Improved Chilled Water Piping Distribution Methodology for ...
Towards an improved methodology for Energy Return on ...
Transcript of Towards an improved methodology for Energy Return on ...
Towards an improved methodology for Energy
Return on Investment (EROI) for electricity supply
Graham Palmer
July, 2018
ORCID:0000-0002-7667-4189
School of Earth Sciences
The University of Melbourne
Melbourne, Australia
Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy
Abstract
The challenge of decarbonising electricity grids is urgent, but pathways towards decarbonisation are
uncertain. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs)
are used to project future low emission scenarios to provide policy guidance. Cost minimization is
a key objective function. Although economic analyses can identify pathways that are economically
infeasible, they cannot identify those that are energetically infeasible. Net-energy analysis (NEA),
using the energy return on investment (EROI) metric, has the potential to supplement conventional
economic and environmental models by providing an energetic valuation of energy supply. However, a
lack of methodological consistency has limited the utility of this approach. This thesis provides a foun-
dation for incorporating NEA into energy modelling by addressing the methodological inconsistencies,
specifically in relation to electricity-based EROI analyses.
The research method is to approach the methodological problem from two ‘directions’, based on
the conceptual idea of hybrid-life cycle assessment (LCA). Firstly, using a top-down approach for
systematic completeness; and a bottom-up approach for a more detailed technology-specific analysis.
The top-down approach includes an environmentally extended input-output analysis (EEIOA) of
the Australian electricity supply industry. This is the first study to calculate the EROI of electricity at
a national level, with a calculated EROI of 40:1. The industry is energetically economic, in the sense
that a relatively small energy investment leverages primary fuels to generate a much greater magnitude
of electricity generation and distribution. However, the leveraging has been achieved at the expense
of a high feedstock extraction rate and commensurate emissions. The result confirms the common
assumption that EROI is not a major factor with respect to the current configuration of the Australian
electricity supply industry. Yet, the study also validates the biophysical economists’ intuition that
a shift away from the existing generation mix risks significantly increasing the energetic costs of the
system, and consequently lowering the system EROI below an energetically viable threshold.
The bottom-up approach includes two technology-focused studies. The first of these introduces
a framework for implementing a consequential EROI analysis of variable renewable energy (VRE)
and storage. Since the underlying function of electricity grids is to ensure that supply meets demand
at a prescribed reliability level, a power based functional unit of 1 kW is adopted to supplement
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the conventional 1 kWh of energy delivered to the grid. This is the first renewable and storage
based EROI study to undertake a power-based analysis, and provides a framework for comparing the
energetic costs of VRE and electricity storage with the value that storage provides.
The second is an evaluation of the factors that contribute to a divergence in the renewables EROI
literature. This is the first comprehensive evaluation of these factors, and establishes a framework for
incorporating process-based LCA into a consequential EROI assessment.
There are two key conclusions from this thesis. Firstly, a technology-specific EROI analysis needs
to establish the impact on the overall system EROI in order to establish the energetic viability of a
transition pathway. Such an approach is termed a consequential analysis. An attributional analysis,
in itself, is insufficient for assessing the viability of an individual technology.
Secondly, where the EROI of an electricity system is estimated to be higher than about 20:1, an
EROI approach may not provide additional guidance about the viability of a transition pathway. In
this case, EROI may be a non-constraining factor. However, much of the energy modelling literature
utilizes electricity generation and storage technologies that have significantly lower EROI ratios than
the typical incumbent system. In those cases, the modelled system may be energetically infeasible
even though it represents a cost-optimised solution.
This thesis provides a foundation for incorporating NEA into integrated, and energy system opti-
misation models.
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Declaration
This is to certify that:
1. the thesis comprises only my original work towards the PhD except where indicated;
2. due acknowledgement has been made in the text to all other material used; and
3. the thesis is less than 100,000 words in length, exclusive of tables, maps, bibliographies and
appendices.
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Acknowledgements
Special thanks to my supervisors and advisory committee Roger Dargaville, Robert Crawford, and
David Byrne.
Thanks to the reviewers for many excellent comments and valuable suggestions.
Thanks to Josh Floyd, Sam Alexander and John Wiseman for introducing me to the Energy and
Climate scholarships, and especially to Josh for co-authoring a NEA paper and for valuable discussions
that contributed to this thesis.
Thanks to many people for discussions that contributed to this work. Many people provided
different perspectives, gave valuable feedback, reviews, and answered my questions, especially Ajay
Gupta, Andrea Bunting, Carey King, Charles Hall, Dave Murphy, Eric Lee, Gail Tverberg, Garvin
Boyle, Gene Preston, George Mobus, Igor Bashmakov, Jack Albert, Jessica Lambert, John Morgan,
John Schramski, Kent Klitgaard, Manfred Lenzen, Marco Raugei, Michael Carbajales-Dale, Pedro
Prieto, Reynir Atlason, Steve Keen, Ted Trainer, Tom Biegler, and Wayne Qu.
Thanks to all those at the Australian German College of Climate and Energy, especially Malte
Meinshausen and Jen Drysdale, and fellow PhD candidates and staff, Adrian, Alex, Alex, Alexei,
Alister, Anita, Anne, Belle, Caro, Cathy, Chang, Cienna, Dimitri, Dylan, Elisabeth, Ellycia, Fiona,
Kate, Kate, Kenny, Kieran, Laura, Madhu, Mandy, Martin, Matthew, Nick, Philip, Rachelle, Raif,
Seb, Skye, Sonya, Steph, Stephen, Tash, Tim, Win, Yann, and Zeb.
Thanks to Greg, Kevin and Steve for encouragement and support.
Finally, I would like to acknowledgement the love and support of Kylie, Lachlan and Sarah.
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Preface
This dissertation is submitted for the degree of Doctor of Philosophy at the University of Melbourne.
The research described herein was conducted under the supervision of Dr Roger Dargaville and Dr
Robert Crawford in the School of Earth Sciences and Architecture respectively, University of Mel-
bourne, between December 2014 and March 2018.
This work is to the best of my knowledge original, except where acknowledgements and references
are made to previous work. Neither this nor any substantially similar dissertation has been or is being
submitted for any other degree, diploma or other qualification at any other university.
I am a recipient of an Australian Postgraduate Award (APA) scholarship – STRAPA 2014.
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Publications Resulting from Work Reported in this Thesis
This thesis contains five papers that have been published in Energy, Energies, and BioPhysical Eco-
nomics and Resource Quality, including:
• Palmer, G. 2017, ‘An input-output based net-energy assessment of an electricity supply indus-
try’, Energy, vol. 141, pp. 1504-1516.
• Palmer, G. 2017, ‘Energetic Implications of a Post-industrial Information Economy: The Case
Study of Australia’, BioPhysical Economics and Resource Quality, vol. 2, no. 2, p. 5.
• Palmer, G. 2017, ‘A Framework for Incorporating EROI into Electrical Storage’, BioPhysical
Economics and Resource Quality, vol. 2, no. 2, p. 6.
• Palmer, G. 2018, ‘A biophysical perspective of IPCC integrated energy modelling’, Energies,
vol. 11, no. 839
The next paper was co-authored, with the lead author contributing 70%:
• Palmer, G. & Floyd, J. 2017, ‘An Exploration of Divergence in EPBT and EROI for Solar
Photovoltaics’, BioPhysical Economics and Resource Quality, vol. 2, no. 4, p. 15.
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Contents
Abstract i
Declaration iii
Acknowledgements iv
Preface v
Publications Resulting from Work Reported in this Thesis vi
Table of contents vii
Nomenclature xiv
List of figures xviii
List of tables xx
Introduction 1
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1 Literature Review 9
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.1.1 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.1.2 The purpose of EROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
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1.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2.1 Definition of biophysical economics . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2.2 Definition of EROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 Historical context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3.1 Thermodynamic origins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3.2 Contributors to biophysical economics . . . . . . . . . . . . . . . . . . . . . . . 15
1.3.3 The notion of energy surplus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.4 Energy and economic valuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.4.2 Contrasting theories of value . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.5 Net-energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.5.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.5.2 EROI as a distinct field of inquiry . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.5.3 Bio-ethanol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.5.4 Electricity generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2 EROI as a supplement to the price system 28
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1.1 An outline of the problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1.2 EROI as an energy productivity indicator . . . . . . . . . . . . . . . . . . . . . 29
2.1.3 EROI as a subset of biophysical economics . . . . . . . . . . . . . . . . . . . . 31
2.1.4 The EROI hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2 Contrasting the circular and linear flow economies . . . . . . . . . . . . . . . . . . . . 32
2.2.1 Neoclassical perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2.2 BPE perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.3 Integrating biophysical with neoclassical perspectives . . . . . . . . . . . . . . . 33
2.3 Limits of energy substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.2 Stylised isoquant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3.3 Energy cost shares . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.4 Oil price shocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4 Relationship between energy and the economy . . . . . . . . . . . . . . . . . . . . . . . 38
2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4.2 The Australian context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5 The EROI metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
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2.5.1 Difference between the output elasticity of energy and its factor share . . . . . 44
2.5.2 The minimum EROI of society . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.5.3 Resource depletion versus technical change . . . . . . . . . . . . . . . . . . . . 46
2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3 Energetic Implications of a Post-industrial Information Economy: The Case Study
of Australia 48
3.1 Overview and context of chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3 ICT-Enabled Remote Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.4 ICT and the Sharing Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.5 Dematerialization Driven by ICT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.6 The Energy Intensity of Australia’s Economy . . . . . . . . . . . . . . . . . . . . . . . 52
3.7 Real-World End-Use Energy Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.8 Unmeasured gains from ICT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4 An input-output based net-energy assessment of an electricity supply industry 59
4.1 Overview and context of chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.1.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.1.4 Study challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.1.5 Study code and data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.1 Net-energy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.2 Definition of EROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.3 Previous studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.4 Motivation and context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4 Overview of the Australian electricity supply industry . . . . . . . . . . . . . . . . . . 63
4.5 Environmentally extended input-output analysis methodology . . . . . . . . . . . . . . 63
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4.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.5.2 Monetary flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.5.3 Energy flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.5.4 Calculation of individual energy pathways . . . . . . . . . . . . . . . . . . . . . 64
4.5.5 Feedstock fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.5.6 Primary energy factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.5.7 Limitations of methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.6 Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.6.1 Monetary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.6.2 Energy data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.6.3 Solar generation data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.6.4 Other data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.6.5 Consumer classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.7 Reconciliation of data for the electricity supply industry . . . . . . . . . . . . . . . . . 68
4.7.1 Allocating energy to all electricity supply . . . . . . . . . . . . . . . . . . . . . 68
4.7.2 Double counting due to on-selling . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.7.3 Coal mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.7.4 Revision of electricity use by the electricity supply industry . . . . . . . . . . . 68
4.8 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.8.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.8.2 Largest components of operational energy . . . . . . . . . . . . . . . . . . . . . 70
4.8.3 Tier analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.8.4 Sankey diagram of the electricity supply industry . . . . . . . . . . . . . . . . . 71
4.8.5 Low energy intensity cost components . . . . . . . . . . . . . . . . . . . . . . . 71
4.8.6 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.8.7 Differences to other energy sources . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.8.8 Implications and further work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5 A Framework for Incorporating EROI into Electrical Storage 75
5.1 Overview and context of chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.1.3 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
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5.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3.2 Definition of EROI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3.3 Storage Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3.4 Renewable Simulation Literature Review . . . . . . . . . . . . . . . . . . . . . 80
5.3.5 Storage Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3.6 Goal Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.4 Goal and Scope of the Present Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.4.1 Different Roles of Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.4.2 Energy Capital Substitution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.4.3 Reformulation of Storage and EROI . . . . . . . . . . . . . . . . . . . . . . . . 81
5.5 Historical Precedents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.5.1 Food Storage as a Pivotal Development . . . . . . . . . . . . . . . . . . . . . . 82
5.5.2 Value as the Vector of Energy Surplus and Storage . . . . . . . . . . . . . . . . 83
5.6 The Role of Energy Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.6.1 Conventional Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.6.2 Connecting the Pre-Industrial with the Contemporary Role of Storage . . . . . 83
5.6.3 Availability Versus Capacity Factor . . . . . . . . . . . . . . . . . . . . . . . . 84
5.6.4 Role for VRE Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.7 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.7.1 EROI Assessments Have Focused on Net-Energy Only . . . . . . . . . . . . . . 85
5.7.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.7.3 Reliability Metrics for Conventional Generation . . . . . . . . . . . . . . . . . . 85
5.7.4 Storage Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.8 The Analysed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.8.1 Reliability of Variable Renewable Generation . . . . . . . . . . . . . . . . . . . 86
5.8.2 Charging Storage with VRE Overbuild . . . . . . . . . . . . . . . . . . . . . . . 86
5.8.3 Marginal Returns to Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.8.4 ERCOT Regional Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.8.5 Embodied Energy of VRE and Storage . . . . . . . . . . . . . . . . . . . . . . . 88
5.9 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.9.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.9.2 The Embodied Energy and Marginal Embodied Energy Curves . . . . . . . . . 90
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5.9.3 Key Differences Between VRE-Storage and Conventional Generation . . . . . . 90
5.9.4 Consequence of Diminishing Returns . . . . . . . . . . . . . . . . . . . . . . . . 92
5.9.5 100% VRE Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.11 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6 An exploration of divergence in EPBT and EROI for solar photovoltaics 97
6.1 Overview and context of chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.3.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.3.2 Definition of EROI and EPBT . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4 Goal definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.1 Defining LCA Goal and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.2 Goal Definition in the Context of NEA . . . . . . . . . . . . . . . . . . . . . . . 100
6.4.3 Analysis Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5 Detailed Investigation of Factors Contributing to Divergence . . . . . . . . . . . . . . 101
6.5.1 Life-Cycle Assessment Methodologies . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5.2 Age of Primary Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.5.3 PV Cell Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.5.4 Treatment of Intermittency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.5.5 Equivalence of Investment and Output Energy Forms . . . . . . . . . . . . . . 108
6.5.6 Differences Between Assumed Values Real-World Performance . . . . . . . . . 110
6.6 Discussion of Overall Implications for EROI and EPBT Metrics . . . . . . . . . . . . . 114
6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7 A biophysical perspective of IPCC integrated energy modelling 119
7.1 Overview and context of chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
7.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
7.1.2 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
7.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
xii
7.3.1 Scenario modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.3.2 Biophysical economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
7.4 Integrated assessment models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.5 Detailed exploration of assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.5.1 Total factor productivity (TFP) and GDP growth . . . . . . . . . . . . . . . . 123
7.5.2 Declining energy intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
7.5.3 Life-cycle assessment methodologies . . . . . . . . . . . . . . . . . . . . . . . . 126
7.5.4 Steel and cement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.5.5 Biofuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.5.6 EROI constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.5.7 Fossil fuel resource availability . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7.6 Discussion and recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.6.1 A proposed net-energy feedback model . . . . . . . . . . . . . . . . . . . . . . . 129
7.6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
7.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
7.8 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
7.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
8 Conclusions and future work 138
8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
8.2 Electrical storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
8.3 What would a low-emission scenario of Australian electricity look like if it was optimised
for EROI? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
8.4 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
References 146
xiii
Nomenclature
ABS Australian Bureau of Statistics
AC alternating current electricity
AEMO Australian Energy Market Operator
ALCA attributional life-cycle assessment
AR5 IPCC Fifth Assessment Report
BP British Petroleum
BPE Biophysical Economics
BREE Australian Bureau of Resources and Energy Economics
CAD computer aided design
CDF cumulative distribution function
CDE energy augmented Cobb Douglas function
CdTe cadmium telluride
CED cumulative energy demand
CIGS copper indium gallium selenide
CLCA consequential life-cycle assessment
CO2eq carbon dioxide equivalent
DC direct current electricity
EC European Commission
EEIOA environmentally extended input-output analysis
EEO Australian Energy Efficiency Opportunities Program
EIA US Energy Information Administration
EPBT energy payback time
EROI energy return on investment
xiv
EV electric vehicle
FOR forced outage rate
GDP gross domestic product
GEA Global Energy Assessment
GEER gross external energy ratio
GEPR gross external power ratio
GHI global horizontal insolation
GJ gigajoule
GPS global positioning system
IAM Integrated Assessment Model
ICT information and communications technology
IEA International Energy Agency
IEA-PVPS International Energy Agency Photovoltaic Power Systems Programme
IIASA International Institute for Applied Systems Analysis
I/O input-output analysis
IPCC Intergovernmental Panel on Climate Change
ISO International Organization for Standardization
kJ kilojoule
kW kilowatt
kWh kilowatt-hours
LCA Life cycle Assessment or Analysis
LOLE loss-of-load-expectation
LOLP loss-of-load-probability
MW megawatts
xv
MWh megawatt-hours
MJ megajoule
NEM Australian National Electricity Market
NEPR net external power ratio
NER net energy ratio
NGER National Greenhouse and Energy Reporting
NWIS North West Interconnected System
OPEC Organization of the Petroleum Exporting Countries
PDF probability distribution function
PE primary energy
POLES Prospective Outlook on Long-term Energy Systems
PJ petajoule
PHS pumped hydro storage
PR performance ratio
PV solar photovoltaic
RCP Representative Concentration Pathways
RE renewable energy
SETAC The Society of Environmental Toxicology and Chemistry
SRES Special Report on Emissions Scenarios
SWIS South West Interconnected System
TDOS electricity transmission, distribution, and on-selling
UNEP United Nations Environment Programme
VRE variable renewable energy
WEC World Energy Council
xvi
WEO IEA World Energy Outlook
WITCH World Induced Technical Change Hybrid
xvii
List of Figures
1 Schematic of research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.1 Net-energy cliff depicting difference between gross energy and net-energy. . . . . . . . 12
2.1 Stylised depiction of standard circular flow of the economy . . . . . . . . . . . . . . . . 34
2.2 Substitution and indirect energy requirements . . . . . . . . . . . . . . . . . . . . . . . 36
2.3 Entropy boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.4 Primary energy consumption and GDP Australia . . . . . . . . . . . . . . . . . . . . . 40
2.5 Change in per-capita primary energy, GDP, and energy productivity . . . . . . . . . . 41
2.6 Sources of per-capita growth Australia, 1940 to 2016 . . . . . . . . . . . . . . . . . . . 43
2.7 Real price of electricity Australia, 1955–2015 . . . . . . . . . . . . . . . . . . . . . . . 43
3.1 Australia primary energy consumption and real GDP 1900–2014 . . . . . . . . . . . . 53
3.2 Australian industry sectors - actual and projected - 1800–2020 . . . . . . . . . . . . . 53
3.3 Persons employed in selected manufacturing industries - post-War . . . . . . . . . . . 54
3.4 Passenger vehicles per capita and average floor area of new detached dwellings . . . . 54
3.5 Real price of electricity 1955–2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.1 Streamlined energy systems diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 Australia’s four major electricity systems. . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Direct versus indirect energy for electricity generation. . . . . . . . . . . . . . . . . . . 65
4.4 ‘Black box’ depiction of the electricity supply industry 2013–14. . . . . . . . . . . . . . 70
4.5 Largest 16 energy pathways disaggregated by generation and TDOS. . . . . . . . . . . 70
4.6 Total electricity supply industry for 2013–14. . . . . . . . . . . . . . . . . . . . . . . . 71
4.7 Sankey diagram of Australian electricity supply industry 2013–14. . . . . . . . . . . . 71
4.8 ABS 4604.0 net-use of electricity by the electricity supply sector. . . . . . . . . . . . . 72
5.1 Stylised graph of energy surplus versus storage. . . . . . . . . . . . . . . . . . . . . . . 82
xviii
5.2 Graph of capacity factor versus availability factor. . . . . . . . . . . . . . . . . . . . . 84
5.3 Stylised effect of storage on baseload and variable renewable energy. . . . . . . . . . . 84
5.4 Available capacity curve and forced outage rate . . . . . . . . . . . . . . . . . . . . . . 85
5.5 Stylised probability density function for a year. . . . . . . . . . . . . . . . . . . . . . . 86
5.6 Storage status for Preston simulation and Henning and Palzer . . . . . . . . . . . . . . 87
5.7 Embodied energy of VRE plus storage for PHS . . . . . . . . . . . . . . . . . . . . . . 90
5.8 Stylised representation of reliability measurement . . . . . . . . . . . . . . . . . . . . . 91
6.1 Definition of boundaries for solar PV life-cycle assessments . . . . . . . . . . . . . . . 102
6.2 Reported cumulative energy demand (CED) . . . . . . . . . . . . . . . . . . . . . . . . 105
6.3 Sankey diagram of annual global energy flows . . . . . . . . . . . . . . . . . . . . . . . 111
6.4 Population GDP per capita and global horizontal insolation by latitude . . . . . . . . 112
6.5 Installed capacity and final annual yield . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.6 Stylised depiction of the scaling impact of methodological factors . . . . . . . . . . . . 115
7.1 Proposed biophysical economic feedback model . . . . . . . . . . . . . . . . . . . . . . 130
xix
List of Tables
1.1 Branches of economics within Ecological Economics . . . . . . . . . . . . . . . . . . . 11
1.2 Comparison of energy versus exergy efficiency . . . . . . . . . . . . . . . . . . . . . . . 13
1.3 Comparison of objectivist theories of value and stores of value. Compare subjectivist
theories, show in table 1.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.4 Comparison of subjectivist theories of value. . . . . . . . . . . . . . . . . . . . . . . . . 23
4.1 Primary energy factors for this study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.2 ABS 4604 fuel types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3 Shares of electricity supply output and net capital expenditure . . . . . . . . . . . . . 68
4.4 Feedstock fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.5 Operational fuels and electricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.6 Costs and energy intensity of selected cost components . . . . . . . . . . . . . . . . . . 72
4.7 Consumption of fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1 Reference VRE plus storage model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.2 Assumptions applied to embodied energy . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.3 Aggregate embodied energy over 50-year time frame . . . . . . . . . . . . . . . . . . . 89
5.4 Summary of 100% VRE with storage based on simulation model . . . . . . . . . . . . 89
6.1 Direct energy inputs for major production processes for crystalline Si . . . . . . . . . . 102
6.2 Comparison of hybrid versus process-based LCA . . . . . . . . . . . . . . . . . . . . . 104
6.3 Methods of adjusting for investment and output energy equivalence . . . . . . . . . . . 110
6.4 Methodological factors affecting EPBT and EROI . . . . . . . . . . . . . . . . . . . . 114
xx
Introduction
Summary
Cost minimization is a key objective function of integrated and scenario modelling. The standard
economics perspective is that the price system embeds all of the relevant information, and therefore
differences between ‘energetic’ and ‘economic’ valuations are irrelevant. The biophysical economics
(BPE) perspective is that energy is a primary factor of production, with a lower bound to substitu-
tion, and therefore understanding the energetic value of energy supply technologies is important for
establishing whether energy transition pathways are energetically feasible.
A clear example of the divergence between the standard and BPE perspective is the treatment
of biomass energy with carbon dioxide capture and storage (BECCS) in the IPCC Fifth Assessment
Report (AR5). The report found that a carbon price of USD 100 per tonne CO2-eq would be sufficient
to drive large scale deployment of BECCS (see chapter 7). BECCS is not economically viable without
a carbon price, but the proper pricing of externalities is presumed to remedy the price disadvantage
relative to incumbent energy sources. Yet from a BPE perspective, the EROI of BECCS is estimated
to be less than 3:1, and therefore below the minimum threshold of around 10:1 required to support
an advanced society. At the margins, the production of high utility and economically useful liquid
fuels with BECCS may be useful, however BECCS is only viable within the context of the incumbent
system. The assumption that BECCS can be scaled up to substitute for fossil fuels ignores biophysical
constraints. A carbon price can alter the relative price of energy substitutes but cannot alter the
underlying energetic value.
But equally, a focus on energetic valuation alone may lead to anomalous results. Such an approach
is conceptually similar to an ‘energy theory of value’, which sets energy as the universal standard of
value. For example, much of the cost of nuclear power is related to quality assurance, regulatory
compliance, and financing costs, all of which are financially costly but energetically inexpensive.
Similarly, electricity networks are costly to build and maintain but not biophysically constrained.
Ideally, EROI should provide a means to supplement conventional economic and environmental
analysis, and inform and shape energy transition analysis. However, a lack of methodological consis-
tency has led to contestation of net-energy analysis’ (NEA) relevance to energy transition feasibility
assessment. Furthermore, although the idea of energetic valuation may be simple enough, a closer ex-
amination shows more detail and complexity. Identifying and codifying this complexity is an objective
of this thesis.
This thesis addresses these issues in relation to electricity supply technologies. Nearly all electricity-
based EROI analyses have focused on electricity supply technologies, measured as the ratio of the life-
1
time electricity generated, and the energy investments required to produce that electricity. However,
evaluating a particular component of electricity supply based on lifetime electricity generation is far
too coarse-grained if the goal is transition feasibility assessment. Electricity supply is delivered by
complex real-time systems, and much of the cost of electricity supply is related to distribution and
system costs.
The research method is to approach the methodological problem from two ‘directions’. Firstly,
using a top-down approach for systematic completeness; and a bottom-up approach for a more detailed
technology-specific analysis. Taken together, these approaches provide a foundation for building
electricity-based EROI assessments that can provide meaningful guidance in relation to transition
feasibility assessment.
Motivation
The motivation for this thesis is to better understand the challenge of decarbonising electricity grids in
biophysical terms, and apply this to decarbonisation planning. The long-term objective is to achieve
decarbonisation as economically and efficiently as practically possible. Although cost-minimized op-
timisation is a valuable research tool, it generally ignores energetic cost constraints and net-energy
considerations. A motivation is to develop a more consistent methodology for electricity-based EROI,
which can be adopted as a constraint, or an additional optimisation factor within scenario models.
Context
Australia’s electricity supply industry is undergoing a transition towards lower greenhouse (GHG)
emission electricity generation. The primary driver is GHG emission abatement targets, but tech-
nological and economic changes are also driving a transition away from coal-fired generation. Wind
power is currently the cheapest form of large-scale generation in Australia. Similar drivers are occur-
ring world-wide.
Unfortunately, there is no ready-made template for defining low-emission pathways. The historical
adoption of hydro and/or nuclear power enabled several regions or nations, including Brazil, France,
New Zealand, Norway, Ontario, and Sweden to substantially decouple GHG emissions from electricity,
but the further expansion of both of these technologies is country specific, and face geographic,
economic or social hurdles. Although these technologies generate low-emission electricity, they are
still embedded in, and dependent on the broader fossil fuelled energy sectors, in particular, petroleum
fuels.
Some form of carbon pricing is widely considered essential, but has been difficult to implement in
Australia and internationally. Until the 1990s, the standard model for electricity delivery was vertically
2
integrated monopoly under public ownership. Like many nations, Australia implemented competition
reforms that included liberalization, reorganization, and disaggregation of electricity functional units.
These reforms placed greater emphasis on prices and markets to drive GHG abatement.
Both of the principle modelling approaches – integrated assessment modelling (IAM), and energy
system optimisation modelling (ESOM) – seek a least cost solution based solely on economic prices.
Aim
The aim of this thesis is to improve the methodological consistency of electricity-based EROI studies
to enable net-energy analysis to be incorporated into energy transition analysis.
3
Thesis outline
(i) Abstract
(ii) Introduction
1. Literature review
Chapter 2 provides a high level overview of EROI. It frames the EROI metric as being based,
in part, on an energy theory of value. It address the most common criticism of net-energy analysis
(NEA), which is the claim that the price system is a more reliable indicator of value and scarcity. It
makes the argument that the price system alone may be insufficient for describing energy transitions,
and that a focus on the underlying physical system offers important insights.
It contrasts the standard economic ‘circular flow of the economy’ conception with the biophysical
linear flow conception, and seeks to integrate the two approaches. It explores the limits of energy
substitution using stylised isoquants, and introduces theories of energy expenditure cost shares.
It includes a case study that links energy consumption and economic growth to make the case
for the primacy of energy as a primary factor of production. The case study is based on a historical
overview of Australia.
The EROI metric is explored in more detail using a narrative approach, and placed in a historical
context. Finally, the role of EROI in describing the tension between resource depletion and technical
change is explored.
2. EROI as a supplement to the price system
The next two chapters adopt a national perspective to energy.
Chapter 3 builds on the previous chapter with an original article that explores the correlation
between Australian energy consumption and gross domestic product for the period 1900 to 2014. It
focuses on the shift to a service-based economy and explores why the shift to an economy based on
low-energy intensity services hasn’t led to ‘strong decoupling’ of energy and economic activity. In this
context, weak decoupling is defined as a relative reduction in energy consumption per unit of GDP,
whereas strong decoupling is also an absolute reduction in national energy consumption. The aim
of the chapter is to test the hypothesis that energy is a primary factor of production, and whether
energy can be displaced by technology.
4
The chapter introduces some of the theories that underlie biophysical economics, including: the
relationship between energy consumption and social complexity; the role of primary industries as
economies evolve towards service economies; the relationship between energy consumption and in-
formation and communications technology (ICT); and how demand for end-use energy services has
evolved.
The study finds that ICT is enabling productivity gains and new business models, but does not
significantly weaken the demand for end-use energy services. The conclusion is that a shift to an
ICT-enabled service economy has not historically enabled strong decoupling of energy and economic
activity.
Chapter 4 continues with a national-scale exploration of Australian energy consumption. It applies
a ‘top-down’, environmentally extended input-output analysis (EEIOA) of Australian electricity sup-
ply for the period 2009-10 to 2013-14. EEIOA provides a method for evaluating the linkages between
economic activities and environmental impacts, including direct and indirect energy consumption.
One technique is to combine the national monetary use-table with the national energy account.
The purpose of the chapter is to calculate the EROI of the Australian electricity supply industry.
This is the first study to assess the EROI of electricity at a system or national level. The EROI
was calculated at 40:1 for 2013-14. The electricity generation industry is energetically economic, in
the sense that a relatively small energy investment leverages a large magnitude of primary energy to
generate a large magnitude of electricity. However, the leveraging has been achieved at the expense of
a high feedstock extraction rate and commensurate emissions. Unlike comparable studies of oil and
gas production, the calculated EROI was relatively high. Much of the economic cost of electricity
supply is related to low energy intensity costs. It is concluded that the industry is cost constrained
rather than EROI constrained.
3. Energetic Implications of a Post-industrial Information Economy: The Case Study of Australia.
Article published (Biophys Econ Resource Qual.)
4. An input-output based net-energy assessment of an electricity supply industry. Article published
(Energy).
The study shifts from a ‘top-down’ national scale to a ‘bottom-up’ technology focus. Two chapters
consider the EROI of renewable energy, storage, and system-level effects.
Chapter 5 adopts a consequential life-cycle assessment (LCA) to electricity-based net-energy anal-
ysis. It applies a functional unit of 1 kW of power delivered to the grid, which is contrasted with the
5
standard functional unit of 1 kWh of energy delivered to the grid. The study introduces a framework
for electricity-based consequential analysis, and applies the framework to a suite of high-penetration
renewable scenarios.
It begins with a discussion of food and energy storage as a historical development, and how
this connects with the contemporary role of storage. It then develops a framework for measuring and
contrasting VRE plus storage, and conventional generation with the use of electrical reliability indices.
Finally, it uses a case study to apply the framework.
Chapter 6 identifies and explains the factors that have contributed to a divergence in variable re-
newable energy (VRE) net-energy analyses. It provides a link between the conventional attributional,
process-based approach, and a consequential approach that includes the systems-engineering qualities
of electricity systems.
The goal of these chapters is to provide a framework for comparing and evaluating EROI studies
and introduce a systems-engineering perspective into net-energy analysis.
5. A Framework for Incorporating EROI into Electrical Storage. Article published (Biophys Econ
Resour Qual)
6. An exploration of divergence in EPBT and EROI for solar photovoltaics. Article published
(Biophys Econ Resour Qual)
Chapter 7 explores the IPCC Fifth Assessment Report (AR5) energy scenario modelling, including
Integrated Assessment Models (IAMs).
Analysis of the costs and benefits of climate mitigation involve extrapolation into the future. Since
the long time horizons of interest extend well beyond the range of standard economic-development
scenarios, ‘transformation pathways’ are derived from integrated assessment models (IAMs). Since the
future evolution of demography, socio-economic development, and technology are highly uncertain,
scenarios have been developed that can be described by the Kaya identity.
A hypothesis of BPE is that the Kaya ‘driving forces’ are not independent variables, but interact
in complex ways. This study explores the assumptions that underlie IAMs. A preliminary proposal
for incorporating NEA into IAMs is presented.
7. A biophysical perspective of IPCC integrated energy modelling. Article published (Energies)
8. Conclusions and further work
6
Research method
This section provides an overview of the research method. Further detailed descriptions are given in
each chapter. Figure 1 outlines the research method schematically.
Chapter 4Input-output analysis
of the Australianelectricity supply industry
Engineering-systemsaspects of electricity
generation
Chapter 6Review of methodologies
for variablerenewable energy
Chapter 5Demand-based
consequential LCAapproach
Chapter 7Need to endogenize EROIin integrated assessment
models (IAMs)
Environmental, energy, economic analysis
EROI-basedenergetic valuation
Process-basedLCA for
electricity generation
Reliability indices
Top-down
Bottom-up
Otherconsequential LCA
approaches
Not incorporated intoIPCC AR5 mitigation
scenarios
Integrated assessmentmodels (IAMs)
LCA is identified but not incorporated intoIPCC AR5 mitigation
scenarios
Figure 1: Schematic of research method
This thesis addresses the two major weakness of NEA in relation to electricity supply. Firstly,
process-based analysis results in a truncated boundary, and therefore understates the indirect embod-
ied energy. In LCAs, there is no requirement to ensure that an analysis meets a prescribed level of
‘completeness’. The international standards for LCAs require that stages, unit processes or inputs are
followed until they ‘lack significance’ within the given scope. This can be problematic when LCAs are
applied to energy transition exercises since a high level of ‘completeness’ is often assumed by modellers
applying LCA data.
Secondly, nearly all NEA studies adopt an attributional approach, which takes into account the
embodied energy of the technology in question, but doesn’t take into account the broader impacts that
might result from the decision to adopt or install a specific technology. In contrast, a consequential
approach considers the broader impacts. However, it can be methodologically difficult to evaluate
consequential changes to energy systems with LCA approaches. Furthermore, the systems-engineering
aspects of electricity supply are generally treated as lying outside the domain of conventional life-cycle
research.
The paucity of consequential NEA studies has precluded the incorporation of consequential analysis
7
into integrated assessment models (IAMs) and IPCC mitigation reports. Other factors that limit the
use of LCA and NEA in integrated modelling include substantial variability in published LCA/EROI
results, and differences in LCA/EROI technique.
Although this thesis is not a hybrid-LCA study, the research method draws on the conceptual
idea of hybrid-LCA. Hybrid analysis brings together a top-down, input-output (I/O) analysis and
a bottom-up, process-LCA. An I/O analysis has the benefit of systematic completeness, but lacks
precision. On the other hand, process-LCA provides a more specific, detailed analysis, but is subject
to truncation error. The respective benefits of each can be combined with hybrid-LCA.
The I/O element of hybrid-LCA is typically used to estimate higher-order embodied energy re-
quirements of a product or service. However, in this thesis, there are two important differences.
Firstly, I/O is being used to calculate the embodied energy on an entire electricity industry, rather
than a specific product or service. Secondly, the primary energy extractions, which are usually the
main parameters of interest in LCA, are deducted to derive the energy-industry-own-use (EIOU).
Whereas the detailed element of hybrid-LCA is usually a process-based analysis, in this thesis
the detailed element is intended to provide a more detailed exploration of a consequential approach,
including the systems-engineering of renewable electricity. It comprises two studies (chapters 5 &
6), which explore a consequential LCA approach to renewable energy and storage, and a detailed
exploration of the factors that contribute to a divergence in the NEA literature.
In order to place the research within a policy-relevant context, the final chapter is a proposal to
supplement and support IAMs, and provides a useful reference point to ‘bookend’ the thesis.
Research questions
The research questions posed by this thesis are:
1. What can NEA contribute to our understanding of low-emission electricity transformation path-
ways?
2. What tools or approaches are appropriate?
3. What are the differences between a net-energy analysis and a conventional financial analysis?
8
Chapter 1
Literature Review
1.1 Introduction
1.1.1 Outline
This chapter reviews the EROI literature. The sections are organised as follows:
1. Introduction to the purpose of EROI.
2. EROI-related definitions.
3. A historical narrative to frame the emergence of net-energy analysis in the 1970s.
4. A brief overview of alternative theories of value. Since EROI relies, in part, on an objectivist
‘energy theory of value’, it is useful to place it into context.
5. A post-1970s overview of the EROI literature,
6. An overview of the current electricity-based EROI literature.
1.1.2 The purpose of EROI
The seminal paper by Cleveland, Costanza, Hall, and Kaufmann, titled Energy and the US Economy:
A Biophysical Perspective (Cleveland et al. 1984) provides a useful opening statement of the role of
EROI:
We approach macroeconomics from a thermodynamic perspective that emphasizes the pro-
duction of goods, rather than the neoclassical perspective that emphasizes the exchange of
goods according to subjective human preferences. Production is the economic process that
9
upgrades the organizational state of matter into lower entropy goods and services. Those
commodities are allocated according to human wants, needs, and ability to pay. Upgrading
matter during the production process involves a unidirectional, one-time throughput of low
entropy fuel that is eventually lost (for economic purposes) as waste heat. Production is
explicitly a work process during which materials are concentrated, refined, and otherwise
transformed. Like any work process, production uses and depends on the availability of
free energy. The laws of energy and matter control the availability, rate, and efficiency of
energy and matter use in the economy and therefore are essential to a comprehensive and
accurate analysis of economic production.
Changes in natural resource quality affect the ease and cost of fuel and matter through-
put in human economies because lower quality resources nearly always require more work
directly and indirectly to upgrade them into goods and services. Technological change
can counter changes in natural resource quality to varying degrees, but historically, many
technical advances that have lowered unit labor costs have been realized by increasing
the quantity of fuel used directly and indirectly to perform a specific task. The degree
to which technological change can offset declining resource quality as some basic natural
resources are depleted (for example, fuel and metal ores) and/or mismanaged (some biotic
resources) is an empirical question and cannot be easily predicted. Nevertheless, such
resource changes have important implications for what is and is not possible in the econ-
omy. Economic theory and policy must incorporate the physical properties of resources if
economic predictions are to be accurate and economic policies effective.
Reflecting the perspective of Cleveland et al. (1984), some of the issues explored in this thesis
include:
1. Cleveland et al. are arguing for a comprehensive and accurate analysis of economic production,
which includes a proper treatment of energy and matter. The application of ‘the laws of energy’
to economic valuation implies a partial form of ‘energy theory of value’. It is therefore useful to
explore how an energy theory of value fits into standard economics.
2. How should we think about ‘value’ in the context of energy, and how does this relate to EROI?
3. How do we assess the tension between declining resource quality and technological change, and
how does resource decoupling link into this?
4. What are methodological approaches to EROI and what is needed to improve them?
5. What are the practical applications of electricity-based EROI measurements?
10
1.2 Definitions
1.2.1 Definition of biophysical economics
‘Biophysical’ refers to the material world, and can be contrasted with an anthropocentric perspective,
of which mainstream economics is a subset (Hall & Klitgaard 2011, p.1). A useful working defini-
tion of biophysical economics (BPE) is ‘the study of the ways and means by which human societies
procure and use energy and other biological and physical resources to produce, distribute, consume
and exchange goods and services, while generating various types of waste and environmental impacts’
(Chevallerau 2017). While BPE pays attention to monetary prices, it is focused on the physical laws
that impose constraints and interact with ‘anthropocentric’ systems.
Peet (1992) classifies the major strands of economic-ecological thought by how they approach the
interactions within, or between, human society and nature. Standard economics is usually concerned
with interactions within human society, while ecology is concerned with interactions within nature. On
the other hand, resource economics and environmental economics are concerned with the interactions
between society and nature.
Biophysical economics is a term most closely associated with Charles Hall and colleagues (see Hall
& Klitgaard (2011)). The emphasis of BPE is understanding the role of energy in society, with a focus
on the tension between resource depletion and technology. Both BPE and Industrial Ecology utilise
the tools of life-cycle assessment (LCA), input-output (I/O) analysis, and environmentally extended
input-output analysis (EEIOA).
To human society To nature
From human Standard economics Environmental economicssociety Ecological economics
Biophysical economics (BPE)From nature Resource economics Ecology
Industrial ecology
Table 1.1: Branches of economics within Ecological Economics. Based on Peet (1992)
1.2.2 Definition of EROI
Net energy analysis (NEA) refers to a class of methods that are physically based, which are used to
determine the efficiency or productivity of energy supply technologies (Brandt & Dale 2011). Results
are presented in the form of energy return ratios (ERR), of which EROI is the most commonly used.
EROI is a unitless ratio, defined as the ratio of the gross flow of energy Eg over the lifetime of the
11
Perc
ent
EROI
Net-energy available
for consumption
Energy industryown-use
60
20
40
100
80
45 40 35 30 25 20 15 10 5 050
0
Figure 1.1: Net-energy cliff depicting difference between gross energy and net-energy. Derived fromMearns (2008)
project, and the sum of the energy for construction Ec, operation Eop, and decommissioning Ed
(Murphy & Hall 2011, Eq. 1). The energy includes the indirect, or embodied energy, and the direct
energy. Murphy & Hall (2010) state that ‘EROI is the ratio of how much energy is gained from an
energy production process compared to how much of that energy (or its equivalent from some other
source) is required to extract, grow, etc., a new unit of the energy in question’.
EROI =Eg
Ec + Eop + Ed(1.1)
The use of a simple ratio leads to the mathematical outcome that significance can be attached
only when the ratio is small (i.e. < 10:1). This can be graphically depicted as the so-called energy
cliff, shown in figure 1.1. A shift from, say, 5:1 to 4:1 carries more significance than a shift from 50:1
to 40:1. This is consistent with the focus of most EROI discourse, which is to explore underlying
energetic barriers of energy supply systems. Understanding and measuring the ‘energy industry own
use’ in figure 1.1 has multiple challenges, especially related to system boundaries and aggregating
different fuels, and is the prime focus of the EROI literature.
EROI is not a universal substitute for conventional energy productivity metrics and should be
considered a specific type of productivity metric that can be useful for exploring underlying limits or
constraints in relation to energy supply. Daly’s (1986) observation in relation to the entropy law is
apt, where he notes that the entropy law should be viewed as a constraint, not as an independent,
sufficient explanation of value.
12
1.3 Historical context
1.3.1 Thermodynamic origins
Although coal had been utilised much earlier for heat, at least from the Chinese Han Dynasty (206
BC to 220 AD) (Deng 2011, p.42), it was not until the early eighteenth century that the heat released
from coal could practically be converted to motion. The study of thermodynamics had proceeded in
response to the development of steam power, and throughout the industrializing period of the nine-
teenth century, physical scientists were calling attention to the underpinnings of thermodynamic laws.
Clausius (1867), Carnot (1825), Kelvin (1852) and others formalized the laws of thermodynamics.
These principles became embedded in the first and second laws, referring to the law of conservation of
energy, and the irreversibility of heat processes respectively. More precisely, the ‘Clausius statement’
that ‘heat cannot by itself pass from a colder to a warmer body’ (Clausius 1867, p.117) states that
work can be completely converted into heat, but that it is impossible to convert all heat back into
work. In any heat engine, the maximum theoretical efficiency is defined by the Carnot temperature
ratio (1−T2/T1) – see table 1.2. The Carnot equation is a re-statement of the entropy law – any heat
process must increase the total entropy. A localised decrease must be accompanied by an increase
somewhere else. These laws provided a theoretical base for the enhancement and further development
of heat and steam engines through the nineteenth century.
Thermodynamic law Parameter Equation
First Energy efficiencyη =
energyoutenergyin
Second Exergy efficiency
ε =useful workoutuseful workin
ε = η
(1 − T2
T1
)
Table 1.2: Comparison of energy versus exergy efficiency. T2 is temperature of energy source in degreeskelvin, T1 is sink temperature, η is efficiency. Source: (Brockway et al. 2014)
13
At a microscopic level, the second law can be statistically described as the state of order within
a system; processes will tend to proceed towards states of disorder through a process of random
commingling, rather than states of structured order by spontaneous separation.
The ‘state variable’, entropy, and its associated term, exergy, derive from the second law and
provide a useful metric for available energy. When applied quantitatively to steam and refrigeration
cycles, entropy and exergy were to become useful metrics; the area enclosed within a pressure-volume
integral equals energy, the area enclosed within a temperature-entropy integral equals heat. The second
law, also commonly referred to as the entropy law, provides an iron law of nature that underlies the
study of energetics. Eddington (1928, p.37) wryly observed —
The law that entropy always increases – the second law of thermodynamics – holds, I
think, the supreme position among the laws of Nature. If someone points out to you that
your pet theory of the universe is in disagreement with Maxwell’s equations – then so
much the worse for Maxwell’s equations. If it is found to be contradicted by observations
– well, these experimentalists do bungle things sometimes. But if your theory is found to
be against the second law of thermodynamics I can give you no hope; there is nothing for
it but to collapse in deepest humiliation.
Entropy can also be understood at a macro scale – linear or rotary mechanical motion represents
a highly ordered, low-entropy state since all the molecules in the moving mass are moving together. It
is easy, in a thermodynamic sense, to extract energy from ordered motion, leaving disordered motion
as a waste by-product.
A practical example may be useful here. Electricity is pure exergy since it represents a highly
ordered transmission of electrons, which can be readily converted into mechanical motion, heat or
light. But once converted, it is impossible to completely revert the heat back into electricity. For
example, if 1 kWh of electrical energy is used to heat a quantity of water from 20 to 80oC using a
resistance heater, the final exergy of the water in the tank calculates to 0.17 kWh using the Carnot
equation since the maximum efficiency of an ideal heat engine is 17% for this set of conditions. Hence,
the conversion from electricity to low-grade heat has destroyed 0.83 kWh of exergy, although no energy
has been gained or lost. In an electrical conductor, an external voltage sets up a highly ordered electric
field that propagates through the conductor. On the other hand, the molecules in warm water are
undergoing rapid, random motion at a microscopic scale, and only a small fraction of that motion can
be usefully extracted.
The distinction between the generic term ‘energy’ and the more specific term ‘exergy’ is important
in net-energy analysis. In the case of combustion-based electricity generation, a significant proportion
of the chemical energy is unavoidably lost as part of the energy upgrading process. In the case of
14
renewable technologies, the upgrading of diffuse and variable energy incurs an embodied energy cost
that must be accounted for in net-energy analyses.
1.3.2 Contributors to biophysical economics
The appeal to the second law of thermodynamics encouraged a range of theories that focused on energy.
One of the most notable was Alfred Lotka (1922), a mathematical biologist, who sought to explain
the biological evolutionary process in terms ‘energy gradients’ and the second law of thermodynamics.
In any isolated system, the second law requires that the equilibrium state will be maximum dis-
order. Yet ordered structures, such as crystalline or biological structures, seem to defy the second
law. Prigogine et al. (1972) brought attention to the nature of open systems in contact with a heat
reservoir, such as the earth, which radiates heat into space while absorbing radiation from the sun.
Such systems were termed non-equilibrium open systems, which could be contrasted with the closed
systems of practical interest, such as heat engines.
More recently, Schneider & Kay (1994) and Schneider & Sagan (2005) connected Lotka’s hypothesis
with complexity theory, postulating that free energy may have driven the chemical cocktail from
which biological processes evolved. According to their hypothesis, systems that are moved away from
equilibrium will tend to resist externally applied energy gradients. Schneider is a marine geologist and
ecological thermodynamicist, and gives the example of isolated deep sea ecosystems that derive their
energy from the temperature and chemical gradients emanating from the sea floor. He argues that the
combination of warm waters entering the cold deep ocean, along with the chemical and acid/alkaline
gradients derived from mineral releases, provided energy gradients from which chemical reactions
could take place. According to his hypothesis, systems will tend to evolve additional complexity since
more complex structures are more effective at dissipating energy within their ecosystem.
The astrophysicist, Chaisson (2011), derives a complexity metric he terms the ‘energy rate den-
sity’, which is defined as the energy flow per second, per gram of material found in a given system
(joule/s/kg) – or equivalently, the power density in watts per kg. When the energy rate density
for systems, including galaxies, the sun, earth, plants, animals, and society, are plotted over time,
a correlation between scale and energy rate density is observed. Chaisson connects the metric with
complexity, arguing that the availability of free energy is a common underlying factor in evolution,
diversity, and complexification.
Chaisson’s conception of energy and complexity has similarities with the anthropologists Joseph
Tainter (Tainter 1990), Leslie White (White 1943), and Ian Morris (Morris 2015). According to
Tainter’s hypothesis, societies become more complex as a response to ‘problem solving’. Social and
political stratification is an example of complexification, and includes new layers of specialisation,
15
bureaucracy, and social class. Tainter notes that whereas hunter-gatherer societies may have contained
up to a few dozen distinct social personalities, modern European censuses recognize 10,000 to 20,000
unique occupational roles. But specialization also has a cost. Investment in education is growing,
more students undertake advanced education, the productive phase of graduates is delayed, and post-
education specialization carries further costs. Although specialisation has been a driver of productivity,
it is nonetheless subject to declining marginal returns.
White places technology at the centre of cultural development, and was the originator of ‘White’s
Law’, which states that ‘culture develops when the amount of energy harnessed by man per capita
per year is increased; or as the efficiency of the technological means of putting this energy to work is
increased; or, as both factors are simultaneously increased.’ (White 1943, p.338).
Morris argues that cultural values, including fairness and equality, have been conditioned by differ-
ent stages of ‘energy extraction’. Morris identifies foraging, farming, and fossil fuels as defining stages
during which societies held different values (Morris 2015). For example, the shift from foraging to
farming entailed the development of hierarchies in which the notion of equality shifted from a highly
egalitarian notion, to an acceptance of social division. Foraging was non-hierarchical
The shift from farming to fossil fuels disrupted the power relationships and politics of societies
(Mitchell 2009). In organic societies, political power and available energy were tied together. The
primary source of focused energy was human labour, and those that controlled that energy, such
as through slavery or aristocracy, also maintained control of the power structures of society. But
fossil fuels broke this basic link. The rise of the industrial class in Europe was a direct result of the
availability of a large quantity of energy, disrupting the traditional hierarchical systems. Mitchell
(2009, p.27) argued that working people in the industrialised West acquired a power that would have
seemed impossible before the late nineteenth century. The logistics of coal permitted coordinated acts
of workers to interrupt flow in order to meet political aims.
Tainter (1990) used twenty-four historical examples of the collapse of civilisations (including the
Roman, Mayan and Cacoan) to argue that increasing complexity carries a greater energy cost. Tainter
posits that voluntary ‘simplification’ (i.e. reverting to a less energy intensive society) is rare, and that
collapse has been historically more common.
For example, as a solar energy based society, the Roman Empire was subject to what Wrigley (2010)
terms the ‘organic limit’. Not only was food derived from the land, but also industrial production,
such as shoes, textiles, bronze, and construction timber. In all cases, the primary source of energy
was the sun, hence the physical and biological limits of production were set by the energy captured
and stored by plants, and to a lesser extent, the solar energy extracted by the wind or flow of water.
In most cases, the seasonal cycle defined the production cycle. Although the availability of mineral
16
ores were not defined by solar energy, the labour embedded in their mining and processing, and the
energy in smelting and processing, remained tied to the solar cycle. For example, iron manufacture
depended on vast quantities of wood for conversion to charcoal to use as a reducing agent and energy
source.
The Roman empire was able to appropriate the stored products of solar energy – precious metals,
works of art, and people (Taylor & Tainter 2016). At the limits of expansion, the empire managed
to sustain itself by consuming its capital resources, but ultimately, these measures were bounded.
The financing of the empire shifted from consuming stored solar energy, to consuming annual solar
energy harvested by agriculture. Coinage was debased, shifting a greater economic burden to the
future. Eventually the Western Empire collapsed, with the Eastern Empire surviving as the much
weaker and less stratified Byzantine Empire. Tainter (2011) suggests that the transition from the
Eastern Roman to Byzantine Empire represents the only example where ‘a large, complex, society
systematically simplified, and reduced thereby its consumption of resources’.
As systems evolve towards greater complexity, they rely on more productive energy supply systems.
But energy supply is also subject to diminishing returns and eventually becomes a constraining factor.
Less developed nations consume a greater proportion of total expenditure on energy, while in the
advanced economies, there seem to be energy cost share thresholds above which economies reconfigure
or contract (Bashmakov 2007, King 2015b). The evolution of national economies towards greater
specialisation and diversification is important in projecting net-energy into the future and covered in
more detail in chapter 3.
Podolinsky (1883), a Ukrainian socialist, was the first to explicitly link energy and economic pro-
cesses. Podolinsky was concerned with the distribution of labour and tried to reconcile the labour
theory of value with thermodynamic principles, concluding that the socialist model was flawed. In
much the same way that the modern limits-to-growth movement argues that globalisation and cap-
italism run counter to resource scarcity, Podolinsky was arguing that ‘scientific socialism’ similarly
failed to account for resource scarcity.
Beginning the twentieth century, a growing body of literature was exploring the connection between
energy and economic processes. In the main, the research lay outside the orthodox economics field,
and much of it offered critiques of standard economic theory. Ostwald (1909) was the first to argue
for something approaching an ‘energy theory of value’ when he suggested that energy was the ‘sole
universal generalization’.
The most significant of the early contributors was Soddy (1933), who sought to explain elements
of economic development with the use of thermodynamics. Soddy was a nuclear chemist, but applied
physical laws to a critique of standard economic theory, debt, and financial practice. Soddy argued
17
that debts are ‘subject to the laws of mathematics rather than physics’ but ‘wealth is subject to the
laws of thermodynamics’ (Soddy 1933, chpt.IV). He noted the contradiction between financial debts,
which grow exponentially, and the ‘real’ economy, which relies on the consumption of exhaustible
stocks of fossil fuels.
Perhaps the most radical example of the application of an energy theory of value was the ‘Tech-
nocrats’, a movement led by professional engineers in the US and Canada in the 1920s and 1930s
(Buenstorf 2004, Cleveland 1987). The technocrats were influenced by Soddy, but also Frederick Tay-
lor’s scientific management, and Thorstein Veblen’s radical political economy (Buenstorf 2004). The
Technocrats proposal was to adopt energy as a universal measure of value – engineers would calculate
the energy content of all materials and work, and use these to derive tradable energy certificates. The
Technocrats argued that the conventional price system was ‘complex, unstable and arbitrary’, but
that ‘engineers, capable of making measurements free from the distorting interests of economics and
politics, would organize society better than politicians.’ (Taylor 1988).
More recently, ‘energy theory of value’ proponents have included ‘no growth’ or steady-state propo-
nents, who argue that a finite world requires a fundamental shift from conventional growth economics.
For example, Sgouridis (2014) proposes a radical shift to energy-backed or energy-referenced currencies
as a means of ‘realigning the economic system to the thermodynamic limits of the physical world’.
Although all single-value theories suffer from an inability to incorporate the complexity of human
interactions, Hau & Bakshi (2004) identify several attractive properties of the principle of energy
based valuations:
1. They provide a bridge that connects economic and ecological systems.
2. They compensate for the inability of money to value non-market inputs in an objective manner.
3. An energy valuation model is scientifically sound and shares the rigor of thermodynamic meth-
ods.
4. The adoption of a common physically-based unit allows all resources to be compared, in principle,
on a ‘fair’ basis 1.
5. Energy analysis provides a more holistic alternative to many existing methods for environmen-
tally conscious decision making.
1 Although the joule (and equivalent) is the common unit of energy, there is no universal measure that permits acomparison of energy across all contexts. The thermal-equivalence approach of the major energy agencies is usuallythe default comparator, but has limitations. Chapter 6 - ‘Equivalence of Investment and Output Energy Forms’provides a more detailed examination.
18
1.3.3 The notion of energy surplus
The first use of the concept of ‘surplus energy’, which is the forerunner to the EROI term, was
the American sociologist W. Fred Cottrell (1955) in Energy and Society. Cottrell linked the idea
of energy quality with economic and social development. He identified the capability of energy to
being able to magnify labour and therefore increase labour productivity. He explored the relationship
between energy quality and surpluses, and social and cultural life. For example, he noted that bulky
raw materials, such as farm produce and timber were often produced in the hinterland and shipped
downstream to cities, where they were processed into more valuable goods, but that the wealth did
not flow back ‘uphill’ (Cleveland 1987).
Similarly, Allen (2012) showed that the ready availability of energy, and the accompanying labour
productivity gains, significantly boosted the real wage of workers in London in the eighteenth century,
such that the typical purchasing power was two to five times higher than other comparable cities.
The application of energy through tools or machines magnified the work that a single worker could
perform.
Although the concept of ‘energy surplus’ had been introduced by Cottrell and others, it was
the work of Hall (1972) that sought to more precisely define EROI with measurable data. In his
early research paper on fish migration in North Carolina, under the tutelage of Howard Odum, Hall
measured ecosystem productivity and discovered some clear patterns. There was a trade-off between
fish investing some of their energy in upstream migration, from which they could feed from more
productive waters, compared to the downstream waters that were less productive, but more stable.
Hall found that fish populations that migrated would return at least four calories for every calorie they
invested in the process of migration. The connection to energy in other areas of ecological became
evident.
In building on the ideas of Lotka, Odum & Pinkerton (1955) developed the ecological ‘maximum
power principle’, which sought to provide a complementary explanation of biological evolution. The
principle was earlier developed in electrical engineering by Jacobi. The electrical Ohm’s Law provides
a demonstration of the relationship between maximum power and maximum efficiency. In electronic
devices, the output resistance of the power source should match the input resistance of the load to
maximise the power throughput. However, the point of optimised power does not match the point of
optimised efficiency. In the case of loudspeakers, for example, the maximum sound is achieved when
the impedance (AC resistance) of the speakers matches the output impedance of the amplifier. Under
this condition, half of the energy is dissipated in the speakers and half in the amplifier. Loudspeakers
with a higher impedance will possess a higher system efficiency but at a lower maximum sound level
volume, requiring a larger amplifier to reproduce an equivalent sound level. In practice, the respective
19
impedances are selected to provide a trade-off between power transfer and efficiency.
Using a forest as an ecological example, Hall (2004) described the relationship between a tree’s
leaf area index (LAI) and the energy capture. The highest efficiency is achieved with a relatively low
LAI since the topmost leaves capture the most sunlight, but each leaf is energetically expensive to
maintain. But the benefit of high efficiency is offset by the limited leaf area and therefore total energy
capture; an efficient plant would be short and out-competed in a forest. Hall showed that there exists
a diminishing return between maximising energy throughput (power) and efficiency – at too great a
leaf area, the energy cost of maintaining the leaves is greater than the useful benefit.
Odum applied the maximum power principle to the economic realm, suggesting that systems tend
to select for processes that maximize their use of available energies. Similarly, Boyle (2016) argued
that business supply chains that consume matter and energy at the greatest rate will tend to persist
and dominate markets.
Another of Odum’s insights was that natural processes can only be properly understood by applying
a systems analysis. Odum was a student of the ecologist G. Evelyn Hutchinson, who delivered a
paper titled Circular Causal Systems in Ecology in 1946. Hutchinson argued that biological and
physical processes were tightly linked (Taylor 1988). He constructed carbon budgets for the biosphere,
phosphorus budgets for lakes, and attempted to balance the respective material flows.
In the tradition of Soddy (1933) and others who connected thermodynamic principles and economic
growth, Herman E. Daly (1972) articulated a view that was to form the basis for the steady-state
economy movement. In Steady-State Economics, Daly argued that the ‘circular flow of exchange value’,
implicit in standard economics, is incompatible with the unidirectional and linear matter-energy flows
of the physical world. This fundamental distinction was to remain as one of the key foundations of
biophysical economics, and is covered in more detail in chapter 2. Daly argued that stocks of physical
wealth needed be held constant by controlling the consumption rate of energy and matter.
1.4 Energy and economic valuations
1.4.1 Introduction
Net-energy analysis implies an objectivist approach to energy since energy possesses intrinsic prop-
erties that are not readily substitutable (covered in more detail in chapter 2). Furthermore, energy
possess quality differences, described in exergy analysis, and quantification differences, related to the
way primary energy is evaluated. In order to contextualise net-energy within the broader economics
field, it is useful to frame net-energy as adopting, in part, an energy theory of value. This section
briefly identifies theories of value.
20
but it has quality differences (exergy) and quantification problems (among practitioners).
1.4.2 Contrasting theories of value
Prior to the so-called marginalist revolution, wealth was believed to derive from intrinsic or objective
qualities. Historically, the principle ‘objectivist’ factors, or primary factors of production, were land
and labour. Many other factors have been suggested as possessing an intrinsic value independent of
price. Similarly, various stores of value have been proposed, such as coinage, gold, or salt. These, and
the later ‘subjectivist’ interpretations are shown in tables 1.3 and 1.4.
From the early modern period, the objectivist notion of value had been undermined by the ‘water
versus diamonds‘ paradox (Hall & Klitgaard 2011, p.110). Smith (1776, p.103) argued that value can
have two different meanings – ‘value in use’ and ‘value in exchange’. Water is essential for life but is
often abundant, and therefore may not command a high price. In contrast, a diamond has little ‘use
value’ but ‘a very great quantity of other goods may frequently be had in exchange for it.’
A resolution to the paradox occurred in the late nineteenth century and came to be termed the
‘marginalist revolution’. It is generally ascribed to Jevons (1871), Menger (1871), and Walras (1896).
Marginalism is the idea that value or utility is related to ‘the next unit’ of a product or service, and
is connected to scarcity and exchange value rather than some intrinsic quality. Standard economics
emerged from the marginalist revolution. Although the adoption of market valuation seemed to resolve
the longstanding paradox, the complete rejection of intrinsic value also led to a major shortcoming
– an inability to properly value non-renewable natural resources, or fully value the cost of wastes and
pollution. The idea of ‘marginalism’ also made it much harder to correctly identify the role of natural
resources, especially energy, in economic growth. Instead, beginning with Solow (1957), ‘technical
innovation’ came to be adopted as the primary explanation of economic growth.
1.5 Net-energy
1.5.1 Context
In the postwar period, economic growth was enabled by rapidly increasing energy resources, with the
link between oil supply and the economy being strongly connected (Hamilton 1983). Up until the
late 1960s, the US had been the pre-eminent supplier of oil. For the period of World War II, the
US supplied 6 out of the 7 billion barrels of oil consumed by the Allies (Miller 2001). Japan and
Germany’s deficiency in oil were key factors in the Allied victories (Jacobs 1998, p.159).
However, US domestic supply peaked in 1970, leaving it vulnerable to supply shocks. The 1973
OPEC oil embargo, which led to a quadrupling of the world oil price, was to be a watershed in
21
Theory Proponent/s Comments
Objectivist theories
Electricity Beaudreau A production function based on capital, labour andelectric power provides a closer correlation to eco-nomic growth in the US, Germany and Japan (Beau-dreau 1999, 2005).
Energy Technocrats, Sgouridis Value is determined by the energy content of all ma-terials and work (Buenstorf 2004, Cleveland 1987,Sgouridis 2014). May include an energy-based cur-rency.
Gold Austrian School of Eco-nomics
Adherence to a gold standard reduces price inflation,and reduces exchange rate volatility.
Human action Graeber Intentional or productive action aimed at a certaingoal produces social relations and in doing so trans-forms the producers themselves (Graeber 2001, p.59).
Information Kurzweil There is a rapidly increasing knowledge and informa-tion content in products and services, and that theseare not constrained by material resources. Economiesare driven by data and knowledge (Kurzweil 1999,p.124).
Land Petty, Quesnay, Cantil-lon, Physiocrats
Land and sunlight are the origins of wealth. Manu-facturing and commerce are ‘unproductive’ or ‘sterile’(Murphy 1993).
Labour Smith, Ricardo, Marx Physical capital embodies previous labour (Smith1776, Ricardo 1891). Ricardo (1891, p.12) noted ‘Pos-sessing utility, commodities derive their exchangeablevalue from two sources: from their scarcity, and fromthe quantity of labour required to obtain them.’
Money Sumner A stable relationship between money and the economyrequires a predictable or modest controlled expansionof nominal GDP (Sumner 2015).
Social relations Strathern, Dodd Objects (e.g. a pig or shell) embody social relations(Strathern 1992). For Dodd, money is a social linkthrough which social relations of interdependence andconflict are resolved (Dodd 2016, p.45).
Sunlight Odum Solar emergy is the available solar energy used up di-rectly and indirectly to make a service or product (Hau& Bakshi 2004, Odum 1996).
Table 1.3: Comparison of objectivist theories of value and stores of value. Compare subjectivisttheories, show in table 1.4
22
Theory Proponent/s Comments
Subjectivist theories
Utility, scarcity, con-sumer preferences
Most economists Capital and labour are the primary factors pf pro-duction. Solow’s residual (i.e. total factor produc-tivity) drives economic growth. Marginal productsequal to factor shares. Energy is important but a mi-nor share of national economic activity, and is substi-tutable with capital.
Capital and labour sup-plemented by energy
Nordhaus Energy’s marginal productivity set to its factor share
Hybrid theories
Capital, labour, energy,and knowledge
Kummel, Ayres, Keen Energy’s marginal productivity much greater than itsfactor share. (Ayres & Warr 2010, Kummel 2011,Kummel et al. 2010, Keen & Ayres 2017)
Table 1.4: Comparison of subjectivist theories of value.
national energy policy for many oil importing states (Scott 1994). Nations adopted supply and
demand strategies, including an expansion of coal and nuclear energy, much greater investments in
alternative energy and synthetic fuels, greater focus on energy efficiency, energy information sharing,
and a strategic oil reserve policy. The supply shock was moderated in Australia by the development
of the Gippsland Basin, and surplus coal resources. At the time, Australia was 60% self-sufficient in
oil, and the oil price was subject to a regulated fixed price of $A2.06/bbl. Nonetheless, as a small
trading economy, Australia soon imported the indirect effects of the oil price rise – inflation, rising
unemployment, and recession (Marks 1986).
This was a formative period for energy research. It was driven by the tangible effects of oil supply
shocks and its responses, along with concerns about ‘limits to growth’. One of the fields of inquiry
that emerged was ‘net-energy’, which sought to explain energy supply from an underlying physical
perspective. Several strands of activity emerged, including:
1. Establishing the energy costs of goods and services with input-output analysis (Bullard & Heren-
deen 1975, Bullard 1975).
2. Assessing the rising per-barrel energy investment of the US oil extraction industry (Hall &
Cleveland 1981, Cleveland et al. 1984).
3. Assessing the degree to which Britain’s expanded nuclear power program reduced reliance on
imported oil (Chapman 1975a,b, Department of Energy England 1975).
4. Conducting a broad-based analysis of fossil fuel extraction in the Western United States (Melcher
et al. 1976).
23
5. Incorporating the principle of net-energy into energy policy – ‘net-energy’ was itemised as one of
several ‘governing principles’ in the US Federal Nonnuclear Energy Research and Development
Act of 1974 (US EPA 1974, sec.5.(a).(5)).
Building on the work of Bullard and others (e.g. Bullard et al. (1978)), who had introduced formal
methods for net-energy, several researchers including Cleveland, Costanza, Hall, and Kaufmann began
to incorporate empirical data on petroleum exploration and production into resource models. Much
of the literature was beginning to incorporate the EROI metric. Although the net-energy concept
was already in use, the EROI statistic was formally introduced by Hall (1972) and elaborated on by
Melcher et al. (1976). The work on petroleum was exploring various metrics as a means of assessing
future energy prospects. For example, Hall & Cleveland (1981) estimated the discovery rate for
petroleum in the United States, using the metric ‘per foot of drilling effort’. They found that the
metric was declining at about 2% per year, implying that greater effort was required to secure a given
quantity of oil and natural gas.
In their seminal paper, Cleveland et al. (1984) presented an argument for energy return on invest-
ment (EROI). The four key hypotheses of biophysical economics were identified:
1. A strong link exists between fuel use and economic output, particularly when adjustments are
made for energy quality.
2. A large component of the increased labour productivity through the twentieth century can be
attributed to the increased use of fuel.
3. Inflation levels can be partly attributed to changes in the cost of obtaining fuel.
4. The general trend is towards an increase in the cost of locating, extracting and refining fuel,
although technical change mitigates this trend to some degree.
Although early research centred on fossil fuels, especially oil, the tension between resource depletion
and technological advances (item 4 above) was to become an important area of research in relation
to electricity generation technologies. It was clear that the depletion rate of non-renewable resources
would reduce the future availability of resources. Furthermore, even renewable energy technologies
were dependent on the incumbent energy system for their production and system support, and none
of the prospective renewable energy alternatives offered the same suite of energy qualities as oil
and natural gas. But it was also apparent that technological advances and learning-by-doing made
previously uneconomic resources available or reduced the cost of renewable electricity technologies.
These competing factors, which could be framed as Malthusian versus cornucopian, were typified
by the ‘Club of Rome’ versus Julian Simon respectively. In The Limits to Growth, Meadows et al.
24
(1972) argued that exponential growth of natural resource extraction is incompatible with a finite
world in the long run. On the other hand, in The Ultimate Resource, Simon (1981) argued that the
capacity for humans to ‘invent and adapt’ outstrips any natural resource constraints.
The question of natural resource depletion and the potential for substitution was being widely
explored. Speaking from a conventional economics perspective, Solow (1974) identified the issue,
noting –
If it is very easy to substitute other factors for natural resources, then there is in principle
no ‘problem’. The world can, in effect, get along without natural resources, so exhaustion
is just an event, not a catastrophe. If, on the other hand, real output per unit of resources is
effectively bounded – cannot exceed some upper limit of productivity, which is in turn not
too far from where we are now, then catastrophe is unavoidable. In-between there is a wide
range of cases in which the problem is real, interesting, and not foreclosed. Fortunately,
what little evidence there is suggests that there is quite a lot of substitutability between
exhaustible resources and renewable or reproducible resources, though this is an empirical
question that could absorb a lot more work than it has had so far.
By the early 1990s, the price of world oil had fallen from its oil-crisis highs. Inflation had subsided
and the world economy returned to trend growth of about 3% a year, leading to a 20-year hiatus
from energy concerns (Hall & Klitgaard 2011, p.26). It wasn’t until the release of the IPCC Third
Assessment Report (TAR), emergence of debates around the efficacy of corn ethanol, and the run
up in oil prices in the 2000s that net-energy re-emerged as a field of inquiry. Energy researchers
were engaged in explorations of energy transitions, with debate centred on the efficacy of fossil fuel
substitutes and the relationship between energy and economies.
1.5.2 EROI as a distinct field of inquiry
By 2011, several elements of EROI had been formalized methodologically (Murphy et al. 2011). Four
key areas of inconsistency were addressed:
1. System boundaries.
2. Energy quality corrections.
3. Energy-economic conversions.
4. Alternative EROI statistics.
Furthermore, Murphy & Hall (2011) had identified the major strands of EROI research as:
25
1. The efficacy of corn ethanol as a net energy producer.
2. A summary of the state of EROI for most major fuels and electricity generators.
3. Alternative applications of EROI, such as energy return on water invested.
4. The relationship between EROI and the economy.
5. The minimum EROI for a sustainable society.
1.5.3 Bio-ethanol
The United States and Brazil have been producing fuel ethanol since the 1970s, however, attention
turned to the efficacy of US corn-based bio-ethanol as a petroleum substitute (Solomon et al. 2007).
Pimentel was an early protagonist of the biofuel efficacy debate in the United States (Hall et al.
2011). He drew attention to the energy intensity of the ethanol life cycle and questioned whether the
net-energy was positive Pimentel (1991). A negative result would undermine the value of the produc-
tion process as a large scale petroleum substitute, although the high utility of the liquid fuel would
somewhat complicate the assessment. Some of the most significant energy inputs included: nitrogen
fertilizer, irrigation, embodied energy of machinery, drying, and on-farm diesel. Hammerschlag (2006)
provides an overview of the literature, with most estimates falling between 1.3:1 to 1.7:1, a range of
figures that are mostly above Pimentel but well below the minimum useful energy surpluses required
for society (Hall et al. 2009).
1.5.4 Electricity generation
Unlike ethanol, which is a direct substitute for petroleum fuels, the role of electricity generators
within an electricity grid is more complex. Different generator types may not be direct substitutes.
Liquid fuels are storable and fungible, but electricity is only useful within the context of a complete
system. Defining the specific role of each component is essential to estimating their respective value.
Furthermore, much of the cost to end users is associated with transmission and distribution, rather
than generation. Yet nearly all electricity EROI studies focus exclusively on generation.
Electricity systems share the same attributes as other utilities and public services, such as water,
telephone, and passenger rail in being built to meet peak demand, requiring expensive and long-lived
infrastructure. Once constructed, costs are sunk and marginal unit costs are small. Utility costs have
traditionally been recovered with unit costing, such as per litre, per phone call, or per ride. Some
utilities include a fixed component, such as a daily or access charge.
In the case of commuter trains for example, consumers pay per ride, but in nearly all major systems,
most of the cost is met through public finances (Walker 2011). Much of the social, environmental,
26
and broader economic benefits of public transport (the so-called ‘public good’) are captured outside
the system (Walker 2010). Monetising these benefits is possible but challenging. The discussion from
Hopkinson (1892) on the challenges of equitable and prudent cost recovery in electricity provision is
still relevant today. Hopkinson observed that a reliance on unit costing for low-use consumers would
provide insufficient revenue to cover the high fixed costs of servicing those consumers.
EROI for electricity generation has been widely studied, especially coal-fired electricity, including
carbon capture (Wu et al. 2016, White & Kulcinski 2000), wind power (Kubiszewski et al. 2010),
solar photovoltaics (Bhandari et al. 2015, Koppelaar 2016, Louwen et al. 2016) and gas-fired genera-
tion (Moeller & Murphy 2016). The boundaries and types of analysis vary between studies, but all
adopt the electricity busbar or inverter output as the EROI numerator2 – electricity distribution and
management of the grid system as a whole is typically excluded from the analysis boundary.
Net-energy analyses usually adopt the tools of life-cycle assessment to establish the lifetime embod-
ied energy. The principal benefit of standard guidelines is that they permit like-for-like comparison.
Many of the differences in findings between conventional analyses can be accounted for via meta-
analyses that harmonise for key performance parameters.
The limitation of a standardised methodology is that studies are then restricted to answering the
range of research questions to which that methodology is suited. An emphasis on improved harmoni-
sation between studies may come at the cost of excluding energy investments that are important for
a comprehensive assessment of energy supply technologies. Furthermore, considerations relating to
the engineering-systems view of electricity supply, which are critical to establishing the value of some
forms of generation at higher grid penetration, are generally treated as lying outside the domain of
conventional life-cycle research.
Some variable renewable energy (VRE) studies include storage and RE overbuild within the anal-
ysis boundary. The problem is that defining the magnitude of RE overbuild and storage that provides
an ‘always available’ role is fraught, and understates the value of VRE in other contexts. Most studies
don’t consider system-level requirements within a broader suite of generation, including geographic
and technology diversity, along with engineering services. These system-level engineering services
ensure quality, stability and reliability of supply. They include parameters such as inertia, spinning
reserve, and rapid ramping. No single generator is required to provide these services, but there must
be sufficient supply of these at a system level.
2 See equation 1.1. The numerator is the energy supplied by the system in question. It is typically defined as the energysupplied over the assumed lifetime.
27
Chapter 2
EROI as a supplement to the price
system
2.1 Introduction
2.1.1 An outline of the problem
A common question posed to proponents of Energy Return on Investment (EROI) is — what will
energy analysis reveal that isn’t already apparent from a conventional financial analysis? The price
system already embeds all of the energy and non-energy costs into the cost of production of energy
supply firms. While it is true that non-market costs1 distort the ‘true cost’ of energy supply, this
doesn’t alter the argument that the price system is the most reliable guide to the value of fuels and
electricity.
The challenge for proponents of EROI is to explain how a hypothesis based, in part, on an ‘energy
theory of value’2 can be reconciled with the empirical observation that market prices regularly reflect
relative scarcity unrelated in any meaningful sense to energy resources. Clearly, energy is only one of
many scarce factors of production. The market price of a van Gogh is a stark example3. Furthermore,
while it is possible to make the claim that without energy, there would be no economic activity, it is
also possible to make the same claim for labour, water, air, land and to a lesser extent many other
factors of production. What makes energy unique?
1 Such as externalities, regulations, differential tax rates, and subsidies.2 ‘Energy theory of value’ is being used here to identify EROI as being grounded in an objectivist conception of energy,
discussed later. A better descriptor may be an energy-based analysis of a physical constraint.3 The sale of a painting is a zero-sum transaction and not measured in GDP, nor are capital gains generally. Hence, the
exchange value of artworks may not immediately contradict an ‘energy theory of value’ as it relates to value addingin GDP measurement. However, capital gains tax and broker fees, for example, would be included in GDP.
28
Since these are fundamental and contested issues, this chapter explores these in detail.
2.1.2 EROI as an energy productivity indicator
Definition of energy productivity
A common indicator of energy productivity is the ratio of economic activity to energy consumption,
or its inverse, energy intensity (Stanwix et al. 2015). The output is expressed in monetary units, such
as dollars. Energy use is expressed in physical units such as megajoules. In 2015, Australia consumed
5,831 PJ of primary energy (BREE 2015, table 1.3) and produced $1,637B of GDP (ABS 2015a),
giving an energy productivity of $0.28 per MJ of primary energy, or equivalently, an energy intensity
of the national economy of 3.6 MJ per dollar of net output.
A related, but distinct metric, end-use service energy intensity, is the ratio of energy consumed,
to end-use service delivered in physical units. An example is road freight energy intensity, expressed
as the diesel consumption per tonne-kilometre (IEA 2017c, p.83). In Australia for 2014, the road
transport sector consumed 488 PJ of diesel fuel (Office of the Chief Economist 2015b) to deliver 196
billion tonne-km of road freight (expressed as the product of the freight weight and the distance
travelled) (ABS 2014b), giving 2.5 MJ per tonne-km, or 0.065 litres of diesel per tonne-km, using the
diesel conversion factor from Australian Department of the Environment (2015).
Fuel price and energy productivity use the same units
The price of energy or fuels is expressed in the same units as energy productivity – dollars per physical
unit of energy. Petroleum fuels are priced in dollars per litre, electricity in dollars per megawatt-hour,
natural gas in dollars per gigajoule. In principle, the energy productivity of an entire economy should
be much greater than the price of energy, otherwise the economy would be producing only as much
output to purchase energy and nothing else. A nation with a high energy productivity is said to be
relatively decoupled 4. For example, Australia’s energy productivity of $0.28 value added per MJ of
primary energy can be contrasted with fuel prices of $0.038 per MJ ($1.30 per litre of petrol) and
$0.07 per MJ of retail electricity ($250 per MWh) 5.
EROI as a specialised energy productivity metric
Whereas energy productivity and related efficiency measurements are related to energy consumption,
EROI is applied to energy supply. The usual way to think about energy supply is to consider i) fuel
4 Although the activity mix of the economy and overseas trade influence energy productivity.5 Note that one unit of electricity requires about 2.9 units of primary energy in Australia with the current generation
mix, giving $0.024 per MJ primary energy.
29
price; and ii) fuel utility. The utility is the usefulness of the fuel in regards to the end-use service
required. For example, electricity and natural gas are substitutes for building space heating. On the
other hand, liquids fuels have the highest utility for transport applications, and therefore petroleum
fuels can command a price premium over less readily substitutable fuels, including natural gas and
electricity.
EROI is a specialised energy productivity metric, in which both the numerator and denominator are
expressed in units of energy, giving a unitless ratio 6. Just as the ‘road freight energy intensity’ given
above is a physical metric, EROI similarly does not directly include monetary costs7. As such, both
metrics reflect underlying physical properties. The EROI ratio provides an energetic valuation of the
fuel or electricity supply technology, which may, or may not, correlate with the conventional economic
valuation. An energetic valuation of fuels and electricity, based on EROI, conveys information about
the sustainability, and the trend of the sustainability indicators, of the energy supply chain that may
not be obvious from an economic valuation. The principle weakness of economic valuation is that
market prices reflect the cost of bringing the next unit of energy to market8, which may be a poor
indication of long-run sustainability and prosperity.
Overlap between energetic and economic valuations
There may be overlap between energetic and economic valuations, determined by the capital and
energy intensity of the energy supply system (King et al. 2015a). For example, the price of oil and gas
production has been shown to be inversely related to EROI (King & Hall 2011). On the other hand,
the price of solar photovoltaics may be a poor proxy for its energetic evaluation (Palmer & Floyd 2017).
Canadian oil sands is both energetically and economically expensive (Wang et al. 2017), while United
States shale oil has been shown to be less energetically expensive than oil sands (Moeller & Murphy
2016) although the economic costs are high due to a scarcity of drilling and support resources serving
the fracking industry (Hughes 2018). Biofuels generally perform poorly from an energetic perspective
(Ketzer et al. 2017, Carneiro et al. 2017), but differential tax treatment and other incentives may
obscure the underlying costs when assessed by an economic evaluation alone.
Nuclear power is subject to economic, regulatory and social constraints (von Hippel et al. 2012,
Lang 2017), rather than underlying biophysical constraints. Much of the cost of modern electricity
supply is related to network infrastructure, retail margins, and other administrative costs that have
a low energy intensity, and therefore the economic valuation may be more pertinent (Fares & King
2017, Palmer 2017b).
6 EROI is usually discussed as an energy ratio, but may also be expressed as a power ratio when applied to annualenergy flows.
7 Although monetary costs are often used to estimate the physical energy consumed.8 Market prices are ideally based on competitive markets, economic equilibrium and rational expectations.
30
2.1.3 EROI as a subset of biophysical economics
Net-energy will be explored through the lens of biophysical economics (BPE). ‘Biophysical’ refers to
the material world and can be contrasted with an anthropocentric perspective, of which mainstream
economics is a subset (Hall & Klitgaard 2011, p.1). A useful working definition of BPE is ‘the study
of the ways and means by which human societies procure and use energy and other biological and
physical resources to produce, distribute, consume and exchange goods and services while generating
various types of waste and environmental impacts’ (Chevallerau 2017). While BPE pays attention
to monetary prices, it is focused on the physical laws that impose constraints and interact with
‘anthropocentric’ systems.
2.1.4 The EROI hypotheses
The argument for EROI is summarised in the suite of hypotheses as follows:
1. Economically useful fuels and electricity are always derived from primary energy resources ex-
tracted from nature 9. But, with few exceptions, energy resources in nature are not suitable for
providing energy services until they have been processed or upgraded to energy products, suit-
able for downstream purposes10. By definition, commercial energy products are ‘commercial’
because some value-adding processes have been undertaken.
2. A corollary to hypothesis 1 is that the upgrading of natural energy resources occurs within
the economic system, but the natural resources themselves are appropriated from outside the
economic system.
3. Energy is a primary factor of economic production, and energy consumption and economic
development are highly correlated. Relative decoupling between energy and economic growth
can be observed in some developed countries over some time intervals, but doesn’t alter the
general pattern across regions and through historical time (Stern et al. 2014).
4. A corollary to hypothesis 3 is that energy is substitutable with capital and labour, but there is a
lower bound to substitution. Certain types of economic activity, such as knowledge production
and personal services are only weakly linked to energy. However, all physical processes are
governed by physical and thermodynamic laws. In respect of energy, civilisation is like any other
physical process; that is, as an open, non-equilibrium thermodynamic system that sustains itself
with the consumption and dissipation of energy (Garrett 2014).
9 Primary energy is defined as energy mined or extracted from nature. This includes non-renewable resources, such ascrude oil and coal; and renewable energy sources, such as sunlight and wind.
10 Upgrading includes extraction, processing, refining and transport. A counter-example is drying clothes outdoors,which is free. It is an example of direct use of energy from nature, not requiring any conversions.
31
5. A further corollary to hypothesis 3 is that the output elasticity of energy is far greater than its
factor share11 (Ayres 2001, Stern & Kander 2012, Lindenberger & Kummel 2011, Keen & Ayres
2017, Kummel et al. 2015). Energy-augmented aggregate production functions have been shown
to provide a closer approximation to output than those relying on capital, labour, and ‘technical
progress’ alone.
6. Energy industries can be conceptualised as functional units that ‘lever’ primary energy from
nature, using commercial energy12, to make additional commercial energy for consumption by
industry and households. EROI is a measure of the mechanical advantage of the ‘lever’. However,
energy is only one scarce factor of production to energy supply firms.
2.2 Contrasting the circular and linear flow economies
2.2.1 Neoclassical perspective
Beginning from the work of Law, Cantillon, and most notably Quesnay’s Tableau Economique in
the eighteenth century (Murphy 1993), the concept of the circular flow economy (e.g. Samuelson &
Nordhaus (2010, fig. 5.1)) conceptualises goods and services flowing from producers to purchasers,
who direct their labour and investment back for more production. The circular flow, or closed system
model, is the basis for the accounting procedures for national accounts (Coulter 2017). All three
approaches to gross domestic product (GDP) – production, expenditure, and income – are based on
accounting rules that bring each side of an accounting ledger into balance, consistent with double
entry bookkeeping. For example, the production approach defines GDP as the sum of all gross value
added, which equates to the difference between total output and intermediate consumption. This
is consistent with treating ‘the economy’ as being purely a monetary-based system, with currency
flowing between economic actors. It is also consistent with the first law of thermodynamics, which
is the law of conservation of energy – equivalently, no money is gained or lost through economic
exchange. Furthermore, since the marginalist revolution of the late nineteenth century, the concept
of economic equilibrium, in which supply and demand curves intersect, is assumed to apply. Closed
systems are characterised by predictable end states, with defined equilibria.
The concept of circular flow was further reinforced with Bill Phillips so-called ‘MONIAC’ – a
mechanical-hydraulic machine that simulated the UK economy with a system of tubes, tanks, valves
11 The standard cost-share theorem states that the output elasticity of a production factor must be equal to its sharein total factor cost (Lindenberger et al. 2017). Hence the ‘economic power’ of energy can be no more than its factorshare.
12 In this discussion, a distinction is being made between ‘primary energy’ and ’commercial energy’ to clarify thehypotheses. In energy statistics, there is usually overlap, particularly for coal, for which the material dug out of theground is the same as the material weighed and shipped.
32
and pumps. It was first demonstrated at the London School of Economics in 1949.
Each tank represented a sector of the economy, including households, business, government, and
foreign sectors (Bollard 2016). A further tank at the bottom represented total national income. The
machine was able to be ‘tuned’ by adjusting valves to simulate real-world aggregate transactions.
At face value, the MONIAC was a brilliant machine in respect of its treatment of monetary flows
and investments. Phillips machine was successfully able to replicate characteristics of the macro-
economy. But as a model of the real world, it contained an underlying flaw – it ignored the flows of
energy and materials upon which ‘real economies’ are dependent (Raworth 2017). In a fitting analogy,
the machine only worked when an electric motor was turned on to power the pumps.
2.2.2 BPE perspective
In Paleolithic societies, energy and material flows could be conceptualised as circular flows energised
by sunlight. Humans consumed food produced by nutrient rich soil and energised by sunlight, and
breathed oxygen released by plant life. Organic wastes were returned to the soil and carbon dioxide
to plants. In a natural ecological cycle, no waste accumulates because nothing is wasted (Commoner
1971, p.126). Hence, in a pre-industrial context, the closed-system model of economic exchange may
have been able to approximately replicate natural biophysical cycles. However, once released from the
‘organic cycle’ by fossil fuels (Wrigley 2010), the entire cyclic process became disrupted.
Whereas neoclassical economics adopts capital and labour as the primary factors of production,
BPE defines energy-matter13 as the only factor that cannot be physically produced from within the
economic system – energy is the only unsubstitutable and unrecyclable input into every human activity
(Smil 2008, p.344);(Stern 2011). This implies, at least in part, an objectivist ‘energy theory of value’.
2.2.3 Integrating biophysical with neoclassical perspectives
The modern physical economy is based on the flow of energy and materials to and from the environ-
ment. The one-way flow of energy – from natural resources, upgrading, through dissipative processes,
and eventually low grade heat, pollutants and degraded materials – is an immutable natural law.
Money 14 facilitates the physical process, but does not define the process itself. Although the circular
flow model is consistent with the first law of thermodynamics, it is not consistent with the second
law, which states that energy quality must degrade when work is extracted from a thermodynamic
system.
13 Energy is not itself ‘stuff’. Energy is a property of objects that characterises their behaviour and their relationshipsto one another. Although the scientific definition is often given as ‘the ability to do work’, there is no single definitionbetween different contexts (Giampietro & Sorman 2012).
14 Not shown in figure 2.1 is the expansion of the money supply by money creation by private and central banks.So-called endogenous money creation occurs within the economic system.
33
Final goods & services
Producers
Purchasers
Productive services(labour, land, etc.)
Wages, rents, profits, etc.
Expenditures
Primaryenergy
Waste,low-grade heat
& pollutants
Energy industryown use Final
consumption
Industrialconsumption
Waste,low-grade heat
& pollutants
Energysupplyfirms
$
$
Intermediateconsumption
Figure 2.1: Stylised depiction of standard circular flow of the economy, augmented with energy.
Figure 2.1 augments the standard economic model of circular flows with the linear flows of energy,
including energy-industry-own-use. In this energy-augmented conception, energy is both a driver and a
facilitator of economic activity – causation between energy and economic activity can be bi-directional.
Biophysical economists emphasise that production functions based on an energy-augmented model
must reflect the physical reality of the economic system (e.g. Keen & Ayres (2017)). Whereas the
standard economics model is taken as a closed system in equilibrium, the biophysical model is an open
system in disequilibrium. Open systems can exhibit complex and unpredictable behaviour patterns
that are far from equilibrium.
However, standard economics does not recognize energy and materials from nature as existing
outside the economic domain, nor the complex behaviour of biophysical systems that are subject to
biophysical constraints or shocks. Rather, the relevant metrics are the capital and labour economic
costs of bringing energy and materials to market.
2.3 Limits of energy substitution
2.3.1 Introduction
Energy is substitutable with capital-labour, but the BPE hypothesis is that there is a lower limit to
energy substitution. Conventional economics assumes that there is, in principle, no limit to resource
and energy substitution, and ascribes a high price elasticity to energy in the long-run in response
34
to rising fuel prices. For example, rising oil prices in the 1970s stimulated policies to improve the
energy efficiency of end-use energy services. This contributed to increasing the capital stock of energy
consuming equipment, but at a lower direct energy consumption. A neoclassical energy-economics
analysis would usually only consider the direct energy effects of this substitution. However, higher
efficiency equipment typically embeds a higher energy content than what would be assumed taking
an economy-wide energy intensity.
2.3.2 Stylised isoquant
One way of depicting the limits of substitution is the use of a stylised isoquant15 comparing energy
on the y-axis with capital and labour on the x-axis. Figure 2.2 depicts a stylised isoquant plotted
against energy versus capital-labour, based on Stern (1997, fig.2).
E = f(K,L) is a neoclassical isoquant (blue dashed line) for a constant level of output; g(K,L)
is indirect energy of capital-labour; and addition of the direct and indirect energy costs results in the
isoquant E = h(K,L). h and g shown for different indirect energy requirements. All physical work,
heat, material transformations, and material processes are governed by physical laws that require a
minimum application of direct energy (Cullen & Allwood 2010).
Figure 2.3 is based on Kumhof & Muir (2014, fig.3); Stern (1997, fig.2); and Ayres & Nair (1984,
fig.3), and depicts the isoquant region to the right of the upward turn as an ‘unviable factor space’
since it represents a more costly region of the isoquant.
Referring to figure 2.2, in the ranges of high and intermediate energy consumption, the neoclassical
(blue) and BPE (black) approach produce a similar isoquant. However, the BPE perspective is that
energy substitution is ultimately constrained by the second law of thermodynamics. As the ‘entropy
boundary’ is approached at lower direct energy consumption (upper edge of red region in figure 2.3),
the isoquant turns upwards because of the energy embodied in energy producing, and consuming,
capital (and to a lesser extent labour)16. Standard economics would normally only consider the
direct energy flows, rather than also incorporating the indirect energy explicitly. An upward turn of
the isoquant implies that proceeding further rightward increases the total (direct plus indirect) energy
consumption, rather than decreasing it, as focusing on the direct energy only would imply. In practice,
economic activity would not be expected to proceed to the right, but rather, shift downwards and to
the left to a different isoquant at a lower level of output (i.e. economic contraction).
Although it may be possible to establish similar isoquant relationships with other scarce factors
15 In economics, an isoquant (equal output) is a contour line depicting the same output for a given set of productionfactors, typically capital and labour, or in this case energy and capital-labour. The gradient of the curve at any givenpoint depicts the elasticity of substitution between the given factors.
16 The energy of labour can be considered either the food intake, where 3,000 daily calories equals 12.5 kJ, or thecontinuously available muscle power of perhaps 100 to 200 watts. This is two orders of magnitude less than theper-capita consumption of commercial energy in the developed economies.
35
f(K,L)h (K,L)1
h 2h 3h 4 g (K,L)1g2
g4g3
Ener
gy
Capital/labour
Figure 2.2: Substitution and indirect energy requirements, based on Stern (1997, fig.2).
of production under certain circumstances, including clean water or mineral ores, a unique aspect of
energy is that it is not possible to avoid the requirement for energy in any physical process. Given
sufficient cheap energy, it is nearly always possible to produce, or substitute away, from non-energy
factors.
2.3.3 Energy cost shares
Empirical data on energy cost shares can be used to test the hypothesis for an entropy boundary
with respect to energy. Evidence in support of the hypothesis is the relatively narrow band of energy
expenditure cost share as a proportion of GDP.
Bashmakov (2007) introduced the so-called ‘Three Laws of Energy Transitions’, one of which was
the observation that the energy cost to income ratio tends to converge towards a stable long-term
ratio. When the energy costs to GDP ratio is below a given threshold, which Bashmakov defines as
less than 11% in OECD countries, energy exhibits a moderate price elasticity. However, when the
threshold is exceeded, price reactions to small changes in demand are much higher, and economic
growth is hampered. Similarly, Fizaine & Court (2016) found that the energy cost share in the United
States must be less than 11% for the national economy to exhibit a positive economic growth rate.
Fizaine & Court explicitly linked the energy cost share to EROI. Based on the current energy intensity
of the US economy, this translates to a societal EROI of around 11:1. King (2015b) collected long-run
energy expenditure data (Fouquet 2010, 2011, 2014) and found similar results.
36
Unviableregion of
factor space
Ener
gy
Capital/labour
h (K,L)1
Figure 2.3: Entropy boundary based on figure 2.2.
For a given energy cost share, the final energy services delivered are a function of a country’s
GDP, energy productivity, and energy price. This implies that wealthier nations consume more
energy services, and that nations with a higher overall energy price tend to possess a higher energy
productivity. This is borne out by international comparisons (King 2015b), and can be explained by
differential national circumstances and policies (Grubb 2014, 2017).
The asymmetry of the price elasticity of energy, with respect to the national energy cost share,
suggests that there is a lower bound to energy substitution. Energy is an primary factor of production
that is only partially substitutable.
2.3.4 Oil price shocks
The oil price shocks of the 1970s produced a rich literature examining the linkages between oil and
economic activity. Much of the analysis focused on conventional economic interpretations, such as ex-
ogenous versus endogenous oil prices, effects of monetary policy and inflation, consumer and producer
expectations, etc. (e.g. Kilian (2008, 2006)). The closest biophysical interpretation by a standard
economist is possibly Hamilton (1983, 2009), who implicated large increases in the crude oil price in
all but one of the post-World War II US recessions.
The challenge for the standard economic interpretation is that the magnitude of macroeconomic
outcomes is far greater than what the relatively minor share of oil expenditures would suggest. For
37
example, the loss of oil (a 20% decline in oil consumption over 2 years) from Cuba’s economy as a
consequence of dissolution of the Soviet Union in 1989 was devastating to the entire economy, even
though the implicit expenditure on oil was minor relative to national GDP. The Cuban socialized
model of agriculture had been sustained by the highly generous terms of trade of Cuban sugar for
Soviet oil (Sinclair et al. 2001). The Cubans did eventually learn to live with less oil (Hall et al. 2008),
and adapted to the loss of oil through decentralization, urban gardening, and a range of market based
reforms (Sinclair et al. 2001).
The hypothesis that energy is a primary factor of production, and that the output elasticity is far
greater than its factor share (Ayres 2001, Stern & Kander 2012, Lindenberger & Kummel 2011, Keen
& Ayres 2017, Kummel et al. 2015), provides a plausible and arguably more coherent explanation
than the standard economics explanation. Furthermore, it appeals to Occam’s razor, which states
that the successful model with the fewest assumptions may capture the underlying structure better.
2.4 Relationship between energy and the economy
2.4.1 Introduction
The close relationship between energy and the economy was recognized at least as early as 1926 in
Frederick Soddy’s declaration that ‘... the flow of energy should be the primary concern of economics’
(Soddy 1933, ch.3). By the 1970s, the unique role of energy had been recognized in standard economics
(e.g. Jorgenson (1984), Nordhaus (1979), Stiglitz (1974)).
Outside of the mainstream economics literature, the standard interpretations were sometimes
considered inadequate to explain the magnitude of economic effects of the oil crises of the 1970s
(Cleveland et al. 1984, Hall & Klitgaard 2011). Meanwhile, ecological (Odum 1973, Hall 1972),
geographical (Smil 2008), anthropological (Tainter 1990), historical (Wrigley 2010), and biophysical
perspectives (Stern 1993, Cleveland et al. 1984, Georgescu-Roegen 1972) drew attention to the essential
role of energy in economic production.
The stable relationship between per-capita energy consumption and per-capita income is one of
several ‘stylized facts’ of the energy-economy nexus (Stern et al. 2014). Using a sample of 33 countries,
energy consumption and GDP have been shown to be cointegrated, with a long-run dependency ratio
of 0.6 to 0.7 (Giraud & Kahraman 2014).
However, despite the large body of research exploring the energy-economy nexus, the question of
the nature of causality between energy and the economy remained contested. Recent literature reviews
(Chen et al. 2012, Kalimeris et al. 2014, Payne 2010, Ozturk 2010) have identified four hypotheses
that explore the direction of causality.
38
1. A cause-effect running from energy to economic growth
2. A cause-effect running from economic growth to energy
3. A bi-directional relationship
4. No causal relation
The ambiguity of the causal relationship was delineated in Fizaine and Court’s (2016) discussion
of motor vehicle fuel consumption per kilometre, and vehicle speed. Fizaine and Court argued that
a test of Granger causality17 should show a causal relation running from the latter variable to the
former. The relation could be proved by referring to the aerodynamic drag equation, which shows
that drag (and hence fuel consumption) increases roughly with the square of speed. Yet no-one would
dispute that fuel consumption causes forward motion of the vehicle – seemingly the opposite causal
relation.
A resolution of the paradox is that both variables are subordinated to the physics variable ‘force’.
Velocity is a function of force and aerodynamic drag. Fuel consumption is a function of force and dis-
tance. Velocity and fuel consumption are therefore linked by force, but are both dependent variables.
2.4.2 The Australian context
Introduction
The case study of Australia will be used to explore the relationship between energy and GDP. Figure
2.4 depicts Australian primary energy consumption and real GDP for the period 1900-2014. Despite
a significant shift towards a service economy over the 115 year span, primary energy consumption has
remained strongly connected to GDP, but overlaid with distinct long-run trends in energy intensity.
The distinct periods have been labelled as ‘agrarian’, ‘urbanisation’, ‘post-war industrialisation’, and
‘service economy’ respectively, and explored further in chapter 3.
In the ‘agrarian’ phase up to around 1920, energy consumption rose faster than GDP, reflecting
the substitution of labour with capital and energy to increase the productivity and output of agricul-
ture and an emerging manufacturing sector. In the ‘urbanisation’ phase in the lead-up to the second
world war and including the Great Depression, there was a gradual shift from rural to urban regions,
and energy intensity remained constant. In the ‘post-war industrialization’ phase, tariff barriers sup-
ported energy-intensive manufacturing, with energy intensity rising. Following the war, the chemical,
electrical, automotive and iron and steel industries were seen as important national industries, and
17 Granger causality testing is commonly used to test for causality in energy-economic models (Stern & Enflo 2013). Itrelies on the intuition of temporal precedence. A variable xt is said to ‘Granger cause’ yt if prediction of the currentvalue of y is enhanced by using past values of x. However Pearl (2009, p.42) cautions that temporal informationalone may be insufficient for disassociating genuine causation from spurious association.
39
-
200
400
600
800
1,000
1,200
1,400
1,600
1,800
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
RealGDP
(AUD
$2016)
Prim
aryen
ergyco
nsum
ptionbyfu
el(PJ)
Blackcoal
Browncoal
Petroleum
Naturalgas
Hydroandrenewables
Real GDP
AGRARIAN POST-WARINDUSTRIALISATION
SERVICEECONOMYURBANISATION
Figure 2.4: Primary energy consumption and GDP, Australia, 1900-2016. Sources Vamplew (1987),Office of the Chief Economist (2015a, 2017), Dyster & Meredith (1990), Butlin (1962), ABS (2017a)
the motor car became a symbol of modernity and affluence. This marked a period of expanding and
growing house size in the major cities and commensurate expansion of car use. In the current ‘service
economy’ phase, energy intensity is falling, reflecting the de-industrialisation of the economy due to
structural changes starting in the 1980s, and ongoing efficiency gains.
Role of energy in economic growth
In order to assess the sensitivity of Australia’s economy to changes in energy consumption and energy
productivity, Giraud and Kahraman’s (2014) mathematical relation was applied to time-series data
of Australia’s primary energy consumption, GDP and GDP deflator. Giraud & Kahraman (2014)
connected GDP and per-capita energy with the simple but useful relation given in equation 2.1. The
expression enables per-capita GDP growth to be decomposed into per-capita energy consumption
growth, and energy productivity improvement respectively.
YtNt
=Et
Nt× YtEt
(2.1)
40
-5%
-3%
-1%
1%
3%
5% An
nualgrowth
5yrmovingaverage changeinenergypercapita
5yrmovingaveragechangeinenergy productivity
($perGJ)
5yrmovingaveragechangeinrealGDPpercapita
POST-WARINDUSTRIALISATION
SERVICEECONOMY
Figure 2.5: Change in per-capita primary energy, per-capita GDP, and energy productivity for Aus-tralia, 1945-2016. Columns are annual change in per-capita GDP.
where
Yt is real GDP as a function of time t
Et is national primary energy consumption as a function of time t
Nt is population as a function of time t
and therefore
YtNt
is per-capita real GDP as a function of time t
Et
Ntis per-capita primary energy consumption as a function of time t
YtEt
is energy productivity of the national economy
which leads to
∆YtNt
= ∆Et
Nt+ ∆
YtEt
(2.2)
It is important to distinguish between the outcome of an algebraic relation, and causation. The
41
relation, in itself, does not prove a relation between any of the variables. Rather, the example is
intended as a case study to test whether the data can produce a sensible and intuitive outcome.
Figure 2.5 is based on equation 2.1, and depicts annual changes in per-capita primary energy,
per-capita real GDP, and energy productivity for Australia, 1945-2016. Since the year-on-year change
is volatile, the annual changes are all drawn as a 5 year moving average. From equation 2.1, the
per-capita growth in GDP is a function of the change in energy productivity and the change in per-
capita energy consumption. Therefore, under the condition of falling energy consumption, the energy
productivity must rise faster to result in an increase in per-capita GDP.
Several observations emerge from figure 2.5. Firstly, energy productivity swung above and below
the zero axis in the post-war period up to the early 1980s, after which it turned positive, except for the
period of the recession of the early 1990s. This is consistent with a shift away from energy-intensive
industries, towards a service economy.
Secondly, the per-capita energy consumption growth remained positive for the entire period up
to around 2000, except for the recession of the early 1980s. However, post-2000, the trend has been
downward, and since the global financial crisis, turned negative. From the data, and accepting the
biophysical hypothesis, the decline in per-capita GDP growth can be largely attributed to falling
per-capita energy consumption. The unresolved question is the causal factors.
In order to explore the hypothesis in more detail, figure 2.6 was constructed with the same data as
figure 2.5. The two independent and single dependent terms were grouped into decades and shown as a
column chart. The per-capita GDP growth is a product of the per-capita energy consumption and the
national energy productivity. From figure 2.6, the period from 1990 to 2016 has shown rising energy
productivity, but a decline in per-capita energy consumption. There has been significantly reduced
per-capita economic growth. The biophysical hypothesis is that much of what has been termed the
‘productivity puzzle’ (e.g. Summers (2016)) can be simply explained by falling energy consumption
without a commensurate improvement in energy productivity. A part explanation for the reduction
in energy is a significant increase in electricity costs, shown in figure 2.7.
42
!2%
!1%
0%
1%
2%
3%
4%
5%
1940!1
950
1950!1
960
1960!1
970
1970!1
980
1980!1
990
1990!2
000
2000!2
010
2010!2
016
Annualise
d6compund6growth6rate6for6p
eriod
Per!capita6GDP Energy6per6capita Energy6productivity
Figure 2.6: Sources of per-capita growth Australia for decades 1940 to 2010, and 6 years up to 2016.Change shown as annualized compound growth over the respective periods.
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Real+price+$2015/kW
h
households
industrial
Figure 2.7: Real price of electricity (2005$) 1955–2015. Calculated from ABS A2325846C CPI index,ABS (2016b), Brady (1996), OECDiLibrary (2015)
43
2.5 The EROI metric
2.5.1 Difference between the output elasticity of energy and its factor
share
The large difference between the output elasticity of energy and its factor share is explained by
the fact that national economic production is a function of the total marketable energy, but energy
expenditures reflect the effort by energy supply firms of bringing energy to market, plus profit. Society
gets the use of the total marketable energy, but essentially only has to pay for the energetic-economic
cost of bringing the energy to market. Gaining access to primary energy from nature18 and disposal
of wastes19 usually comprises a small proportion of overall cost.
2.5.2 The minimum EROI of society
Introduction
A key question of EROI research is – what is the minimum EROI that is required to support an
industrial society (Hall et al. 2009, Lambert et al. 2014, Brandt 2017)? Empirically, the energy
cost share must be below 11% for economic growth in developed nations (King et al. 2015b, Fizaine &
Court 2016). Where the energetic and economic valuations approximately align, the EROI is inversely
related to the energy cost share, implying an EROI of roughly 9:1. The inverse of EROI is conceptually
equivalent to energy expenditures cost share.
Historically, societies have flourished and grown with a higher cost share, but with a different
configuration. The hierarchy of human wants and needs has evolved markedly since industrialisation.
Earlier configurations exhibited much higher proportions of primary and secondary industry sectors,
such as agriculture and mining, and much less of discretionary sectors (Hall & Klitgaard 2011, Palmer
2017c). The minimum EROI of society is defined by the configuration of society.
The lower the energy cost share, the easier it is to satisfy basic needs. In contemporary developed
societies, the direct20 energy cost share is sufficiently low that it is not a major determinant of
household discretionary expenditures. Where the energetic costs are much greater than the economic
costs, the effects may be pervasive.
18 For example, mining royalties and resource taxes.19 The obvious exception is carbon pricing, where applicable. There may also be other waste disposal or rehabilitation
fees, although these costs are minimal in relation to the cost of externalities.20 In Australia, household energy comprises 11% of non-transport energy consumption (Office of the Chief Economist
2015b), hence most energy is embodied in goods and services, or related to the provision of public goods
44
Historical overview
Historically, four main phases can be identified in relation to the prevailing societal EROI:
1. In hunter-gatherer societies, the relevant EROI metric was the caloric value of the food captured
or gathered, versus the calorific expenditure of the hunt or gathering expedition. Studies of
hunter-gatherers show an EROI of 10:1 to as high as 50:1 (Lee 1969, Glaub & Hall 2017).
However, the limited capacity for food storage and settlement was a significant impediment to
development.
2. High population and overexploitation of resources has been identified as a possible driver of early
domestication (Weitzel & Codding 2016). In pre-industrial agriculture, the EROI was 5:1 or
less (Day et al. 2018). Agriculture requires intense effort over long periods, often with variable
results. A high proportion of activity was related to production of food, fodder and fuelwood.
However, farming had the benefit of food storage (Palmer 2017a), dedicated fruit trees and crops,
established settlements, and concentrated labour (Diamond 2005). These advances permitted
surplus food production and seasonal food storage for the first time, increased population density,
and represented a prelude to the development of non-farming specialization and villages.
3. Early industrialized societies benefited from rising EROI and energy surpluses. Capital and
energy substituted for labour. Food, fodder and fuel could be provided with fewer workers,
permitting an expansion of non-primary sectors. The range of goods and services expanded. In
the United Kingdom, energy and food expenditures fell to 20% as a proportion of GDP in 1830
(Fouquet 2011, 2014) from 50 to 80% prior to the industrial revolution (Day et al. 2018).
4. In developed nations with advanced service economies, more spending is available for consump-
tion of discretionary goods and services, and more advanced activities, including advanced health
care, the arts, and higher education. (Hall & Klitgaard 2011, p.318);(King 2015a). The wellhead
EROI of global oil reached its maximum value of 50:1 in the 1930s-40s, with a current EROI of
around 10-15:1 (Court & Fizaine 2017). In general, the EROI of oil and gas extraction are in
decline (Court & Fizaine 2017), while the EROI of mine-mouth coal extraction, and the EROI of
renewable electricity are improving (Day et al. 2018). In the contemporary period, resource de-
pletion and technology can be conceptualised as opposing factors that either improve, or worsen,
the EROI. Furthermore, economic forces that may offset the natural resource constraints include
the substitution with man-made factors of production (discussed earlier), and returns to scale
(Stiglitz 1974).
45
2.5.3 Resource depletion versus technical change
Resource depletion
Energy extraction has generally conformed to the ‘best first’ principle, meaning that the largest and
most accessible natural resources tend to be the first exploited 21. The ‘Lucas 1 on Spindletop’ Texas
oil gusher was discovered in 1901 by a small group of wildcatters, and went on to produce seventy-
five thousand barrels per day (Yergin 2011, pp. 66-71). On the other hand, contemporary offshore
platforms operate in much more demanding conditions, carry more technical risk, and are costly to
build and operate (Tainter & Patzek 2011). The Berkut rig operating in the Russian Far East produces
around one-hundred thousand barrels per day at a rig cost of USD$12 billion (Cunningham 2015).
Lower-quality and difficult to access resources require more work to locate, upgrade, and refine than
their higher-quality counterparts.
Technical change
Human ingenuity continually pushes the technological frontier forward. Innovation, learning-by-doing
and incremental improvements push the cost of technologies downwards as they progress along a
learning curve. Technical knowledge is cumulative. The development of hydraulic fracturing (Moeller
& Murphy 2016), the rapid decline in production cost of solar photovoltaics (Louwen et al. 2016),
and the incremental advances in scaling, design, capacity factor, and durability of wind power (Wiser
et al. 2016), are the outstanding examples of technical change in relation to energy supply, leading to
technology-specific economic and energetic cost declines.
Future uncertainty
King (2015b) compiled energy cost share estimates for forty-four countries for the period 1978-2010,
combined these with longer-run historical data, and concluded that the period around 2000 represented
a historical nadir in global energy cost share. More recent analysis confirms that the upward trend in
cost shares since around 2000 has continued (King & Rhodes 2018).
Yet in the period in the lead up to and since 2000, there have been substantial technological de-
velopments in the energy supply technologies listed above, along with energy and electricity markets,
greater use of information and communications technologies, among many other technical develop-
ments. This implies that future outcomes of the competing factors of resource depletion and technology
are uncertain, and understanding how these factors interact is important to transitions research.
21 Although generally true, Norgaard (1990) identified the so-called ‘Mayflower Problem’, referring to the landing of thePilgrims in North America – the Pilgrims were initially unaware that richer soils lay far inland. Similarly, naturalresource development has not historically conformed to Hotelling’s theoretical model of resource scarcity. Investmentflows to projects that can be commercially and profitably developed.
46
In a review of the energy transition literature, Day et al. (2018) found that few studies discuss
the thermodynamic and biophysical implications of transitions. Critical biophysical feedbacks include
diminishing returns to resource use (Hall & Klitgaard 2011), energetic limits to build-out rates of
capital intensive energy supply infrastructure (Floyd 2016), ecosystem health (Schramski et al. 2015),
earth system dynamics (Rockstrom et al. 2009), societal stability (Ahmed 2016), the energy-complexity
nexus (Tainter 1990), and the relationship between energy costs and economic structure (King 2016).
2.6 Conclusions
The EROI ratio provides an energetic valuation of the fuel supply chain or energy supply technology,
which may, or may not, correlate with the conventional economic valuation. Where the energetic
valuation, expressed as the EROI ratio, is much higher than the conventional economic valuation, a
conventional economic approach is warranted. On the other hand, a low, or reducing, EROI may
indicate biophysical limits that may not be apparent from the price system alone. Identifying these
energetic bottlenecks is critical to assessing the efficacy of low-emission pathways.
47
Chapter 3
Energetic Implications of a
Post-industrial Information
Economy: The Case Study of
Australia
3.1 Overview and context of chapter
3.1.1 Introduction
The previous chapter made the case for the primacy of energy. However, given the long-run shift of
the composition of the Australian economy from energy-intensive primary to lower energy-intensive
service-based industries, intuitively one would expect energy to have been decoupled from economic
activity. For this study, ‘strong’ decoupling is defined as an absolute reduction in energy use, while
‘weak’ decoupling is defined as a relative reduction. Strong decoupling would undermine the primacy
of energy as a primary factor of production. Since EROI rests on several hypotheses, identified in
section 2.1.4, which link energy and economic activity, it was considered important to collect the data
and perform a time-series regression in the early stage of this thesis.
3.1.2 Research questions
The research questions posed by this chapter are:
48
1. What is the long-run correlation between energy and economic activity in Australia?
2. Has the shift towards a service economy led to strong decoupling of energy from economic
activity?
3. Do the conclusions support or refute the hypothesis that energy is a primary factor of production?
3.1.3 Research method
The question was explored by applying a time-series regression between real GDP and primary energy
consumption for the period 1900-2014. A qualitative approach was adopted to investigate the role of
energy in service and information technologies.
From inspection of the regression residuals, four phases were classified based on the changing
energy intensity of GDP. In the ‘agrarian’ phase up to around 1920, energy consumption rises faster
than GDP; during the ‘urbanisation’ phase, energy intensity remains constant; during the ‘post-war
industrialization’ phase, once again energy consumption rises faster than GDP; in the current ‘service
economy’ phase, energy intensity is falling.
49
Vol.:(0123456789)1 3
Biophys Econ Resour Qual (2017) 2:5 DOI 10.1007/s41247-017-0021-4
COMMENTARY
Energetic Implications of a Post-industrial Information Economy: The Case Study of Australia
Graham Palmer1
Received: 29 August 2016 / Accepted: 28 March 2017 © Springer International Publishing Switzerland 2017
An explanation for the long-run connection is two-fold. The evolution towards greater social and industrial com-plexity has been underpinned by the ready availability of cheap fuels. The deepening of the service economy towards the infotronics phase should be seen partly as a conse-quence of available energy supply and productive primary and secondary sectors. Several specific examples of ICT are explored to test the hypothesis, including ICT-enabled remote work, online retail, the ‘sharing economy’, and productivity-enhancing ICT applications. Second, energy consumption is driven by demand for end-use energy ser-vices, including transport, buildings and food. Demand for these services is a function of human wants, needs and income, and a changing industrial structure does not alter their underlying demand. The conclusion is that ICT is ena-bling productivity gains and new business models, but does not significantly weaken the demand for these services, and therefore does not enable strong decoupling.
ICT-Enabled Remote Work
The increasing role of ICT-driven service sectors should, in principle, offer opportunities to enable ‘strong’ decoupling by substituting ICT for real-world interactions. Australian service sectors have a much lower energy intensity (energy consumed per dollar of value added) than the primary and secondary sectors, including agriculture, mining, manu-facturing and transport (Stanwix et al. 2015). To the extent that ICT-enabled services can substitute for higher energy intensity products, energy consumption should be reduced, both in relative and absolute terms.
The case of ICT substituting for travel is one such exam-ple—passenger road transport makes up 40% of Australia’s transportation energy (Energy Information Agency (EIA)
Abstract The potential for decoupling of energy and resources from economic growth should enable economic development while improving environmental sustain-ability indicators. Relative decoupling of energy has been a characteristic of developed nations as a consequence of efficiency gains and productivity growth. The trend has strengthened in recent decades as economies have advanced further into the service economy phase. The next phase of development (the so-called ‘Infotronics’ phase) is being enabled by the rapid growth of information and com-munications technology (ICT), and artificial intelligence. The question explored in this commentary is whether the Infotronics phase will shift energy consumption in abso-lute rather than relative terms (so-called ‘strong’ vs. ‘weak’ decoupling), using Australia as a case study. In this context, weak decoupling is defined as a relative reduction in energy consumption per unit of GDP, whereas strong decoupling is also an absolute reduction in national energy consump-tion. Historic data on Australian primary energy consump-tion, gross domestic product, GDP deflator, and industrial sectors have been assembled for the period 1900–2014. A time-series linear regression between energy and real GDP was undertaken to explore the historic relationship between changes in the changing structure of the Australian econ-omy and energy consumption. Despite a significant shift towards a service economy, primary energy consumption has remained strongly connected to GDP, but overlaid with distinct long-run trends in energy intensity.
* Graham Palmer [email protected];
1 Australian-German College of Energy and Climate, The University of Melbourne, Melbourne, Australia
50
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5 Page 2 of 9
2015). Indeed, the example of communications-enabled decoupling has historic precedents—the eighteenth century telegraph, later telephone, transatlantic cable, right up to the late twentieth century internet are cases in which it was hypothesised that advanced communications would substi-tute for travel (Mokhtarian 2009).
However despite early optimism for ICT-enabled remote work, the substitution effect has not been as evident as originally assumed (Van Wee et al. 2013). Email and video conferencing has not been able to fully compensate for the richness of face-to-face contact, and the collegiality of a regular work group. Whatever reduction in travel that might have been expected from remote work has been over-whelmed by the economic growth enabled by ICT. By low-ering the relative cost of information gathering, processing and enabling complementary business innovations, ICT has in fact provided a direct stimulant to business (Brynjolfs-son and Hitt 2000). The expansion of ICT has occurred at the same time as rising travel congestion—Australian met-ropolitan travel distance tripled from 1970 to 2014 (Bureau of Infrastructure Transport and Regional Economics 2016, Fig. 1), while the population doubled.
ICT and the Sharing Economy
The emergence of ‘sharing economy’ applications, such as Uber, Lyft and Airbnb, provide examples in which software is impinging on real-world activities. Andreessen (2011) describes the foray of software into the physical world as ‘software eating the world’. Once again, the substitution of low energy intensity software development, defined in the Australian National Accounts (Australian Bureau of Statis-tics (ABS) 2016a) as a low energy intensity service sector, seems to present a pathway of low energy development.
However, the significance of ICT seems to be more reflective of a business model disruption than a funda-mental change to the physical and energetic outcomes. For example, Uber has been shown to have a higher utilisation factor than traditional taxis (Cramer and Krueger 2015). Nonetheless, the Uber service consists of conventional vehicles being driven on public roads, consuming the same public infrastructure and energy as conventional vehicles, albeit possibly at a slightly higher passenger–km efficiency. In Australia, Uber was estimated to have provided 6% of taxi industry rides during 2015, with strong continuing growth (Deloitte 2016). The Australian experience is that Uber is drawing in consumers who may not have otherwise taken a cab, and in some cases is substituting for travel modes that would have carried a lower energy intensity (energy per km), such as trains. Similarly, in principle, Airbnb should be substituting for hotel rooms and increas-ing the utilisation of the built environment (i.e. fewer hotels
would need to be constructed). However, Airbnb mainly competes in the more price-elastic leisure market, which has less impact on the established hotel market (Moody’s 2016). Schor (2016) makes the observation that the produc-tivity growth of sharing economy businesses will lead to macroeconomic growth effects that may overwhelm energy reduction effects.
Dematerialization Driven by ICT
An early version of dematerialization was Buckminster Fuller’s concept of ‘Ephemeralization’—doing more and more with less and less until eventually you can do every-thing with nothing (Fuller 1973, pp. 252–259). In a con-temporary ICT-based version, Kurzweil (1999) hypoth-esised that computing power will eventually cross a critical boundary (the so-called singularity), after which dema-terialized economic growth will accelerate sharply. Kur-zweil (1999, p. 124) argued that there is a rapidly increas-ing knowledge and information content in products and services, and that these are not constrained by material resources.
Using Fuller as a backdrop, Lee (2011) uses the concrete example of the introduction of Google Maps onto smart-phones to argue that information technology is a ‘magic wand’ that ‘in one stroke, transformed millions of Android phones into sophisticated navigation devices’. In Lee’s conception, the smartphone is assumed to be a low energy footprint device that substitutes for a host of real-world products—at zero marginal cost, Google Maps is said to be substituting for paper maps and dedicated navigation devices.
But the reverse is true—Nokia, Google and Apple all have multi-billion dollar ‘real world’ investments in map-ping hardware, software development and data. Further-more, GPS piggy backs onto the large sunk investment of the Navstar GPS satellite system. Google has bundled ‘free’ maps to improve the perceived value of Android, from which it reportedly made $31 billion in revenue and $22 billion in profit during the past seven years (Curry 2016). Furthermore, GPS devices are penetrating cameras and fitness devices, far exceeding the material and energy footprint of paper-based maps and atlases. Hence far from dematerialising, the ‘magic’ of GPS-enabled devices car-ries a far reaching energy and material footprint.
Smil (2013) provides a similar example in which appar-ent ICT dematerialisation has contributed to system growth effects that exceed the direct energy and resource-saving effects. The introduction of computer-aided design (CAD) and machining significantly reduced the man-hours and office infrastructure to produce a drawing and then pro-duce the final product. But in a sort of rebound effect,
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CAD enabled orders-of-magnitude greater complexity and sophistication of advanced products, such as jetliners. Fur-thermore, the ease of translating ideas into products has contributed to an overwhelming diversity and choice of consumer and industrial products. Rather than dematerial-izing, CAD is better described as a complex form of mate-rial substitution.
The emergence of online retail demonstrates the multi-faceted effects of ICT that may be difficult to quantify. For example, online women’s fashion has grown at 15% in recent years in Australia (Magner 2016b), at the same time as the emergence of ‘fast fashion’, which has grown at 12% (Magner 2016a). Yet despite strong online growth, Austral-ian suburban shopping malls continue with major redevel-opment projects (Zhou and Robb 2016). A part explanation is that women’s fashion is being driven by social media and online shopping, exposing consumers to the latest designer fashions (Magner 2016a). Furthermore, the environmental implications of online versus conventional shopping can be difficult to estimate when a broader perspective of shopper behaviour is considered (Edwards et al. 2010).
All of these examples illustrate the inter-relationships between growing complexity, a deepening of service sec-tors, energy consumption, and ICT. At face value, ICT applications may seem to represent low marginal cost, dematerialized solutions, but are in fact examples of Taint-er’s (1990) energy–complexity spiral in which software is embedded in an increasingly complex system with a far reaching energy footprint.
The Energy Intensity of Australia’s Economy
The hypothesis that ICT is not leading to ‘strong’ decou-pling was explored with the case study of Australia. Real GDP was derived by dividing nominal GDP by the GDP deflator. Primary energy consumption refers to the con-sumption of primary feedstock fuels for end-use consump-tion and electricity generation. In the case of electricity derived from renewable fuels, there are two alternative methods to calculate their primary energy equivalent. This commentary uses the IEA methodology, which simply assumes that 1 MJ of electricity from hydro or renewables is equivalent to 1 MJ of primary fuels (Modahl et al. 2013). The alternative method adopted by the EIA is to multiply the electricity generation by three to approximate the fuels that would otherwise have been combusted if the electric-ity was derived from coal, gas or petroleum. With a rising penetration of renewable fuels, the IEA method will tend to indicate greater decoupling (i.e. one unit of renewable elec-tricity substitutes for three units of coal or gas).
Figure 1 compares the Australian real GDP and the pri-mary energy consumption for the period 1900–2014. Over
the period, the per capita real GDP increased 5.7-fold, and the per capita primary energy rose 7.6-fold. Viewed over a century, the primary energy consumption has been strongly connected to the gross domestic product, despite a strong shift towards a service economy (see Fig. 2). A time-series linear regression between real GDP as independent and primary energy as the dependent variable, with 115 annual observations, results in an R-squared of 0.973, providing support for a correlation.
A closer inspection of the regression residuals reveals four phases. In the ‘agrarian’ phase up to around 1920, energy consumption rises faster than GDP, reflecting the substitution of labour with capital and energy to increase the productivity and output of agriculture and an emerg-ing manufacturing sector. In the ‘urbanisation’ phase in the lead-up to the second world war and including the Great Depression, energy intensity remains constant. This marks a period of electricity substituting for steam power—the proportion of factory horsepower in Australia delivered by electricity had increased from 24% in 1918, to 81% in 1939 (Australian Bureau of Statistics (ABS) 1920, 1940).
In the ‘post-war industrialization’ phase, tariff barriers support energy-intensive manufacturing, with energy inten-sity rising (Fig.3). Following the war, the chemical, elec-trical, automotive and iron and steel industries were seen as important national industries, and the motor car became a symbol of modernity and affluence (Frost and Dingle 1995).
This marks a period of expanding and growing house size in the major cities and commensurate expansion of car use. In 1945, there were only 0.12 motor vehicles per person, rising threefold to 0.36 by 1968 (Frost and Dingle 1995, p. 34). Passenger vehicles per capita rose linearly until an inflection in the 1980s led to slowing trend growth (Fig. 4). The post-war spread of Australian cities up until the 1990s exceeded one million hectares (Buxton 2006). New detached housing floor area rose steadily before peak-ing in 2008/09 (Commsec 2016). Near-complete electrifi-cation wasn’t reached until around 1970 (Australian Bureau of Statistics (ABS) 1970), and for the period 1945 to 1970, national electricity consumption rose seven fold. By 1973, manufacturing consumed 38% of total primary energy. Electricity consumption continued to rise before moderat-ing post-2010.
In the current ‘service economy’ phase, energy intensity is falling, reflecting the de-industrialisation of the economy due to structural changes starting in the 1980s, and ongoing efficiency gains (Stanwix et al. 2015). Household energy consumption has stabilised, with rising building and appliance efficiency being off-set by rebound factors. In an engineering energy study, Palmer (2012) showed that despite significant and sus-tained improvements in appliance and building fabric
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Fig. 1 Australia primary energy consumption and real GDP 1900–2014. Sources: Australian Bureau of Statistics (ABS) (2015a); Butlin (1962); Dyster and Meredith (1990); Office of the Chief Economist (2015); Vamplew (1987)
Fig. 2 Australian industry sectors, actual and projected, 1800–2020, Source: author estimates from Ruthven (2013)
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Fig. 3 Persons employed in selected manufacturing industries, post-War. Sources: Australian Bureau of Statistics (ABS) (1955, 1960, 1965)
Fig. 4 Passenger vehicles (excluding commercial and other vehi-cles) per capita 1945–2016, and average floor area of new detached dwellings 1984–2016. Sources: Australian Bureau of Statistics (ABS)
(1962, 1976, 1988, 1999, 2013a, 2014, 2015b); Commsec (2016); Frost and Dingle (1995)
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efficiency, the per capita heating energy consumption in Melbourne remained stable over the studied period of 1960–2010. This was attributed to higher expectations of thermal comfort, such as larger heated areas, extended heating periods and higher thermostat temperatures, and lower per-household occupancy rates.
Since the mid-1990s, per capita energy consumption has plateaued at around 240 GJ per capita per annum. The recent fall in the total consumption for 2013 and 2014 is due to a reduction in the electricity demand as a consequence of a doubling of retail electricity prices during the period 2007–2012, which has mostly impacted coal consumption (Australian Productiv-ity Commission 2013). The price rise has accelerated the recent structural changes, including the closure of energy-intensive production, and a trend towards greater efficiency (Fig. 5). Based on a projected average annual economic growth rate of 2.7% and using an equilibrium model with ongoing efficiency gains, the growth in pri-mary energy consumption is projected at 1% per annum for the period up to 2050 (Bureau of Resources and Energy Economics 2014). The BREE projection should be taken as a scenario that satisfies an equilibrium model for a given economic growth rate rather than a forecast.
Real-World End-Use Energy Services
At face value, the low energy intensity of service sec-tors and a concentration of wealth in Australian localities associated with ICT services would seem to strengthen the decoupling hypothesis (Ruthven 2012). However, the growth of service sectors and the relative decline of energy-intensive sectors (see Fig. 2) do not seem to have led to strong decoupling. Part of the explanation is that the ongoing demand for the real-world end-use services that consume energy has not diminished. End-use ser-vices include passenger and freight transport, construction materials for buildings and other products, food, hygiene, thermal comfort, communications and illumination (Cul-len and Allwood 2010). These end-use services are a func-tion of human wants, needs and income. At a global level, a consumption-based approach to energy and material flows (vs. the conventional production approach) shows that the ‘material footprint’ is still strongly correlated with GDP (Wiedmann et al. 2015).
Furthermore, the ‘physical dimensionality’ of goods places practical limits on efficiency gains (Bithas and Kalimeris 2013). For example, the fuel economy of motor vehicles is a function of their mass, shape, and required performance. The path dependency of urban development establishes commute distances and the associated energy
Fig. 5 Real price of electricity (2005AUD) 1955–2015. Calculated from ABS A2325846C CPI index, Australian Bureau of Statistics (ABS) (2016b); Brady (1996); OECDiLibrary (2015)
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footprint. In buildings, people require the full suite of mechanical services, including heating and cooling, lifts and lighting. All of these products and services involve direct and embodied energy consumption. It is not appar-ent that service and information economies have a funda-mentally different need for these services. Indeed, even ICT services are themselves responsible for an expanding and significant magnitude of energy consumption, including the rapid growth of cloud computing and data centres (Aebi-scher and Hilty 2015, Fig. 7; Corcoran and Andrae 2013).
Unmeasured gains from ICT
An alternative postulate is that ICT is not significantly contributing to GDP but has led to unmeasured gains in welfare, and therefore material well-being is much higher than that measured by conventional metrics (Brynjolfsson and McAfee 2014). The prime example related to ICT is the claim that social media enhances welfare, but there are many measures of welfare unrelated to economic devel-opment, such as relationships and a healthy environment (Australian Bureau of Statistics (ABS) 2013b). If true, this would be equivalent to low energy intensity development, and is sometimes said to explain the so-called ‘productivity puzzle’ of post-industrial economies.
A resolution to the puzzle is that the recent smartphone developments produce non-market benefits (i.e. consum-ers are more productive in using their non-market time to consume services they value), but that the impact on con-ventional standard-of-living metrics is minor (Byrne et al. 2016). In much the same way that the earlier introduc-tion of colour television contributed to unmeasured gains in consumer surplus, social media has supplanted tradi-tional forms of entertainment and communication. From a consumer perspective, social media appears to have zero marginal cost because cellphone and internet services are often fixed monthly cost services. This tends to obscure the costs that lie behind the provision of the services and the telephony infrastructure. Furthermore, social media is not substantially substituting for real-world products such as transport, buildings and food.
Summary
A key question in sustainability research is whether ‘strong’ decoupling of energy and resource consumption from eco-nomic activity is occurring. An observation supporting the decoupling hypothesis is that national economic activity is increasingly dominated by low energy intensity ICT-driven service sectors—the Australian quaternary and quinary
service sectors now comprise 47 and 10% of the national economy, respectively.
The question was explored by applying a time-series regression between real GDP and primary energy consump-tion. The regression showed that economic activity and energy consumption has remained linked, but overlaid with distinct long-run trends in energy intensity. Two hypotheses that sought to explain the connection were presented. The first argued that the demand for energy end-use services is a function of human wants, needs and income, irrespective of the relative composition of the national economy. The second argued that ICT-enabled service sectors are driv-ing new business models but not significantly altering the underlying demand for end-use energy services. The deep-ening of the service economy towards the Infotronics phase should be seen partly as a consequence of sufficient energy supply and productive primary and secondary sectors. The conclusion is that ICT is enabling productivity gains and new business models, but does not significantly weaken the demand for these services, and therefore does not enable strong decoupling.
Acknowledgements The author would like to thank the anonymous reviewers for their valuable comments that substantially improved the commentary.
Compliance with Ethical Standards
Conflict of Interest The author states that there is no conflict of in-terest.
References
Aebischer B, Hilty LM (eds) (2015) ‘The energy demand of ICT: a historical perspective and current methodological challenges’ ICT Innovations for Sustainability, Springer, Cham, pp. 71–103
Andreessen M. (2011) ‘Why Software Is Eating The World’. Wall Str. J. 20:C2
Australian Bureau of Statistics (ABS) (1920) Year book of Australia. ABS, Canberra
Australian Bureau of Statistics (ABS) (1940) Year book of Australia. ABS, Canberra
Australian Bureau of Statistics (ABS) (1955) Year book of Australia. ABS, Canberra
Australian Bureau of Statistics (ABS) (1960) Year book of Australia. ABS, Canberra
Australian Bureau of Statistics (ABS) (1962) Census of motor vehi-cles. ABS, Canberra
Australian Bureau of Statistics (ABS) (1965) Year book of Australia. ABS, Canberra
Australian Bureau of Statistics (ABS) (1970) Year book of Australia. ABS, Canberra
Australian Bureau of Statistics (ABS) (1976) Census of motor vehi-cles. ABS, Canberra
Australian Bureau of Statistics (ABS) (1988) Census of motor vehi-cles. ABS, Canberra
Australian Bureau of Statistics (ABS) (1999) Census of motor vehi-cles. ABS, Canberra
56
Biophys Econ Resour Qual (2017) 2:5
1 3
5 Page 8 of 9
Australian Bureau of Statistics (ABS) (2013a) 8752.0 - Building activity, Australia, featured article: Average floor area of new residential dwellings. ABS, Canberra, http://www.abs.gov.au/AUSSTATS/[email protected]/Previousproducts/8752.0FeatureArticle1Jun2013
Australian Bureau of Statistics (ABS) (2013b) Measures of Aus-tralia’s progress—is life in Australia getting better? ABS, Can-berra, http://www.abs.gov.au/ausstats/[email protected]/mf/1370.0
Australian Bureau of Statistics (ABS) (2014) 3105 - Australian his-torical population statistics. ABS, Canberra
Australian Bureau of Statistics (ABS) (2015a) 5206.0 - Australian national accounts: National income, expenditure and product - series A2304334J. ABS, Canberra
Australian Bureau of Statistics (ABS) (2015b) Census of motor vehicles. ABS, Canberra
Australian Bureau of Statistics (ABS) (2016a) 5209.0.55.001-Aus-tralian national accounts: Input–output tables, 2013-14. ABS, Canberra, http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/5209.0.55.001Main+Features12013-14?OpenDocument
Australian Bureau of Statistics (ABS) (2016b) 6401.0 consumer price index - series A2328141J–electricity. ABS, Canberra
Australian Productivity Commission (2013) Electricity network regulatory frameworks report. Australian Productivity Com-mission, Canberra, http://www.pc.gov.au/inquiries/completed/electricity/report
Bithas K, Kalimeris P (2013) ‘Re-estimating the decoupling effect: Is there an actual transition towards a less energy-intensive economy?’ Energy 51:78–84
Brady F (1996) ‘Contribution on Australia: A dictionary on elec-tricity’, paper presented to the international conference on large high voltage electrical systems. Australia National Com-mittee of CIGRE
Brynjolfsson E, Hitt LM (2000) ‘Beyond computation: Information technology, organizational transformation and business perfor-mance’. J Econ Perspect 14(4):23–48
Brynjolfsson E, McAfee A (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company, New York
Bureau of Infrastructure Transport and Regional Economics (2016) Traffic and congestion cost trends for Australian capital cities. Australian Department of Infrastructure and Regional Devel-opment, Canberra, https://bitre.gov.au/publications/2015/files/is_074.pdf>.
Bureau of Resources and Energy Economics (2014) Australian energy projections to 2049-50. ACT, Canberra
Butlin NG (1962) Australian domestic product, investment and for-eign borrowing, 1861–1938/39. Cambridge University Press, Cambridge
Buxton M (2006) Urban form and urban efficiency. Department of the Environment and Energy, Canberra, http://www.environ-ment.gov.au/node/22564
Byrne DM, Fernald JG, Reinsdorf MB (2016) ‘Does the United States have a productivity slowdown or a measurement prob-lem?’, Brookings Pap Econ Act 2016:109–182
Commsec (2016) US overtakes Australia to build biggest homes - CommSec Home Size Trends Report. Commonwealth Bank of Australia, Sydney, https://www.commsec.com.au/content/dam/EN/ResearchNews/Eco_Insights31.10_US_overtakes_Aus-tralia_to_build_biggest_homes.pdf>.
Corcoran P, Andrae A (2013) ‘Emerging trends in electric-ity consumption for consumer ICT’. Rep. NUI Galway Irel 10379:3563
Cramer J, Krueger AB (2016) ‘Disruptive Change in the Taxi Business: The Case of Uber’, National Bureau of Economic Research, no. 22083.
Cullen JM, Allwood JM (2010) ‘The efficient use of energy: Tracing the global flow of energy from fuel to service’. Energy Policy 38(1):75–81
Curry, D (2016), Court records reveal how much revenue and profit Google has made from Android, Digital Trends, http://www.digi-taltrends.com/mobile/google-android-revenue-revealed/. Viewed 10 March 2016
Deloitte Access Economics (2016) Economic effects of ridesharing in Australia. Deloitte Access Economics, Sydney, https://www2.deloitte.com/content/dam/Deloitte/au/Documents/Econom-ics/deloitte-au-economic-effects-ridesharing-australia-010216.pdf>.
Dyster B, Meredith D (1990) Australia in the International Economy: in the twentieth century, CUP Archive. Cambridge University Press, New York
Edwards JB, McKinnon AC, Cullinane SL (2010) ‘Comparative anal-ysis of the carbon footprints of conventional and online retailing A “last mile” perspective’. Int J Phys Distrib Logistics Manag 40(1/2):103–123
Energy Information Agency (EIA) (2015) Passenger travel accounts for most of world transportation energy use. EIA, Wash-ington D.C., https://www.eia.gov/todayinenergy/detail.php?id=23832>.
Frost L, Dingle T (1995) ‘Sustaining suburbia: An historical perspec-tive on Australia’s urban growth’. In: Troy PN (ed) Australian Cities: Issues, strategies and policies for urban Australia in the 1990s, 1st edn. Cambridge University press, Cambridge, p 20
Fuller RB (1973) Nine chains to the moon. Cape, LondonKurzweil R (1999) The age of spiritual machines: When computers
exceed human intelligence. Penguin, New YorkLee T (2011) The Great Ephemeralization. http://timothyblee.
com/2011/04/26/the-great-ephemeralization/.Magner L (2016a) Fast fashion in Australia. IBIS World, MelbourneMagner L (2016b) Online women’s clothing sales in Australia. IBIS
World, MelbourneModahl IS, Raadal HL, Gagnon L, Bakken TH (2013) ‘How meth-
odological issues affect the energy indicator results for different electricity generation technologies’. Energy Policy 63:283–299
Mokhtarian P (2009) ‘If telecommunication is such a good substitute for travel, why does congestion continue to get worse?’ Transp Lett 1(1):1–17
Moody’s (2016) Continued hotel construction a greater threat to US lodging sector CMBS than Airbnb, Moody’s Investor Ser-vices, Boston, https://www.moodys.com/research/Moodys-Continued-hotel-construction-a-greater-threat-to-US-lodging–PR_345701>.
OECDiLibrary (2015) Dataset: OECD - Electricity/heat supply and consumption. http://stats.oecd.org/OECDStat_Metadata/Show-Metadata.ashx?Dataset=ELE_BALANCE&ShowOnWeb=true&Lang=en.
Office of the Chief Economist (2015) Table B1 - Australia popula-tion, GDP and energy consumption. http://www.industry.gov.au/Office-of-the-Chief-Economist/Publications/Documents/aes/data/2015/Table-B.xlsx. Viewed 25 August 2016
Palmer G (2012) ‘Does energy efficiency reduce emissions and peak demand? A case study of 50 years of space heating in Mel-bourne’. Sustainability 4(7):1525–1560
Ruthven P (2012) A snapshot of Australia’s digital future to 2050. IBIS World, Melbourne, http://www-07.ibm.com/au/pdf/1206_AustDigitalFuture_A4_FINALonline.pdf
Ruthven P (2013) The Phenomenon of Industry Cycles. IBIS World, Melbourne, http://www.ibisworld.com.au/common/pdf/phil/Phil_April_2013_FINAL.pdf
Schor J (2016) ‘Debating the sharing economy’. J Self-Gov Manage Econ 4(3):7–22
57
Biophys Econ Resour Qual (2017) 2:5
1 3
Page 9 of 9 5
Smil V (2013) Making the modern world: Materials and demateriali-zation. John Wiley & Sons, Chicester
Stanwix G, Pham P, Ball A (2015) End-use energy intensity in Aus-tralia. Office of the Chief Economist, Canberra
Tainter J (1990) The collapse of complex societies. Cambridge Uni-versity Press, Cambridge
Vamplew W (1987) Australians: a historical library. v10: Australians: historical statistics. vol 10, Syme & Weldon, Fairfax
Van Wee B, Geurs K & Chorus C (2013) ‘Information, communica-tion, travel behavior and accessibility’. J Transp L Use 6:1–16
Wiedmann TO, Schandl H, Lenzen M, Moran D, Suh S, West J, Kanemoto K (2015) ‘The material footprint of nations’. Proc Natl Acad Sci 112(20):6271–6276
Zhou C, Robb K (2016) Shopping centre redevelopments are putting suburbs back on the map. domain.com.au, Sydney, https://www.domain.com.au/news/shopping-centre-redevelopments-are-put-ting-suburbs-back-on-the-map-20161025-gsa64f/>.
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Chapter 4
An input-output based net-energy
assessment of an electricity supply
industry
4.1 Overview and context of chapter
4.1.1 Introduction
Nearly all net-energy assessments of electricity generation have focused on specific technologies, and
adopted an attributional, process-based LCA. The functional unit is nearly always given as kWh (or
equivalently, MJ) of electricity delivered to the grid over the assumed life of the device.
However, much of the cost of delivering electricity is related to networks and ensuring sufficient
system redundancy. Hence there are system-level properties that are not addressed in technology-
specific analyses. Furthermore, nearly all EROI and LCA studies of electricity generation adopt
a process-based methodology, resulting in truncation error. Chapter 5 addresses similar issues by
adopting adopting a ‘bottom-up’ approach, using a functional unit of 1 kW of power delivered to the
grid. This study takes a ‘top-down’ approach using an input-output analysis.
A challenge with this study was that, to the best of my knowledge, there hasn’t been a study that
assessed the EROI of electricity at a system or national level. There has been progression towards
identifying the need for a broader, or consequential approach. Furthermore, some of the issues have
been widely explored, such as the costs of renewables integration, but much of the LCA or net-energy
approach has been ad-hoc and incomplete. Therefore, the initial challenge was ascertaining how to
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approach the analysis.
4.1.2 Research questions
The research questions posed by this chapter are:
1. Is it possible to calculate the EROI of a national electricity industry?
2. If so, what techniques are appropriate?
3. What data is available and what are the limitations of the data?
4. What are the highlights of the analysis?
4.1.3 Research method
This study adopts an environmentally extended input-output analysis (EEIOA). EEIOA provides a
method for evaluating the linkages between economic activities and environmental impacts, including
direct and indirect energy consumption. One technique is to combine the national monetary use-table
with the national energy account. The main strength of input-output (I/O) analysis is systematic
completeness – all national energy is allocated to specific industrial production or consumer end-use.
Its main weakness is homogeneity, or the assumption that each sector of the economy products a
single, homogeneous good or service.
Since the electricity supply industry produces a single product, the homogeneity weakness is partly
alleviated, although electricity costs vary across customer class, location and time. I/O also suffers
from uncertainty related to sampling, reporting and imputation. For this study, the benefit of an
economic input-output approach is that it includes all elements of electricity supply, including trans-
mission, distribution, and retailing.
The adoption of Australia as the country of interest has two benefits:
1. Most of the energy inputs are sourced within Australia. This reduces error associated with
multi-regional analyses and simplifies the analysis.
2. Australian electricity is geographically confined nationally, and therefore completely bounds the
analysis.
I/O analysis is widely adopted in several fields, especially economics. There is a also a large body
of literature on EEIOA. Most of the energy related EEIOA is related to embodied greenhouse gas
emissions and other pollutants. Since no comparable study was able to be identified, the matrix
algorithm needed to be developed. The study was conducted with Matlab.
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4.1.4 Study challenges
There were several challenges with the study. Firstly, the Australian Bureau of Statistics (ABS) system
of national accounts treats household electricity consumption and gross fixed capital formation as
final demand. Therefore the energy intensity of electricity for each sector is calculated mostly relative
to a unit of household consumption. But this study also treated industrial/commercial electricity
consumption as final demand. This required adaptions to the Matlab algorithms to correctly allocate
energy intensities and reconcile energy flows.
The second main challenge was that Australian economic and energy statistics are not designed for
net-energy analysis. The ABS produces a ‘net-use’ energy account, but the ABS definition of net-use
differs from the BPE definition. Similarly, the IEA ‘energy industry own use’ appears to be a statistic
that can be readily incorporated into net-energy analysis. However, closer analysis of the ABS and
IEA data, and further investigations, revealed that the ‘net-use’ or ‘own use’ statistics are not directly
completely translatable to ‘net-energy’ analysis. The ABS was able to assist with clarifying specific
issue with the ‘net-use’ account.
The third main challenge was dealing with the problem of differential tariffs and costs across fuels
and end-users. The standard approach in I/O analysis is to adopt a homogenous price for a product
across industries. For example, gas consumption can be calculated from purchases from the ‘gas
supply’ industry, coal from the ‘coal mining’ industry, and so on. The average tariff is estimated as
the quotient of the fuel industry revenue and fuel sold. However, it is difficult to establish precise
estimates for fuel tariffs, there is no single tariff for all consumers, and the assumption of a fixed tariff
does not correctly reflect energy flows across industries. The approach of this study was to allocate
energy consumption for all 15 of the ABS fuel types directly to industries and final demand. This
required building fuel allocation algorithms into the Matlab code.
4.1.5 Study code and data
The Matlab code and data utilised in the model can be found at:
https://github.com/grahampalmer/I_O_study
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An input-output based net-energy assessment of an electricity supplyindustry
Graham PalmerAustralian-German College of Climate and Energy, The University of Melbourne, Parkville 3010, Australia
a r t i c l e i n f o
Article history:Received 9 May 2017Received in revised form10 November 2017Accepted 12 November 2017Available online 15 November 2017
Keywords:Net-energyEROIInput-output analysis
a b s t r a c t
Electricity systems process and upgrade crude feedstock energy from the natural environment using highquality energy inputs, including diesel and electricity. The ratio of electricity output to the energy inputsis termed the energy-return-on-investment (EROI) and may be an important metric linking energyconsumption and standard of living. Environmentally extended input-output analysis (EEIOA) can beused to determine the energy flows through an economy with a monetary use-table and a satelliteenergy account. This study applies an EEIOA analysis to the Australian electricity supply industry, dis-aggregating the feedstock from the energy inputs, and further disaggregating electricity generation fromtransmission, distribution and on-selling. We calculated the system EROI at 40:1 for 2013e14. Theelectricity generation industry is energetically economic, in the sense that a relatively small energy inputleverages a much greater magnitude of electricity generation and distribution. However, the leveraginghas been achieved at the expense of a high feedstock extraction rate and commensurate emissions. Wefind that the industry is cost constrained rather than EROI constrained. This is the first study to examinethe net-energy of electricity at a system level, and establishes a baseline for exploring future low-emissions scenarios.
© 2017 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Net-energy analysis
Energy systems process and upgrade crude feedstock energyfrom the natural environment using high quality energy inputs,such as diesel and electricity. The energy-return-on-investment(EROI) is the ratio of the net energy output to the low entropyenergy inputs that are ‘diverted from society’ [78]. Input-output (I/O) modelling can be used to determine the energy flows through aneconomy with a monetary use-table and a satellite energy account.However most I/O studies are concerned with the primary feed-stock energy extracted from the environment. Net-energy analysisrequires that the feedstock energy is subtracted from the cumula-tive energy demand (CED) [17,18].
1.2. Definition of EROI
EROI is a dimensionless ratio, defined as the ratio of the gross
flow of energy inputs Eg , over the lifetime of the project, and thesum of the energy for construction Ec, operation Eop, and decom-missioning Ed ([73]; eq. (1)). More generally, Murphy and Hall [72]state that ‘EROI is the ratio of how much energy is gained from anenergy production process compared to how much that energy (orits equivalent from some other source) is required to extract, grow,etc. a new unit of the energy in question.’
EROI is an important metric for understanding the relationshipbetween energy and standard of living [63]. At a high EROI (>20:1),EROI is not a constraint on economic activity, however at low andfalling EROI, society may contract or reconfigure to stay above someminimum [56]. Large differences in the energy intensity of energysupply technologies can result in significant differences betweeneconomic and energetic costs.
EROI is the most commonly used net-energy ratio (NER), butthere are several NERs that take slightly different mathematicalforms. This study adopts the gross external power ratio (GEPR),introduced in King et al. [58] and discussed in Ref. [48]; p.130. TheGEPR is the power-based equivalent to the life-cycle based ‘grossexternal energy return’ (GEER) ratio given in Brandt and Dale [26].A ‘gross external’ ratio has been applied because: i) this studyadopts a complete system boundary based on a top-down analysis;
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Energy 141 (2017) 1504e1516
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ii) only marketed (i.e. external) energy is included in the analysis;and iii) electricity output is expressed as gross output. A powerratio has been applied because the study is considering annual,rather than life-cycle energy flows. Given the relatively highcalculated GEPR, the equivalent ‘net’ ratio (NEPR) would be onlyslightly lower (see Fig. 1).
EROI ¼ EgEc þ Eop þ Ed
(1)
1.3. Previous studies
There is a large body of literature exploring the EROI of variousenergy sources, especially oil production, biofuels, and electricitygeneration [35,49,73]. The focus of most EROI studies is exploringthe biophysical constraints of energy technologies and whether theenergy source is energetically ‘worth doing’ [47]. Further work in-cludes exploring the relationship between net-energy and prices[57]. Electricity generation has beenwidely studied, especially coal-fired electricity, including carbon capture [87,90], wind power [62],solar photovoltaics [61] and gas-fired generation [71]. Importantly,the boundaries and types of analysis vary between studies, but alladopt the electricity busbar or inverter output as the EROInumerator e the distribution and management of the electricitysystem lies beyond the analysis boundary. Recent research suggeststhat the fixed costs of networks may be a greater constraint thanthe net-energy of conventional generation [41], however a risingpenetration of variable renewable energy, electrical storage, andbiomass will increase the energetic burden [49]. Wolfram et al. [88]adopted a hybrid-LCA to explore the carbon footprint of lowemission scenarios for Australian electricity, and were the first toadopt a full life-cycle approach to electricity generation in Australia.To the best of our knowledge, this is the first study to estimate theEROI/GEPR of electricity supply at a national level, as well as thefirst to disaggregate generation, transmission and distribution.
1.4. Motivation and context
Australia's electricity system is undergoing a transition towardslower greenhouse emission electricity generation [43]. The primarydriver is greenhouse emission abatement targets, but technologicaland economic changes are also driving a transition away from coal-fired generation. Wind power is currently the cheapest form of newlarge scale generation in Australia [22].
Several studies have explored Australian low-emission sce-narios, including AEMO [12]; Blakers et al. [24]; Elliston et al. [38];
Elliston and Riesz [39]; Jeppesen et al. [53]; Lenzen et al. [67]. Alladopt a least-cost optimisation methodology, with differing ap-proaches to incumbent generation, geographic diversity and tech-nology mix. However, all scenarios adopt a gross-energy approach,rather than a net-energy approach, and thereby overstate fuelsubstitution in cases where the net-energy is much less than theincumbent system. Financial and energetic costs should be assessedat a system level in order to capture system-level effects, such asgeographic and technology diversity, curtailment, storage, demandmanagement, and additional transmission. This study provides abaseline for exploring low-emission scenarios.
2. Overview of the Australian electricity supply industry
The Australian electricity supply industry includes the twomajor energy markets, the National Electricity Market (NEM),which covers the majority of Australia's population and businessactivity on the east coast and Tasmania, and the South WestInterconnected System (SWIS) in Western Australia (see Fig. 2).These comprise 85% and 13% of national electricity consumption,respectively [23]; Fig. 4.4. Smaller networks include the NorthWestInterconnected System (NWIS) in Western Australia and theNorthern Territory Electricity Network (NTEN).
In 2013e14, Australian electricity was generated by black coal(43%), brown coal (19%), natural gas (22%), diesel (2%), hydro (7%),and other renewables, including wind (4%), solar (2%), and biomass(1%). Mine mouth dedicated coalmines supplied all the coal-firedpower stations in the state of Victoria and some in Queensland[10]. All NSW generators and some Queensland generators sourcedcoal from a mix of open-cut and underground mines. For the studyperiod, South Australian coal-fired generators sourced coal from anopen-cut mine. Transport costs may comprise a substantial pro-portion of the delivered cost of coal. Natural gas is supplied viaextensive pipeline infrastructure from large conventional gas ba-sins and increasingly, also coal-seam gas [30].
3. Environmentally extended input-output analysismethodology
3.1. Introduction
Environmentally extended input-output analysis (EEIOA) pro-vides a method for evaluating the linkages between economic ac-tivities and environmental impacts, including direct and indirectenergy consumption. One technique is to combine the nationalmonetary use-table with the national energy account. The mainstrength of I/O analysis is systematic completeness e all nationalenergy is allocated to specific industrial production or consumer
Fig. 1. Streamlined energy systems diagram. Derived from Raugei and Leccisi [78].
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end-use. Its mainweakness is homogeneity, or the assumption thateach sector of the economy products a single, homogeneous goodor service [60]. Since the electricity supply industry produces asingle product e electricity e the homogeneity weakness is partlyalleviated, although electricity costs vary across customer class,location and time. I/O also suffers from uncertainty related tosampling, reporting and imputation. For example, in a hybrid-I/Ostudy of wind turbines, Lenzen and Wachsmann [68] calculated astandard error of 24e29%.
3.2. Monetary flows
Following Leontief [69]; Lenzen [65] and Common and Salma[34]; let y be a vector ðn� 1Þ of final demand in monetary units,where n is the number of industry sectors and Z is a matrix of in-termediate demand of industries, then the vector of total output, x,can be expressed as -
xi ¼ zi1 þ zi2 þ…þ zij þ yi (2)
A matrix ðn� nÞ of technological coefficients, A, can be given byZ and x, shown in Eq. (3).
aij ¼zijxj
(3)
From Leontief [69]; the vector x can be expressed as a function ofy, including the total direct and indirect requirements for eachsector with the ‘Leontief Inverse’, L, and technology matrix, A,shown in Eq. (4).
x ¼ ðI � AÞ�1y ¼ Ly (4)
The Leontief Inverse, L, can also be expressed as the geometricseries, shown in Eq. (5), which can be used to calculate the indi-vidual pathways (see Section 3.4).
L ¼ ðI � AÞ�1 ¼hI þ Aþ A2 þ…
i(5)
3.3. Energy flows
One technique of allocating fuel use is to connect the monetary
Fig. 2. Australia's four major electricity systems. Sources: PowerWater [77]; ERAWA [40]; AEMO [14].
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flows with the respective energy industries. Gas consumption canbe calculated from purchases from the ‘gas supply’ industry, coalfrom the ‘coal mining’ industry, and so on. However, it is difficult toestablish precise estimates for fuel tariffs, and the assumption of afixed tariff does not correctly reflect energy flows across industries[66]. This study allocates energy consumption directly to industries,and indirect energy is approximated indirectly through the mon-etary flows. The functional unit for this study is annual energyconsumption in petajoules (PJ) for the respective electricity sectors.
Let G be a matrix ðf � sÞ of the industrial consumption of f fueltypes and s industry sectors given in the Energy Account. Since theAustralian Energy Account uses a higher level of aggregation thanthe National Account ðs<nÞ, the respective industry sectors, n, ofthe National Account were grouped under respective sectors, s,given in the Energy Account. Electricity supply has its own sector inthe Energy Account. The vector for energy consumption for each ofthe Energy Account sectors, g, was allocated to respective NationalAccount sectors on a pro-rata basis based on the total output, xi,giving a matrix H ðf � nÞ.
The fuel intensity vector ðn� 1Þ for each fuel type is shown inEq. (6).
e ¼ Hx�1 (6)
The total direct and indirect fuel attributed to each industrysector, i, can be calculated as shown in Eq. (7).
Fueli ¼ be � L� by (7)
3.4. Calculation of individual energy pathways
Fig. 3 illustrates the application of direct and indirect energypathways. Stage 0 are the direct energy inputs for the two elec-tricity supply sectors (generation; transmission, distribution & on-selling). The ‘stage 1’ inputs of a process are the direct energy inputsof the respective industries supplying the electricity supply sector(e.g. coal mining, construction services, road transport), ‘stage 2’supply the ‘stage 1’ industries, and so on. The total energy for eachstage, or for all stages, can be calculated using the Leontief matrix.However, disaggregated individual pathways require an expansionroutine. Inserting the geometric series from Eq. (5) into Eq. (7)([60]; eq. 7), gives Eq. (8).
Fueli ¼ ½be � I � by� þ ½be � A� by� þ ½be � A� A� by� þ… (8)
where the first bracketed term is stage 0 (direct) energy, the secondis stage 1 (indirect), and so on.
From Eq. (8), stage 1 and higher ordered pathways are individ-ually calculated as shown in Eqs. (9)e(11).
pathwayji ¼ ej � aji � yi (9)
pathwaykji ¼ ek � akj � aji � yi (10)
pathwaylkji ¼ ek � alk � akj � aji � yi (11)
and so on.The number of possible pathways evaluates to nt , where n is the
number of industry sectors and t is the number of stages. At n of 114and t of 5, there are 19.3 billion pathways. The processing time and
Fig. 3. Direct versus indirect energy for electricity generation. Derived from Treloar et al. [85].
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size of the itemised lists is improved by applying the pruningtechnique described in Treloar [84]; which truncates subsequentpaths when a pathway is below a given threshold. The contributionto indirect energy generally lessens for each subsequent stage, suchthat five stages encompasses nearly all of the indirect energy e bytrial and error, five stages and a threshold of 0.00001 PJ were foundto provide 97.6% of total energy. The process was repeated for eachof the fuel types, f, making it possible to explore pathways by fueltype (see Table 2).
3.5. Feedstock fuels
The feedstock fuels dominate the CED of electricity generation.However net-energy analysis requires that the feedstock energy issubtracted from the CED ([27,78]; Fig. 1). For example, in 2013e14,feedstock fuels totalled around 2200 PJ, electricity generationtotalled 894 PJ, but the energy investment to build and operate thesystem was less than 100 PJ. From a net-energy perspective, allfeedstock energy inputs, including coal, natural gas, sunlight andthe kinetic energy of wind, are treated more-or-less equivalently.
3.6. Primary energy factors
After evaluating the evaluation of energy pathways, it isnecessary to apply a scaling factor based on fuel type to take ac-count of the differing economic usefulness of a fuel. For example, amegajoule of coal energy has a different value to a megajoule ofelectricity, although they both represent the same quantity of en-ergywhen expressed as the ‘first-law’ (of thermodynamics) heatingvalue. Since electricity is a secondary energy carrier, it is necessaryto determine the primary energy consumed to generate a mega-joule of electricity. The electricity conversion factor is specific forthe particular electricity system in question, and given as the ratioof the primary energy consumed to the electricity output. In the LifeCycle Assessment (LCA) literature, primary energy is expressed asthe life-cycle primary energy, including the embodied energy andenergy harvested from nature [44]. The major energy agenciesmeasure primary energy as the ‘physical energy content’ of the fuelat the point where it first becomes an economically useful energyproduct [51,52]. For grids composed primarily of fossil fuels, theratio is numerically similar to the average power plant heat rateratio of 2.6e3.3 MJ per MJ of electricity.
However, there is no consensus within the energy agencies onthe treatment of non-thermal power generation, such as hydro-power and wind power [45,70]; Section 1.A.3. The ‘substitutionmethod’ is adopted by BP, EIA, IIASA, and WEC, and adopts athermal conversion of 1 MJ electricity equals � 3 MJ primary en-ergy. The ‘direct equivalent’ method is used by the UN, IEA andEurostat, and uses a one-to-one equivalence between electricityand primary energy (i.e. 1 MJ electricity equals 1 MJ primary en-ergy). Various other approaches have been explored in relation toaggregating energy flows, including economic approaches usingprices ormarginal product, the emergy analysis approach pioneeredby Odum, and the thermodynamic approach using exergy [33,55].
Taking the annual generation for Australia for 2013e14, the‘primary energy factor’ (PEF) equates to 2.9 using the EIA non-fossilconversion, and 2.6 using the IEA non-fossil conversion factor ofunity. A prospective future grid composed of less thermal
generation would require a different conversion factor.In life-cycle analysis, primary energy factors are applied to each
fuel type. Treloar [84] applied amultiplier of 1.2 for coal, oil and gas;1.4 for petroleum and coal products; 3.4 for electricity; and 1.4 forgas. The factors used for this study are shown in Table 1.
3.7. Limitations of methodology
A single-region model has been adopted. This assumes thatimported products are produced with the same energy intensity asif they were produced domestically [79]. For products that are notproduced in Australia, or for other elaborately transformed manu-factures, the implicit energy intensity may not apply and thereforean error will be introduced. This will becomemore important as theexpenditure on renewable energy components, such as solarpanels, increases. The Australian electricity supply industry sourcesall of the electricity feedstock inputs, and much of the energyintensive capital stock locally. In contrast, fuel importing countriesrequire a multi-regional model as a minimum requirement (e.g.Brand-Correa et al. [25]).
In 2013e14, imports comprised 9% of electricity supply industryvalue added. In the case of coal-fired and conventional thermalgeneration, most of the embodied energy cost is associated withcoal mining, oil and gas extraction, and transport. Construction andmaterials of the capital stock is estimated at 5% for coal-firedgeneration [87] to 11% [90]. Since nearly all of the energy foot-print of renewable energy is embodied in the build/constructionphase, a future system with a higher penetration of importedrenewable energy components will require the use of expandedboundaries, including multi-regional tables.
4. Data sources
4.1. Monetary data
The primary economic data source was the Australian Bureau ofStatistics (ABS) ‘input-output Table 5’ from data series5209.0.55.002, ‘industry by industry flow table (direct allocation ofimports)’ [8]. The ABS has continually evolved the input-outputframework, requiring slightly different mapping of the energysatellite account, G, to the National Account. The first year availableonline (1998e99) used 106 industry sectors, 2001e02 through2005e06 used 109 sectors, 2006e07 through 2008e09 used 111sectors, and subsequent years have used 114 industries. There hasbeen continuity with most industries, but shifts must be accountedfor in a time-series analysis. In particular, the change from 109 to111 sectors in 2006e07 included some aggregation of sectors,
Table 1Primary energy factors for this study.
Coal, coke, briquettes, wood, bagasse Diesel, petrol, LPG other-refined, natural gas Electricity
1.0 1.2 2.9
Table 2ABS 4604 fuel types.
1 Black coal 9 Diesel2 Brown coal 10 Other refined3 Coke 11 LPG4 Coal by products 12 Biofuels5 Briquettes 13 Wood6 Natural gas 14 Bagasse7 Crude oil 15 Electricity8 Petrol
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especially farming-related, and an expansion of other serviceindustries.
With the introduction of 111 industries in 2005e06, ‘electricitysupply’ was split into 2 sectors e ‘electricity generation’ and‘electricity transmission, distribution, on-selling and electricitymarket operation’ (TDOS). The transmission/distribution sectorincludes purchase and sales of distribution costs as part of on-selling, hence caution must be exercised to avoid double countingwhen deriving sales and revenue data from the ‘use table’. For theperiod 2008e09 onwards, the industry spending on transmission/distribution can be calculated as the difference between ‘total in-dustry uses’ and sales of transmission/distribution to itself (i.e. thediagonal of the I/O table). However, this calculation does not workfor the period 2007e08 and earlier. An explanation is that duringthe process of privatisation and disaggregation, formerly singleentities became multiple entities [4]. Furthermore, many activitiesthat were once conducted by the single, state-owned corporation,are now carried out by specialist businesses, which are classified toother industries. These include businesses associated with con-struction, repair and maintenance. Since the ABS does not have aspecific ‘solar industry’ sector, it was not possible to disaggregatethe solar industry. The majority of enterprises that operate in theindustry are small-scale contractors and tradespeople [64], andtherefore sales and installation is allocated to the ‘service sector’.
4.2. Energy data
4.2.1. Feedstock fuelsFeedstock refers to fuel that is directly combusted for electricity
generation. The feedstock fuels were derived from BREE, Table F1,‘Australian energy consumption, by industry and fuel type, energyunits’ [74]. Four feedstock fuels were included: black and browncoal, natural gas, and diesel, which combined comprised 98.8% of‘fuels consumed’, excluding consumption of electricity. Since elec-tricity is a secondary fuel, consumption of electricity by the elec-tricity supply industry is treated as a net-use, rather than afeedstock.
Since Table F1 represents gross energy consumption, this studyassumes that nearly all of the consumption of the four fuels by theelectricity supply industry can be attributed to feedstock ratherthan net-use fuels. In recent years, brown coal has only beenconsumed as a feedstock fuel (historically, it was also a feedstockfor briquettes and syngas) and it is assumed that most direct blackcoal and natural gas consumption is also a feedstock fuel. This isconfirmed by the net-use data from ABS 4604. Diesel is consumedas both a feedstock and a direct energy input, but from inspection,around 95% of diesel consumption can be attributed to a feedstockfuel.
4.2.2. Net-use energyThe energy net-use data sources were derived from three
sources: ABS data series 4604.0, ‘Australian net use of Energy2002e03 to 2013e14’; BREE Table F1, ‘Australian energy con-sumption, by industry and fuel type, energy units’ [74]; and ABSdata series 4660.0, ‘Energy Use, Electricity Generation and Envi-ronmental Management, Australia’.
The ABS net use of energy is derived from three sources e theEnergy, Water and Environment Survey (EWES), which is con-ducted every three years; Australian Energy Statistics (AES) data;and NGER reported data. Some entries in the ABS 4604.0 energyaccount are labelled ‘np’, which refers to data that is not itemised inorder to preserve confidentiality of the reporting industries, but arenonetheless included in totals. A process of summing rows andcolumns and ‘guestimating’ permits estimates to be made of themissing entries.
Electricity net-use data was cross referenced with the publisheddata from the Australian Clean Energy Regulator for the NationalGreenhouse and Energy Reporting (NGER) scheme. The schemerequires reporting of greenhouse emissions for generators andcorporate groups that exceed a threshold of 25 kt of CO2-eq orproduction of greater than 100 TJ (27.8 GWh). This includes all ofthe major generators and nearly all electricity generation, includingwind farms with a capacity of greater than around 10 MW. Smalldistributed generators not owned by large corporate entities androoftop solar fall below the reporting threshold.
Scope 1 (direct emissions) result from the combustion of fossilfuels for electricity generation, and scope 2 (indirect) emissionsresult from the purchase and consumption of electricity. This im-plies that self-generated self-use within a facility is not included.Scope 2 emissions are calculated as the electricity multiplied by theemission factor for the state or territory in which the consumptionoccurs. Therefore, the electricity can be back-calculated given theestimated emission intensity.
From NGER reporting, the Victorian Loy Yang A facility is treatedas an integrated generation-mine operation and therefore theelectricity consumption associated with mining is reported as ageneration activity. All Victorian coal-fired generators consumemine-mouth brown coal, and operate as integrated open-cut-generator facilities [10]. Loy Yang B is separately owned and pur-chases coal from the owner-operator of Loy Yang A. From 2002, theYallourn mine was operated by a joint venture company [82]. Forthis study, it is assumed that the Victorian coal-fired generators arevertically integrated generator-mines, which report as ‘generators’.All other generators in other states purchase fuel from the ‘coalmining’, ‘natural gas supply’, or ‘oil & gas extraction’ industriesrespectively.
A check of the ABS 4604.0 data related to mining and oil and gasextractionwas enabled by cross-referencing ABS data to the EnergyEfficiency Opportunities (EEO) Program. The EEO was a FederalGovernment program running from 2006 to 2014. It required cor-porations that consumed greater than 0.5 PJ per annum to report atleast 80% of their total energy use by the end of the first five-yearcycle. Several reports covering industry groups were published in2013 covering the period 2006e11 [19,20].
Since NGER only applies to generators, it should be possible tosubtract generator estimates of electricity consumption from theABS 4604 data to derive an estimate for TDOS. However, since it isassumed that generation is much more electricity intensive thanTDOS, and it was not possible to directly disaggregate purchasedelectricity by TDOS, an alternative method was applied. An elec-tricity intensity per dollar of value added was estimated from thecalculated intensity of the non-energy intensive industry sectorsfrom ABS 5204.5, Table 5. This study uses an intensity of 50 MWhper $million gross value added based on a range of around 20 to 60,giving 0.9 TWh for 2013e14.
4.2.3. Electricity generation and loss dataElectricity generation data were sourced from BREE table O1
[75]. Further electricity supply data was derived from AEMO [13]archived generation data, including the supply of electricity fromAustralia's three pumped-hydro schemes (Wivenhoe, Shoalhavenand Jindabyne). Transmission and distribution losses of 2.5% and3.5% respectively were estimated from Australian data from WorldBank [89] and ABS [5].
4.3. Solar generation data
For the period of this analysis, the contribution of rooftop solarwas small enough that Australian PV Institute estimates areadequate. However future expansion will require more detailed
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assessments. For the period of the study, nearly all installed solarwas rooftop. Since rooftop solar is embedded within the low-voltage distribution network and partly consumed on the house-holder side of the meter, self-consumed solar generation isunmetered and treated as a demand reducer from the perspectiveof the electricity supply industry. Feed-in tariffs are based onmetered net-exports rather than gross generation. Gross-meteringwould permit more precise estimates. However only a limitednumber of systems in NSW and ACT use a dual meter [54] and theinformation is not publicly accessible. Most of the estimated solargeneration is based on system capacity and postcode data from theClean Energy Regulator, which can be calibrated against knownperformance data from gross-metered systems [11]. The AustralianPV Institute website provides estimated real-time performancedata based on solar insolation and selected in-field solar systemsvia the ‘http://pvoutput.org’ portal. Johnston and Egan [54] calcu-lated a national weighted average annual solar output from theRenewable Energy Certificate registry of 1400 kWh/kW, or3.8 kWh/kW per day. In relation to monetary data, the ABS does notdisaggregate the Australian solar industry, however industry-basedreports are available which enable some estimates to be made,including Ledovskikh [64] and Solar Business Services [81].
4.4. Other data
A set of time-series data, including electricity consumption,prices, feedstock fuels and GDP, was built up from several historicand contemporary sources including Vamplew [86]; Butlin [31];Dyster and Meredith [37]; ABS [2,3]; Office of the Chief Economist[74,75]. Further disaggregation of the ‘transmission, distribution,and on-selling’ sector was provided by IBIS World reports. IBISpublishes industry reports based on firm-level and other data. Thisincludes separate reporting of the transmission [42], distribution[15], and retail electricity sectors [16]. The capital depreciationmetric was used as a proxy for the proportion of energy expendi-ture for each of the 3 non-generation sub-sectors, with therespective proportions calculated as 26% transmission, 62% distri-bution, 12% on-selling.
4.5. Consumer classes
Some jurisdictions apply several consumer classes to energyconsumption, including: industrial; commercial; government; andresidential/households. For this study, only 2 consumer classes aredisaggregated based on ABS reporting: industrial, comprising allbusiness and government; and residential/households.
5. Reconciliation of data for the electricity supply industry
5.1. Allocating energy to all electricity supply
Since this study is considering the energy inputs into the elec-tricity industry, several adaptions to the basic methodology wereadopted to reconcile energy consumption. The ABS system of na-tional accounts treats household electricity consumption and grossfixed capital formation as final demand. Therefore the energy in-tensity of electricity for each sector will be calculated mostly rela-tive to a unit of household consumption. But this study is alsotreating industrial/commercial electricity consumption as finaldemand. This was remedied by allocating all direct energy inputs tothe electricity supply industries directly, rather than just the energyattributed to final demand. The problem this creates is that non-energy purchases by the electricity supply industry, from sectorsthat have purchased from the electricity supply industry, becomedouble counted inputs. This was remedied by directly allocating
only the remainder of total electricity after processing the energypathway routine.
5.2. Double counting due to on-selling
A further issue relates to the on-selling of electricity in the‘transmission, distribution and on-selling’ sector. During the periodof state-owned utilities, electricity was sold directly to consumersby vertically integrated entities. However, the functional separationof retailing post-privatisation meant that electricity becamecounted twice in the National Account e firstly as purchases by theretail sector, then as sales plus markup by the retail sector to finalconsumers. This is a characteristic of the accounting systemadopted by the ABS. The IBIS report on electricity retailing [16] alsoshares this characteristic. It only applies from 2008 to 09 onwards.
The issue this creates is that it increases the intermediate use ofthe TDOS sector relative to the generation sector, and therefore agreater proportion of the direct energy of the entire ‘electricityindustry’ is incorrectly attributed to TDOS when direct energy isallocated on a pro-rata basis (see Section 3.3). In order to betteralign the monetary flows with the physical flow of electricity, theapproach taken in this study was to subtract the estimated costs oftransmission and distribution from the diagonal of the intermedi-ate matrix, Z, as though retailing was vertically integrated withTDOS. This subsequently reduced the total output, xi, of the‘transmission, distribution and on-selling’ sector and brought therespective electricity supply components back into the typicalrelative proportion given in the National Accounts in 2012, shownin Table 3.
5.3. Coal mining
Diesel consumption by the coal mining sector is the singlelargest indirect pathway. From the monetary use table, the pur-chase of ‘coal mining’ product by the ‘electricity generation’ in-dustry made up 3.6% of total supply. But from the energy account,the consumption of black coal for electricity generation constituted8.6% of total production, with exports constituting 89% of produc-tion. This implies that ‘electricity generation’ is purchasing blackcoal at a much lower price than exported coal, and/or there areadditional costs associated with exported coal, including transport.There may also be a cost difference associated with the quality ofcoal, whereby local generators can utilise relatively inferior de-posits at lower cost, but presumably at the same, or higher, energycost of mining and processing. Furthermore, product that is sup-plied for local generation is usually offered at a discount due to thestability of long-run contracts [10]. For this study, the indirect en-ergy of ‘coal mining’ that was attributed to ‘electricity generation’was adjusted upwards two-fold, equating to 7.2%.
The GEPR of coal supply was estimated from data from theEnergy Efficiency Opportunities Program. Direct energy consump-tion for the coal mining industry for 2010e11 included: diesel 50 PJ;and electricity 12 PJ [19]. From BREE [28]; total production of blackcoal was 9215 PJ in 2010e11. Applying a primary energy multiplierof 1.0 for coal, 1.2 for diesel and 2.9 for electricity equates to a GEPR
Table 3Shares of Electricity supply output and net capital expenditure by ANZSIC group,2006e2007, source [6].
Industry value added (%) Net capital expenditure (%)
Generation 35 30Transmission 11 18Distribution 47 48On-selling 7 4
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of 97:1 for direct energy inputs only, which comprise most of theenergy footprint [90].
5.4. Revision of electricity use by the electricity supply industry
The ABS data series 4604.0 ‘net-use’ includes electricity con-sumption by the electricity supply industry itself. It includes: self-generated electricity for own use; purchased electricity for ownuse; electricity losses due to transmission and distribution; andelectricity purchased to charge pumped hydro storage (PHS) [9].The IEA [51,52] Energy Balance data for ‘electricity own use’ ap-pears to be derived from the ABS data, and therefore does not addadditional information. Some studies (e.g. Brand-Correa et al. [25];King et al. [59] have used IEA ‘energy industry own use’ (EIOU) datato calculate EROI/GEPR of energy at a national level, but the highlevel of aggregationmay not properly reflect the boundaries usuallyadopted for the EROI denominator.
Generator self-use is referred to as auxiliary load or parasiticuse. This includes drive power components such as pumps, fans,and conveyors for fuel handling, furnace draft, and feedwaterpumping; power conversion, protection and distribution compo-nents such as transformers; and instruments and control systems.In total, thesewill typically consume around 7e15% of generation insteam power plants ABB [1]; down to around 2% in gas-firedcombined-cycle plants [46]. Anti-pollution devices in pulverisedcoal plants, including precipitators, can consume 5% of generationoutput. Prospective estimates for coal with carbon capture are25e35% [1]; p.338. From ABS [9]; it is estimated that the ABS hasused an estimate of 2.8% across all generation, however it is notclear which components have been included. The re-charging ofpumped hydro constituted around 0.1% of Australian electricitysupply and could be ignored. The estimated losses in PHS charge-discharge cycling were estimated at 20%.
Self-generated electricity for own use within an energy supplyfacility can be treated in two ways e either as a loss within thefacility that effectively reduces the efficiency of production but isnot added to the denominator on the EROI equation; or treated as adiversion of energy ‘from society’ that needs to be added to thedenominator [48,71]; pp.130e131. Consistent with an ‘external’approach Brandt and Dale [26]; this study uses the former.
For 2013e14, ABS4604.0 reports electricity consumption by theelectricity supply industry as 96 PJ. Taking transmission and dis-tribution losses at 6% calculates to 55 PJ; gross inputs for PHS isestimated at 0.6 PJ; ownuse electricity for generators only is esti-mated at 25 PJ (from personal contact with [9]); leaving 15 PJ ofpurchased electricity, which was allocated mostly to generation. Itwas subsequently found that the last figure is the most significantcomponent of the overall energy footprint, and therefore the studyaccuracy is dependent upon procuring unpublished data. Unfor-tunately, the ABS net-use account is not designed for EROI analysis.
6. Results and discussion
6.1. Overview
Tables 4 and 5 summarise the results derived from the methodsdescribed in detail above.
These results provide the primary data for depicting the entireelectricity system as a ‘black box’ that shows all inputs and outputse feedstocks, operational fuels, electricity output and waste (heat).That ‘black box’ is shown schematically in Fig. 4.
One can see that in 2013e14, 894 PJ (248 TWh) of electricity wassupplied by the Australian electricity supply industry. Spending onelectricity was estimated at $29.8 B or 1.9% of GDP for industrialconsumers, and $14.4B or 0.9% of GDP for households. The GEPR has
been calculated as 40:1 for 2013e14 using primary energy equiv-alent scaling, or 29:1 using unity scaling for all fuel types andelectricity.
6.2. Largest components of operational energy
Fig. 5 itemises the 16 largest net-use energy pathways. Thelargest net-use is the purchase of electricity by the electricitygeneration sector. This refers to purchased electricity and does notinclude generator self-use (see Section 5.4). Using primary energyequivalent scaling, electricity comprises 75% of the operationalenergy. The second largest component is diesel consumption by thecoal mining industry for transport and machinery. ‘Other refined’,including bunker oil, is consumed by the water and pipeline in-dustry for the generation sector. Other industry sectors that areresponsible for a significant energy footprint supplying the elec-tricity supply industry include oil and gas extraction, constructionservices, gas supply, and road transport.
6.3. Tier analysis
Fig. 6 itemises the aggregated energy pathway totals for the first4 stages, for the 6 most significant fuels, where stage 0 is the directenergy inputs into the electricity supply industry. Natural gas,diesel and ‘other refined’ are dominated by stage 1 inputs. Themostsignificant consumption of natural gas includes: oil and gasextraction; gas supply; coal mining; and construction services.Diesel consumption is dominated by the coal mining industry fol-lowed by road transport. ‘Other refined’ includes water and pipe-line transport and coal mining. Most of the crude oil and petrolconsumption relates to purchases by the electricity sectorsthemselves.
6.4. Sankey diagram of the electricity supply industry
Fig. 7 presents the Sankey diagram representation of the results.The figure graphically depicts the feedstock fuels, operational en-ergy inputs, and annual energy flows of the all fuels consumed inthe Australian economy. With the exception of exported crude oil,exports are not included in the diagram since these have no directimpact on the electricity supply industry. Major exports for2013e14 include black coal (9485 PJ), liquefied natural gas (1303 PJ)and uranium oxide (given as 3944 PJ) [29]. The results for trans-mission, distribution and on-selling have been disaggregated usingthe capital depreciation metric discussed in Section 5.2.
6.5. Low energy intensity cost components
A characteristic of the EEIOA methodology is that energy isallocated on an industry-by-industry basis, whether directly to theelectricity supply industry, or indirectly through purchases by theelectricity supply industry. Using this framework, payments to la-bour, taxes and gross profit margins have, by definition, a zeroenergy intensity since these payments re-enter the economy asconsumption or capital purchases. Labour compensation includesremuneration in cash or in kind, and employers' social contribu-tions [7]. Some studies argue that an energy intensity should beapplied to labour [48]; pp.139e40, however labour is usually
Table 4Feedstock fuels, 2013-14.
Black coal Brown coal Natural gas Hydro & RE Diesel
1018 622 531 133 39
G. Palmer / Energy 141 (2017) 1504e1516 1511
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explicitly excluded in life cycle assessments [80]. Using thisframework, the application of an energy intensity to labour wouldresult in double counting since the same energy would be allocated
to household consumption.Other significant low-intensity cost components include finance
and insurance. The energy intensity of service industries includes
Table 5Operational fuels and electricity >0.01 PJ, 2013-14.
Black coal Natural gas Crude oil Petrol Diesel Other refined LPG Biofuels Wood Bagasse Elect.
0.1287 2.2856 1.0916 1.5179 6.4177 2.1667 0.0701 0.0691 0.0640 0.0101 16.8802
Fig. 4. ‘Black box’ depiction of the electricity supply industry, 2013e14.
Fig. 5. Largest 16 energy pathways, disaggregated by generation and TDOS. Petroleum includes diesel, petrol, ‘other refined’, and LPG.
G. Palmer / Energy 141 (2017) 1504e15161512
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the direct consumption of electricity (e.g. for office buildings) andpetroleum (e.g. to run vehicles), along with the indirect energycosts of those industries respectively. Significant costs for thefinance and insurance industries include computer systems design,
internet services, employment services, and travel agency services.From Table 6 it is apparent that much of the cost of electricity can beattributed to low energy intensity costs that have no bearing on thebiophysical limitations of energy supply.
Fig. 6. Total electricity supply industry for 2013e14. Direct (left/blue), and the first 3 stages of indirect, for the most significant 6 fuel types. (For interpretation of the references tocolour in this figure legend, the reader is referred to the web version of this article.)
Fig. 7. Sankey diagram of Australian electricity supply industry 2013e14, figures shown without primary energy equivalent scaling.
G. Palmer / Energy 141 (2017) 1504e1516 1513
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6.6. Sensitivity analysis
Since the studied period was a single year, a sensitivity analysiswas undertaken to establish whether the period was representa-tive. The I/O analysis was repeated for the years 2009e10 and2012e13 which have an identical use-table structure to 2013e14.The ABS did not produce input-output tables for 2010e11 and2011e12, and 2013e14 is the most recent year for which I/O tablesare available. The four most significant fuels, excluding electricity,are shown in Table 7. By inspection, these are sufficiently close tosuggest that 2013e14 was representative for the 5-year time span.The ABS 4604.0 reported net-use of electricity consumption for theperiod 2003e03 through 2013e14 is plotted in Fig. 8. By inspection,there has been a slight downward trend since 2011-12 but it is notclear whether this is an artefact of the ABS accounts or a distincttrend.
6.7. Differences to other energy sources
The cost structure of the electricity system differs from otherenergy sources in several important ways. Firstly, Australian
electricity supply is geographically constrained and therefore notsubject to direct international cost competition. Second, unlike allliquid and solid fuels, electricity cannot be stored directly andtherefore the infrastructure must be built to meet peak demand.The high reliability standards imposed by Australian regulatorsnecessitate quality engineering, redundancy, and regulatory su-pervision, all of which add to cost. By definition, this lead to sub-optimal usage of infrastructure since system capacity far exceedsaverage utilisation. Third, since electricity network services arenatural monopolies, there is little scope for direct competition [21];p.65. Network businesses are compensated through rate of returnregulation, which may suffer from the Averch-Johnson effect e
firms have incentives to over-invest to increase the capital basefrom which they derive a return [83]; p.19). These factors lead toover investment, regulatory, administration, and other service coststhat carry a low energy intensity.
6.8. Implications and further work
Scenario analyses typically adopt least-cost optimisationmethods [50]. All of the published Australian studies adopt a gross-energy approach, which implicitly assumes that the energy in-tensity of the modelled system is commensurate with the incum-bent system. However, if the EROI of substituting electricity supplycomponents is much less than the incumbent system, the net-energy flows may be much less than anticipated, and thereforethe scenario analyses are overstating the degree to which low-emission substitution is occurring.
Nearly all EROI studies focus on a specific generation type, and
Table 6Costs and energy intensity of selected low energy intensity cost components, 2013-14.
Costs or flows ($M) Energy intensity (MJ/$)
Generation TDOS Diesel N/gas Electricity
Labour compensation 1618 5009 0 0 0Gross operating surplus 3157 9343 0 0 0Taxes 2897 2949 0 0 0Auxiliary Finance and Insurance Services 1285 1975 0.01 0.01 0.02Finance 1063 1907 0.01 0.01 0.02
Table 7Consumption of fuels, excluding electricity.
Diesel Petrol Natural gas Other refined
2009e10 6.33 1.00 3.46 3.912012e13 5.10 0.98 2.46 2.782013e14 6.42 1.52 2.29 2.17
Fig. 8. ABS 4604.0 net-use of electricity by the electricity supply sector. See discussion Section 5.4.
G. Palmer / Energy 141 (2017) 1504e15161514
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adopt the electricity busbar or inverter output as the EROInumerator. However, scenario analyses reveal system level char-acteristic that are not easily evaluated at the level of a specifictechnology. For example, geographic and technology diversity, anddemand management are important tools for optimising a suite ofvariable renewable generation.
The benefit of evaluating EROI at a system level is that synergies,trade-offs and system-level properties can be explored. Forexample, from this study, transmission is not a significant factor inthe system-wide embodied energy, and therefore geographic di-versity may be a strategy that can be implemented withoutadversely affecting the system-wide EROI. On the other hand,electrical storage [76] and biofuels [32]; Fig. 8 generally incur ahigher embodied energy debt, and may constrain low-emissionscenarios. The adoption of coal with CCS will increase both theprimary energy extraction, and the embodied energy [87,90].
Future work should include estimating the EROI/GEPR of lowemission scenarios to assess potential energetic constraints.Another approach is to include an EROI constraint in optimisationalgorithms to ensure that a minimum EROI is met e for exampleDupont et al. [36] calculated a global wind energy potential atvarious minimum EROI constraints.
7. Conclusions
The EROI/GEPR of energy supply technologies is an importantmetric linking energy consumption and standard of living. Thisstudy aimed to fill a global gap in net-energy related to the treat-ment of electricity at a systems level. An environmentally extendedinput-output analysis was undertaken to calculate the grossexternal power ratio (GEPR) of the Australian electricity supplyindustry. We calculated the GEPR at 40:1 for 2013e14. The highGEPR implies that the industry is not EROI/GEPR constrained.
The results of this study differ from similar studies on oil supplyand biofuels, which show a stronger inverse relationship betweenEROI and price. A part explanation is that much of the cost ofproviding reliable electricity comprises low energy intensity costsassociated with networks, regulation and administration. Largecost increases in recent years are due to network and other lowenergy intensity costs, which are not reflected in a significant fall inGEPR/EROI.
This work establishes a EROI/GEPR baseline for Australia. Futurework should include overlaying low emission scenarios with anEROI/GEPR analysis to assess potential energetic constraints.
Conflicts of interest
Wewish to confirm that there are no known conflicts of interestassociated with this publication and there has been no significantfinancial support for this work that could have influenced itsoutcome.
Acknowledgements
The author gratefully acknowledges Roger Dargaville, RobertCrawford, Josh Floyd, Carey King, and Tom Biegler for helpfulcomments and feedback. Thanks also to the anonymous reviewersfor helpful comments and feedback.
References
[1] ABB. Power generation - energy efficient design of auxiliary systems in fossil-fuel power plants. Report. Zurich, Switzerland: ABB; 2009.
[2] ABS. Year book of Australia. Report. Canberra, Australia: Australian Bureau ofStatistics; 1954.
[3] ABS. Year book of Australia. Report. Canberra, Australia: Australian Bureau of
Statistics; 1970.[4] ABS. Electricity Industry - industry restructing and the effects on statistics.
Report. Canberra, Australia: Australian Bureau of Statistics; 2001.[5] ABS. 4648.0.55.001-Detailed energy statistics, Australia, 2001-02. 2004. http://
www.abs.gov.au/ausstats/[email protected]/mf/4648.0.55.001.[6] ABS. 5206.0 Australian national accounts: national income, expenditure and
product. 2012. http://www.abs.gov.au/ausstats/[email protected]/mf/5206.0.[7] ABS. Labour statistics: concepts, sources and methods - employee remuner-
ation. Report. Canberra, Australia: Australian Bureau of Statistics; 2013.[8] ABS. 5209.0.55.001-Australian national accounts: input-output tables, 2013-
14. 2016. http://www.abs.gov.au/ausstats/[email protected]/mf/5215.0.55.001.[9] ABS. Personal communication: electricity consumption by the electricity
supply industry. 31 Jan 2017.[10] ACIL Tasman. Fuel resource, new entry and generation costs in the NEM,
0419e0035. Report. Melbourne, Australia: AEMO; 2009.[11] AEMO. Rooftop PV information paper. Report. Melbourne, Australia: Austra-
lian Energy Market Operator; 2012.[12] AEMO. 100% renewables study - modelling outcomes. Report. Melbourne,
Australia: Australian Energy Market Operator; 2013.[13] AEMO. NEMWEB archived dispatch data. 2016. http://www.nemweb.com.au/
REPORTS/ARCHIVE/Dispatch_SCADA/.[14] AEMO. Regional boundaries for the national electricity market. 2016. https://
www.aemo.com.au/-/media/Files/Electricity/NEM/Planning_and_Forecasting/Maps/2016-NEM-Regional-Boundaries-Map-WEB.pdf.
[15] Allday A. IBISWorld industry report D2630 electricity distribution in Australia.IBIS World; 2015a.
[16] Allday A. IBISWorld industry report D2640 electricity retailing in Australia.IBIS World; 2015b.
[17] Arvesen A, Hertwich EG. More caution is needed when using life cycleassessment to determine energy return on investment (EROI). Energy Policy2015;76:1e6.
[18] Arvidsson R, Fransson K, Fr€oling M, Svanstr€om M, Molander S. Energy useindicators in energy and life cycle assessments of biofuels: review and rec-ommendations. J Clean Prod 2012;31:54e61.
[19] Australian Department of Industry. Energy efficiency Opportunities program -the first 5 Years: 2006-2011-the mining sector. Report. Canberra, Australia:Department of Industry; 2013a.
[20] Australian Department of Industry. Energy efficiency Opportunities program -the first 5 Years: 2006-2011-the oil & gas sector. Report. Canberra, Australia:Department of Industry; 2013b.
[21] Australian Productivity Commission. Electricity network regulatory frame-works report. Report. Canberra, Australia: Productivity Commission; 2013.
[22] Baldwin K. Is coal still cheaper than renewables as an energy source?. 2017.http://theconversation.com/factcheck-qanda-is-coal-still-cheaper-than-renewables-as-an-energy-source-81263.
[23] Ball A, Bernie K, Feng A, McCluskey C, Stanwix G, Willcock T, et al. Energy inAustralia 2014. Bureau of Resources and Energy Economics; 2014.
[24] Blakers A, Lu B, Stocks M. 100% renewable electricity in Australia. Energy2017;133:471e82.
[25] Brand-Correa L, Brockway P, Carter C, Foxon T, Owen A, Taylor P. Developingan Input-Output based method to estimate a national-level EROI (energyreturn on investment). Energies 2017;10(4).
[26] Brandt AR, Dale M. A general mathematical framework for calculatingsystems-scale efficiency of energy extraction and conversion: energy returnon investment (EROI) and other energy return ratios. Energies 2011;4(8):1211e45.
[27] Brandt AR, Dale M, Barnhart CJ. Calculating systems-scale energy efficiencyand net energy returns: a bottom-up matrix-based approach. Energy 2013;62:235e47.
[28] BREE. Energy in Australia 2013. Report. Bureau of Resources and EnergyEconomics; 2013.
[29] BREE. Energy in Australia 2014. Report. Bureau of Resources and EnergyEconomics; 2014.
[30] BREE. Energy in Australia 2015. Report. Bureau of Resources and EnergyEconomics; 2015.
[31] Butlin NG. Australian domestic product, investment and foreign borrowing,1861e1938/39. Cambridge University Press; 1962.
[32] Carneiro MLN, Pradelle F, Braga SL, Gomes MSP, Martins ARF, Turkovics F,et al. Potential of biofuels from algae: comparison with fossil fuels, ethanoland biodiesel in Europe and Brazil through life cycle assessment (LCA). RenewSustain Energy Rev 2017;73:632e53.
[33] Cleveland CJ, Kaufmann RK, Stern DI. Aggregation and the role of energy in theeconomy. Ecol Econ 2000;32(2):301e17.
[34] Common MS, Salma U. Accounting for changes in Australian carbon dioxideemissions. Energy Econ 1992;14(3):217e25.
[35] Dale M, Krumdieck S, Bodger P. Global energy modellingA biophysicalapproach (GEMBA) part 1: an overview of biophysical economics. Ecol Econ2012;73:152e7.
[36] Dupont E, Koppelaar R, Jeanmart H. Global available wind energy withphysical and energy return on investment constraints. Appl Energy 2017.https://doi.org/10.1016/j.apenergy.2017.09.085.
[37] Dyster B, Meredith D. Australia in the International Economy: in the twentiethcentury. CUP Archive; 1990.
[38] Elliston B, MacGill I, Diesendorf M. Comparing least cost scenarios for 100%renewable electricity with low emission fossil fuel scenarios in the Australian
G. Palmer / Energy 141 (2017) 1504e1516 1515
73
National Electricity Market. Renew Energy 2014;66:196e204.[39] Elliston B, Riesz J. Future high renewable electricity scenariosInsights from
mapping the diversity of near least cost portfolios. In: Power and energyengineering conference (APPEEC), 2015 IEEE PES Asia-Pacific. IEEE; 2015.
[40] ERAWA. The electricity industry. Report. Perth, WA: Economic RegulationAuthority Western Australia; 2015.
[41] Fares RL, King CW. Trends in transmission, distribution, and administrationcosts for US investor-owned electric utilities. Energy Policy 2017;105:354e62.
[42] Finch C. IBISWorld industry report D2620, electricity transmission inAustralia. IBIS World; 2015.
[43] Finkel A, Moses K, Munro C, Effeney T, OKane M. Independent review into thefuture security of the national electricity market. Commonwealth of Australia;2017.
[44] Frischknecht R, Wyss F, Kn€opfel SB, Lützkendorf T, Balouktsi M. Cumulativeenergy demand in LCA: the energy harvested approach. Int J Life Cycle Assess2015;20(7):957e69.
[45] Grubler A, Johansson TB, Muncada L, Nakicenovic N, Pachauri S, Riahi K, et al.Chapter 1-energy primer in global energy assessment - toward a sustainablefuture. Cambridge, UK: Cambridge University Press; 2012.
[46] Gulen SC. Importance of auxiliary power consumption for combined cycleperformance. J Eng Gas Turbines Power 2011;133(4), 041801.
[47] Hall CA. Introduction to special issue on new studies in EROI (Energy Returnon Investment). Sustainability 2011;3(10):1773e7.
[48] Hall CA. Energy return on investment: a unifying principle for biology, eco-nomics, and sustainability, vol. 36. Springer; 2016.
[49] Hall CA, Lambert JG, Balogh SB. EROI of different fuels and the implications forsociety. Energy Policy 2014;64:141e52.
[50] Hart EK, Stoutenburg ED, Jacobson MZ. The potential of intermittent renew-ables to meet electric power demand: current methods and emerginganalytical techniques. Proc IEEE 2012;100(2):322e34.
[51] IEA. Commentary: understanding and using the energy balance. 2017. http://www.iea.org/newsroom/news/2017/september/commentary-understanding-and-using-the-energy-balance.html.
[52] IEA. Energy balance - Australia. Report. France: International Energy Agency;2017.
[53] Jeppesen M, Brear M, Chattopadhyay D, Manzie C, Dargaville R, Alpcan T. Leastcost, utility scale abatement from Australia's NEM (National Electricity Mar-ket). Part 1: problem formulation and modelling. Energy 2016;101:606e20.
[54] Johnston W, Egan R. National Survey report of PV power applications inAustralia 2015. Australian PV Institute; 2015.
[55] King CW. Matrix method for comparing system and individual energy returnratios when considering an energy transition. Energy 2014;72:254e65.
[56] King CW. Net energy in context of macroeconomic trends and indicators. In:GCEP/Stanford net energy analysis workshop, March 31- April 1, 2015.Stanford University; 2015.
[57] King CW, Hall CA. Relating financial and energy return on investment. Sus-tainability 2011;3(10):1810e32.
[58] King CW, Maxwell JP, Donovan A. Comparing World economic and net energymetrics, Part 1: single technology and commodity perspective. Energies2015a;8(11):12949e74.
[59] King CW, Maxwell JP, Donovan A. Comparing World economic and net energymetrics, Part 2: total economy expenditure perspective. Energies2015b;8(11):12975e96.
[60] Kitzes J. An introduction to environmentally-extended input-output analysis.Resources 2013;2(4):489e503.
[61] Koppelaar R. Solar-PV energy payback and net energy: meta-assessment ofstudy quality, reproducibility, and results harmonization. Renew Sustain En-ergy Rev 2016;72:1241e55.
[62] Kubiszewski I, Cleveland CJ, Endres PK. Meta-analysis of net energy return forwind power systems. Renew energy 2010;35(1):218e25.
[63] Lambert JG, Hall CA, Balogh S, Gupta A, Arnold M. Energy, EROI and quality oflife. Energy Policy 2014;64:153e67.
[64] Ledovskikh A. IBISWorld industry report OD4042 solar panel installation in
Australia. IBIS World; 2016.[65] Lenzen M. Primary energy and greenhouse gases embodied in Australian final
consumption: an inputoutput analysis. Energy policy 1998;26(6):495e506.[66] Lenzen M. Errors in conventional and input output based life cycle in-
ventories. J Ind. Ecol 2000;4(4):127e48.[67] Lenzen M, McBain B, Trainer T, Jtte S, Rey-Lescure O, Huang J. Simulating low-
carbon electricity supply for Australia. Appl Energy 2016;179:553e64.[68] Lenzen M, Wachsmann U. Wind turbines in Brazil and Germany: an example
of geographical variability in life-cycle assessment. Appl energy 2004;77(2):119e30.
[69] Leontief W. Input-output economics. New York: University Press; 1966.[70] Lightfoot HD. Understand the three different scales for measuring primary
energy and avoid errors. Energy 2007;32(8):1478e83.[71] Moeller D, Murphy D. Net energy analysis of gas production from the mar-
cellus shale. BioPhys. Econ Resour Qual 2016;1(1):1e13.[72] Murphy DJ, Hall CA. Year in review - EROI or energy return on (energy)
invested. Ann N. Y Acad Sci 2010;1185(1):102e18.[73] Murphy DJ, Hall CA, Dale M, Cleveland C. Order from chaos: a preliminary
protocol for determining the EROI of fuels. Sustainability 2011;3(10):1888e907.
[74] 25 August 2016 Office of the Chief Economist. Table F1-Australian energyconsumption, by industry and fuel type, energy units. Report. Department ofIndustry, Innovation and Science; 2015a.
[75] 25 August 2016 Office of the Chief Economist. Table O1-Australian electricitygeneration, by fuel type, physical units. Report. Department of Industry,Innovation and Science; 2015b.
[76] Palmer G. A framework for incorporating EROI into electrical storage. BioPhys.Econ Resour Qual 2017;2(2):6.
[77] PowerWater. Northern territory electricity network. Report. Australia: Dar-win; 2017.
[78] Raugei M, Leccisi E. A comprehensive assessment of the energy performanceof the full range of electricity generation technologies deployed in the UnitedKingdom. Energy Policy 2016;90:46e59.
[79] Rocco MV. Primary exergy cost of goods and services: an input-ouputapproach. Italy: Springer; 2016.
[80] Rugani B, Panasiuk D, Benetto E. An input-output based framework to eval-uate human labour in life cycle assessment. Int J Life Cycle Assess 2012;17(6):795e812.
[81] Solar Business Services. Solar businesses in Australia. Report. Australia: RecAgents Association; 2014.
[82] Thiess. Yallourn mine alliance. 2017. https://www.thiess.com/projects/yallourn-mine-alliance/detail.
[83] Train KE. Optimal regulation: the economic theory of natural monopoly. MITPress Books; 1991.
[84] Treloar GJ. Extracting embodied energy paths from inputoutput tables: to-wards an inputoutput-based hybrid energy analysis method. Econ Syst Res1997;9(4):375e91.
[85] Treloar GJ, Love PE, Holt GD. Using national input/output data for embodiedenergy analysis of individual residential buildings. Constr Manag Econ2001;19(1):49e61.
[86] Vamplew W. Australians: a historical library. v10: australians: historical sta-tistics, vol. 10. Fairfax: Syme & Weldon; 1987.
[87] White SW, Kulcinski GL. Birth to death analysis of the energy payback ratioand CO2 gas emission rates from coal, fission, wind, and DT-fusion electricalpower plants. Fusion Eng Des 2000;48(3):473e81.
[88] Wolfram P, Wiedmann T, Diesendorf M. Carbon footprint scenarios forrenewable electricity in Australia. J Clean Prod 2016;124:236e45.
[89] World Bank. Electric power transmission and distribution losses. Report.World Bank; 2014.
[90] Wu X, Xia X, Chen G, Wu X, Chen B. Embodied energy analysis for coal-basedpower generation system-highlighting the role of indirect energy cost. ApplEnergy 2016;184:936e50.
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Chapter 5
A Framework for Incorporating
EROI into Electrical Storage
5.1 Overview and context of chapter
5.1.1 Introduction
This study is intended as one variant of a consequential approach to electricity-based net-energy
analysis.
Nearly all electricity-based net-energy and LCA studies adopt a functional unit of 1 kWh (or equiv-
alent) of electrical energy delivered to a grid. This study appraised the limitation of this functional
unit, and argued that a functional unit of 1 kW of power delivered to the grid is more useful in the
context of large-scale transitions. The main rationale for testing this hypothesis is that all electricity
systems are built and managed to meet maximum power ; annual energy supplied is important for
revenue for much less important from an operational perspective.
5.1.2 Research questions
The research questions posed by this chapter are:
1. There is a lack of consensus in the EROI literature as to how to incorporate electrical storage
into EROI assessments. Is it possible to formulate a repeatable and consistent framework?
2. What are the conceptual differences between conventional electricity generation and variable
renewable electricity (VRE)?
75
3. Is it possible to normalize EROI estimates for the different classes of generation?
4. What are the roles of electrical storage in electricity systems?
5. How feasible are high-penetration VRE studies that rely on electrical storage?
5.1.3 Research method
Organisation
The paper was conceptualized as three parts.
1. A discussion of food and energy storage as a historical development, and how this connects with
the contemporary role of storage.
2. The use of electrical reliability indices to provide a repeatable and consistent method of mea-
suring and contrasting VRE plus storage, and conventional generation.
3. An application of the framework to a suite of renewable simulations to demonstrate the frame-
work.
Storage as an intrinsic quality
The origins of the paper were based in trying to understand the concepts of energy (and food) stocks
and flows in human society. Much of the early drafting of the paper was focused on the ‘Historical
Precedents’ section, with an anthropological perspective. This led to the conclusion that energy (or
food) storage is an intrinsic property, from which value is derived. It is argued that food storage was
pivotal to the evolution of human society and culture. Similarly, the value of fossil fuels has been due
to their high energy density, utility, and the capability of producing power on demand.
The formulation of the framework
The second part derived a technical framework. It was built on the principles derived in the first part,
but using electrical power system reliability indices to measure and contrast VRE plus storage.
It is commonly understood that conventional generation is ‘dispatchable’, but there are also a
diverse range of technical specifications between different generator types. The challenge was in for-
mulating a framework that enables VRE plus storage to be compared with conventional generation
that captured the intrinsic value of respective configurations or sub-systems. Since no single conven-
tional generator is perfectly reliable, it was necessary to consider reliability metrics at a system level.
Similarly, it is not necessary for individual renewable sub-systems to possess the same dispatchable
76
qualities as conventional generation to contribute value to a system. Some characteristics can be
understood at a system level, such as geographic and technology diversity.
The proposed framework has the benefit of trading off the embodied energy of VRE-overbuild and
storage, with the additional value that it provides. Conceptually, this results in an optimal magnitude
of storage in a given context. In much the same way that an economic analysis may be intended to
evaluate an optimal solution to reduce costs, it is possible to evaluate an optimal solution based on
‘energetic costs’.
Applying the framework to VRE simulations
In order to demonstrate the framework, it was necessary to collate a suite of demand-balance models.
The third part consisted of combining LCA data with a suite of simulations to demonstrate the
framework.
Discussion
The framework was able to produce sensible comparisons of VRE-storage sub-systems versus conven-
tional generation. The first units of storage are the most valuable, with a diminishing return with
additional storage. Using the framework, it is apparent that an optimal solution based on an energetic
evaluation would evaluate to much less storage than anticipated in some high-penetration renewable
scenarios, and that some scenarios may be infeasible from an energetic perspective.
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Biophys Econ Resour Qual (2017) 2:6 DOI 10.1007/s41247-017-0022-3
ORIGINAL PAPER
A Framework for Incorporating EROI into Electrical Storage
Graham Palmer1
Received: 24 July 2016 / Accepted: 11 April 2017 © Springer International Publishing Switzerland 2017
was modelled as a limiting case of VRE plus storage, and is therefore not intended as a comprehensive cost-optimised solution to high-penetration VRE. A shift from an elec-trical system based mostly on energy stocks to one based mostly on natural flows is constrained by the quantity of storage required, and the quantity of VRE overbuild to charge the stores. The application of the framework shows that the value of electrical storage and overbuild exhibits a marked diminishing returns behaviour at rising VRE pen-etration and therefore the first units of storage are the most valuable. The framework is intended to stimulate further research into using EROI to better understand the role of VRE and storage in prospective energy transitions.
Keywords Storage · EROI · Renewables · Energy surplus
Introduction
Overview
Regardless of climate goals, the contribution from renew-able energy is projected to increase significantly over the medium to long term (Krey and Clarke 2011). The IPCC AR5 450 ppm suite of energy scenarios (Edenhofer et al. 2014, p. 12) are characterised by rapid improvements in energy efficiency, and a significant scaling up of the share of low-carbon energy supply. Nearly all substantially increase the deployment of renewables, many deploy an increasing share from nuclear and/or fossil fuels with car-bon capture and sequestration (CCS), and some include the possibility of bioenergy with carbon capture and storage (BECCS). All of these options have net-energy implica-tions, and some (e.g. nuclear and CCS) face additional bar-riers and risks (Edenhofer et al. 2014, pp. 20–22).
Abstract The contribution from variable renewable energy (VRE) to electricity generation is projected to increase. At low penetration, intermittency can usually be accommodated at low cost. High-penetration VRE will displace conventional generation, and require increased grid flexibility, geographic and technology diversity, and the use of electrical storage. Energy return on investment (EROI) is a tool that gives greater weight to the principles of energetics over market prices, and may provide a long-term guide to prospective energy transitions. The EROI of electrical storage may be critical to the efficacy of high-penetration renewable scenarios. However, there is no generally agreed upon methodology for incorporating stor-age into EROI. In recent years, there have been important contributions to applying net-energy analysis to storage, including the development of storage-specific net-energy metrics. However, there remains uncertainty as to how to apply these metrics to practical systems to derive useful or predictive information. This paper will introduce a frame-work for evaluating storage at a system level. It introduces the surplus energy-storage synergy hypothesis as a general principle for exploring the role of storage. It is argued that the useful energy available to society is determined by both the net-energy of the energy source and the stored energy as stocks. This hypothesis is translated across to electric-ity systems with the use of electrical reliability indices to evaluate the value of storage. A case study applies the framework to a suite of VRE simulations. The case study
* Graham Palmer [email protected];
1 Australian-German College of Energy and Climate, The University of Melbourne, Melbourne, Australia
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At low penetration, intermittency can usually be accom-modated at low cost (Gross et al. 2006). High-penetration RE will displace conventional generation, and require increased grid flexibility, geographic and technology diver-sity, and the use of electrical storage (Denholm and Hand 2011).
The need for storage increases the energy burden of elec-tricity supply systems, and thus lowers the EROI (Carba-jales-Dale et al. 2014). However, there is ongoing debate as to how much storage will be required in a shift to an elec-trical system based mostly on VRE, and whether this rep-resents an additional but affordable cost, or a fundamental constraint on VRE penetration (Morgan 2014). Although there are estimates of the embodied energy of storage from the life-cycle literature, there is no agreed methodology on how to apply these estimates to a comprehensive estimate of EROI (Pickard 2014a).
Definition of EROI
EROI is a unitless ratio, defined as the ratio of the gross flow of energy Eg over the lifetime of the project, and the sum of the energy for construction Ec, operation Eop, and decommissioning Ed (Murphy et al. 2011, Eq. 1). More generally, Murphy and Hall (2010) state that “EROI is the ratio of how much energy is gained from an energy produc-tion process compared to how much of that energy (or its equivalent from some other source) is required to extract, grow, etc., a new unit of the energy in question”.
Although there is usually general agreement on the con-cept of EROI (i.e. energy out/energy in), there is a diver-gence of objectives and methodologies (Carbajales-Dale et al. 2015; Pickard 2014a). The divergence is perhaps most acute with VRE, compared to say oil, because VRE does not substitute one-for-one with conventional generation, with uncertainty related to the allocation of energy costs for integration and storage. Unlike oil, which is pervasive and substitutable, VRE produces electricity, which has high utility but is not globally fungible and currently makes up only 18% of global total final energy consumption (Interna-tional Energy Agency (IEA) 2016, p. 28).
Storage Literature Review
Much of the literature that explores the value of storage has focused on the relationship between storage and markets, and the potential for storage to add value within incumbent systems [e.g. McConnell et al. (2015); Salles et al. (2016)]. Some of the analyses explore a strategic network role (i.e.
(1)EROI =Eg
Ec + Eop + Ed
supporting networks rather than contributing to bulk gener-ation) and the potential role of technological disruption of distributed solar and storage, although much of the disrup-tion is occurring within markets, rather than in transforma-tions of the physical systems.
There have been a number of approaches taken to include the embodied energy of storage into a system-level EROI analysis. Much of the analysis has attempted to iso-late storage devices to determine a device-specific EROI. The main weakness to date has been establishing a dynamic function for incorporating storage into EROI.
Barnhart and Benson (2013) defined the metric Energy stored on invested (ESOI), defined as the ratio of electri-cal energy stored over the lifetime of a storage device, to the embodied primary energy required to build the device. In contrast, Weißbach et al. (2013) considered the storage capacity of the storage device (in this case, pumped hydro storage) required to provide an equivalent baseload role for VRE. Both of these approaches try to ascertain the embod-ied energy debt of storage, but may not reflect the differ-ing value of storage depending on their role and context. Weißbach assumed a baseload role with 10 days of storage; however, it is not clear why 10 days should be a reference and a high-VRE scenario would value other characteristics, especially flexibility. Furthermore, Raugei (2013) noted some methodology inconsistencies that reflect differing approaches to net-energy analysis.
Barnhart et al. (2015) provided a broader context for the role of storage by exploring the various trade-offs between curtailment, storage, and greenhouse emissions. The trade-off was illustrated with the use of a graphic that depicted energy intensity (as a proxy for EROI) versus carbon inten-sity. The plot encompassed both generation and storage devices. It was dissected into four quadrants; ‘do today’, reflecting options that are worthwhile; ‘reduce emissions’ and ‘improve EROI’, as prospective options requiring fur-ther development; and ‘avoid’, reflecting options that have a poor EROI and poor emission performance.
Using the specific context of off-grid rooftop solar, Palmer (2013) considered the role of storage within the context of an overall system. In this case, the role of storage and surplus solar capacity (to ensure adequate winter sup-ply) in a rooftop solar system was evaluated to establish the lifetime EROI of the complete system.
Denholm and Kulcinski (2004) is representative of stud-ies that are measuring the embodied energy of storage devices, but not attempting to account for the relative value that storage provides. In this case, pumped hydro storage (PHS) was evaluated, along with compressed air storage (CAES) and large-scale batteries. Denholm and Kulcinski were concerned with establishing the life-cycle parameters for a given power capacity (GW) rather than a given stor-age capacity (GWh).
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Renewable Simulation Literature Review
This study will apply the framework to a renewable energy simulation. Although there are many published regional VRE simulations [e.g. Budischak et al. (2012); Jacobson et al. (2015)], few apply or publish a loss-of-load proba-bilistic assessment, or explicitly include reserve margins. Where a reserve margin has been applied, it is usually applied as a simple exogenous multiplier rather than emerg-ing from the probabilistic assessment [e.g. (MacDonald et al. 2016b, pg. 32)]. These are described as ‘first order’ demand-balance simulations (see Hart et al. (2012)) and could be regarded as high-level, exploratory studies rather than the ‘second-order’ system-level reliability analyses typically carried out by electrical system operators.
Storage Requirements
Storage requirements of VRE are dominated by stretches of low-wind and solar resource (the ‘big gaps’ problem) (Len-zen et al. 2016). The ratio between the average monthly solar insolation between summer and winter varies greatly across geographic regions (PV Education 2016). Low lati-tude regions, such as Singapore, show a relatively small summer/winter ratio of 1.3–1.7, rising to 3–4 in the mid-latitudes such as Nevada USA, and above 10 for higher lati-tudes, such as London. Cloud cover is also region depend-ent, with clear skies and low aerosols typically located in the latitudes from 15° to 40° north or south (International Energy Agency (IEA) 2014). Concentrated solar thermal requires high direct normal radiation (DNI) and is usu-ally located in arid regions in the subtropical band. Winter insolation and extended cloudy periods define the storage requirements for systems reliant on solar.
In the case of wind, geographic dispersion, the physical inertia of wind machines, and aggregation provide smooth-ing of bulk wind power at sub-hourly scales (Archer and Jacobson 2007). At an hourly and above scale, wind speeds are highly correlated within wind regimes, which may span distances of hundreds to thousands of kilometres (Huva et al. 2016). Seasonal variation is usually much less than for solar but multi-day low-wind conditions appear to be a characteristic of many regions (MacKay 2008, p. 187; Oswald et al. 2008), and these low-wind stretches define the quantity of storage or backup capacity required.
In a review of VRE storage, Pickard (2014b) assumed that around 7 days of storage would be required in a high-penetration VRE scenario. In a simulation for the PJM network in the US, Budischak et al. (2012) used between 9 and 72 h storage, with 3-times VRE overbuild, which included maintaining a significant share of the legacy fos-sil fuel capacity, operating as infrequent reserve capacity. Abdulla et al. (2014) found that 58 h of storage (calculated
at 1500 GWh and 25.5 GW demand) would be required for a high PV scenario in Australia in an explorative assess-ment. Oswald et al. (2008) found that around 6 days of stor-age would be required to cope with continental scale low-wind conditions in Europe.
In a 100% renewable simulation, including sector cou-pling (i.e. heat and electricity) for Germany, 55 GWh of battery storage, 60 GWh of PHS, and 62,000 GWh of methane storage was used in the ‘medium’ scenario (Palzer and Henning 2014, Fig. 3.4). At 500 TWh annual con-sumption, the storage capacity represents around 45 days of full-load electrical capacity (equated at average annual demand). Likewise for North America (Canada, USA, and Mexico), Aghahosseini et al. (2016) used around 3-times VRE overbuild, around 2000 GWh of battery storage and 174,000–230,000 GWh of methane storage. At 6059 TWh annual consumption, the storage capacity represents around 14 days of full-load electrical capacity.
Much of the scenario literature avoids the problem of large-scale storage by maintaining a significant share of conventional capacity [e.g. Budischak et al. (2012)] or assuming the ready availability of large-scale biomass. For example, Lenzen et al. (2016, Table 2) did not employ conventional electrical storage but used concentrated solar thermal with 15 h storage (equating to a theoretical 917 GWh of storage), and generated 16,500 GWh with bio-fuelled powered gas turbines in a simulation for Australia. Assuming the biofuels were used for seasonal storage equates to 21 days of full-load electrical capacity (equated at average annual demand). In a simulation covering the eastern populated regions of Australia, Elliston et al. (2012) employed a similar generation suite, with biofuelled tur-bine capacity set at 71% of annual peak demand but at low capacity factor. The resulting 28,000 GWh of electricity generated from biofuels equates to 50 days full-load electri-cal capacity, assuming seasonal storage. Both of these sim-ulations set the minimisation of biofuels as an objective.
This study is not seeking to prescribe a storage type and only considers the role of electrical storage. Sector cou-pling (e.g. power-to-heat, power-to-gas, power-to-desal-ination, vehicle-to-grid), and other forms of storage (e.g. thermal storage in concentrated solar thermal) may offer opportunities to substitute for electrical storage.
Goal Definitions
Carbajales-Dale et al. (2015) identifies three distinct goal definitions for EROI as it applies to VRE: a descrip-tive assessment of a particular technology; a compara-tive assessment of alternative energy technologies; and an exploration of the viability of emerging technologies to completely substitute for the incumbent system.
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As a tool for comparative assessment, net-energy analy-sis is frequently used to assess the degree to which VRE displaces fossil fuel consumption. For example, Raugei et al. (2012) derived a ‘primary energy equivalent’ multi-plier in conjunction with EROI to describe the equivalent primary energy that is ‘virtually returned to society’ (i.e. coal or gas that is preserved for alternative uses). Raugei et al. argue that a unit of energy invested in solar signifi-cantly reduces the depletion of fossil fuels over the lifetime of the solar system. This is consistent with much of the discussion of VRE as an abatement tool within an incum-bent system. From this perspective, it is assumed that the problem is that there is too much readily available fossil fuel and that without policy intervention the IPCC ‘repre-sentative concentration pathways’ (RCPs) at the high-end are more likely (e.g. RCP 6.0, RCP 8.5). Although RCP 8.5 should not be taken as a no-climate-policy reference sce-nario (Moss et al. 2010), it nonetheless assumes continuing growth in fossil fuel demand (Riahi et al. 2007).
Goal and Scope of the Present Study
Different Roles of Storage
This study is focused on the use of storage to buffer or complement VRE, modelled as natural renewable flows. But to date, most of the use of storage in electricity grids has been for arbitrage and time-shifting in conjunction with baseload (Yang and Jackson 2011). It will be argued that the role of storage as a complement to VRE is different to its traditional role with baseload. Furthermore, the output of electrical storage is usually directly substitutable with energy stocks, especially flexible generation (Denholm and Hand 2011).
Storage can also be reduced to the extent that renewable flows coincide with demand, or demand can be modulated with demand management (Schreiber et al. 2015). Further-more, geographic diversity (Grossmann et al. 2015; Huva et al. 2016; MacDonald et al. 2016a) and resource diversity (Bogdanov and Breyer 2016) provide a quasi-storage role by raising the effective availability factor.
Energy Capital Substitution
This study will frame EROI in the context of energy capi-tal substitution, rather than fuel substitution (i.e. to what degree can VRE plus storage replace the capital stock of fossil fuel extraction and conversion rather than just sup-plant the flow of fossil fuels). It is more concerned with assessing the possibility of future energy systems being able to both sustain themselves, and provide society with sufficient surplus energy (Hall et al. 2009).
A justification for focusing on substitution of capital is the German Energiewende. The term ‘Energiewende’ can be dated to a study by the German Öko-Institut in 1980 (Krause et al. 1981), with the title ‘Energy turnaround, growth and prosperity without oil and uranium’ (Joas et al. 2016; Maubach 2014). Between the starting point of the EEG in 2003 and 2014, total installed power gen-eration capacity grew by 51%, although total annual gen-eration was virtually unchanged (Bundesministerium für Wirtschaft und Technologie 2015).
The Energiewende has been accompanied by grid expansions and tariff increases, although wholesale spot prices have declined (Poser et al. 2014). Hence the Ener-giewende is reducing the direct fuel load and CO2 emis-sions, at a cost, but not replicating the substitution of infra-structure that has typically accompanied historic transitions (i.e. the incentive policies encourage a process of adding to the capital stock rather than substituting away from the leg-acy capital stock). The tariff increases need to be balanced against the broader societal costs of energy, including sub-stantial German fossil fuel and nuclear subsidies (Morris and Jungjohann 2016; van der Burg and Pickard 2015). One interpretation is that the Energiewende is succeeding in reducing the environmental burden of electricity genera-tion, but that the shift is reliant on the strength of the Ger-man economy, the relatively high economic output per unit of energy, and the high EROI of the global energy system.
Reformulation of Storage and EROI
The contribution of this study is to reformulate the analysis of storage. Firstly, it will be argued that energy storage is a fundamental property of net-energy. An understanding of the transition from hunter-gatherer, through pastoralism and agriculture, the Industrial Revolution, and twentieth cen-tury economic development, can be enhanced through sup-plementing the concept of net-energy with the concept of the energy surplus-storage synergy. This approach follows the precedent of applying behaviours observed in natural ecology to industrial ecology [e.g. Brown et al. (2011)]. In the pre-industrial context, energy referred mostly to food and fodder, but by the twentieth century, energy included a broader range of traded energy sources and biomass (King et al. 2015).
Secondly, the cost structure of electricity systems is dominated by capital-intensive infrastructure and fixed operating costs (Brown and Faruqui 2014). It is the nature of all utilities (e.g. electricity, water, telecommunications) and many public services (e.g. passenger rail) that the infrastructure must be built to meet peak demand. Some services, such as urban transit systems and water supply systems, can dampen peak demand by permitting conges-tion or using built-in storage. For example, trains can queue
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passengers on platforms during peak periods, allowing con-gestion to buffer peak demand without the train system fail-ing; the pressure of water supply can fall during summer heat waves, permitting a stressed system to remain func-tional but at a lower quality of service.
In contrast, the real-time flow limitation of electrical sys-tems does not permit ‘soft’ congestion management during peak periods. Furthermore, unlike gas or water pipelines, which may store several hours (or days) of product within distribution networks that can be readily drawn down, AC networks do not possess an equivalent ‘draw down’ capa-bility without storage devices. Instead, infrastructure must be maximally sized to meet reliability standards, although infrastructure requirements could be marginally reduced if reliability standards were relaxed (Australian Productivity Commission 2013).
Finally, a framework for incorporating storage into EROI is presented. This requires calculating EROI of the VRE and storage as a sub-system, and measuring the resulting EROIVRE+storage relative to the displaced capacity of conventional generation. The benefit of measuring the result relative to the displaced capacity is that it is possible to trade-off the rising availability factor with the diminish-ing returns of marginal storage, thereby overcoming the limitation of arbitrarily setting the quantity of storage and RE overbuild. The result reflects the energy investment to substitute for energy infrastructure, rather than displacing fossil fuel consumption.
Historical Precedents
Food Storage as a Pivotal Development
The contemporary value of energy storage is not unique, and a historical parallel can be drawn from the Neolithic transition, beginning around 10,000 years ago. Prior to this period, Palaeolithic man lived a subsistence life of hunting and gathering, governed by the diurnal and seasonal cycles (Diamond 2005).
In the transition, humans began domesticating plants and animals (Hibbs and Olsson 2004). There are different hypotheses for why humans chose to adopt agriculture in the first place (Diamond 2005). But it gradually led to spe-cialised crop cultivation, land clearing, and basic irrigation. These advances permitted seasonal food storage for the first time, increased population density, and represented a prel-ude to non-farming specialists, villages and later, cities.
The decisive break came from collecting of grain, then the evolution of grain, and cereal farming. The cru-cial advantage of grains and cereals was that it permitted agricultural surpluses to be converted into seasonal stor-age. But in the early period at least, the relative calorific
return from agriculture may have been less than tradi-tional foraging and hunting, and it is not obvious that farming would have been worth the effort. Agriculture requires intense effort over long periods, often with vari-able results.
The capacity for storage was to prove crucial. On the one hand, grains have several useful properties. They are hard and dry, and do not readily rot and go bad. Since they are dry, they have a high calorific-to-weight ratio, making them both storable and readily transportable (Laudan 2015). But on the other hand, they are excep-tionally difficult to turn into something useful and digest-ible. Most food gathered or caught by hunter-gatherers is eaten in its raw state or cooked over a fire, but grains require several processing and conversion stages. Wheat must be harvested, threshed, winnowed, and ground. Finally, the flour can be mixed with water and baked to produce unleavened bread. The case of wheat suggests that there must have been a strong evolutionary advan-tage to adopting agriculture and perhaps derive an easily storable product.
The history of salt provides another example of the importance of food storage. Salt was used since antiquity for curing foods such as beef, pork, fish, and later butter. Such was its importance that it was sometimes a strate-gic commodity and was used as an early form of currency (Cowen 2005).
Fig. 1 Stylised graph of energy surplus versus storage. ‘Value’ is defined as the vector of surplus and storage
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Value as the Vector of Energy Surplus and Storage
The evolution of storage is depicted in Fig. 1 in which ‘value’ is defined as the vector of energy surplus and stor-age. The early adoption of farming may have had a lower calorific return than foraging but storage contributed to a greater vector of value than with no storage.
In the early industrial era, steam engines were extremely inefficient in converting chemical energy to mechanical motion, and the net-energy of early steam must have been remarkably low by contemporary standards. Newcomen’s early engines were around 1% efficient and Watt’s early innovations lifted this to 2–4% (Buenstorf 2004). None-theless, as ‘stored sunlight’, coal was able to provide copi-ous quantities of power on demand. Coal and steam power evolved within a virtuous cycle to leverage the power out-put of steam, and were to break the ‘organic limit’ imposed by reliance on solar energy flows (Wrigley 2010).
The Role of Energy Storage
Conventional Storage
Nearly all electrical storage to date has been pumped hydro storage (PHS), which makes up 97% or 142 GW of global power capacity for electrical storage (United States Depart-ment of Energy (DOE) 2016). The three leading PHS coun-tries are Japan with 26 GW, China at 24 GW, and the US at 22 GW. The Eurelectric region comprising the 34 Euro-pean countries that are part of the five European synchro-nous regions has a total installed capacity of 35 GW. These figures relate to power but comprehensive data on storage capacity (GWh) are less readily available.
From facility-level author calculations, the storage capacity of most PHS facilities in the US, Japan, and China range from 8 to 25 GWh per GW of installed capacity, cor-responding to a typical daily arbitrage cycle with spare capacity. In Europe, the storage capacity of 2500 GWh is dominated by Spain with 1530 GWh in 17 PHS plants comprising around 4.8 GW (Pirker et al. 2011).
From Pickard (2012), the ten largest capacity facilities in the US total 13.4 GW with 332 GWh of storage capacity, equating to 25 GWh per GW. If the storage ratio is extrap-olated to all 36 facilities, the storage capacity equates to 545 GWh.
PHS has historically operated in unison with coal and nuclear baseload. In the US, the deployment of PHS was relatively slow until the 1960s, but developed in parallel with nuclear during the 1960s and 1970s, and subsequently slowed in the 1980s when nuclear deployment came to a standstill (Yang and Jackson 2011). Baseload-PHS usu-ally operates with a daily arbitrage cycle between inflexible
overnight off-peak and daytime peak. The daily cycling maximises energy throughput for a given storage capacity and underpins the economic return for PHS, while provid-ing a demand sink for surplus off-peak baseload (Barnes and Levine 2011). Since the deregulation of electricity markets, the use of pumped hydro has expanded to cover a range of additional services, including rotational inertia, load following, frequency control, spinning reserve, and voltage regulation (MWH 2009).
At a global scale, other utility scale storage includes thermal storage (e.g. concentrated solar thermal) at 1.7 GW, which assuming 6 h storage equates to around 10 GWh. Other storage includes electro-mechanical (e.g. flywheel) at 1.4 GW, battery at 0.75 GW, and hydrogen at 0.003 GW (United States Department of Energy (DOE) 2016).
Connecting the Pre‑Industrial with the Contemporary Role of Storage
The question is—how to connect the pre-industrial or organic conception of surplus-storage with the role of storage within a modern electricity system? In the pre-industrial context, the value of storage was in guaranteeing survival during winter or times of poor harvest. Survival during austere times was more important than feasting dur-ing good harvests—favourable harvests were valuable to the extent that food could be stored for later consumption. This aligns with a marginalist interpretation of energy sup-ply (i.e. the first units of energy are the most valuable but there is a diminishing return to surplus energy).
In the contemporary interpretation, the guarantee of electricity supply is provided by the built infrastructure and a high ‘availability factor’ from a suite of generators. Modern electricity markets are founded on the concept of marginal cost pricing, in which there is always a surplus of available power. Generators with the lowest short-run marginal cost (SRMC) bid into the market first, with higher marginal cost generators providing load following.
Most of the value to residential and industrial consum-ers, and the cost of providing that service, is in the guar-anteed connection and available power flow, rather than the cumulative energy consumption per se. For example, the electrification of the United States during the twentieth century was a major contributor to rising productivity and output (Jorgenson 1984; Schurr 1990). Much of the produc-tivity gain from industry was a result of the ‘fact of elec-trification’ and the associated re-organisation of industrial production. In the early industrial period, the shift away from line-drive permitted organisations to adopt flexible and more efficient production processes (Rosenberg 1994). Electricity was a key enabler of innovation in industrial and consumer devices through the twentieth century.
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The value of storage is therefore proportional to the degree that storage contributes to the assurance of demanded power flow. Fortunately, the electricity supply industry has a suite of probabilistic reliability indices that fall under the broad categories of availability factor and forced outage (Billinton and Allan 1996). The availability factor defines the proportion of nameplate power that is available on demand, or in the case of VRE, the quantity of dispatchable power that is displaced (Preston 2015b).
Availability Versus Capacity Factor
The value of stored energy is evident in the availability factor versus capacity factor graph (see Fig. 2). Conven-tional generators convert stocks of energy into an elec-trical power flow. Providing the stocks are available, the energy converters (generators) can be dispatched in real-time, subject to technological and physical constraints, such as ramp-rate limits (the time rate of change of power output). In contrast, the capacity factor (often described as the full-load hours) describes the usage of the genera-tor, and is driven mostly by the short-run marginal cost (SRMC) for dispatchable generators, or the availability of the natural flows in the case of VRE (Tasman 2009).
From the perspective of an individual generator, con-ventional generation can be considered to have access to unlimited fuel storage within a planning time frame (i.e. sufficient fuel at all times), and therefore possess high availability factors. In contrast, solar PV without storage has a low availability factor, except to the extent that solar insolation coincides with annual peak demand (Denholm et al. 2015). The rising availability of the vari-ous types of concentrated solar is a direct result of solar tracking, solar field multipliers, built-in storage, and natural gas backup, which are reflected in progressively higher costs (International Renewable Energy Agency (IRENA) 2012).
Role for VRE Storage
From an operational viewpoint, the purpose of storage is to increase operational flexibility. From the perspective of VRE, the role of storage is to boost the availability—that is, a greater proportion of the nameplate power of the VRE is considered ‘firm’ when power is demanded. From the perspective of large thermal generators, the role of storage is to increase utilisation during off-peak periods (see Fig. 3).
Fig. 2 Graph of capacity factor versus availability factor with typi-cal ranges shown as whisker plots. Availability and capacity factor representative figures from Sims et al. (2011, Table 8.1), Sims et al. (2007), Bashmakov et al. (2014), Australian Energy Market Opera-tor (AEMO) (2012, Table 6), US Energy Information Administra-tion (EIA) (2015), International Renewable Energy Agency (IRENA) (2012, Table 1)
Fig. 3 Stylised effect of storage on baseload and variable renewable energy
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Methods
EROI Assessments Have Focused on Net‑Energy Only
In EROI assessments, storage is rarely, if ever, explicitly incorporated. This is probably due to the universality of storage being embedded within fossil fuels - the notion of stored sunlight is simply built into the energy sources (Schramski et al. 2015). Many analyses have incorpo-rated ‘energy quality’, or exergy, as a scaling factor in EROI analysis (e.g. Ayres and Warr (2010); Murphy et al. (2011)) to differentiate between the economic use-fulness of energy in its various states—a megajoule of electricity is more useful than a megajoule of coal.
Overview
The proposed framework requires calculating the embodied energy of the VRE-storage sub-system rela-tive to the displaced capacity of conventional generation. The methodology can be applied at any point along an energy transition pathway. In this context, conventional generation refers to thermal generation or hydro. The key concept is that the EROI of conventional genera-tion substitutes should be compared against the capacity they are replacing. In contrast, the conventional abate-ment approach is to consider the cost of displacing fossil fuel consumption as a consequence of applying environ-mental taxes or regulation on energy (Enevoldsen et al. 2009).
Reliability Metrics for Conventional Generation
The availability of conventional generation is approxi-mately proportional to the inverse of the forced outage rate (Billinton and Allan 1996, chpt.11). The forced outage rate is defined as the proportion of operating time that a unit is out of service due to unexpected problems or fail-ures. Since forced outages are usually uncorrelated between conventional generators (i.e. the distribution functions are independent random variables), the system reliability at a given power asymptotes towards 100% as additional gen-erators are added to the system.
To illustrate this, Fig. 4 plots the cumulative density function (CDF) (left) and probability density function (PDF) of a given capacity being available in any hour for a hypothetical suite of 10 generators, each 100 MW with a forced outage rate (FOR) of 0.1 (or equivalently, an avail-ability of 0.9). An FOR of 0.1 means that there is a 10% probability of an outage in a given hour. The plot has been calculated using the recursive convolution algorithm given in Preston (2016a). The left plot indicates that there is a 93% probability of at least 800 MW being available. The right plot indicates that there is 19% probability of exactly 800 MW being available. The CDF of annual demand is often referred to as the ‘load duration curve’. The electric-ity supply industry uses many indices to describe availabil-ity, including seasonal derating factors and planned mainte-nance [see IEEE (2007)]. In this context, availability refers to effective availability factor (EAF) in IEEE (2007).
The PDF, such as the right side of Fig. 4, can be com-bined with the projected demand function, to produce a probabilistic reliability assessment for each hour. The
Fig. 4 Available capacity curve for 10 generators, each 100 MW, and forced outage rate (FOR) of 0.1. Left—cumulative density function (CDF), right—probability density function (PDF)
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probability of demand exceeding supply for each hour is termed the loss-of-load-probability (LOLP). The LOLPs for every hour are added to give the loss-of-load-hours (LOLH) over the projected time frame (e.g. 1 year). Alter-natively, the LOLP for the peak hour only for each day can be added to give the loss-of-load-expectation (LOLE). There may be slight differences in methodology across regional operators. For example, LOLE can be expressed as either days/year, or hours/year (OFGEM 2013). An alterna-tive energy-based metric is the expected unserved energy (EUE), which is the sum of the unserved energy over all hours in a year.
Figure 5 illustrates this graphically for a stylised exam-ple of the Texas ERCOT grid over a year. The intersection of the areas bounded by the demand and supply curves gives the probabilistic LOLH. Note that this does not imply that demand will exceed supply but that there is a small but finite probability of unmet demand.
Since surplus generator capacity is costly, and outages are also costly, the optimal arrangement is to install only as much capacity as is required to provide a given (small) probability of unmet demand. In economics terminology, this can be framed as the intersection of the reliability-cost and reliability-worth curves (Billinton and Allan 1996, Fig. 1.3).
The standard criteria for generator reserve margin plan-ning vary between jurisdictions. The United States standard is a LOLE of ‘one day in ten years’ (North American Elec-tric Reliability Corporation (NERC) 2011). The ‘one day in ten years’ means that an outage (of any duration) should only occur on one day in 10 years on average. For compari-son, a LOLE of 2.9 h per year is used within the reliability
standards used by France, Ireland, and Belgium (OFGEM 2013). Australia applies an EUE standard of 0.002% of annual consumption (Australian Energy Market Operator (AEMO) 2013, Table 1). In this context, ‘outage’ refers to the adequacy of the bulk system and excludes outages due to local network interruptions due to storms etc.
The Analysed System
Reliability of Variable Renewable Generation
Renewable converters (e.g. solar panels, wind turbines) convert natural flows into electrical power flows, but have limited capability of controlling power on demand (although turning down is an obvious mode of control). Since renewable converters within a region are highly cor-related (i.e. it is sunny or relatively windy everywhere), VRE cannot be modelled as random independent variables using a convolution algorithm. Instead, they must be mod-elled as load reducers (Preston 2015b). The distinction between ‘adding to supply’ and ‘subtracting from demand’ may appear subtle, but is important because the value of VRE depends on both the supply and demand functions. VRE can be conceptualised as changing the shape of the PDF of demand (left side of Fig. 5). The degree to which VRE changes the LOLE is related to the degree that the rightward tail of the distribution is shifted leftward (i.e. the reliability value of VRE is dominated by the co-incidence of VRE with annual peak loads).
The first units of wind (or solar) produce the highest availability factor, with additional wind power subject to
Fig. 5 Stylised probability den-sity function (PDF) for Texas ERCOT system for a year. LOLE is equal to the intersec-tion of the areas bounded by the demand and supply curves
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diminishing returns. The reason for this is that if the first units of wind reduce peak loads, those hours are no longer peak hours, and the system reliability is dominated by other load hours not well served by wind (Preston 2015b). Therefore additional wind during those (now) non-peak hours reduces net-demand but does not contribute further to peak demand mitigation. This general property applies regardless of the composition of the grid. The sub-system of interest could also be composed of multiple VRE types (e.g. wind + solar PV), which would yield a composite den-sity function.
Therefore, the availability of a suite of VRE is defined as the reduction in net-demand as a proportion of nameplate capacity, at a given probabilistic reliability. For example, if 100 MW of wind power permits an additional 10 MW of demand at a given reliability level, the availability is expressed as 10% or 10 MW.
Charging Storage with VRE Overbuild
A key question is the quantity of storage and the VRE overbuild that is required to substitute for generation over and above inflexible or baseload generation. Storage must be accompanied by VRE overbuild in order to charge stores. Overbuild is defined as capacity that is surplus to demand—it can be defined at both a local or system level. For example, at a household level, rooftop solar that pro-duces surplus solar power could be either stored locally or exported into the grid. At a system level, wind power that exceeds a given usable proportion of system supply could be exported to an adjoining grid, stored, or curtailed (Den-holm and Hand 2011).
Marginal Returns to Storage
The first units of storage and overbuild are the most use-ful. The reason is that the PDF (i.e. the histogram describ-ing the number of annual hours for each ‘bin’ of power) for typical electricity systems is approximately bell shaped with a tail extending rightward representing short duration peak loads (see Fig. 5). Therefore a relatively modest quan-tity of storage can shift the PDF of generator availability leftward (right side of Fig. 5), ‘filling’ the highest peak loads that make up the rightward tail of the demand distri-bution. These peak loads are often the most valuable and easiest to fill, particular if the peaks tend to correlate with VRE power (e.g. solar power and air conditioning). How-ever, progressively greater amounts of energy must be used to fill the (new) peaks after the first peaks are filled. The marginal returns to storage aligns with market-based esti-mates of the value of storage [e.g. McConnell et al. (2015)] and net-energy-based assessments [e.g. Barnhart and Ben-son (2013)].
ERCOT Regional Grid
The framework will be applied to a suite of renewable simulations from Preston (2015a, 2016b), with reliability methodology described in detail in Preston et al. (1997), a simplified procedure in Preston (2015b) and detailed out-put files at http://egpreston.com/cases.htm. The simulations are for the Texas ERCOT network. ERCOT is one of nine independent system operators (ISO) in the US. It supplies electric power to around 24 million Texas customers. The ERCOT region has abundant wind and solar resources.
Fig. 6 Storage status for Preston simulation (left) for 1 Jan 2010–31 Dec 2010 and Henning and Palzer (2014, Fig. 4.5) gas storage (right). Nor-malised as days of full-load storage, based on annual average load
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The Preston simulations were modelled as time-series, demand-balance simulations using Fortran, optimised for least cost but constrained to VRE plus electrical stor-age, with a further reliability constraint (LOLE). Since the simulations were modelled with higher cost seasonal storage, they are not intended as a comprehensive cost-optimised solution to high-penetration VRE. Indeed, commercially available electrical storage is economic only for storage up to several hours (Luo et al. 2015, Table 12). Since this study is concerned with VRE plus storage, they provide a valuable tool to establish refer-ence points to demonstrate the framework. Furthermore, the magnitude of seasonal storage (of any type) at high-VRE penetration is consistent with the published sce-nario literature in relation to the days of full-load storage required (see “Storage requirements” Section and Fig. 6).
The simulations provide a base case, two intermedi-ate steps, and a 100% renewable simulation (see Table 1). They are based on projected demand in 2017, using his-torical wind and solar data from NREL, and hourly loads from 2010 to 2012. Wind and solar data were derived from NREL datasets. Wind was located along the coast, Panhandle, and west Texas. Solar sites included cen-tral Texas (Austin), south central Texas (San Antonio), and west Texas (Pecos). They are optimised for the US standard LOLE of 0.1. The intermediate steps and LOLE fit readily into the proposed framework, and provide multiple reference points for establishing framework parameters.
Preston has started with the incumbent ERCOT system, assumed zero wind power, but with 76 GW of fossil thermal generation and 5.15 GW of nuclear power (see Table 1). Each of the simulations traced 3 consecutive years with a maximum demand of around 71 GW and minimum 28 GW. Annual energy consumption is around 340 TWh. Addi-tional wind and solar PV was added, while thermal genera-tion was reduced, to maintain a LOLE of 0.1. Hence, the VRE has replaced the installed capacity of thermal plant at the given system reliability level. Nextly, storage was added while reducing thermal generation to also maintain a LOLE of 0.1. Figure 6 (left) displays the storage status for 2010
for the full year of the simulation. The right side is derived from Henning and Palzer (2014) as a comparison. In both cases, the requirement for seasonal storage is evident.
Embodied Energy of VRE and Storage
Embodied energy has been calculated for two storage types for comparison: PHS and Li-ion. These have been selected to provide low and high embodied energy references for commercially mature storage devices. PHS is the domi-nant form of electrical storage globally, providing a valu-able reference for a near lower bound for embodied energy and high round-trip efficiency. Li-ion is projected to expand and provides a distributed generation reference. In practice, PHS is not a large-scale option in Texas—Hall and Lee (2014) identified several possible PHS projects in Texas but these were relatively small. Compressed air energy storage (CAES) possesses intermediate embodied energy (Den-holm and Kulcinski 2004). Power-to-gas combined with gas turbine is a potential future seasonal storage option (Schiebahn et al. 2015), which could be assessed with the proposed framework.
In this study, embodied energy for storage is defined as the embodied energy per unit of storage (MJ per kWh of storage). There are uncertainties with Li-ion embod-ied energy, which is largely related to accessing firm-level energy consumption and production data. Furthermore, nearly all battery life-cycle assessments are carried out as process-based attributional assessments, which understate embodied energy relative to systematically complete hybrid assessments [see Crawford (2009)].
This study uses data from Ellingsen et al. (2014), and is based on primary data for a traction battery cell. Ell-ingsen’s medium ‘ASV’ value of 960 MJ/kWh was used, which is representative of the broader literature [e.g. Zack-risson et al. (2010) 790 MJ/kWh, Rydh and Sandén (2005, Table 6) 1510–1870 MJ/kWh for recycled and virgin materi-als, respectively]. The current learning rate (the cost reduc-tion following a cumulative doubling of production) of vehi-cle battery packs using Li-ion is 6–9% (Nykvist and Nilsson 2015). Barnhart and Benson (2013) explored the theoretical
Table 1 Reference VRE plus storage model from Preston (2015a)
Scenario Wind (GW) Solar PV (GW)
Storage (GW) Storage (GWh) Fossil Nuclear
Actual installed capacity at Dec 2015
16 0.3 0 0 72 5.15
Reference 0 0 0 0 76 5.15Case 4 24 24 0 0 63 5.15Case 6 68 76 50 16,500 0 5.15Case 7 76 84 54 18,970 0 0
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and resource constraints of battery manufacture and con-cluded that embodied energy of batteries could be reduced ‘at most by a factor 2 to 3’, but also note that the great-est potential for reducing the per-cycle embodied energy lies in increasing the cycle life (Barnhart and Benson 2013, Sect. 3.3). The recyclability of Li-ion packs is constrained by the complexity of cell and battery construction, and diffusion of elements throughout the anode, cathode, and electrolyte (Gaines 2014). Therefore the energetic benefit of recycling Li-ion chemistry is relatively low (Rydh and Sandén 2005, Fig. 4). Repurposing of EV battery pack cells for grid storage to extend their service life has also been proposed, with as yet uncertain commercial value (Neubauer et al. 2012). The repurposing has been described as ‘cascading reuse’ in the
life-cycle literature, and requires allocating the environmental burden across multiple uses. Various procedures have been proposed for allocation, including ‘quality based’, ‘cut-off’, and ‘50/50’ (Richa et al. 2015). The use of vehicle-to-grid storage would similarly entail allocation across multiple uses.
The embodied energy for PHS of 400 MJ/kWh is taken from Denholm and Kulcinski (2004). PHS is a mature civil engineering technology and therefore not subject to a pro-jected significant decline in embodied energy. An advantage of PHS is its long life of 60 years or greater.
The embodied energy for wind and solar PV were cal-culated from the respective assumed EROI data as shown in Table 2. For wind, a meta-review by Kubiszewski et al. (2010) estimated 19.8–25.2 in 2009. Since wind has pro-gressed in recent years, this study assumes 30:1. For solar PV, Raugei et al. (2012) calculated 5.9–11.8 for data from 2009 to 2011. Since solar PV has progressed significantly in recent years, this study takes a high-end estimate of around 25 from a review from Dale and Benson (2013). A sensitivity analysis was included with varying EROI for the wind and solar PV, with wind from 20:1 to 40:1 and solar PV from 15:1 to 40:1.
The embodied energy and resulting EROI are shown in Tables 3 and 4. In this work, the EROI is defined for the VRE-storage sub-system—EROI is not defined for storage as a stand-alone unit. EROI VRE−storage is equal to the ratio of the gross lifetime energy supplied by the subsystem E g , and the sum of the lifetime embodied energy of the VRE, E VRE and storage, E storage . The lifetime of the subsystem has been defined as 50 years. The benefit of applying this methodology is that it is possible to trade-off the energetic costs of stor-age with the value that storage provides, with different grid mixes, VRE penetration, and geographic regions.
(2)EROIVRE+storage =Eg
EVRE + Estorage
Table 2 Assumptions applied to embodied energy calculation for Texas
[1] Representative figure derived from global horizontal irradiance averaged between Austin and El Paso (The University of Texas at Austin 2016) [2] (Denholm and Kulcinski 2004) [3] (Ellingsen et al. 2014), use ‘ASV’ value [4] (Kubiszewski et al. 2010) meta-review ranged 15–30 years, most were 20 years, assume 25 [5] most studies assume 25 or 30 years, assume 30 years [6] assume 50 + years [7] Li-ion is cycled limited, cycle life is dependent on depth-of-discharge and capacity degrades with cycling, assume 10 years as high-end estimate for warranties [8] Kubiszewski et al. (2010) estimated EROI of 19.8–25.2 in 2009, assume improvement to 30:1. Embodied energy calculated from EROI data [9] taken as a high-end estimate from Dale and Benson (2013). Embodied energy calculated from EROI data
Wind Solar PV PHS storage Li-ion storage
Capacity factor 40% 18.7% [1] N/A N/AEmbodied energy 10,512 MJ/kW [8] 5911 MJ/kW [9] 400 MJ/kWh [2] 960 MJ/kWh [3]Modelled lifetime 25 years [4] 30 years [5] 50 years [6] 10 years [7]Lifetime EROI of VRE only 30:1 [8] 25:1 [9] N/A N/AAnnual energy production 3504 kWh/kW 1752 kWh/kW N/A N/A
Table 3 Aggregate embodied energy over 50-year time frame
50-year VRE embodied energy (PJ)
50-year storage embodied energy (PJ)
Wind Solar PV PHS Li-ion
Case 4 505 227 0 0Case 6 1430 718 6583 79,200Case 7 1598 794 7569 91,056
Table 4 Summary of 100% VRE with storage based on simulation model for 76 GW wind, 84 GW solar PV, 54 GW, and 18,970 GWh storage, for two types of storage, pumped hydro storage and lithium-ion
50-year embodied energy of VRE + storage (PJ)
50-year EROI of VRE + storage
PHS Li-ion PHS Li-ion
Case 7 9961 93,448 7.2:1 0.8:1
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Results and Discussion
Overview
Figure 7 depicts the embodied energy and marginal embod-ied energy for the generalised case with pumped hydro storage. Referring to the figure, the embodied energy (blue curve) below 14 GW (curtailment lower bound) was calcu-lated from Preston case 4 (zero storage) and assumed to be linear up to 14 GW. For greater than 14 GW, a trend curve was estimated from the method of least squares, applied to a second-order polynomial. The second-order polynomial was selected as the closest fit based on previous work with off-grid solar PV and storage (Palmer 2013). The R2 cor-relation was 0.9997. The solid curves are for an assumed EROI for wind of 30 and solar PV of 25 (see from Table 2). The shaded areas bounding the solid lines show sensitivity for varying wind and solar EROI, from wind at 20 and solar at 15, up to wind at 40 and solar at 40.
From the first derivative of the embodied energy curve, it is possible to estimate the marginal embodied energy, and from this, the marginal EROI. The marginal EROI refers to the EROIVRE−storage of the additional VRE storage required to provide an additional gigawatt of displaced capacity. Importantly, the marginal EROI varies depending on the penetration of VRE. Since the trend was assumed a second-order polynomial, the first derivative is necessarily linear.
As shown in Fig. 7, the marginal embodied energy per unit of displaced capacity is around four times higher at the high end-point of the line for PHS, but around 41-fold for Li-ion (not shown in graph). This implies that it is 4 to 41 times (PHS versus Li-ion) more energetically expensive to displace a gigawatt of conventional capacity at near-100% VRE versus low penetration VRE.
The sensitivity shows that the EROI of the wind and solar is much less important at high penetration since the embodied energy is dominated by the storage. This aligns with other studies that suggest it may be energetically pref-erable to curtail renewable energy at times, rather than store energy [e.g. Barnhart et al. (2013)].
The Embodied Energy and Marginal Embodied Energy Curves
From this work, the most important outcome is the shape and behaviour of the embodied energy and marginal embodied energy curves. The first units of VRE and stor-age have the lowest embodied energy (and therefore highest EROI). This implies that the first hour of storage produces the highest marginal EROIVRE+storage, the second hour, less so, and so on. Conceptually, VRE changes the shape of the ‘demand distribution’ density function (Fig. 5), and storage shifts the ‘generator availability’ density function rightward.
The ERCOT simulation is intended to demonstrate the framework, but different regional grids, assumptions, and simulations would provide different results. Furthermore, ERCOT is a single region in the US, and the simulation is not seeking to capture the benefits of geographic diversity. However, the qualitative relationships would be expected to be broadly similar—all large grids exhibit a similarly shaped load duration curve [e.g. Billinton and Allan (1996, Fig. 2.4)] giving the characteristic PDF shown in Fig. 5. Demand management technologies that address peak demand effectively ‘blunt’ the rightward tail of the ‘annual demand distribution’ (Fig. 5), shifting the right-hand tail leftwards.
Wind and solar PV exhibit broadly similar shaped CDF curves across different regions, shown in the bottom sec-tion of Fig. 8. The parameters and size will vary depending on geographic location but the characteristic shape will be similar.
Key Differences Between VRE‑Storage and Conventional Generation
There are two key differences between a VRE-storage sys-tem and a suite of conventional generators. In the case of conventional generation, the risk of outage between genera-tors is mostly uncorrelated. Therefore each generator can be treated as a random independent variable, cumulatively adding to system available capacity. In contrast, VRE of a particular type is highly correlated within a region, limiting the available firm capacity. Technology diversity and geo-graphic dispersion are two methods to de-correlate VRE availability.
Fig. 7 Embodied energy of VRE plus storage for PHS for ERCOT grid, displaced generation based on simulation by Preston (2015a)
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Fig. 8 Stylised representation of reliability measurement for system composed of conventional generation, VRE, and storage
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Nextly, conventional generation can be considered to have unlimited storage available from the perspective of an individual generator within a planning time frame. VRE does not have access to storage except through discrete storage devices. The devices can only be charged through overbuilding VRE capacity to produce a surplus at times of high wind speeds or solar irradiation.
Consequence of Diminishing Returns
Rising VRE penetration is subject to diminishing returns such that the marginal EROI may fall below the mini-mum useful EROI for society. Hall et al. (2009) identifies an EROI of around 3:1 as being the absolute lowest use-ful EROI for oil or corn-based ethanol to provide unsubsi-dised energy to society, but an EROI of 10:1 being required to deliver unsubsidised energy for a modern developed society. Lambert et al. (2014) suggest a societal EROI of 20–30:1 as being necessary for a high standard of living.
A shift from an electrical system based mostly on energy stocks to one based mostly on natural flows is handicapped by the energetic demands of surplus VRE and storage. From this case study, it is readily apparent that Li-ion bat-teries could only usefully contribute a short-term role to buffering VRE. However, the energetic requirements of PHS are sufficiently low to enable a greater penetration of VRE. Future storage devices may be energetically superior to PHS.
100% VRE Scenarios
In a prospective 100% VRE scenario, it is not necessary to consider the displaced capacity as shown in Fig. 7 since it is assumed that there is zero fossil fuel capacity. The sys-tem-wide EROIsystem is calculated as a standalone estimate for the given probabilistic reliability (e.g. LOLE = 0.1) as shown in Eq. 3, where EROIsystem is the ratio of the gross lifetime energy supplied by the entire system Eg, and the sum of the lifetime embodied energy of the VRE, EVRE and storage, Estorage.
Similarly, in scenarios that retain conventional capac-ity (e.g. hydro), the methodology can be used to assess the EROIVRE−storage of the non-conventional portion. Since demand-balance simulations in the academic literature are usually modelled without probabilistic reliability, it may be challenging to compare alternative scenarios on a like-for-like basis. Rigorous application of the methodology will require the incorporation of loss-of-load estimates into sce-nario models.
(3)EROIsystem =
Eg
EVRE + Estorage
In much the same way as scenarios can be compared based on cost (e.g. total system cost or $/kWh), they can be compared for system-wide EROI. A high EROI estimate would imply that the system is energetically viable from a net-energy perspective, and therefore capable of converting natural energy flows into demand-based electricity. A low EROI estimate (<10:1) would imply that the system is not energetically viable without access to an external source of inexpensive energy (i.e. the system is only energetically viable due to the importation of components from regions with inexpensive energy).
Availability of Storage
This study makes no assumptions about the prospec-tive availability of storage. To put the scale of the PHS into perspective, the US is the third leading country in pumped hydro storage, with an estimated 547 GWh of stor-age capacity, and Preston’s 100% VRE ERCOT scenario requires 18,970 GWh of storage. ERCOT (340 TWh) rep-resents around 9% of total annual US electrical generation (3764 TWh).
The methane or hydrogen storage option, via the power-to-gas and gas turbine pathways described in Palzer and Henning (2014), offers another prospective long-term path-way with as yet uncertain costs (Götz et al. 2016; Sterner 2009) and embodied energy. The high energy density of methane, stored either within pipelines, underground stor-age, or as liquefied LNG, potentially offers seasonal stor-age. For example, it has been estimated that the storage capacity of the German gas network is of the order of ‘hun-dreds of TWh’ (i.e. >300,000 GWh) (Sterner 2009, p. 105). The use of existing gas infrastructure would be favourable from a net-energy perspective. Götz et al. (2016) details the various technical and economic barriers that would need to be resolved for successful commercialisation, including the availability of CO2 sources, the dynamic behaviour of the various processes, a low round-trip efficiency, and high capital cost.
Conclusions
It was argued that energy storage, along with net-energy, is a fundamental property of the useful energy available to society, and can be described by the vector of surplus energy and storage. The examples of the development of agriculture, especially grains, provide an example of the value of storage. Despite the relatively high energetic demand of early agriculture, grains provided calories when they were needed—access to food during austere times was more important than feasting during good harvests. The early Industrial Revolution provides a similar lesson.
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Early coal-fired steam was extremely inefficient and ener-getically expensive but gave access to essentially unlimited ‘stored sunlight’, providing copious quantities of power on demand.
These historic lessons inform a methodology for evalu-ating the value of variable renewable energy and storage in a modern context. The methodology is based on the EROI metric, which gives greater weight to the principles of ener-getics over market prices.
Much of the contemporary global shift to variable renewable energy is adding to the capital stock rather than substituting away from legacy infrastructure. This could be contrasted with earlier transitions in which the substituting energy source replaced legacy infrastructure and enhanced productivity. The case of coal substituting for wood, and oil for coal provides early examples. However, it may not be necessary to completely substitute to provide value. The addition of VRE to existing electrical grids reduces the fos-sil fuel that would otherwise have been consumed, thereby providing emission abatement, usually measured as the marginal abatement cost in $/tonne CO2Eq.
This study argues that a long-run transition is better measured by substitution of generation capacity. Through the use of demand-balance simulations and probabilistic reliability assessments, the embodied energy of alternative VRE-storage options can be plotted against displaced gen-eration to compare their efficacy in substituting for capac-ity. In a 100% VRE scenario, the system-wide EROI is calculated as a stand-alone estimate. The most important conclusion is that rising VRE and storage exhibit marked diminishing returns, and therefore the first units of storage are the least energetically expensive. Unlike conventional generation, which has access to essentially unlimited stor-age in the form of fuels, VRE is handicapped by the ener-getic demands of surplus VRE and storage.
The rate of diminishing returns is dominated by the embodied energy of the storage device. Pumped hydro stor-age is currently the dominant form of electrical storage, and gives a much shallower diminishing return than Li-ion. It is apparent that the EROI of a system reliant on Li-ion (and other similar electro-chemical storage devices) would rapidly fall below the minimum useful EROI for society. In principle, if regional topography and water availability per-mitted, the large-scale use of pumped hydro storage would permit VRE to displace a substantial proportion of conven-tional generation capacity. Since electrical storage is not currently economic for seasonal storage, the scenario lit-erature adopts lower cost strategies, such as demand man-agement, maintaining a significant share of conventional capacity, or assuming the ready availability of large-scale biomass. In the future, other storage options may emerge, such as methane via the power-to-gas and gas turbine pathways.
The goal of this study was to introduce a framework for exploring the role of storage with net-energy and stimulate further research. Further work should include renewable simulations across different regions that are tailored to net-energy analysis and loss-of-load reliability metrics.
Acknowledgements The author would like to thank Roger Dar-gaville of the Melbourne Energy Institute at the University of Mel-bourne for valuable comments and feedback. Thanks also to the anonymous reviewers for their valuable comments that substantially improved the paper.
Compliance with Ethical Standards
Conflict of interest The author declares no conflict of interest.
References
Abdulla K, Wirth A, Halgamuge SK, Steer KC (2014) ‘Selecting an optimal combination of storage & transmission assets with a non-dispatchable electricity supply’, in Information and Auto-mation for Sustainability (ICIAfS), 2014 7th International Con-ference on, pp. 1–6
ACIL Tasman (2009), Fuel resource, new entry and generation costs in the NEM, 0419–0035. AEMO, Melbourne
Aghahosseini A, Bogdanov D, Breyer C (2016) ‘100% Renewable Energy in North America and the role of Solar Photovoltaics’, in EU-PVSEC conference June 20–24, 7DV.4.8, Munich
Archer CL, Jacobson MZ (2007) ‘Supplying baseload power and reducing transmission requirements by interconnecting wind farms’. J Appl Meteorol Climatol 46(11):1701–1717
Australian Energy Market Operator (AEMO) (2012) ‘2012 Planning Studies Input Tables’, viewed 20 Oct 2015. https://www.aemo.com.au/media/Files/Other/planning/Modelling_Assumptions_and_Data_2012_v3.xls
Australian Energy Market Operator (AEMO) (2013) Power system adequacy for the national electricity market. AEMO, Mel-bourne. https://www.aemo.com.au/media/Files/Other/electrici-tyops/Power_System_Adequacy-Two_Year_Outlook_2013.pdf.pdf
Australian Productivity Commission (2013) Electricity network regu-latory frameworks report. Productivity Commission, Canberra, http://www.pc.gov.au/inquiries/completed/electricity/report
Ayres RU, Warr B (2010) The economic growth engine: how energy and work drive material prosperity. Edward Elgar Publishing Limited, Cheltenham
Barnes FS, Levine JG (2011) Large energy storage systems hand-book. CRC Press, Boca Raton
Barnhart CJ, Benson SM (2013) ‘On the importance of reducing the energetic and material demands of electrical energy storage’. Energy Environ Sci 6(4):1083–1092
Barnhart CJ, Dale M, Brandt AR, Benson SM (2013) ‘The energetic implications of curtailing versus storing solar-and wind-gener-ated electricity’. Energy Environ Sci 6(10):2804–2810
Barnhart C, Carbajales-Dale M, Benson SM (2015) ‘Flexible Power Grid Resources - an NEA Analysis’, viewed 1 Sep 2015. Stan-ford University Global Climate and Energy Project. http://gcep.stanford.edu/pdfs/events/workshops/Barnhart_NEAStanfor-dApril2015.pdf
Bashmakov I, Bruckner T, Mulugetta Y, Chum H, Navarro A, Edmonds J (2014), ‘Energy systems’, In: Edenhofer O, Pichs-Madruga R, Sokona Y (eds), Climate change 2014: mitigation
93
Biophys Econ Resour Qual (2017) 2:6
1 3
Page 17 of 19 6
of climate Change, Cambridge University Press, New York. http://www.ipcc.ch/pdf/assessment-report/ar5/wg3/ipcc_wg3_ar5_chapter7.pdf
Billinton R & Allan RN (1996) Reliability evaluation of power sys-tems, 2nd edn, Springer, New York
Bogdanov D, Breyer C (2016) ‘North-East Asian Super Grid for 100% renewable energy supply: optimal mix of energy tech-nologies for electricity, gas and heat supply options’. Energy Convers Manag 112:176–190
Brown T, Faruqui A (2014), Structure of electricity distribution network tariffs: recovery of residual costs. The Brattle Group, Sydney
Brown JH, Burnside WR, Davidson AD, DeLong JP, Dunn WC, Hamilton MJ, Mercado-Silva N, Nekola JC, Okie JG, Wood-ruff WH (2011) ‘Energetic limits to economic growth’. Bio-science 61(1):19–26
Budischak C, Sewell D, Thomson H, Mach L, Veron DE, Kemp-ton W (2012) ‘Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time’. J Power Sources 225:60–74
Buenstorf G (2004) The economics of energy and the production process: an evolutionary approach. Edward Elgar Publishing, Cheltenham
Bundesministerium für Wirtschaft und Technologie (2015) ‘Gen-erating capacity, gross electricity generation and gross con-sumption Germany’, viewed 10 Feb 2016. Federal Ministry for Economic Affairs and Energy. http://www.bmwi.de/BMWi/Redaktion/Binaer/Energiedaten/energietraeger10-stromerzeugungskapazitaeten-bruttostromerzeugung,property=blob,bereich=bmwi2012,sprache=de,rwb=true.xls
Carbajales-Dale M, Barnhart CJ, Benson SM (2014) ‘Can we afford storage? A dynamic net energy analysis of renewable electric-ity generation supported by energy storage’. Energy Environ Sci 7(5):1538–1544
Carbajales-Dale M, Raugei M, Fthenakis V, Barnhart C (2015) ‘Energy return on investment (EROI) of solar PV: an attempt at reconciliation’. Proc IEEE 103(7):995–999
Cowen R (2005) Exploiting the Earth. Johns Hopkins University Press (unpublished). http://mygeologypage.ucdavis.edu/cowen/~gel115/salt.html
Crawford RH (2009) ‘Life cycle energy and greenhouse emissions analysis of wind turbines and the effect of size on energy yield’. Renewable Sustain Energy Rev 13(9):2653–2660
Dale M, Benson SM (2013) ‘Energy balance of the global photo-voltaic (PV) industry-is the PV industry a net electricity pro-ducer?’ Environ Sci Technol 47(7):3482–3489
Denholm P, Hand M (2011) ‘Grid flexibility and storage required to achieve very high penetration of variable renewable electric-ity’. Energy Policy 39(3):1817–1830
Denholm P, Kulcinski GL (2004) ‘Life cycle energy requirements and greenhouse gas emissions from large scale energy storage systems’. Energy Convers Manag 45(13):2153–2172
Denholm P, O’Connell M, Brinkman G, Jorgenson J (2015), Over-generation from solar energy in California: a field guide to the Duck Chart. NREL (National Renewable Energy Laboratory (NREL), Golden, http://www.nrel.gov/docs/fy16osti/65023.pdf
Diamond JM (2005) Guns, germs, and steel, vintage, LondonEdenhofer O, Pichs-Madruga R, Sokona Y, Farahani E (2014), ‘Sum-
mary for Policymakers’, In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner P, P Eickemeier KB, Savolainen J, Schlömer S, von Stechow C, Zwickel T, Minx JC (eds), Climate change 2014: mitigation of climate change. contribution of working group III to the Fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, New York. http://
www.ipcc.ch/pdf/assessment-report/ar5/wg3/ipcc_wg3_ar5_summary-for-policymakers.pdf
Ellingsen LAW., Majeau-Bettez G, Singh B, Srivastava AK, Valøen LO, Strømman AH (2014) ‘Life cycle assessment of a lithium-ion battery vehicle pack’. J Ind Ecol 18(1):113–124
Elliston B, Diesendorf M, MacGill I (2012) ‘Simulations of scenarios with 100% renewable electricity in the Australian National elec-tricity market’. Energy Policy 45:606–613
Enevoldsen MK, Ryelund A, Andersen MS (2009) ‘The impact of energy taxes on competitiveness: a panel regression study of 56 European industry sectors’. In: Andersen MS, Ekins P (eds) Carbon-energy taxation. Oxford Univeristy Press, New York
Gaines L (2014) ‘The future of automotive lithium-ion battery recy-cling: charting a sustainable course’. Sustain Mater Technol 1:2–7
Götz M, Lefebvre J, Mörs F, Koch AM, Graf F, Bajohr S, Reimert R, Kolb T (2016) ‘Renewable Power-to-Gas: a technological and economic review’. Renewable Energy 85:1371–1390
Gross R, Heptonstall P, Anderson D, Green T, Leach M, Skea J (2006), The Costs and impacts of intermittency: an assessment of the evidence on the costs and impacts of intermittent gen-eration on the British electricity network. UK Energy Research Centre, London
Grossmann W, Grossmann I, Steininger KW (2015) ‘Solar electric-ity supply isolines of generation capacity and storage’. Proc Natl Acad Sci USA 112(12):3663–3668
Hall D, Lee R (2014), Assessment of opportunities for new united states pumped storage hydroelectric plants using existing water features as auxiliary reservoirs. Idaho National Laboratory (INL), Idaho Falls
Hall CAS, Balogh S, Murphy DJR (2009) ‘What is the mini-mum EROI that a sustainable society must have?’ Energies 2(1):25–47
Hart EK, Stoutenburg ED, Jacobson MZ (2012) ‘The potential of intermittent renewables to meet electric power demand: cur-rent methods and emerging analytical techniques’. Proc IEEE 100(2):322–334
Henning H-M, Palzer A (2014) ‘A comprehensive model for the Ger-man electricity and heat sector in a future energy system with a dominant contribution from renewable energy technolo-gies—part I: methodology’. Renewable Sustain Energy Rev 30:1003–1018
Hibbs DA, Olsson O (2004) ‘Geography, biogeography, and why some countries are rich and others are poor’. Proc Natl Acad Sci USA 101(10):3715–3720
Huva R, Dargaville R, Rayner P (2016) ‘Optimising the deployment of renewable resources for the Australian NEM (National Elec-tricity Market) and the effect of atmospheric length scales’. Energy 96:468–473
IEEE (2007), IEEE Std 762–2006 - IEEE Standard Definitions for Use in Reporting Electric Generating Unit Reliability, Avail-ability, and Productivity. IEEE Power Engineering Society, New York
International Energy Agency (IEA) (2014) Technology roadmap—concentrating solar power. IEA, Paris. https://www.iea.org/pub-lications/freepublications/publication/TechnologyRoadmapSolarThermalElectricity_2014edition.pdf
International Energy Agency (IEA) (2016) Key world energy sta-tistics—2016. IEA, Paris. https://www.iea.org/publications/freepublications/publication/KeyWorld2016.pdf
International Renewable Energy Agency (IRENA) (2012), Concen-trating solar power. IRENA, Abu Dhabi. http://www.irena.org/documentdownloads/publications/re_technologies_cost_analy-sis-csp.pdf
Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015), ‘Low-cost solution to the grid reliability problem with 100%
94
Biophys Econ Resour Qual (2017) 2:6
1 3
6 Page 18 of 19
penetration of intermittent wind, water, and solar for all pur-poses’. Proc Natl Acad Sci, 112:15060–15065
Joas F, Pahle M, Flachsland C, Joas A (2016) ‘Which goals are driving the energiewende? Making sense of the German energy transformation’. Energy Pol 95:42–51
Jorgenson DW (1984) ‘The role of energy in productivity growth’. Energy J 5(3):11–26
King CW, Maxwell JP, Donovan A (2015) ‘Comparing world eco-nomic and net energy metrics, part 1: single technology and commodity perspective’. Energies 8(11):12949–12974
Krause F, Bossel H, Müller-Reißmann K-F (1981), Energie-wende: wachstum und wohlstand ohne erdöl und uran, S. Fischer, Berlin
Krey V, Clarke L (2011) ‘Role of renewable energy in climate mitigation: a synthesis of recent scenarios’. Climate Pol 11(4):1131–1158
Kubiszewski I, Cleveland CJ, Endres PK (2010) ‘Meta-analysis of net energy return for wind power systems’. Renewable Energy 35(1):218–225
Lambert JG, Hall CA, Balogh S, Gupta A, Arnold M (2014) ‘Energy, EROI and quality of life’. Energy Pol 64:153–167
Laudan R (2015) Cuisine and Empire: Cooking in World History. University of California Press, Berkeley
Lenzen M, McBain B, Trainer T, Jütte S, Rey-Lescure O, Huang J (2016) ‘Simulating low-carbon electricity supply for Aus-tralia’. Appl Energy 179:553–564
Luo X, Wang J, Dooner M, Clarke J (2015) ‘Overview of current development in electrical energy storage technologies and the application potential in power system operation’. Appl Energy 137:511–536
MacDonald AE, Clack CT, Alexander A, Dunbar A, Wilczak J, Xie Y (2016a) ‘Future cost-competitive electricity systems and their impact on US CO2 emissions’. Nat Clim Change 6:526–531
MacDonald AE, Clack CT, Alexander A, Dunbar A, Wilczak J, Xie Y (2016b) ‘Supplementary information: future cost-competitive electricity systems and their impact on US CO2 emissions’. Nat Clim Change 6:526–531
MacKay D (2008) Sustainable Energy-without the hot air. UIT, Cambridge
Maubach K-D (2014) Energiewende: wege zu einer bezahlbaren ener-gieversorgung, Springer, Berlin
McConnell D, Forcey T, Sandiford M (2015) ‘Estimating the value of electricity storage in an energy-only wholesale market’. Appl Energy 159:422–432
Morgan J (2014) ‘The catch-22 of energy storage’, Chem Aust, 2014:22
Morris C, Jungjohann A (2016) Energy democracy: Germany’s Ener-giewende to renewables. Springer, Berlin
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T (2010) ‘The next generation of scenarios for climate change research and assessment’. Nature 463(7282):747–756
Murphy DJ, Hall CA (2010) ‘Year in review—EROI or energy return on (energy) invested’. Ann N Y Acad Sci 1185(1):102–118
Murphy DJ, Hall CA, Dale M, Cleveland C (2011) ‘Order from chaos: a preliminary protocol for determining the EROI of fuels’. Sus-tainability 3(10):1888–1907
MWH (2009), Technical analysis of pumped storage and integration with wind power in the pacific northwest : MWH-HDC-T12, MWH Americas, Inc., Bellevue, http://www.hydro.org/wp-content/uploads/2011/07/PS-Wind-Integration-Final-Report-without-Exhibits-MWH-3.pdf
Neubauer JS, Pesaran A, Williams B, Ferry M, Eyer J (2012), A techno-economic analysis of PEV battery second use: repur-posed-battery selling price and commercial and industrial
end-user value, SAE Technical Paper, 0148–7191, http://papers.sae.org/2012-01-0349/
North American Electric Reliability Corporation (NERC) 2011, Plan-ning resource adequacy analysis, assessment and documenta-tion. BAL-502-RFC-02, NERC, Washington DC, http://www.nerc.com/files/BAL-502-RFC-02.pdf
Nykvist B, Nilsson M (2015) ‘Rapidly falling costs of battery packs for electric vehicles’. Nat Clim Change 5(4):329–332
OFGEM (2013), Electricity capacity assessment report 2013. OFGEM, London
Oswald J, Raine M, Ashraf-Ball H (2008) ‘Will British weather pro-vide reliable electricity?’ Energy Pol 36(8):3212–3225
Palmer G (2013) ‘Household solar photovoltaics: supplier of marginal abatement, or primary source of low-emission power?’ Sustain-ability 5(4):1406–1442
Palzer A, Henning H-M (2014) ‘A comprehensive model for the Ger-man electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies–part II: results’. Renewable Sustain Energy Rev 30:1019–1034
Pickard WF (2012) ‘The history, present state, and future prospects of underground pumped hydro for massive energy storage’. Proc IEEE 100(2):473–483
Pickard WF (2014a) ‘Energy return on energy invested (EROI): a quintessential but possibly inadequate metric for sustain-ability in a solar-powered world? [point of view]’. Proc IEEE 102(8):1118–1122
Pickard WF (2014b) ‘Smart grids versus the Achilles’ heel of renewa-ble energy: can the needed storage infrastructure be constructed before the fossil fuel runs out?’ Proc IEEE 102(7):1094–1105
Pirker O, Argyrakis I, Babkin V, Chudy M, Crosnier G, Dahlback N, Gianatti R, Gomez Martin P, Gudnason EG, Hellsten K, Kreiss G, Nikolov I, Oesch P, o`Mahony B, Pala R, Reinig L, Kreiken-baum D, Polak D, Romer N, Saturka Z, Stanojevic V, Stettler A, Marin C, Freitas, J.C.T., Lobacz J, Weisrock G, Jenko J, SEE-LOS K, Timm M. 2011, Hydro in Europe: powering renewa-bles, Eurelectric, Brussels
Poser H, Altman J, ab Egg F, Granata A, Board R (2014), ‘Devel-opment and integration of renewable energy: Lessons learned from Germany’. Finadvice, FAA Financial Advisory, Sood-strasse 55
Preston EG, Grady WM, Baughman ML (1997) ‘A new plan-ning model for assessing the effects of transmission capac-ity constraints on the reliability of generation supply for large nonequivalenced electric networks’. IEEE Trans Power Syst 12(3):1367–1373
Preston G (2015a) 100% solar and wind power simulation for ERCOT, http://egpreston.com/100percentrenewables.pdf
Preston G (2015b) ‘A simple calculation procedure for LOLE, LOLH, and EUE, calculation of probabilistic transmission line flows, and study results for extreme renewables in ERCOT’, Paper presented to Institute of Electrical and Electronics Engineers (IEEE) LOLE Working Group, Golden, http://egpreston.com/Presentation3.pdf
Preston G (2016a) ‘Direct calculation example convolving ten ran-domly outaged generators’, viewed Dec 1 2015, http://egpres-ton.com/DC.txt
Preston G (2016b) ‘Microgrids Can Play An Important Role In Reducing ERCOT’s Fossil Fuel Dependency’, Renewable Energy Law Conference, http://egpreston.com/PrestonFeb2016.pdf
PV Education (2016) Average Solar Radiation, http://www.pveduca-tion.org/pvcdrom/average-solar-radiation
Raugei M (2013) ‘Comments on” energy intensities, EROIs (energy returned on invested), and energy payback times of electricity generating power plants"-making clear of quite some confu-sion’. Energy, 59:781
95
Biophys Econ Resour Qual (2017) 2:6
1 3
Page 19 of 19 6
Raugei M, Fullana-i-Palmer P, Fthenakis V (2012) ‘The energy return on energy investment (EROI) of photovoltaics: method-ology and comparisons with fossil fuel life cycles’. Energy Pol 45:576–582
Reedman LJ (2012) High penetration renewables studies: a review of the literature, report prepared for the Australian energy market operator (AEMO), report no. EP 127113. CSIRO, Melbourne. https://publications.csiro.au/rpr/download?pid=csiro:EP127113&dsid=DS2
Riahi K, Grübler A, Nakicenovic N (2007) ‘Scenarios of long-term socio-economic and environmental development under climate stabilization’. Technol Forecast Soc Change 74(7):887–935
Richa K, Babbitt CW, Nenadic NG, Gaustad G (2015) ‘Environmen-tal trade-offs across cascading lithium-ion battery life cycles’. Int J Life Cycle Assess. doi:10.1007/s11367-015-0942-3pp
Rosenberg N (1994) Exploring the black box: technology, economics, and history. Cambridge University Press, New York
Rydh CJ, Sandén BA (2005) ‘Energy analysis of batteries in photo-voltaic systems. Part I: performance and energy requirements’. Energy Convers Manag 46(11):1957–1979
Salles M, Aziz M, Hogan W (2016) ‘Potential arbitrage revenue of energy storage systems in PJM during 2014’, In:In Proceedings of the 2016 IEEE PES General Meeting (PIPGS16), Boston
Schiebahn S, Grube T, Robinius M, Tietze V, Kumar B, Stolten D (2015) ‘Power to gas: Technological overview, systems analysis and economic assessment for a case study in Germany’. Int J Hydrog Energy 40(12):4285–4294
Schramski JR, Gattie DK, Brown JH (2015) ‘Human domination of the biosphere: Rapid discharge of the earth-space battery foretells the future of humankind’. Proc Natl Acad Sci USA 112(31):9511–9517
Schreiber M, Wainstein ME, Hochloff P, Dargaville R (2015) ‘Flex-ible electricity tariffs: power and energy price signals designed for a smarter grid’. Energy 93:2568–2581
Schurr SH (1990) Electricity in the American economy: Agent of technological progress. Greenwood Publishing Group, Westport
Sims RE, Schock RN, Adegbululgbe A, Fenhann JV, Konstantina-viciute I, Moomaw W, Nimir HB, Schlamadinger B, Torres-Martínez J, Turner C (2007) ‘Energy supply’, In: Climate change 2007: Mitigation. Contribution of working group iii to
the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press, New York. http://www.ipcc.ch/publications_and_data/ar4/wg3/en/ch4.html
Sims R, Mercado P, Krewitt W, Bhuyan G, Flynn D, Holttinen H, Jan-nuzzi G, Khennas S, Liu Y, Nilsson LJ (2011), ‘Integration of renewable energy into present and future energy systems’, In: Edenhofer O, Pichs-Madruga R, Sokona Y (eds), IPCC special report on renewable energy sources and climate change mitiga-tion, Cambridge University Press, New York. http://www.ipcc.ch/pdf/special-reports/srren/Chapter 8 Integration of Renewable Energy into Present and Future Energy Systems.pdf
Sterner M (2009) Bioenergy and renewable power methane in inte-grated 100% renewable energy systems: limiting global warm-ing by transforming energy systems. vol 14, Universität Kassel, Kassel
The University of Texas at Austin (2016), ‘Texas Solar Radiation Database’, viewed 31 March 2016, http://www.me.utexas.edu/~solarlab/tsrdb/tsrdb.html.
United States Department of Energy (DOE) (2016) DOE Global Energy Storage Database, viewed 10 Sep 2015, http://www.energystorageexchange.org/projects/data_visualization
US Energy Information Administration (EIA) (2015) Electricity—Form EIA-923 detailed data, http://www.eia.gov/electricity/data/eia923/index.html
van der Burg, L., Pickard, S (2015) G20 subsidies to oil, gas and coal production: Germany, Oil Change International, Washington, DC
Weißbach D, Ruprecht G, Huke A, Czerski K, Gottlieb S, Hus-sein A (2013) ‘Energy intensities, EROIs (energy returned on invested), and energy payback times of electricity generating power plants’. Energy 52:210–221
Wrigley EA (2010) Energy and the English industrial revolution. Cambridge University Press, New York
Yang C-J, Jackson RB (2011) ‘Opportunities and barriers to pumped-hydro energy storage in the United States’. Renewable Sustain Energy Rev 15(1):839–844
Zackrisson M, Avellán L, Orlenius J (2010) ‘Life cycle assessment of lithium-ion batteries for plug-in hybrid electric vehicles–Criti-cal issues’. J Cleaner Prod 18(15):1519–1529
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Chapter 6
An exploration of divergence in
EPBT and EROI for solar
photovoltaics
6.1 Overview and context of chapter
6.1.1 Introduction
Solar photovoltaics (PV) is widely regarded as one of the most promising renewable energy technolo-
gies. However, there is a large divergence in EROI and energy payback time (EPBT) across studies.
This contributes to ongoing uncertainty in relation to the sustainability, and energetic value of PV.
Although this study focused on solar PV, the same general lessons hold for other renewable energy
technologies.
This study undertook a review of the primary and review literature and make an original con-
tribution to explaining the underlying methodologies. It finds that most of the apparent divergence
between studies is accounted for by six factors – life-cycle assessment methodology, age of the primary
data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy
forms, and assumptions about real-world performance. The apparent divergence in findings between
studies can often be traced back to different goal definitions, which are not usually explicitly identified.
6.1.2 Research questions
The research questions posed by this chapter are:
97
1. What are the underlying reasons for the wide divergence in EROI in renewable energy studies?
2. How significant are these factors?
3. What is the magnitude of each of these factors?
4. How do these factors relate to energy transition feasibility assessments?
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BioPhysical Economics and Resource Quality (2017) 2:15 https://doi.org/10.1007/s41247-017-0033-0
ORIGINAL PAPER
An Exploration of Divergence in EPBT and EROI for Solar Photovoltaics
Graham Palmer1 · Joshua Floyd2
Received: 19 September 2017 / Accepted: 16 November 2017 © Springer International Publishing AG, part of Springer Nature 2017
AbstractSolar photovoltaics (PV) is widely regarded as one of the most promising renewable energy technologies. Net energy analysis (NEA) is a tool to evaluate the energetic performance of all energy supply technologies, including solar PV. Results across studies can appear to diverge sharply, which leads to contestation of NEA’s relevance to energy transition feasibility assess-ment and contributes to ongoing uncertainty in relation to the critical issue of the sustainability of PV. This study explores how PV NEA approaches differ, including in relation to goal definitions, methodologies and boundaries of analysis. It focuses on two principal NEA metrics, energy return on investment (EROI) and energy payback time (EPBT). Here we show that most of the apparent divergence between studies is accounted for by six factors—life-cycle assessment methodology, age of the primary data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance. The apparent divergence in findings between studies can often be traced back to different goal definitions. This study reviews the differing approaches and makes the case that NEA is important for assess-ing the role of PV in future energy systems, but that findings in the form of EROI or EPBT must be considered with specific reference to the details of the particular study context, and the research questions that it seeks to address. NEA findings in a particular context cannot definitively support general statements about EROI or EPBT of PV electricity in all contexts.
Keywords Solar PV · EROI · EPBT · Net energy
Introduction
Summary
Net energy analysis (NEA) is a tool used to evaluate the energetic performance of energy supply technologies. The two net-energy metrics commonly applied to electricity generation technologies are the energy return on investment (EROI) and energy payback time (EPBT). EROI for electric-ity generation has been widely studied, especially coal-fired electricity, including carbon capture (Wu et al. 2016; White and Kulcinski 2000), wind power (Kubiszewski et al. 2010), solar photovoltaics (Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016) and gas-fired generation (Moeller and Murphy 2016). The boundaries and types of analysis vary between studies, but all those just cited adopt the electricity
busbar or inverter output as the EROI numerator—electricity distribution and management of the grid system as a whole is typically excluded from the analysis boundary.
Net energy analyses for solar photovoltaic (PV) systems have mostly conformed to mainstream life-cycle assessment (LCA) guidelines, with different values often assumed for key performance parameters, such as insolation and operat-ing life. Further reinforcement of a standard methodology was provided by the IEA PV Power Systems Programme (IEA-PVPS) guidelines (Frischknecht et al. 2016). Since the IEA-PVPS guidelines are seen by many investigators as the consensual outcome of debate over methodology, this study adopts them as its reference point for standard practice.
The principal benefit of standard guidelines is that they permit like-for-like comparison between different types of solar PV system (e.g. between systems employing different cell technologies), and assessment of the variance between different production contexts for systems of the same type. Many of the differences in findings between conventional analyses can be accounted for via meta-analyses that har-monise for key performance parameters (e.g. Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016).
* Graham Palmer [email protected]
1 Australian-German College of Climate and Energy, The University of Melbourne, Parkville 3010, Australia
2 The Rescope Project, Melbourne, Australia
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The limitation of a standardised methodology is that stud-ies are then restricted to answering the range of research questions to which that methodology is suited. An emphasis on improved harmonisation between studies may come at the cost of excluding energy investments that are important for understanding the broader socio-economic consequences of providing an increasing proportion of final energy supply via PV electricity. Furthermore, considerations relating to the engineering-systems view of electricity supply, which are critical to establishing the value of solar PV at higher grid penetration, are generally treated as lying outside the domain of conventional life-cycle research.
This study explores the differing approaches to under-stand why there is apparently such divergence in EROI find-ings. We show that much of the difference between stud-ies can be attributed to six factors—life-cycle assessment methodology, age of the primary data, PV cell technology, the treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance.
Definition of EROI and EPBT
EROI is a unitless ratio, defined as the ratio of the gross energy output over the operating lifetime for an energy supply system, and the sum of the energy for manufacture, construction, operation and maintenance, decommissioning and disposal/recycling over the system’s project life-cycle (Murphy et al. 2011, Eq. 1). Murphy and Hall (2010) state that ‘EROI is the ratio of how much energy is gained from an energy production process compared to how much of that energy (or its equivalent from some other source) is required to extract, grow, etc., a new unit of the energy in question.’ EPBT is the length of time, in years, for a PV system to generate the same amount of energy (in terms of primary energy equivalent) that was used to produce the sys-tem itself (Frischknecht et al. 2016, Sect. 3.4.2). The energy invested (embodied energy) is established by LCA. For a PV system, this is the same as the cumulative energy demand (CED). However, the CED of PV electricity differs markedly depending on whether the ‘energy harvested’ or the ‘energy harvestable’ concept is applied (see “Output Energy form Equivalence in the Context of PV NEA” section). Energy investment and output can be expressed in different forms depending on the goal of the study, as discussed in detail in “Equivalence of Investment and Output Energy Forms” section.
(1)EROI =Eout
Einv
(2)EPBT =Einv
Eoutyr
,
where Eout is the life-cycle electrical energy delivered by the PV system at the inverter output, Einv is the life-cycle energy investment and Eoutyr
is the annual primary energy equivalent
output (see “Equivalence of Investment and Output Energy Forms” section).
Goal Definitions
Defining LCA Goal and Scope
ISO 14040 (ISO 2006) sets out the requirements for LCA goal and scope definition. The scope definition reflects the underlying purpose of the LCA. Since EROI is defined as the ratio of ‘energy out’ to ‘energy investment’, the goal and scope may sometimes be interpreted as self-evident. Stud-ies published in the LCA-specific literature generally adopt more explicit goal and scope definitions than has been typi-cal for EROI-focused NEA studies.
Goal Definition in the Context of NEA
Carbajales-Dale et al. (2015) identified three applications of NEA with distinctly different goal definitions:
1. descriptive assessment of the viability of a particular technology (e.g., solar PV satellite);
2. comparative assessment of alternative energy technolo-gies; and
3. calculation of the (minimum) EROI to support an indus-trial society, or alternatively assessing the feasibility of some technology to (single-handedly) support an indus-trial society.
For the present study, we reframe and expand on these appli-cations and their respective goal definitions as follows:
1. Energy in, lifetime energy out. Most life-cycle-focused EROI analyses adopt the ‘basic net-energy’ goal defini-tion of ‘lifetime energy out’ versus ‘life-cycle energy in’. The functional unit is typically 1 kWh (or alternatively, 1 MJ) of AC electricity delivered to the grid. Further-more, most adopt a process-based LCA methodology, which focuses on the energy-intensive production pro-cesses. Within this broad category, there are slightly different approaches to boundaries. System-level con-siderations, such as the functional role of PV within an electricity grid treated as a whole, lie outside of the scope. Examples: Fthenakis and Kim (2011), Alsema (2000), Leccisi et al. (2016).
2. Energy in, dispatchable equivalent out. Considers an expanded role beyond the lifetime electricity generation. Includes PV overbuild and storage to provide an equiva-
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lent role to dispatchable generation such as gas turbine or hydro. Does not consider system-level requirements within a broader suite of generation, including geo-graphic and technology diversity. Defining the mag-nitude of PV overbuild and storage that provides an ‘always available’ role is fraught, and understates the value of PV in other contexts. Example: Weißbach et al. (2013), see also Weißbach et al. (2014), Raugei (2013), Raugei et al. (2015).
3. Comparative assessment in relation to substitution of generation capacity. Considers an expanded role beyond the lifetime electricity generation. Includes the embod-ied energy of storage and solar PV overbuild. Consid-ers the value of generated electricity and the degree to which solar PV substitutes for generation power capac-ity. The functional unit is 1 kWh of AC electricity deliv-ered to the grid, but concern for the energy cost of 1 kW of supply capacity is implicit in the study context. Permits a trade-off between storage capacity and power capacity substitution. Considers the role of solar PV within a suite of different generation types. Requires technical and reliability analysis of electricity systems and geographic and technology diversity. Examples: Palmer (2013, 2017).
4. Comparative assessment in relation to fossil-fuel con-sumption. Compares the life-cycle embodied energy with the fossil fuels displaced over the lifetime of the PV system. The focus is on the substitution of fuels rather than substitution of capital infrastructure. The functional unit is 1 kWh of AC electricity delivered to the grid, though this is implicitly a proxy for an equivalent quan-tity of fossil fuel displaced from conventional thermal generation. No weighting applied to energy out on the basis of system-level considerations. Examples: Raugei et al. (2012), Dale and Benson (2013).
5. Comparative assessment in relation to greenhouse gas emissions. The greenhouse gas emissions embodied in the manufacture of solar PV systems are estimated using life-cycle inventories. Most greenhouse-focused analy-ses adopt a similar framework to approach (1). Func-tional unit is CO2-equivalent emissions per kWh, with no weighting of lifetime energy out. Example: Nugent and Sovacool (2014).
6. Comparative assessment in relation to substituting for all primary energy. A conceptually and technically demand-ing goal definition, based on the functional unit of 1 MJ of final energy service (e.g. work or heat of various forms, or some mix of these) delivered to the ‘rest of the economy’ by the overall economy’s energy supply sub-system. Hypothesises the substitution of solar PV electricity for incumbent fuels and their associated sup-ply infrastructure. May include, for example, considera-tion of solar PV electricity as a transport fuel including
conversion to liquid fuels, and possibly broader electri-fication of final energy uses currently reliant on direct use of liquid, gaseous or solid fuels.
Analysis Boundaries
Figure 1 provides a conceptual overview of the range of boundary definitions used across PV NEA studies. Boundary definition varies depending on study goals. As such, it could be considered as a ‘meta-factor’ in terms of its implications for the apparent divergence in study findings. However, in terms of the range of factors identified in this study, the implications of boundary definition flow through directly to (i) the selection of life-cycle assessment methodology; and (ii) the treatment of intermittency (if this is considered at all). We therefore consider the effects of boundary definition specifically through the consequences for these two factors, rather than treating analysis boundaries as a separate factor in its own right. That said, there is no essential relationship between the analysis boundary and LCA methodology. Stud-ies with the same boundary could employ different method-ologies, and studies with different boundaries could employ the same methodology. As such, recognising that analysis boundaries differ between studies for legitimate reasons, and taking into account the specific boundary employed in any given study, is essential for accurately interpreting NEA study findings.
In this study, the ‘Level 2’ boundary from Raugei et al. (2016, Sect. 3.4.4) is adopted as the conventional frame of reference with which to compare alternative definitions. This boundary may be defined either as cradle-to-gate or cradle-to-installation, capturing the most important direct energy inputs of the solar PV panel manufacturing process chain, including the related ‘balance of system’ (BOS) com-ponents, comprising inverter, wiring and support structure, and possibly end-of-life energy inputs. This study focuses on crystalline silicon PV cell technologies because they com-prise ∼ 94% of global production (IEA 2016b, p. 5).
Detailed Investigation of Factors Contributing to Divergence
Life‑Cycle Assessment Methodologies
Process‑Based LCA
The most commonly adopted LCA technique, and that rec-ommended by the IEA-PVPS Programme, is attributional, process-based life-cycle assessment (ALCA) (Frischknecht et al. 2016, p. 6). Attributional approaches contrast with con-sequential approaches. “Attributional Versus Consequential
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LCA” section discusses the distinction between these, and the contexts for which each is most relevant.
In process analysis, the focus is on identifying the most energy-intensive stages in the process chain. In the case of crystalline silicon, the direct energy inputs for the major pro-duction processes are shown in Table 1. For example, silica sand undergoes carbothermic reduction, driven by heat and reducing agents in electric arc furnaces, to produce metal-lurgical grade silicon, consuming 11 kWh electricity per kg of product. The metallurgical grade silicon is processed via the Siemens process to produce PV grade silicon, and so on.
Alternative pathways are also available, including the Elkem pathway, which bypasses the Siemens process (Glöckner and de Wild-Scholten 2012).
The researcher steps through the process chain, identify-ing the process-specific data. Firm-level data are required but are often hard to acquire—a key objective of IEA-PVPS ‘Task 12’ is to gather and compile life-cycle inventory data (Frischknecht et al. 2015a). In the case of PV modules, each of the processes is based on the primary flow of wafer-based materials, but differences in process implementation and operational characteristics from firm to firm will result in
Silica sand
Metallurgical grade Si
Si - production mix
Czochralski- mc-Si
Solar grade Si - modified Siemens
process
Electronic grade Si
Multi-Si Single -Si
Multi-Si PV Single -Si PV cell
Multi-Si PV panel/
Single -Si PV cell
Aluminium, glass, EVA, copper, other
electricity
electricity,wood chips
coke, charcoal
electricity, natural gas
Indi
rect
ene
rgy
&
capi
tal g
oods
IEA-PVPS boundary
Inverter, etc.
PV system hardware
Admin
Insurance
Network integration
Faulty modules, inverters, tracking
OPERATION & MAINTENANCE
Operation & maintenance
Module washing
CONSTRUCTION
Inverter/busbar output
Accesses, fences
Foundations for mounts
Security & surveillance
PV Integration
electricity
electricity electricity
electricity electricity
electricity
Rights-of-way
electricityelectricity
electricity
Trades & fairs
INSTALLATION
ADMIN.
direct & indirect energy direct & indirect energy
electricity, natural gas
Electrical network extension
Offices, HVAC
Vehicles
direct & indirect energy
UP
ST
RE
AM
TR
UN
CA
TIO
N
SIDEWAYS TRUNCATION
Electrical storage
Curtailment
Thermal cycling losses
Force majeure
Faulty panels, inverters, trackers
Capacity firming
Lifetime generation
Availability- adjusted output
direct & indirect energy
DO
WN
ST
RE
AM
TR
UN
CA
TIO
N
Installation
Tran
spor
t
diesel, bunker fuel
Installed PV system
Distribution network
capacity = ƒ(storage)
direct & indirect energy
Degradation
IEA Performance Ratio
Energy losses
END-OF-LIFE
Decommissioning
Disposal
Reduction in assumed output
Complete boundary with input-output/hybrid analysis (e.g. Yao, Zhai)
IFIA
S le
vel 3
- p
lant
Note: new Elkem process
Complete boundary including integration & storage
Cradle to gate boundary (Bhandari et al.)
Broader boundaries for installation and administration (Prieto & Hall)
+2 to 5 %
Extended booundary may include 30 to 50%
of embodied energy
Distribution losses
Aggregation error - potential for overestimating embodied energy
with I/O
Storage
Cradle to installation boundary
Delivered electricity
Fig. 1 Definition of boundaries for this study for solar PV life-cycle assessments. Green coloured regions indicate conventional boundaries rec-ommended by the IEA-PVPS Programme. Size of regions not related to magnitude of energy investment
Table 1 Direct energy inputs for major production processes, for mono- and multi-crystalline silicon wafers Source Frischknecht et al. (2015a)
Energy inputs vary depending on region, and change with technology development and production learning
Process input Process output Major process Major process energy inputs
Silica sand Metallurgical grade silicon (MG-Si) Electric arc furnace at 2000–2200 °C 11 kWh electricity per kg MG-SiMG-Si PV grade silicon (SoG-Si) Siemens process 110 kWh electricity and 185 MJ
natural gas per kg SoG-SiSoG-Si Mono-Si Czochralski process (mono-Si) 68 kWh electricity per kg mono-SiSoG-Si Multi-Si Casting and crystallisation process (multi-Si) 56 kWh electricity per kg multi-SiMono- or poly-Si Single wafers Wafering process 75–93 kWh electricity per m2 Si
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variations in material and energy flows at a more detailed level. Depending on time and effort, the researcher contin-ues with a detailed assessment for each process. Ideally, all flows should be followed until they are elementary—i.e. to the boundary between the technical system and the natural system (Tillman 2000). However, since the number of con-nections in the ‘energy flow web’ rapidly accumulates as the researcher proceeds through the upstream processes, there is a practical need to ‘prune’ sub-branches that are deemed insignificant (Suh and Huppes 2002). Furthermore, the importance of each energy flow rapidly diminishes through indirect relations, requiring greater effort for diminishing significance.
Truncation Error
The practical necessity to adopt a finite boundary leads to the omission of contributions that lie outside this bound-ary. The magnitude of these contributions is termed trunca-tion error. There are broadly three types of truncation error (Crawford 2011):
1. Upstream truncation. This includes higher-order (or background) processes, such as products further up the value chain, or capital goods, including production equipment. PV studies often include an allowance for ‘capital plant’, but process-based analyses do not com-prehensively account for capital goods.
2. Sideways truncation. This includes the omission of minor goods or services that are not part of the main process chain, such as inputs associated with office administrative costs. These costs are generally of low energy intensity and comprise a small (but in aggregate, material) proportion of the overall energy footprint. The IEA-PVPS LCA guidelines (Frischknecht et al. 2016, Sect. 3.2.3) recommend against including administra-tion, marketing, and research and development. Prieto and Hall (2013) investigated such costs, including plant accesses, administration, insurances and promotions among others, and found them to account for a signifi-cant proportion of their total inputs.
3. Downstream truncation. This is usually defined as the exclusion of processes in the ‘use’ and ‘end-of-life’ phase. In this study, ‘downstream’ is defined as the additional inputs that lie beyond the busbar or inverter. These are explored further in “Treatment of Intermit-tency” section.
Energy-intensive products generally carry the lowest trunca-tion error since most of the energy investment is embodied in a limited number of direct and first-order inputs. Services exhibit higher truncation error because much of the energy footprint is in higher-order (or background) paths. Lenzen
(2000) notes that most goods carry a truncation error of the order of 50%.
Level of ‘Completeness’
Since the ISO 14041 standard for goal and scope defini-tion does not define system boundaries as absolute, but as dependent on the goal of the study (Lenzen 2000), there is no requirement to ensure that the analysis meets a pre-scribed level of ‘completeness’. ISO (1998, Sect. 6.4.5; 2006, Sect. 5.2.3) requires that stages, unit processes or inputs are followed until they ‘lack significance’ within the given scope. This can be problematic when applied to NEA since a high level of ‘completeness’ is often assumed by NEA practitioners applying LCA data.
Benefits of Standard Boundaries and Methodology
The consistent treatment of system boundaries is useful for comparison of findings between studies with similar goals. For example, Bhandari et al. (2015) collected 232 refer-ences for PV studies published between 2000 and 2013, the vast majority of which adopted conventional LCA-based boundaries. This biases meta-analyses towards conventional boundaries.
If all products within a product class are used within a similar context, the truncation associated with defined boundaries is less important than establishing the differences between products. For example, in considering the life-cycle differences between timber and concrete railway sleepers, it may not be necessary to consider the life-cycle energy of the steel tracks or installation, since these are common to both types of sleepers. However, if the study goal was to com-pare rail freight to road freight, then much wider boundaries would be required.
The level of completeness need not be a limitation pro-vided the goal definition is stated clearly and results are presented with appropriate qualifications. However, results that have been obtained with a process-based framework are often presented as though they represent a comprehensive inventory of all energy investments. The common use of expressions such as ‘cradle-to-grave’ and ‘full life-cycle’ implies a high degree of completeness, but is only accurate in the context of the process-based methodology.
Attributional Versus Consequential LCA
The objective of ALCA is to track energy and material flows using a bottom-up accounting approach, for the purpose of attributing energy and material quantities to a unit of product or service delivered at the analysis boundary (Tillman 2000). This allows comparison of functionally equivalent products and services on the basis of embodied energy, materials and
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pollutants. Consequential life-cycle assessment (CLCA), on the other hand, involves estimating how flows to and from the environment would be affected by the adoption of par-ticular products or services, or the substitution of alternative products or services for those currently in use. This requires broader boundaries, which may overlap with other LCAs and potentially lead to double counting of energy inputs. The IEA-PVPS LCA guidelines recommend a system bound-ary ending at the inverter output (Frischknecht et al. 2016, Sect. 3.2.2). The marginal or consequential changes in net-work costs due to intermittency are not generally included (Jones et al. 2016, Sect. 3.1.2). The guidelines argue that ‘aspects of dispatchability or intermittency’ should be addressed at a system level rather than at a technology level (Frischknecht et al. 2016, p. 9). The third edition of the IEA-PVPS LCA methodological guidelines has maintained its recommendation of process-based ALCA, but has suggested that a consequential approach should be adopted for inves-tigating a large-scale, long-term energy supply transition (Frischknecht et al. 2016, Sect. 3.2.1.d).
Input–Output and Hybrid LCA
An alternative to the process-based approach is the use of economic input–output (I/O) tables with a satellite energy account, classified as environmentally extended input–out-put analysis (EEIOA) (Suh and Huppes 2005). I/O analysis connects energy flows to monetary flows using national I/O tables, which are a comprehensive account of national mon-etary flows. Analyses can be expanded with multi-region I/O tables to account for imports and exports. Since financial data are usually more commonly available than energy-based data, they enable researchers to access industry informa-tion that may otherwise be difficult to obtain. Furthermore, EEIOA is systematically complete—all energy within the region/s of the analysis is included. The main weakness is that I/O tables combine products that are heterogeneous in terms of energy inputs, introducing aggregation error. Other weaknesses of I/O analysis include excessive age of data, inconsistent classification schemes and inadequate docu-mentation (UNEP/SETAC 2011).
The respective benefits of process and I/O analysis—spec-ificity for the former and completeness for the latter—can
be combined through the use of hybrid analysis. There are three broad types of hybrid analysis (Suh and Huppes 2005):
1. tiered-hybrid approach;2. IO-based hybrid approach; and3. integrated hybrid approach.
In a tiered-hybrid approach for example, some important direct requirements are examined with a detailed process analysis, while higher-order requirements that are less easily tracked are covered with an I/O analysis (Crawford 2011, p. 53). In the PV NEA literature, very few studies have adopted a hybrid approach, and for those that do, embod-ied energy values significantly higher than for comparable process-based analyses are calculated—see Table 2.
We note here that since I/O analysis uses financial costs as the basis for determining energy inputs, results are sensi-tive to the energy intensities attributed to different cost com-ponents. It is sometimes assumed that embodied energy and PV prices (and by inference, the installed cost incurred by PV plant owners) should exhibit a strong positive correlation (Bhandari et al. 2015, p. 140). However, differences between financial costs incurred in manufacture, and prices paid by owners, can lead to anomalous results.
Several factors have contributed to declines in prices that do not reflect corresponding reductions in embodied energy. ‘Soft costs’ are defined as the costs associated with regula-tion and compliance (IEA 2016b, pp. 43, 57), and comprise from around 10% of system costs (e.g. Spain), up to around 60% (e.g. the US and Canada) (IEA 2016b, Fig. 26). These costs relate to low energy intensity administrative functions, which are independent of the PV production system and its energy investments. The greater the contribution that soft costs make to overall price reduction over time, the weaker the correlation will be between installed prices and embod-ied energy. As such, declining cost of ownership may be a poor proxy for embodied energy reduction.
A similar issue arises in relation to the effect of market distortions on PV system price. For instance, overcapacity in Chinese PV production has led to dumping in several markets, starting from around 2011 (European Commis-sion 2017, Sects. 4.7, 3.3.2). Some jurisdictions, including the EU, implemented anti-dumping measures (European
Table 2 Comparison of hybrid versus process-based LCA for the same or comparable projects
Study Hybrid LCA Process-based LCA
Building integrated photovoltaic systems Crawford et al. (2006, Table 1) 41.0 GJ (total) 18.4 GJ (total)
Major Chinese manufacturers Yao et al. (2014, Table 3) 4.7-year EPBT 1.5–2.6-year EPBT
Multi-Si PV System in 2007 Zhai and Williams (2010, Table 7) 4.4 GJ/m2 2.7 GJ/m2
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Commission 2017, Sect. 1.1). The further markets deviate from the ideal of perfect competitive behaviour, the weaker the correlation is likely to be between price and embodied energy. Comparison of experience curves for price and CED makes apparent the overall effect of considerations such as these—Louwen et al. (2016, Fig. 3) show a learning rate of 20% for selling price and around 12% for CED. If an LCA methodology makes use of an assumed relationship between cost of ownership and embodied energy in order to calculate energy inputs, different assumptions about the strength of any correlation would contribute to divergence in values for EROI and EPBT.
Primacy of Precision or Comprehensiveness Depends on Study Context
Sonnemann et al. (2013, p. 1171) differentiate between ‘traditional’ process-based data and ‘adaptive’ approaches, including I/O and hybrid. While the I/O and hybrid approaches are recognised in the IEA-PVPS LCA guidelines (Frischknecht et al. 2016, p. 6), the guidelines recommend against the I/O approach due to a lack of confidence, citing Sonnemann et al. (2013) and UNEP/SETAC (2011). How-ever, UNEP/SETAC (2011, p. 97) note that ‘LCAs should use the most appropriate datasets and modelling approaches to meet the specific goal and scope required to satisfactorily answer the questions posed.’
The IEA-PVPS LCA guidelines endorse the ‘traditional’ process-based approach, emphasising the primacy of data-sets based on ‘complete and verifiable documentation’ (Sonnemann et al. 2013, p. 1170). Furthermore, process
analysis is generally seen to be more accurate (within the given scope) and relevant. A counter argument here is that when the context for NEA is feasibility assessment for large-scale energy transition, providing a comprehensive account of the situation from a net-energy perspective may be a higher priority than data precision, especially if precision necessarily comes at the cost of narrowing the focus for data collection.
Age of Primary Data
Solar PV is a developing technology. Various manufacturing improvements have led to lower energy intensity produc-tion, including processing of metallurgical grade silicon to solar grade silicon, and more productive wafering processes. Louwen et al. (2016, Fig. 3) estimated a CED learning rate (per doubling of cumulative capacity) of between 11 and 13%. NEA findings are particularly sensitive to increases in energy output due to improvement in PV cell efficiency over time (Fthenakis, personal communication, 2 February 2017).
Since published studies utilise a mix of primary and sec-ondary data, the publication date may not reflect the age of the primary data. In some cases, studies cite secondary sources that themselves cited earlier primary data. For exam-ple, Ferroni and Hopkirk (2016) cited Kannan et al. (2006), who had adopted primary data from studies from 1997 and 2002 (i.e. the primary data were 14–19 years old at the time of publication). The use of older data (see Fig. 2) confounds the age-adjustment process for PV EROI and EPBT meta-analyses, unless the primary data sources are traced and adjusted accordingly (Koppelaar 2016).
Fig. 2 Reported cumulative energy demand (CED) data by year. Note logarithmic y-axis. Data from Louwen et al. (2016)
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Of the factors considered in this study, this stands out as potentially contributing to actual rather than apparent divergence in NEA findings, where studies are otherwise equivalent. If the purpose of a study is to consider present or future PV deployment, then the use of earlier data will lead to errors. If the purpose of a study is to investigate historical performance, then this may require the intentional selection of older data.
PV Cell Technologies
This study focuses on crystalline silicon (mono-Si, multi-Si) cell technologies, which account for ∼ 94% of global production (IEA 2016b, p. 5). Most of the remainder com-prises thin-film technologies, including amorphous silicon, copper indium gallium selenide (CIGS) and cadmium tellu-ride (CdTe). The so-called III–V semiconductor compounds with gallium arsenide (GaAs) or similar substrates allow production of high-efficiency multi-layer cells, but are cur-rently used in niche applications only. Process analyses for thin-film technologies calculate an EPBT of around half that of crystalline Si (Ito et al. 2016; Fthenakis and Kim 2011). Findings from a meta-study by Bhandari et al. (2015, Fig. 7) indicate mean harmonised PV module plus BOS EROI (fac-tory-gate boundary) for multi-Si approximately 33% higher than for mono-Si.
Treatment of Intermittency
A significant area of difference in NEA goal definition is the treatment of output power quality, specifically the dif-ference between PV electricity when an inverter bound-ary is assumed, and electricity from dispatchable sources. Since most studies consider the lifetime aggregate energy output only, issues associated with real-time electrical power characteristics are not usually assessed. If such issues are considered in the goal definition though, this can contribute significantly to the apparent divergence in findings.
Defining Reliability
The reliability of large electrical systems is defined by the loss-of-load-expectation (LOLE), which is the primary relia-bility metric for generation adequacy planning (NERC 2011; OFGEM 2013). The value of the LOLE metric for a given grid is prescribed based on priorities that are particular to the prevailing socio-political-economic values for the terri-tory in question. A developing country may place much less value on a high level of reliability than a developed country, depending on electricity consumers’ needs and expectations.
The LOLE is mostly a function of two factors: (i) the ‘availability factor’ of individual generators; and (ii) the pro-jected demand function of the power system. The availability
factor is defined as the inverse of the probability of a forced outage in a given period (Billinton and Allan 1996, Chap. 11). No single generator is completely reliable, but since forced outages are usually uncorrelated between generat-ing units (i.e. the distribution functions are independent random variables), the system reliability converges asymp-totically towards 100% with a large enough number of gen-erators. Within this conventional framework, the role of energy storage, such as pumped hydro storage (PHS), is to arbitrage between low-cost overnight baseload supply and higher-value peak load supply. The value of PHS is there-fore determined by the economics of arbitrage (Yang and Jackson 2011).
In contrast to thermal and hydro generators, PV output exhibits very strong correlation across geographic regions (i.e. it is simultaneously either day-time or night-time eve-rywhere across a region), and therefore individual PV sys-tems cannot be modelled as independent random variables. Instead, since the reliability contribution of PV is domi-nated by the correlation between PV output and demand on the peak-demand days, PV is often modelled as a demand reducer (Preston 2015b). The potential role of demand man-agement becomes more apparent in this context, since the value of PV is also dependant on the potential to voluntarily shed load or time-shift it to periods of high insolation.
The question of whether or not storage should be defined as falling within the PV system boundary arises because PV systems can be designed to exhibit an availability compara-ble with conventional generation when sufficient storage and PV overbuild is deployed. The issue then is not, as Leccisi et al. (2016, p. 4) suggest, whether a single generator can ‘single-handedly follow the dynamics of societal electricity demand’, but how PV, whether considered at the scale of individual systems in isolation, or collectively at the grid-scale, can contribute to system reliability.
Capacity Firming and Storage to Accommodate Temporal and Spatial Output Variability
Solar PV exhibits variability over timescales ranging from seconds to seasons, and at local, regional and national spatial scales (Sayeef et al. 2012). At a regional level, geographic diversity smooths short-term cloud flicker, generally reduc-ing aggregate output variability from all PV systems in a given region. However, weather systems can sometimes extend for thousands of kilometres, reducing output across entire regions (Huva et al. 2016; Sayeef et al. 2012). Sun-rise and sunset are each effectively co-incident for different locations at the regional scale. This results in almost simul-taneous diurnal ramp-up and ramp-down of output from optimally oriented PV modules across regions.
On a seasonal timescale, the ratio of the average monthly insolation between summer and winter varies greatly across
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geographic regions and latitudes (NASA 2017; PV Educa-tion 2016). Low-latitude regions, such as Singapore, show a relatively small summer-to-winter ratio of 1.3–1.7, rising to 3–4 in the mid-latitudes such as Nevada, USA, and above 10 for higher latitudes, such as London. Latitude is the primary determinant of the seasonal ratio, but regional climate fac-tors, including the cloud index, are also important.
At low penetration, variable output from solar PV is read-ily integrated into electricity systems (Gross et al. 2006). At higher penetration, maintaining system reliability is more challenging and costly, and, in addition to demand manage-ment, will require complementary flexible generation and storage (Sims et al. 2011, pp. 15–16).
Defining Integration Costs
Integration costs are usually defined as additional expenses associated with load following, the provision of ancillary services and curtailment (Kirby et al. 2003; Heptonstall et al. 2017). In most cases, the additional costs are small at low solar PV penetration, but will be material at a higher pen-etration; however, the relationship between penetration and integration costs is highly context specific (Heptonstall et al. 2017, Sect. 2.3).
Integration costs can also be conceptualised more broadly as the additional buffering, connection and ancillary service costs necessary for PV to provide a substantive role in a transition from fossil fuel to low-carbon electricity supply. The physical characteristics of conventional synchronous generators, including inertia and reactive power response, have provided some ancillary services by default, and so these have traditionally been uncosted. The retirement of synchronous generators will reduce the contribution of these uncosted services. Since solar PV and wind are asyn-chronous, the substitution of these electricity sources for synchronous generation will require some form of market mechanism or ‘security obligation’ to ensure that equivalent ancillary services are provided with rising variable renew-able energy (VRE) penetration (Finkel et al. 2017, recom-mendations 2.1, 3.3). Additional energy inputs associated with providing ancillary services by alternative means need to be included in NEA where study goals relate to large-scale energy transition.
Transmission Infrastructure
Transmission infrastructure consists of meshed networks that are shared by multiple supply and demand nodes. As such, high-voltage transmission infrastructure is usually assumed to lie outside the study boundary for electricity generation NEA [for exceptions, see Ito et al. (2008, 2016)].
Sometimes, however, dedicated transmission network extensions are required where new generation assets
(whether solar PV or otherwise) are situated geographically outside the existing grid boundary. Many of the most favour-able locations for solar PV lie in sparsely inhabited regions. For example, Ito et al. (2008) assumed that 100 km of trans-mission lines would be required to connect a 100-MW PV system to existing transmission, and found that transmis-sion infrastructure comprised between ∼ 10 and 15% of system CED over the lifetime of the project (Ito et al. 2008, Table 8). Furthermore, Ito et al. (2005) estimated losses due to transformer, reactive power compensation and transmis-sion line losses of 5.8–8.2% for a 100 km line in a hot desert.
In such situations, it is legitimate to ask whether the dedi-cated transmission infrastructure should be treated as falling inside the generation NEA boundary. LCA studies are usu-ally seeking to answer a narrower question than that posed by NEA studies, and therefore the question is resolved by simply stating the scope. However, if the goal of the NEA study is to assess the feasibility of a large-scale energy transition, then the additional transmission costs must be accounted for somewhere, whether attached to a genera-tion asset or considered as part of the broader system-level changes. The lifetimes of transmission assets are usually longer than generation assets. If included within the genera-tion boundary, this would require allocating the transmission CED across multiple PV lifetimes, mitigating the impact on EROI and EPBT.
High‑Penetration PV and Storage Scenarios
An estimate for the quantity of storage required to meet a supply–demand balance for a given period can be derived from studies with a high penetration of solar PV. However, much of the electricity system scenario literature avoids the problem of large-scale storage by maintaining a significant share of legacy thermal generation capacity at low capacity factor (Budischak et al. 2012), or by assuming the ready availability of large-scale biomass-fuelled thermal genera-tion (Lenzen et al. 2016). In the context of energy transi-tion feasibility assessment, we note that studies that reduce emissions while retaining legacy generation capacity involve fundamentally different goals to those focused on transition to 100% renewable electricity supply.
For scenarios where conventional thermal generation capacity is not retained (and where this is not replaced by biomass generation), the storage capacity required for a given level of reliability escalates rapidly with increasing VRE penetration, exhibiting a sharply diminishing return in terms of the reliability outcome obtained for each unit of energy investment (Palmer 2017). This is mostly due to the ‘big gaps’ problem of extended cloudy periods during winter (Lenzen et al. 2016). More generally, the shift from an electricity system based on ‘stored sunlight’ (i.e. fossil fuels) to the one based mostly on uncontrollably variable
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energy flows is constrained by the storage capacity required and the quantity of VRE overbuild necessary to maintain energy stores at adequate levels to ride through low insola-tion periods. Sufficient generation capacity overbuild can substitute to some extent for inter-seasonal variation in PV output. Regardless of storage medium though, in the absence of backup thermal generation capacity, for 100% renewable electricity supply with high PV penetration, some combina-tion of capacity overbuild and large-scale storage sufficient for inter-seasonal balancing of generation and demand is likely to be essential.
For example, in a 100% renewable simulation with wind and solar PV for Germany, 45 days of full-load electric-ity supply capacity (based on average annual demand) was required (Palzer and Henning 2014, Fig. 3.4). In a study encompassing Canada, USA and Mexico, Aghahosseini et al. (2016) found that around 14 days of supply capacity was required. Both of these studies assumed power-to-gas for inter-seasonal storage. Preston (2015a) modelled a set of wind, solar and storage scenarios for Texas, finding that 21 days of supply capacity was required.
Effect of Storage and Overbuild on NEA Findings
There is no single ‘best answer’ to the appropriate quantity of storage (if any) and solar PV overbuild. However, it is possible to state some general principles. Summer peaking grids benefit much more from solar PV than winter peaking grids, and a wide latitudinal diversity improves the value of solar PV when interconnected between regions. Relatively modest storage can improve the value of solar PV in some contexts, such as systems that have high air conditioning loads in summer. Palmer (2017) formulated a framework for calibrating EROI based on the generation capacity displaced, which provided a method to trade-off storage capacity versus the value it provided in a specific context. The low capacity factor and seasonality of PV results in strongly diminish-ing return with increasing solar PV penetration (i.e. the first units are the most valuable but as penetration increases, the marginal value of adding further capacity decreases). In win-ter peaking grids, the role of PV is restricted to displacing fuel consumption of thermal generators, rather than displac-ing their contribution to the generating capacity required by the overall system, unless inter-seasonal storage and/or a very high level of PV overbuild is implemented.
With reference to the goal definitions in “Goal Defini-tions” section, different study contexts explore different questions, and will arrive at different answers in relation to the quantity of storage required. Approaches (1), (4) and (5) do not consider system-level implications at all, and therefore do not consider storage. Approach (2) specifically assumes that ‘PV plus storage’ substitutes for dispatch-able generation, and therefore requires solar overbuild and
substantial storage. Weißbach et al. (2013, p. 213) adopted a 2-time solar PV overbuild, resulting in a halving of EROI. The EROI was reduced by a further ∼ 20 % due to the inclu-sion of pumped hydro energy storage.
Approach (3) takes the incumbent system as given and assumes that a supply system transition proceeds by incre-mentally substituting solar PV and storage for conventional generation capacity. For example, in summer peaking grids, at low PV penetration 2 h of battery storage without solar overbuild improves the network value of PV, but reduces the EROI by a modest 15% (based on data from Palmer 2017, Table 2). But at near 100% wind and solar penetration, the EROI of the last unit of solar PV with battery storage is reduced by 98.8% due to the problem of diminishing returns (Palmer 2017, Fig. 7).
Approach (6) considers the complete substitution of the fossil-fuel energy system by renewable sources, including for transport. As such, a range of alternative storage media may be considered, including liquid and gaseous fuels, with NEA accounting for the attendant conversion losses.
Equivalence of Investment and Output Energy Forms
Primary Energy Basis for LCA Energy Input Accounting
For all LCA methodologies, it is conventional to account for each energy input in terms of the primary energy required to make it available at the point where it enters the analysis boundary. In the broadest physical sense, primary energy is considered to be energy that is available from resources as they exist in nature: chemical energy of fossil fuels, gravita-tional potential energy of water in a reservoir, electromag-netic energy of sunlight, etc. (Nakicenovic et al. 1996). For the purpose of accounting for energy supply and use at the national and global level, the primary energy value of an energy source is typically treated as its ‘physical energy content’ at the point where it first becomes an economically useful ‘energy product’ suitable for multiple downstream purposes (IEA 2017). With combustible fuels in the form of wood, biomass and, most significantly, fossil fuels domi-nating energy trade, by convention, this type of high-level energy accounting has been recorded in thermal energy units, including BTUs or joules. For the purpose of most LCA studies on the other hand, the primary energy input from a given source is defined as that source’s life-cycle CED (Frischknecht et al. 2007b, 2015b).
Since electricity is a secondary energy carrier, each elec-tricity input is adjusted to account for the primary energy required for its supply. For instance, if a particular process in the PV production chain requires 1 kWh of electricity sourced from the grid at the point of manufacture, then this is accounted for as 1∕�grid kWh of primary energy, where
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�grid is the grid-average life-cycle efficiency. This is calcu-lated as the ratio of the annual electrical energy supplied to the total primary energy (renewable and non-renewable) har-vested from the environment for the operation of the grid in the same year (Raugei et al. 2016, p. 8). Where combustion-based thermal generation dominates supply, the life-cycle efficiency is slightly lower than the grid-average thermal efficiency (the ratio of electricity delivered to heating value of all fuel used) since combustion fuels constitute most of the life-cycle primary energy. However, grids composed of greater shares of nuclear and/or renewables may produce a markedly different grid life-cycle efficiency depending on the approach for determining the energy resource inputs.
Output Energy form Equivalence in the Context of PV NEA
While primary energy input accounting is a standard con-vention across LCA methodologies, there is no universal standard for establishing the energy content equivalence of different energy sources. Within the LCA literature, vari-ous conventions are followed for assigning primary energy values to different final energy carriers (Frischknecht et al. 2007b, pp. 31–32). In the case of solar PV, for which the natural resource is unlimited on a human time scale, but for which the conversion efficiency is low, it may be appropriate to register the amount of energy harvested rather than the amount of solar radiation ultimately required (Frischknecht et al. 2007a). Ecoinvent, a widely applied LCA databases, defines the energy harvested by a solar panel as equal to the electrical energy transmitted from the panel to the inverter (Frischknecht et al. 2007b, Sect. 2.2.1). However, several other LCA databases apply the ‘energy harvestable’ concept to solar PV, for which the renewable energy input is the amount of solar energy needed to produce the electricity generated by the PV module (Frischknecht et al. 2015b). From a LCA perspective, the most important considera-tion is to clearly state the methodology and assumptions for a given study context. However, where the study context relates to NEA, there is a potential for misalignment between NEA study goals and standard LCA practice.
Among the major agencies that report on national and global energy statistics, there are two conventions for deter-mining the primary energy equivalence of non-thermal renewables (Lightfoot 2007, Sect. 1.A.3; Grubler et al. 2012). A ‘substitution method’ is used by BP, EIA, IIASA and WEC. This is based on thermal energy equivalence and considers the fossil fuels displaced by renewables, giv-ing a conversion of 1 MJ PV electricity equals ∼ 3 MJ pri-mary energy. The ‘direct equivalent’ method is used by the UN, IEA and Eurostat. It adopts a one-to-one equivalence between electricity and primary energy (i.e. 1 MJ electric-ity equals 1 MJ primary energy). Within the LCA literature, no method corresponds exactly with the ‘direct equivalent’
method. However, the ‘energy harvested’ method used in LCA for determining the primary energy for PV electricity returns a numerically similar result.
Given that the output from solar PV systems is typically in the form of AC electricity delivered to grids supplied by a range of generation types, questions naturally arise in relation to how this output should be treated in terms of its equivalence to other energy sources. There are two distinct contexts in which questions relating to the energy content equivalence of the electricity output from PV can be consid-ered. The first relates to its equivalence to other electricity sources within the context of the grid for which a PV system is deployed. The second relates to the output electricity’s equivalence to the full range of energy sources required for manufacture and deployment of the PV system itself, and hence that are accounted for as energy inputs for the purpose of NEA. This includes a range of liquid and solid fuels, and electricity from grids other than that for which the PV sys-tem is deployed. This second context is discussed further at the end of this section.
In relation to equivalence questions arising in the first of these contexts, the IEA-PVPS Programme recognises two principal approaches: (i) accounting for the output in terms of the physical energy content of the electrical energy delivered; and (ii) accounting for the output in terms of the equivalent primary energy for the grid to which the PV system is connected (Raugei et al. 2016, p. 5). In the context of grids where combustion-based thermal genera-tion dominates, the second approach returns numerically similar results to the ‘substitution method’ described above in relation to conventions used by energy reporting agen-cies. It is, however, a methodologically distinct approach, and results diverge as the contribution of non-combustion generation sources increases. The IEA-PVPS Programme recommends the second approach, on the basis of what it describes as a ‘replacement logic’, where a unit of electric-ity from any source is considered equivalent to the primary energy required to produce a unit of electricity from the overall mix of sources for the grid in question (Raugei et al. 2016, p. 5). Where NEA indices are calculated on this basis, their values must be viewed as deployment-context specific, and findings interpreted accordingly.
In the PV NEA literature more broadly, three approaches to treating the equivalence of investment and output energy forms are recognised, as shown in Table 3. The IEA-PVPS Programme’s recommended method is shown as Method 1 (Frischknecht et al. 2016, pp. 16–17). Methods 1 and 2 are both forms of the ‘substitution method’ and give essentially the same result. Method 3 is the most common method in the wind power EROI literature (e.g. Kubiszewski et al. 2010), and is the alternative method recognised by the IEA-PVPS Programme (Raugei et al. 2016, Eq. 2, p. 8). It is also widely adopted in the PV EROI literature. Of the
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three meta-analyses considered in this study, all apply a ‘substitution method’ equivalence adjustment to either the investments or output, with primary energy to electricity conversion factors of 0.311 (Louwen et al. 2016) and 0.35 (Bhandari et al. 2015; Koppelaar 2016). A related study adopts 0.30 (Leccisi et al. 2016).
Since PV produces electricity, and most of the direct energy inputs in the manufacturing process are in the form of electricity (see Fig. 3), it could be argued that EROI should be expressed as the ratio of the electrical energy output to the electrical equivalent of all energy investments. Arithmet-ically, this requires expressing all inputs as el−eq (Method 2), or expressing the PV output as PE−eq (Method 1)—see Table 3.
On the other hand, some essential non-electrical energy inputs, while smaller in relative magnitude, may not be read-ily substituted by electricity. If an energy input is essential, but cannot in practice be provided by electricity, then a ques-tion arises as to how the scaling for an electricity-equivalent adjustment should be determined. Freight transport provides an interesting case in point. There is no imminent substitute for diesel for heavy vehicles and ocean-going shipping, or jet fuel for air transport (Sims et al. 2014, p. 615). Calculat-ing the electricity equivalent of a given quantity of liquid fuel would require the assumption of a suitable conversion pathway from electricity through to a synthetic fuel, such as hydrogen, and estimation of the ‘electricity-to-wheels’ (or equivalent, depending on end-use) efficiency for the full fuel cycle.
Implications for Interpreting PV NEA Findings
The appropriate method for treating investment and output energy form equivalence depends on the study context and the questions it seeks to answer. In light of this, our interest is in understanding the different approaches and the con-texts in which they might be applied, rather than arguing for a single ‘correct’ method. For example, goal definitions (4) and (5) from “Goal Definitions” section are exploring
the magnitude of displaced fossil fuels within an incumbent system, and therefore Method 1 (convert electricity output to ‘primary energy equivalent’) may be most appropriate. On the other hand, goal definition (6) considers the potential role for solar PV in providing all energy services, includ-ing transport, via renewably generated electricity, and the adoption of a universal energy content scaling factor may be inappropriate given the complex energy conversion path-ways involved.
Further to this, we point out that the established conven-tions discussed here treat equivalence exclusively in terms of a ‘physical energy content’ criterion. We note that, par-ticularly in light of considerations discussed in “Treatment of Intermittency” section, a broader concept of functional equivalence could provide clearer guidance with respect to the most appropriate scaling adjustment to make to the energy output from a PV system in any given situation. Closer investigation of this seems to be warranted, but is beyond the scope of the present study.
From the point of view of understanding the apparent divergence in PV NEA findings, the particular method for treating investment and output energy form equivalence adopted in any given study context clearly plays a major role. Just by adopting a different reporting convention, the findings of a study can change by a factor of three or more. Being informed about the convention adopted for a particu-lar study, and making appropriate adjustments when com-paring findings across studies, is fundamentally important for accurate interpretation of findings.
Differences Between Assumed Values for Key Performance Parameters and Real‑World Performance
Actual performance of PV systems depends on many fac-tors, including insolation of the region in question, ori-entation and shading of panels, and the actual (versus rated) performance of the PV modules. The IEA-PVPS Programme adopts a ‘performance ratio’ (PR) of 0.75 or
Table 3 Methods of adjusting for investment and output energy equivalence in the PV and related EROI literature
PE−eq primary energy equivalent, el−eq electricity equivalent, el electricity. �grid is the life-cycle energy efficiency of the grid in question (typi-cally 0.29–0.35)
Methodology Examples Equation
1 Convert electricity output to ‘primary energy equivalent’
Frischknecht et al. (2016), Raugei et al. (2012), Bhandari et al. (2015), Fthenakis and Kim (2011), Ito et al. (2016), Louwen et al. (2016)
EROIPE−eq =EoutPE−eq
Einv,
where EoutPE−eq =Eoutel
�grid
2 Convert primary energy investments to ‘electricity equivalent’
Koppelaar (2016), Dale and Benson (2013) EROIel−eq =Eoutel.
Einvel−eq, where
Einvel−eq = EinvPE × �grid
3 Adopt ‘direct equivalent’ to electricity output Ito et al. (2003), Fu et al. (2015), Moeller and Murphy (2016), Kubiszewski et al. (2010)
EROIel =Eoutel.
Einv
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0.80 to account for differences between rated performance and actual AC electricity generation (Frischknecht et al. 2016, p. 4). The PR accounts for non-optimal siting and shading, panel degradation, dust, DC to AC conversion losses and other factors. LCAs and NEAs are carried out with assumed factors that are intended to approximate real-world conditions in a given deployment context.
Insolation
The IEA-PVPS LCA guidelines (Frischknecht et al. 2016, Sect. 3.1.2) recognise three approaches for treating insola-tion, depending on the goal of the study. These are industry
average and best-case insolation; insolation with modules optimally orientated and tilted, or with single-axis track-ing; and average insolation for installed systems in a grid network. Insolation should be reported with the given ori-entation and inclination (Frischknecht et al. 2016, Sect. 3.5).
Meta-analyses (e.g. Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016) typically adopt a reference in-plane insolation of 1700 kWh/m2 year. However, there is sometimes ambiguity as to whether the insolation refers to in-plane or global horizontal insolation (GHI). Many LCA studies explicitly identify the insolation as being in-plane (e.g. de Wild-Scholten 2013; Ito et al. 2016, Table 2, Sect. 5; Louwen et al. 2016, Eq. 1). However, some studies
Coal101 EJ
Natural gas49 EJ
Oil 12 EJ
Nuclear 27 EJ(EIA method)
Hydro 14 EJ
Wind 2.2 EJ
Geothermal 2.5 EJ
Combustion &heat transformations
Biomass8.7 EJ
Solar PV 0.5 EJ
Electricity total70 EJ
Commercialheat11 EJ
Electric motor19 EJ
Electric heat 20 EJ
Cooling 11 EJ
Lighting 7 EJ
Electronics 6 EJNon-thermalconversions
Electricityend-use
Primary energyfor electricity
Conversions
Waste heat
Primary energyfor direct use
Coal60 EJ
Natural gas56 EJ
Oil167 EJ
Biomass43 EJ
Non-electricityend-use
Freight transport73 EJ
Passenger transport60 EJ
Heat194 EJ
Other motion, 3 EJ
Solar PV
109 GJ-el
[25 yrs, 0.8 PR1,500 kWh/m2/yr]
~ 75%
~ 25%Heat, transport
Motion, heat
Non-electric primary energy
330 EJ
High grade (> 400C)
Med. grade (100 to 400C)
Low grade (< 100C)
10.2 4.5 GJ-el/kW29.0 12.5 GJ-pe/kW
10.0 4.4 GJ-pe/kW
Fig. 3 Sankey diagram of annual global energy flows, focused on electricity. PV output is lifetime output Source Adapted from IEA (2016a). Data based on median from Koppelaar (2016). Relative proportion of PV inputs estimated from Frischknecht et al. (2015a). (Color figure online)
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also refer to horizontal insolation in relation to an ‘aver-age’ 1700 kWh/m2 year insolation (e.g. Fthenakis and Kim 2011; Phylipsen and Alsema 1995, p. 42). In other studies, it is unclear whether insolation values are meant to be inter-preted as in-plane or horizontal (e.g. Bhandari et al. 2015, Sect. 2.4.3; Koppelaar 2016, Sect. 2.3), although tracing references usually resolves ambiguity. Solar maps usually depict annual solar exposure as the total amount of solar radiation falling on a horizontal surface. From Breyer and Schmid (2010, Appendix), it has been estimated that the in-plane global insolation for an optimally tilted, fixed array is ∼ 145–272 kWh/m2 year greater than the GHI. The actual difference is dependent on the latitude, and the relative con-tributions from direct versus diffuse insolation at the given region in question.
In principle, for the purpose of comparing the relative performance of different PV systems, any consistently applied in-plane insolation value will suffice. However, for NEA findings to be representative of actual field per-formance, the reference insolation value must match the actual conditions at the deployment location. EROI and EPBT values must be adjusted accordingly to account for any difference between reference and site-specific values. In this respect, it is interesting to consider also how the reference in-plane insolation of 1700 kWh/m2 year com-pares with the average insolation for all PV deployed glob-ally to date, when this is weighted for conditions at the deployment location.
The geographic distribution of solar PV reflects factors including population, wealth, the availability of transmis-sion infrastructure, and historically political support and PV subsidies. Hot, arid regions generate the highest out-put but do not generally favour high population densities over widespread territories. Figure 4 provides a graphical depiction of the global relationship between installed PV capacity, population, wealth and insolation. It plots repre-sentative summer and winter GHI across latitudes versus population and GDP per capita. Insolation plots are aver-aged across longitudes and are intended to depict differences across latitude rather than between specific regions—there can be significant variation between locations at similar lati-tude. Also plotted is the estimated distribution of solar PV capacity by latitude. This was calculated from national-level reporting of the regional distribution of solar PV for the 15 leading countries by installed capacity. The distributions were extrapolated for the reported PV capacity at the end of 2015 from IEA (2016b), and for China up to the end of 2016 due to around 34 GW being installed during 2016. IEA (2016b) provides an average country final yield [i.e. annual AC electricity at inverter output in kWh per kW of installed capacity (Frischknecht et al. 2016, Sect. 3.1.3)] and installed capacity, shown in Fig. 5.
Based on these data sources, the deployment-location-weighted average final annual yield (i.e. the ratio of AC electricity out to rated PV capacity) equates to 1204 kWh/kW year. Population-weighted country insolation data from Breyer and Schmid (2010, Appendix) was used to calculate a
80 N
60 N
40 N
20 N
0
20 S
40 S
60 S
GDP per capita (2007)
Population (2005)
Winter averageglobal horiz.insolation
Summer averageglobal horiz.insolation
kWh/m -day86420
GDP (PPP) per capita (USD) 2007010,00030,000 20,000
Installed PVcapacity of largest
15 countries
Installed PV capacity (GW)30 20 10 0
California 1,900Arizona 1,900
Queensland 2,010
NSW 1,710
Victoria1,680
South Australia1,750
Italy 1,500
Castilla-La Mancha1,700
Extremadura 1,800
Castile and León 1,500
1,100east UKSouth-west UK
1,000
Gansu1,640
Hebei1,570
Inner Mongolia
1,600
Jiangsu1,460
Qinghai1,820
Xinjiang1,570
Tokyo 1,350
Kyushu 1,390
Chubu 1,390
Kansai 1,420
Nouvelle-Aquitaine 1,300
S.Korea1,500
Provence-Alpes-Côte d'Azur 1,500
Lower Saxony
2
Fig. 4 Population, GDP per capita, and winter and summer average global horizontal insolation by latitude. Sources Kummu and Varis (2011); NASA (2017). Insolation values are averaged across all lon-gitudes for 22-year period 1983–2005. Winter uses northern hemi-
sphere January/southern hemisphere July; Summer uses northern July/southern January. Orange dots indicate regions with substantial solar installations, and number is average annual insolation in kWh/m2. (Color figure online)
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corresponding deployment-location-weighted average inso-lation for fixed, optimally tilted PV installations, returning a value of 1550 kWh/m2 year. We used Breyer and Schmid’s population-weighted country insolation data, rather than the area-weighted data that they also provide, on the assumption that geographic distribution of population within countries will act as a proxy for geographic distribution of PV capac-ity. Dividing the deployment-location-weighted average final annual yield by the fixed optimally tilted insolation returns a corresponding average PR of 0.78. This is in close agree-ment with the typical PR range of 0.75 (rooftop) to 0.80 (ground-mounted) specified in the IEA-PVPS LCA Meth-odological Guidelines (Frischknecht et al. 2016, p. 4). The deployment-location-weighted average in-plane insolation (fixed, optimally tilted installations) of 1550 kWh/m2 year is 9 % below the reference in-plane insolation typically adopted for meta-analyses.
Longevity
The operational lifetime of solar panels can vary signifi-cantly. There are reported examples of earlier panels with operational lives well in excess of 25 years, but there are also reports of premature failure and abandonment after only a few years (Jordan et al. 2016). During the 1990s, 5- to 10-year product warranties were common, but almost all manufacturers now offer a 25-year performance warranty, in addition to the 5- to 10-year product failure warranty. The most common standard factory warranty for the leading inverter brands is 5 years.
We observe what seem to be two competing views on system performance. On the one hand, solar PV has proven
to be mostly robust and durable, particularly in the emerging period of high-cost panels. High-cost components placed a floor on quality and provided motivation to maintain systems.
In contrast, under-performance has emerged as a signifi-cant issue in recent years due to the proliferation of budget-priced rooftop systems, exposing structural problems in some markets (Johnston 2017; Pulsford 2016). It is not yet clear how rooftop systems will be maintained following the failure of minor parts or inverters. The use of net-metering in rooftop systems obscures actual solar generation, making it difficult to establish precise generation statistics. Commer-cial enterprises are usually better equipped to conduct due diligence and quality assurance. Profit-seeking enterprises have an interest in maintaining systems for the duration of the operational life.
Researchers legitimately hold differing perspectives in relation to the appropriate operating life to assume for PV NEA. Most studies now default to a lifetime of 25 or 30 years for PV modules (20 years was common in the past), reflecting manufacturers’ expectations. In the context of life-cycle assessment, the statistically representative oper-ating life based on actual field experience should be used, rather than the manufacturer’s anticipated operating life for an individual facility in isolation. Notably, DNV-GL report that 85% of the current installed global PV capac-ity is less than 5 years old (Meydbray and Dross 2016). Hence, it will be some decades before a clear empirical picture of statistically representative operating life can be determined for PV systems presently being deployed. In the meantime, researchers will be required to make assumptions about the long-term performance of systems.
Germany Japan China Italy
20 40 60 80 100 120 140 160 180 200
USA
Australia
Spain
Korea
FranceCanada
ThailandNetherlandsBelgium
UK
Switzerland
Final
annu
al yie
ld (kW
h/kW
-yr)
1,800
Weighted average 1,204 kWh/kW-yr
Cumulative installed capacity (GW)
1,500
1,250
1,000
Horiz
ontal
glob
al ins
olatio
n (kW
h/m2-
yr)
1,000
1,400~ 1,360 kWh/m2-yr
Fig. 5 Installed capacity and final annual yield of countries with greater than 1 GW installed PV capacity. Yield data from IEA (2016b). Horizontal insolation estimated, based on Fig. 4. Note that
countries with a higher proportion of on-ground installations and sys-tems with tracking generate a higher yield for a given insolation than countries with a higher proportion of rooftop systems
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Discussion of Overall Implications for EROI and EPBT Metrics
A summary of the methodological factors discussed in “Detailed Investigation of Factors Contributing to Diver-gence” section is shown in Table 4 and Fig. 6. The table pro-vides a heuristic for contextualising studies that investigate different questions and that adopt different methodologies and boundaries. It is not intended as a precise harmonisation
tool—the factors can, however, be compounded to compare studies that have adopted different assumptions.
It is apparent that any factor considered in isolation can alter the EROI or EPBT significantly, and that studies con-ducted in relation to essentially equivalent situations can produce markedly different results. For example, the use of primary data that were sourced 10 years prior to conducting a study would be expected to roughly halve EROI and dou-ble EPBT. Choosing between the ‘direct equivalent method’ versus the ‘substitution method’ for treatment of investment
Table 4 Methodological factors affecting EPBT and EROI
Factor Range of reported values Comments
1 LCA methodology Process-based or hybrid I/O I/O reduces sideways and upstream trunca-tion, increases EPBT ∼ 43–100% (reduces EROI ∼ 30–50 %)
2 Time between study publication and sourced data
∼ 2–19 years 10-year-old data increase EPBT ∼ 100 % (reduces EROI ∼ 50%), 20 years ∼ 400% (reduces EROI ∼ 80%) (Louwen et al. 2016)
3 PV technology Mono-Si, multi-Si, amorphous silicon, CIGS, CdTe
Crystalline silicon (mono/multi-Si) tech-nologies account for ∼ 94 % of global production (IEA 2016b, p. 5). Relative to multi-Si, mono-Si has 33 % longer EPBT (25 % lower EROI). Amorphous silicon, CIGS and CdTe, respectively, have 20, 42 and 66 % shorter EPBT than multi-Si (Bhandari et al. 2015, Fig. 7)
4a Transmission infrastructure Rarely considered. Only applicable to remote solar farms. Ito et al. (2008) considered 100 km
Inclusion of 100 km transmission to con-nect 100 MW PV, and including losses, increases EPBT 16–25 % (reduces EROI 14–20 %) over the lifetime of the PV project. Transmission lifetime is longer than PV
4b Capacity firming Zero storage up to 10 days. Battery or pumped hydro storage
2-h Li-ion storage increases EPBT ∼ 20 % (reduces EROI ∼ 17 %). With 2-day Li-ion storage and PV overbuild for off-grid solar, EPBT increases 400 % (reduces EROI ∼ 80 %) (Palmer 2013, 2017)
5 Investment and output energy form equiva-lence
‘Direct equivalent method’ or ‘Substitution method’
Applying the ‘Substitution method’ adjust-ment to either investments or output increases EROI ∼ 200 %
6a In-plane insolation 800–2344 kWh/m2 year Meta-analyses usually adopt 1700 kWh/m2 year (Bhandari et al. 2015; Koppelaar 2016; Louwen et al. 2016). Deployment-location-weighted average insolation for fixed, optimally tilted installations is 1550 kWh/m2 year. Applied globally, an assumed in-plane insolation of 1700, rela-tive to 1550 kWh/m2 year, reduces EPBT 9 % (increases EROI 10%)
6b System lifetime 20–30 years Studies take into account manufacturers’ expectations and historic experience. 85% of the current installed global PV capac-ity is less than five years old (Meydbray and Dross 2016). Future performance is uncertain. An increase from 20 to 30 years reduces EPBT 33% (increases EROI 50%)
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and output energy form equivalence involves a straightfor-ward scaling of either the investments or output, but results in a threefold difference in reported values for EROI. Both methods can be legitimate in any given situation, though one may be preferred over the other depending on study purpose.
Importantly, the meta-analyses discussed in this study harmonise only for parameters related to factors 2, 3, 6a and 6b, leaving a significant gap in relation to factors 1, 4a, 4b and 5. Findings from studies that have adopted differ-ent methodologies, boundaries or investment-output energy form equivalence adjustments are therefore not directly com-parable unless appropriate adjustments are first made for these methodological factors.
Conclusions
NEA is a tool that has been widely applied to energy supply technologies to explore their present and potential future physical economic roles. However, apparent wide divergence in findings for solar PV has cast doubt on NEA’s relevance to energy transition feasibility assessment. We have shown that most of the apparent divergence between studies can be
attributed to six factors—life-cycle assessment methodology, age of the primary data, PV cell technology, treatment of intermittency, equivalence of investment and output energy forms, and assumptions about real-world performance. The apparent divergence in findings between studies due to these factors can often be traced back to different goal definitions. NEA’s contribution to understanding PV performance, espe-cially in the context of a large-scale renewable energy transi-tion, will be improved if study purposes and goals are clearly and fully stated.
Similarly, there is a role for interpreters of NEA findings to take into account the many contextual factors that underlie NEA studies. We believe the findings of this study support the view that NEA is an essential tool for making sense of economic situations in biophysical terms. In turn, this is essential for coming to grips with questions about sustain-ability of current forms of social organisation, viability of alternatives and feasibility of transition pathways between them. We hope that this study might support increased awareness of contextual issues affecting PV NEA findings, and in doing so contribute to more widespread appreciation for the role that NEA can play in investigating energy transi-tion questions.
Modelledlifetime(years)
Age ofprimary data(years)
Investment & outputenergyequivalence
LCAmethod
30
20
20
Recent
10
2 days full-load storage & 2.5 times
PV overbuild
Process-based
I/O-b
ased
Directequivalent
method
Substitution method
1,700
2,344
800
PVtechnology
mono-Si
CdTe
multi-Si
CIGS
a-Si
25
Sca
ling
fact
or fo
r E
RO
I rel
ativ
e to
met
a-an
alys
es
3.0
0.2
1.0
1.2
0.8
Sca
ling
fact
or fo
r E
PB
T r
elat
ive
to m
eta-
anal
yses
0.4
5.0
1.0
0.8
0.6
0.4
2.0
1.5
0.6
2.0
1.5
0.9
0.7
0.5
4
In-planeinsolationkWh/m -yr2
2 hours
12 hours
Full load storage
onlyTransmission
Not included
Fig. 6 Stylised depiction of the scaling impact of methodological fac-tors from Table 4. Left scale gives approximate scaling factors for EROI for different assumptions relative to meta-analyses considered in this study. Right scale expressed in relation to EPBT. Length of columns depicts sensitivity of aggregate result to respective factors. Factors can be compounded. For example, studies that applied an insolation of 2200 kWh/m2 year have an EROI around 1.2 times (20
%) higher, but studies using data that are 10 years old calculate an EROI around 0.5 times lower (50 % lower). The meta-analyses con-sidered in this study adopted a 25- or 30-year lifetime. PV technology shown scaled relative to multi-Si. Hybrid LCA method shown with 30 to 50 % of CED falling outside process-based boundaries. Stor-age and overbuild estimated from Palmer (2017, Table 2) using Li-ion with 70 % depth-of-discharge
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15 Page 18 of 20
Acknowledgements The authors would like to acknowledge the con-tribution of correspondence with Charlie Hall, Rembrandt Koppelaar, Pedro Prieto, Marco Raugei and Gail Tverberg, and others at the BPE Workshop: Society in Transition; and separately with Vasilis Fthena-kis. Thanks also to anonymous reviewers for valuable comments and feedback.
Funding This research was not funded by any specific grants from agencies in the public, commercial or not-for-profit sectors.
Compliance with Ethical Standards
Conflict of interest The authors declare no conflicts of interest.
References
Aghahosseini A, Bogdanov D, Breyer C (2016) 100 % Renewable energy in North America and the role of solar photovoltaics. In: EU-PVSEC conference, June 20–24, 7DV.4.8
Alsema E (2000) Energy payback time and CO2 emissions of PV sys-tems. Prog Photovolt 8:17–25
Bhandari KP, Collier JM, Ellingson RJ, Apul DS (2015) Energy pay-back time (EPBT) and energy return on energy invested (EROI) of solar photovoltaic systems: a systematic review and meta-analysis. Renew Sustain Energy Rev 47:133–141
Billinton R, Allan RN (1996) Reliability evaluation of power systems, 2nd edn. Springer, New York
Breyer C, Schmid J (2010) Population density and area weighted solar irradiation: global overview on solar resource conditions for fixed tilted, 1-axis and 2-axes pv systems. In: 25th European photovol-taic solar energy conference and exhibition/5th World conference on photovoltaic energy conversion, 6–10 September 2010, Valen-cia, Spain, pp 6–10
Budischak C, Sewell D, Thomson H, Mach L, Veron DE, Kempton W (2012) Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9 % of the time. J Power Sources 225:60–74
Carbajales-Dale M, Raugei M, Fthenakis V, Barnhart C (2015) Energy return on investment (EROI) of solar PV: an attempt at reconcili-ation. Proc IEEE 103:995–999
Commission European (2017) Commission implementing regulation EU 2017/367 of 1 March 2017. Report, European Commission, Brussels
Crawford R (2011) Life cycle assessment in the built environment. Spon Press, Oxfordshire
Crawford R, Treloar GJ, Fuller R, Bazilian M (2006) Life-cycle energy analysis of building integrated photovoltaic systems (BiPVs) with heat recovery unit. Renew Sustain Energy Rev 10:559–575
Dale M, Benson SM (2013) Energy balance of the global photovol-taic (PV) industry—is the PV industry a net electricity producer? Environ Sci Technol 47:3482–3489
de Wild-Scholten MM (2013) Energy payback time and carbon foot-print of commercial photovoltaic systems. Sol Energy Mater Sol Cells 119:296–305
Ferroni F, Hopkirk RJ (2016) Energy return on energy invested (ERoEI) for photovoltaic solar systems in regions of moderate insolation. Energy Policy 94:336–344
Finkel A, Moses K, Munro C, Effeney T, OKane M (2017) Independent review into the future security of the National Electricity Market. Commonwealth of Australia, Canberra
Frischknecht R, Althaus HJ, Dones R, Hischier R, Jungbluth N, Nemecek T, Primas A, Wernet G (2007a) Renewable energy
assessment within the cumulative energy demand concept: chal-lenges and solutions. In: Proceedings from SETAC Europe 14th LCA case study symposium: energy in LCA-LCA of Energy, 3–4 Dec 2007
Frischknecht R, Jungbluth N, Althaus HJ, Bauer C, Doka G, Dones R, Hischier R, Hellweg S, Humbert S, Köllner T, Loerincik Y, Margni M, Nemecek T (2007b) Implementation of life cycle impact assessment methods, Data v2.0, ecoinvent Report No. 3. Ecoinvent
Frischknecht R, Itten R, Sinha M, de Wild-Scholten M, Zhang V, Fthenakis V, Kim H, Raugei M, Stucki M (2015a) Life cycle inventories and life cycle assessment of photovoltaic systems report T12-04:2015
Frischknecht R, Wyss F, Knöpfel SB, Lützkendorf T, Balouktsi M (2015b) Cumulative energy demand in LCA: the energy harvested approach. Int J Life Cycle Assess 20:957–969
Frischknecht R, Heath G, Raugei M, Sinha M, de Wild-Scholten M, Fthenakis V, Kim H, Alsema E, Held M (2016) Methodology guidelines on life cycle assessment of photovoltaic electricity, 3rd edn. IEA PVPS Task 12 Report IEA-PVPS T12-08:2016
Fthenakis V, Kim HC (2011) Photovoltaics: life-cycle analyses. Solar Energy 85:1609–1628
Fu Y, Liu X, Yuan Z (2015) Life-cycle assessment of multi-crystalline photovoltaic (PV) systems in China. J Clean Prod 86:180–190
Glöckner R, de Wild-Scholten M (2012) Energy payback time and carbon footprint of Elkem Solar Silicon. In: 27th EUPVSEC, Frankfurt, Germany, 24–28 Sept 2012
Gross R, Heptonstall P, Anderson D, Green T, Leach M, Skea J (2006) The costs and impacts of intermittency: an assessment of the evi-dence on the costs and impacts of intermittent generation on the British Electricity Network. UK Energy Research Centre, London
Grubler A, Johansson TB, Muncada L, Nakicenovic N, Pachauri S, Riahi K, Rogner HH, Strupeit L (2012) Energy primer. In: Global energy assessment: toward a sustainable future. Cambridge Uni-versity Press, Cambridge
Heptonstall P, Gross R, Steiner F (2017) The costs and impacts of intermittency 2016 update. UK Energy Research Centre, London
Huva R, Dargaville R, Rayner P (2016) Optimising the deployment of renewable resources for the Australian NEM (National Electric-ity Market) and the effect of atmospheric length scales. Energy 96:468–473
IEA (2016a) Energy technology perspectives: data visualisation. https://www.iea.org/etp/explore/. Accessed 10 May 2016
IEA (2016b) IEA-PVPS trends in photovoltaic applications: survey report of selected IEA countries between 1992 and 2015. Report, IEA Photovoltaic Power Systems Programme, Paris, France
IEA (2017) Commentary: understanding and using the energy balance. http://www.iea.org/newsroom/news/2017/september/commentary-understanding-and-using-the-energy-balance.html. Accessed 11 Sept 2017
ISO (1998) ISO 14041: environmental management life cycle assess-ment goal and scope definition and inventory analysis. Report, International Organization for Standardization
ISO (2006) ISO 14040: environmental management-life cycle assess-ment-Principles and framework. Report, International Organiza-tion for Standardization
Ito M, Kato K, Sugihara H, Kichimi T, Song J, Kurokawa K (2003) A preliminary study on potential for very large-scale photovol-taic power generation (VLS-PV) system in the Gobi desert from economic and environmental viewpoints. Sol Energy Mater Sol Cells 75:507–517
Ito M, Kato K, Komoto K, Kichimi T, Kurokawa K (2005) Analy-sis of transmission losses of very large-scale photovoltaic power generation systems (VLS-PV) in world desert. In: Photovoltaic specialists conference, 2005. Conference record of the thirty-first IEEE, IEEE, pp 1706–1709
116
BioPhysical Economics and Resource Quality (2017) 2:15
1 3
Page 19 of 20 15
Ito M, Kato K, Komoto K, Kichimi T, Kurokawa K (2008) A com-parative study on cost and lifecycle analysis for 100 MW very largescale PV (VLSPV) systems in deserts using mSi, aSi, CdTe, and CIS modules. Prog Photovolt 16:17–30
Ito M, Lespinats S, Merten J, Malbranche P, Kurokawa K (2016) Life cycle assessment and cost analysis of very largescale PV systems and suitable locations in the world. Prog Photovolt 24:159–174
Johnston W (2017) How long will your solar panels last, and how well will they perform? ReNewEconomy, London
Jones C, Gilbert P, Raugei M, Mander S, Leccisi E (2016) An approach to prospective consequential life cycle assessment and net energy analysis of distributed electricity generation. Energy Policy 100:350–358
Jordan DC, Kurtz SR, VanSant K, Newmiller J (2016) Compendium of photovoltaic degradation rates. Prog Photovolt 24:978–989
Kannan R, Leong K, Osman R, Ho H, Tso C (2006) Life cycle assess-ment study of solar PV systems: an example of a 2.7 kW p dis-tributed solar PV system in Singapore. Solar Energy 80:555–563
Kirby B, Milligan M, Makarov Y, Hawkins D, Jackson K, Shiu H (2003) California renewables Portfolio standard renewable genera-tion integration cost analysis. The California Energy Commission and The California Public Utilities Commission, San Francisco
Koppelaar R (2016) Solar-PV energy payback and net energy: meta-assessment of study quality, reproducibility, and results harmoni-zation. Renew Sustain Energy Rev 72:1241–1255
Kubiszewski I, Cleveland CJ, Endres PK (2010) Meta-analysis of net energy return for wind power systems. Renew Energy 35:218–225
Kummu M, Varis O (2011) The world by latitudes: a global analysis of human population, development level and environment across the north-south axis over the past half century. Appl Geogr 31:495–507
Leccisi E, Raugei M, Fthenakis V (2016) The energy and environ-mental performance of ground-mounted photovoltaic systems: a timely update. Energies 9:622
Lenzen M (2000) Errors in conventional and input output based life cycle inventories. J Ind Ecol 4:127–148
Lenzen M, McBain B, Trainer T, Jtte S, Rey-Lescure O, Huang J (2016) Simulating low-carbon electricity supply for Australia. Appl Energy 179:553–564
Lightfoot HD (2007) Understand the three different scales for measur-ing primary energy and avoid errors. Energy 32:1478–1483
Louwen A, van Sark WG, Faaij AP, Schropp RE (2016) Re-assessment of net energy production and greenhouse gas emissions avoid-ance after 40 years of photovoltaics development. Nat Commun 7:13728
Meydbray J, Dross F (2016) PV module reliability scorecard report 2016. DNV-GL
Moeller D, Murphy D (2016) Net energy analysis of gas production from the Marcellus Shale. BioPhys Econ Resour Qual 1:1–13
Murphy DJ, Hall CA (2010) Year in review: EROI or energy return on (energy) invested. Ann N Y Acad Sci 1185:102–118
Murphy DJ, Hall CA, Dale M, Cleveland C (2011) Order from chaos: a preliminary protocol for determining the EROI of fuels. Sustain-ability 3:1888–1907
Nakicenovic N, Grubler A, Ishitani H, Johansson T, Marland G, Moreira J, Rogner HH (1996) Energy primer. Climate change 1995, impacts, adaptations and mitigation of climate change: scientific-technical analyses. Cambridge University Press, Cambridge
NASA (2017) NASA surface meteorology and solar energy: global data sets. https://eosweb.larc.nasa.gov/cgi-bin/sse/global.cgi. Accessed 6 May 2017
NERC (2011) Planning resource adequacy analysis, assessment and documentation, BAL-502-RFC-02. http://www.nerc.com/files/BAL-502-RFC-02.pdf. Accessed 26 March 2017
Nugent D, Sovacool BK (2014) Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: a critical meta-survey. Energy Policy 65:229–244
OFGEM (2013) Electricity capacity assessment report 2013. Report, OFGEM, London
Palmer G (2013) Household solar photovoltaics: supplier of marginal abatement, or primary source of low-emission power? Sustain-ability 5:1406–1442
Palmer G (2017) A framework for incorporating EROI into electrical storage. BioPhys Econ Resour Qual 2:6
Palzer A, Henning HM (2014) A comprehensive model for the Ger-man electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies—Part II: results. Renew Sustain Energy Rev 30:1019–1034
Phylipsen GJM, Alsema EA (1995) Environmental life-cycle assess-ment of multicrystalline silicon solar cell modules. Department of Science, Technology and Society, Utrecht University, Utrecht
Preston G (2015a) 100% solar and wind power simulation for ERCOT. http://egpreston.com/100percentrenewables.pdf. Accessed 9 May 2016
Preston G (2015b) A simple calculation procedure for LOLE, LOLH, and EUE, calculation of probabilistic transmission line flows, and study results for extreme renewables in ERCOT. http://egpreston.com/Presentation3.pdf. Accessed 5 Nov 2016
Prieto P, Hall C (2013) Spain’s photovoltaic revolution: the energy return on investment. Springer, New York
Pulsford S (2016) PV module quality: challenges for the Australian market. Clean Energy Council, Melbourne
PV Education (2016) Average solar radiation. http://www.pveducation.org/pvcdrom/average-solar-radiation. Accessed 2 Oct 2016
Raugei M (2013) Comments on “Energy intensities, EROIs (energy returned on invested), and energy payback times of electricity generating power plants”: making clear of quite some confusion. Energy 781:1088–1091
Raugei M, Fullana-i Palmer P, Fthenakis V (2012) The energy return on energy investment (EROI) of photovoltaics: methodology and comparisons with fossil fuel life cycles. Energy Policy 45:576–582
Raugei M, Carbajales-Dale M, Barnhart CJ, Fthenakis V (2015) Rebut-tal: comments on “Energy intensities, EROIs (energy returned on invested), and energy payback times of electricity generating power plants”: making clear of quite some confusion. Energy 82:1088–1091
Raugei M, Frischknecht R, Olson C, Sinha P, Heath G (2016) Meth-odological guidelines on net energy analysis of photovoltaic elec-tricity, IEA-PVPS Task 12, Report T12- 07:2016. IEA-PVPS
Sayeef S, Heslop S, Cornforth D, Moore T, Percy S, Ward JK, Berry A, Rowe D (2012) Solar intermittency: Australia’s clean energy challenge: characterising the effect of high penetration solar inter-mittency on Australian electricity networks. CSIRO, Canberra
Sims R, Mercado P, Krewitt W, Bhuyan G, Flynn D, Holttinen H, Jannuzzi G, Khennas S, Liu Y, Nilsson LJ (2011) Integration of renewable energy into present and future energy systems In: IPCC special report on renewable energy sources and climate change mitigation
Sims R, Schaeffer R, Creutzig F, Cruz-Nunez X, DAgosto M, Dimitriu D, Figueroa Meza M, Fulton L, Kobayashi S, Lah O, McKinnon A, Newman P, Ouyang M, Schauer J, Sperling D, Tiwari G (2014) Transport. In: Climate change 2014: mitigation of climate change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
Sonnemann G, Vigon B, Rack M, Valdivia S (2013) Global guidance principles for life cycle assessment databases: development of training material and other implementation activities on the pub-lication. Int J Life Cycle Assess 18:1169–1172
117
BioPhysical Economics and Resource Quality (2017) 2:15
1 3
15 Page 20 of 20
Suh S, Huppes G (2002) Missing inventory estimation tool using extended input-output analysis. Int J Life Cycle Assess 7:134–140
Suh S, Huppes G (2005) Methods for life cycle inventory of a product. J Clean Prod 13:687–697
Tillman AM (2000) Significance of decision-making for LCA meth-odology. Environ Impact Assess Rev 20:113–123
UNEP/SETAC (2011) Global guidance principles for life cycle assess-ment databases: a basis for greener processes and products. UNEP/SETAC Life Cycle Initiative, Paris, United Nations Envi-ronment Programme
Weißbach D, Ruprecht G, Huke A, Czerski K, Gottlieb S, Hussein A (2013) Energy intensities, EROIs (energy returned on invested), and energy payback times of electricity generating power plants. Energy 52:210–221. https://doi.org/10.1016/j.energy.2013.01.029. Accessed 23 Sep 2016
Weißbach D, Ruprecht G, Huke A, Czerski K, Gottlieb S, Hussein A (2014) Reply on ‘Comments on “Energy intensities, EROIs (energy returned on invested), and energy payback times of
electricity generating power plants” Making clear of quite some confusion’. Energy 68:1004–1006
White SW, Kulcinski GL (2000) Birth to death analysis of the energy payback ratio and CO2 gas emission rates from coal, fission, wind, and DT-fusion electrical power plants. Fusion Eng Des 48:473–481
Wu X, Xia X, Chen G, Wu X, Chen B (2016) Embodied energy analy-sis for coal-based power generation system-highlighting the role of indirect energy cost. Appl Energy 184:936–950
Yang CJ, Jackson RB (2011) Opportunities and barriers to pumped-hydro energy storage in the United States. Renew Sustain Energy Rev 15:839–844
Yao Y, Chang Y, Masanet E (2014) A hybrid life-cycle inventory for multi-crystalline silicon PV module manufacturing in China. Environ Res Lett 9:114001
Zhai P, Williams ED (2010) Dynamic hybrid life cycle assessment of energy and carbon of multicrystalline silicon photovoltaic sys-tems. Environ Sci Technol 44:7950–7955
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Chapter 7
A biophysical perspective of IPCC
integrated energy modelling
7.1 Overview and context of chapter
7.1.1 Introduction
This chapter concludes the main body of the thesis with a policy focus.
Analysis of the costs and benefits of climate mitigation involve extrapolation into the future. How-
ever, the long time horizons of interest extend well beyond the range of standard economic-development
scenarios. In order to guide policy makers, ‘transformation pathways’ are derived from integrated as-
sessment models (IAMs). These models represent interactions between human and natural systems,
including energy, agriculture, the carbon cycle, and economic systems.
IAMs adopt simplified, stylised, and numerical approaches to complex systems. Since the future
evolution of demography, socio-economic development, and technology are highly uncertain, scenarios
have been developed that describe plausible alternative pathways for the key socio-economic drivers
of greenhouse gas (GHG) emissions.
The IPCC scenario series includes the SA90, IS92, and SRES, published in 1990, 1992 and 2000
respectively. Beginning with the Fifth Assessment Report (AR5), ‘representative concentration path-
ways’ (RCPs) were introduced to serve the dual purpose of new socio-economic and emissions scenar-
ios, and form the basis for new climate model simulations. The RCPs are being replaced with ‘share
socioeconomic pathways’ (SSPs).
Scenario drivers can be described by the Kaya identity. The Kaya identity includes population,
per-capita income, energy intensity of the economy, and carbon intensity of energy. Since the identity
119
is multiplicative, the component growth rates are additive. Therefore, growth of per-capita income is
compatible with a decline of GHG emissions, provided energy and carbon intensity decline sufficiently
rapidly. A hypothesis of biophysical economics (BPE) is that per-capita income growth is in fact an
emergent parameter from the biophysical-economic system. Rather than being independent variables,
the Kaya parameters are interlinked in complex ways.
This study focuses on the key drivers of economic growth, how these are derived, and whether
IAMs properly reflect the underlying biophysical systems.
It proposes that GDP and productivity growth are integrated as feedbacks with an expanded
environmentally extended input output analysis (EEIOA). A preliminary proposal is presented.
7.1.2 Research questions
The research questions posed by this chapter are:
1. How can the results of this thesis be incorporated into climate and energy policy?
2. What are the key drivers of economic growth in the IAM literature, and do these properly reflect
the energy-economic nexus?
3. How can IAMs be improved using the principles of biophysical economics?
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energies
Article
A Biophysical Perspective of IPCC IntegratedEnergy Modelling
Graham Palmer ID
Australian-German College of Energy and Climate, The University of Melbourne, Melbourne 3010, Australia;[email protected]; Tel.: +61-428-325-555
Received: 5 February 2018; Accepted: 2 April 2018; Published: 4 April 2018�����������������
Abstract: The following article conducts an analysis of the Intergovernmental Panel on ClimateChange (IPCC) Fifth Assessment Report (AR5), specifically in relation to Integrated AssessmentModels (IAMs). We focus on the key drivers of economic growth, how these are derived andwhether IAMs properly reflect the underlying biophysical systems. Since baseline IAM scenariosproject a three- to eight-fold increase in gross domestic product (GDP)-per-capita by 2100, but withconsumption losses of only between 3–11%, strong mitigation seems compatible with economicgrowth. However, since long-term productivity and economic growth are uncertain, they are includedas exogenous parameters in IAM scenarios. The biophysical economics perspective is that GDPand productivity growth are in fact emergent parameters from the economic-biophysical system.If future energy systems were to possess worse biophysical performance characteristics, we wouldexpect lower productivity and economic growth, and therefore, the price of reaching emission targetsmay be significantly costlier than projected. Here, we show that IAMs insufficiently describe theenergy-economy nexus and propose that those key parameters are integrated as feedbacks with theuse of environmentally-extended input-output analysis (EEIOA). Further work is required to build aframework that can supplement and support IAM analysis to improve biophysical rigour.
Keywords: Integrated Assessment Models; economic growth; energy; biophysical; optimisation;energy return on investment (EROI)
1. Introduction
1.1. Scenario Modelling
Much of the Intergovernmental Panel on Climate Change (IPCC) Work Group 3 (Mitigation)reporting relies on prospective energy modelling based on Integrated Assessment Models (IAMs) [1].Baseline scenarios assume a three- to eight-fold increase in gross domestic product (GDP)-per-capitaby 2100 ([1], p. 426). GDP growth is composed of GDP per-capita and population growth and connected toemissions with the Kaya identity, which underlies the emissions scenario literature ([2], p. 105). The Kayaidentity states that total CO2 emissions are the product of population, GDP per-capita, energy intensityand carbon intensity, shown in Equation (1).
CO2Emissions = Population × GDPPopulation
× EnergyGDP
× CO2
Energy(1)
Since strong mitigation scenarios result in global consumption (in this context, consumptionrefers to economic goods and services) losses in 2100 of only between 3–11% relative to thebaseline ([1], p. 419), strong mitigation seems consistent with economic growth. In the IPCCFifth Assessment Report (AR5), consumption losses are based on incremental costs relative to a
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baseline ([3], p. 1221) and do not take into account co-benefits or the adverse side-effects of mitigationactions ([4], p. 1292).
However, as Stern [5] notes, with assumed growth of over 1% over a century and with modestdamages, the nature of compound growth ensures that ‘future generations are assumed to be muchbetter off’. When the assumption of growth is relaxed, the cost of mitigation, as reflected in the socialcost of carbon (SCC), can increase by orders of magnitude [6]. The challenges of the long time framesof interest in climate mitigation are reflected in the wide divergence in SCC estimates that resultfrom different assumptions in parameters such as the discount rate and ‘pure rate of time preference’(e.g., Smith [7], Pindyck [8]).
Since there are no economic-development scenarios available in the literature that extend to thelong-term time horizons required for climate scenarios, GDP growth and total factor productivityare exogenous inputs. However, the biophysical economics (biophysical economics (BPE) is definedas ‘the study of the ways and means by which human societies procure and use energy and otherbiological and physical resources to produce, distribute, consume and exchange goods and services,while generating various types of waste and environmental impacts’ [9]. ‘Biophysical’ refers to thematerial world and can be contrasted with an anthropocentric perspective, of which mainstreameconomics is a subset ([10], p. 1)) perspective is that these are in fact emergent parameters from theeconomic-biophysical system.
Furthermore, the assumption that future energy systems will possess equivalent or superiorbiophysical and economic qualities is contested within the biophysical literature [11–13]. If futureenergy systems were to possess worse biophysical performance characteristics, productivity andeconomic growth may be overstated in the IAM literature, and therefore, the price of reaching emissiontargets may be significantly costlier than projected.
Section 1.2 defines and describes biophysical economics. Section 2 briefly describes integratedassessment models. Section 3 explores assumptions in the modelling, with an emphasis on a biophysicalperspective. Section 4 identifies two approaches to connecting conventional economic approacheswith a biophysical perspective. Section 4.1 introduces a preliminary framework for supporting andsupplementing integrated models, and finally, Section 5 provides some overarching conclusions.
1.2. Biophysical Economics
Biophysical economics (BPE) is related to the field of industrial ecology, which uses the tools oflife-cycle assessment (LCA) and environmentally-extended input-output analysis (EEIOA) to explorethe full environmental assessment of the life cycle of products and services. The focus of BPE is theenergy-environment-economic nexus.
Net energy analysis (NEA) refers to a class of methods that are physically based, which are usedto determine the efficiency or productivity of energy supply technologies [14]. Results are presented inthe form of energy return ratios (ERR), of which EROI is the most commonly used (see Equation (2)).EROI is a unitless ratio, defined as the ratio of the gross flow of energy Eg over the lifetime of theproject, and the sum of the energy for construction Ec, operation Eop and decommissioning Ed ([15]Equation (1)). The energy inputs include both the direct and indirect energy. Murphy et al. [16] statethat ‘EROI is the ratio of how much energy is gained from an energy production process compared tohow much of that energy (or its equivalent from some other source) is required to extract, grow, etc.,a new unit of the energy in question’.
EROI =Eg
Ec + Eop + Ed(2)
The ratio provides an energetic valuation of the fuel, which may, or may not, correlate with theconventional economic valuation. An energetic valuation of fuels and electricity, based on EROI,conveys information on the potential benefit of using that energy source that may not be obvious fromthe price system alone.
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There may be overlap between energetic and economic valuations, determined, in part, by thecapital and energy intensity of the energy supply system [17]. For example, the cost of oil and gasproduction has been shown to be inversely related to EROI [18,19], but the market price is overlaidwith cycles and subject to many factors [20]. On the other hand, the price of solar photovoltaics maybe a poor proxy for its energetic evaluation [21]. Nuclear power is subject to economic, regulatoryand social constraints (e.g., von Hippel et al. [22]), rather than underlying biophysical constraints,depending on factors such as reactor type, ore grade and enrichment method [23].
Ideally, EROI should provide a means to supplement conventional economic and environmentalanalysis and inform and shape energy transition analysis. However, a lack of methodologicalconsistency has led to contestation of NEA’s relevance to the broader and IAM scenario literature [21].
2. Integrated Assessment Models
Stabilizing greenhouse gas emissions will require transformation of energy supply and energyend-use services ([1], p. 420). Net global CO2 energy supply emissions must eventually be broughtto, or below, zero. The main IPCC modelling tool for assessing transformation pathways areIAMs ([1], p. 420). IAMs employ a simplified and stylised approach to physical and social systems,but integrate energy, the carbon cycle and economic systems. They typically incorporate all of themajor energy sources (i.e., coal, oil, gas, nuclear, renewables), but may constrain specific sources, suchas nuclear, or coal with carbon capture.
A key use of IAMs has been to produce energy scenarios for representative concentrationpathways (RCPs) as part of the IPCC climate mitigation process. AR5 defined seven CO2eqconcentration categories (see Clarke et al. [1], Table 6.2) and selected four that corresponded with RCPpathways: RCP2.6, 4.5, 6.0 and 8.5. The RCP scenarios are considered to be plausible and illustrative,but do not have probabilities attached to them [24]. In AR5, the RCPs superseded the Special Reporton Emission Scenarios (SRES) ‘storylines’. The storylines permit harmonisation across models, and aredefined as the A1, A2, B1 and B2 scenario families ([2] Box SPM-1). GDP growth is one of several‘driving forces’, with the A1 storyline adopting the highest economic growth, followed by B1, then A2and B2. A total of 1184 scenarios was reviewed for AR5 [25], of which a quarter were baseline andthree-quarters were mitigation scenarios ([26], p. 9).
Other economic explorations of mitigation measures include energy supply cost curves, marginalabatement cost (MAC) curves (e.g., McKinsey) and cost-benefit studies (e.g., Tol [27], Nordhaus andBoyer [28]). However, AR5 has adopted integrated modelling as the preferred approach due to its highstructural detail ([29], p. 534).
Although modelling approaches and objectives differ, all models use economics as thebasis for decision making by minimizing the aggregate economic costs of achieving mitigationoutcomes ([1], p. 422; [30]). The BPE perspective is that an economic valuation of fuels and electricitymay not convey complete information about the potential benefit, or costs, of that energy source.This study is not intended as a systematic review of the IAM scenario database, but a contributionto supplementing and supporting the economic valuation perspective of IAMs with a biophysicalenergetic valuation perspective.
3. Detailed Exploration of Assumptions
3.1. Total Factor Productivity and GDP Growth
In economic texts (e.g., (Mankiw [31], pp. 249–250), three sources of economic growth areidentified: changes in the amount of capital, changes in the amount of labour and changes in totalfactor productivity (TFP). Since TFP is not directly observable, it is measured indirectly as a residual,or as the growth that remains after accounting for changes in capital and labour.
The concept of TFP can be traced to Robert Solow’s discovery that output rose faster than capitaland labour inputs in U.S. time-series data for the period 1909–1949. He appended At, calling it
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‘technical change’, to describe what appeared to be a shift in the production function (see Equation (3)).He explained that the term was meant in the broadest sense, including ‘slowdowns, speed-ups,improvements in the education of the labour force’ and ‘other things’ [32]. It was only later that it wasto become a stylized fact that ‘technical change’ (Solow later noted that ‘technical change’ was simply‘a measure of our ignorance’, which has since been variously termed ‘Solow’s residual’ [33], ‘mannafrom heaven’ [34], the ‘dark matter of growth’ [35] and most commonly ‘total factor productivity’in mainstream economics texts [31]) was the primary contribution to national economic growth.In contemporary use, the expression describes a broad range of factors, including technology, educationand the role of institutions. The importance of At is that productivity is the main driver of per-capitagrowth and a rise in the standard of living [36].
Yt = AtLαK1−α (3)
where Y, L and K are output, labour and capital. respectively, and α is labour’s factor share.Beginning with Romer [37], many variants of ‘endogenous growth theory’ were developed
that sought to explain and disaggregate At. Endogenous theory holds that the primary drivers of‘technological progress’ are human capital, innovation and knowledge, and therefore investment ineducation, research and development and knowledge production contribute spillovers and positiveexternalities. The essential feature of endogenous growth theory is the primacy of knowledge capital.For example, Buonanno et al. [38] (Equation (1)) augment the Solow equation with knowledge capital(see Equation (4)).
Yt = AtKβRLαK1−α (4)
where KR is knowledge capital and β is output elasticity of knowledge capital.Although there are many approaches for accounting for endogenous technological change [39],
nearly all energy-economic models take productivity growth as exogenous and assume averageglobal TFP growth of ∼2–3% per year ([40], Section 3.1). Since there are no economic developmentscenarios available in the literature that extend to the long-term time horizons required for climatescenarios, GDP growth and total factor productivity are exogenous in the IAM literature. In AR5,all models assume increasing per capita income ([1], p. 426). Income growth is modelled as anexogenous improvement based on productivity growth during the Twentieth Century. The averagebaseline per-capita growth rate for OECD countries is mostly clustered between 1.2 and 2.0%, whilefor non-OECD, 3–4% ([1], Figure 6.2). On average, baseline scenarios assume a three- to eight-foldincrease in global GDP-per-capita by 2100 ([1], Figure 6.1b). However, recent data on per-capita growthshow a marked decline for wealthier nations ([41], Figure 2.6), with attention turning to what has beentermed ‘secular stagnation’ and falling productivity growth (e.g., [42–44]).
‘Technical change’ also contributes to improved energy efficiency and therefore a decline in energyintensity, discussed in Section 3.2; and a reduction in the cost of energy supply technologies [40,45].In the IAM literature, ‘endogenous technological change’ is sometimes applied to energy supply,resource availability and end-use technologies through the use of learning curves. Examples includeMESSAGE-MACRO [46] and DEN21+ [47]. In modelling, induced technological change tends toreduce the costs of environmental policy and accelerates the learning rates of low-emission energysupply technologies [48].
Whereas neoclassical economics adopts capital and labour as the primary factors of production,with ‘technical change’ or ‘knowledge capital’ driving TFP growth, BPE also adopts energy as aprimary factor (e.g., Ayres [49], pp. 385–389). Energy-matter is the principle factor that cannot bephysically produced from within the economic system. With respect to energy, civilisation is like anyother physical process; that is, as an open, non-equilibrium thermodynamic system that sustains itselfwith the use and dissipation of energy [50]. The simplest energy-augmented production function,given as indexed parameters, from Lindenberger and Kümmel [51] is given as:
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Y = y0KαLβE1−α−β (5)
where Y, y0, L, K and E are output, output at time zero, labour, capital and energy, respectively, and α
and β are capital and labour’s factor share, respectively.Whereas neoclassical economics assumes that there is, in principle, no limit to the
substitution of energy, the BPE perspective is that there is a lower bound to substitution.Kumhof and Muir [52] term this lower bound the ‘entropy boundary’, alluding to the second lawof thermodynamics. Kümmel et al. [53], Kümmel [54], Giraud and Kahraman [55], Keen and Ayres [56]and Ayres and Warr [33] have modelled variations on the neoclassical Cobb–Douglas aggregateproduction function and found that the inclusion of energy accounts for around half to two-thirds ofthe observed economic growth that is usually attributed to TFP. This implies that the output elasticity,with respect to energy, is much greater than its factor share.
IAMs generally include energy as a factor of production, but implicitly set the outputelasticity as equal to the factor share, which is typically less than 10% in the developed economies(e.g., Edenhofer et al. [45], Equation (1)). If the BPE hypothesis is correct, it would require that the At
term (Scientific equations usually include dimensions for dimensional analysis. However, economicmodelling of production functions of this type usually apply dimensionless indexes. Barnett [57]argued that the use of dimensions may be either meaningless or economically unreasonable. In thiscase, the At term is dimensionless.) be endogenous and a key feedback loop in IAMs.
3.2. Declining Energy Intensity
A declining energy intensity permits higher economic growth for a given greenhouse gas (GHG)emission budget. The historical trend is towards declining energy intensity since GDP growth isgenerally greater than energy use growth. The reduction in energy intensity for AR5 baseline scenariosis ∼61–80% up to 2100 ([1], Figure 6.17), equating to an averaged ∼−1.0–−1.8%/year. However, thehistorical change in global energy intensity over the period 1970–2010 averaged −0.8%/year ([1],Figure 6.1c). EIA [58] shows a return to trend decline since a plateau around 2010 (Different primaryenergy conventions across energy reporting agencies [59] and differences in GDP measurement resultin differences in reported changes in energy intensity. For example, EIA [58] shows greater declinethan Clarke et al. [1]. EIA [60] (Table J3) adopt $PPP, while Clarke et al. [1] (Figure 6.1) adopt marketexchange rates. This study relied on Clarke et al. [1] to ensure like-for-like comparisons.).
IAMs are, on average, modelling a baseline reduction in energy intensity of 25–125% greater thanthe average for the period 1970–2010 [13,61]. Some of the bottom-up optimisation models cited in theAR5 project even greater decoupling: Teske [62] assume −3.4%/year and Jacobson and Delucchi [63]assume −3.6%/year. Clarke et al. [1] (Figure 6.2) shows 11 OECD baseline projected projectionsfor 2010–2050 at higher than ∼−2.0%/year and 11 non-OECD at higher than ∼−3.0%/year.The 530–580-ppm and 430–480-ppm CO2-eq scenarios project an additional reduction over the baselineof ∼18–45% for the period up to 2100.
In a review of the AR5 430-530 ppm scenarios, the additional annual investment for efficiencyfar exceeded the additional investment in energy supply ([3], Figure 16.3). However, models do notendogenize the increased embodied energy of the technical efficiency measures of vehicles, buildingsand energy-consuming equipment. IAMs account for costs in the construction stage, but are implicitlyassuming that the energy intensity of manufacturing, construction and energy consuming equipmentis the economy-wide average. Since service sectors are both less energy intensive and comprisea significant share of developed economies, the economy-wide intensity is typically less than formanufacturing and construction (e.g., Hertwich and Peters [64]). In some cases, the improved efficiencyduring the use stage (The ‘use stage’ is one of several stages in life-cycle assessment. Other stagesinclude raw material extraction, processing, manufacturing or production, use and maintenance andend-of-life [65]) is significantly offset by the additional embodied energy during the production stage.Examples include electric vehicles [66] and housing that conforms to the Passive House Standard [67].
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Since energy efficiency protocols almost universally apply only to energy used during the use stage,the problem can arise of ‘overshooting’ the optimal lifetime efficiency.
Projections of energy intensity are partly based on historical energy efficiency improvements.However, it can be difficult to model the human behavioural and income effects resulting fromJevon’s paradox [68]. The direct rebound effect posits that efficiency lowers the cost of an energyservice, leading to increased use of that service, termed the income effect [69]. However, the indirect,economy-wide and transformational effects are significant and pervasive in producer economiessuch as China [70]. On the one hand, the technical efficiency gains of delivering end-use energyservices are very large [71], but translating technical gains into sustained reductions has provenallusive absent sustained rises in energy prices [72]. AR5 identified the efficiency rebound effect inBruckner et al. [29] (Section 7.12.1), Kolstad et al. [73] (Section 3.9.5) and Blanco et al. [74] (Section 5.6.2);however, the adoption of exogenous energy intensity as part of the scenario ‘storylines’ essentiallyinvalidates any macro integration of rebound.
3.3. Life-Cycle Assessment Methodologies
The IPCC Special Report ‘Renewable Energy Sources and Climate Change Mitigation’ containeda chapter [75] on sustainable development, which included a broad discussion of life-cycle analysis(LCA) and net-energy. The report identified the limitations of attributional, process-based life cycleassessments (LCAs) ([75], p. 730) and the need to identify uncertainties, but nonetheless, used anattributional approach to present the main results. Since non-fossil technologies are generally morecapital intensive, the importance of indirect energy, and therefore a comprehensive LCA, will likelyincrease in the future [76].
The two main issues are, firstly, process-based analysis results in a truncated boundary andtherefore understates the indirect embodied energy [77]. In LCAs, there is is no requirement toensure that an analysis meets a prescribed level of ‘completeness’. ISO [78] (Section 6.4.5) andISO [79] (Section 5.2.3) require that stages, unit processes or inputs are followed until they ‘lacksignificance’ within the given scope. This can be problematic when LCAs are applied to energytransition exercises since a high level of ‘completeness’ is often assumed by modellers applyingLCA data [21]. Environmentally-extended input-output analysis (EEIOA) connects energy flowsto monetary flows using national input-output (I/O) tables and ensures systematic completeness.However, the assumption of heterogeneity introduces aggregation error. The respective benefits ofprocess and I/O analysis can be combined with hybrid-LCA [65].
Secondly, an attributional approach considers the embodied energy of the technology in question,but does not consider the broader impacts that might result from the decision to adopt or install aspecific technology ([75], p. 730). In contrast, a consequential approach considers the broader impacts.However, it can be methodologically difficult to evaluate consequential changes to energy systems withLCA approaches (e.g., Jones et al. [80]). Consequential analyses are generally more comprehensive,but also carry greater uncertainty and the risk of double counting due to overlap between studies.The paucity of energy technology consequential studies reflects this, and Sathaye et al. [75] (p. 730)note that the limited number of studies precludes the incorporation of consequential analysis intoIAMs. Other factors that limit the use of LCAs in integrated modelling include substantial variabilityin published LCA results and differences in the LCA technique ([75], p. 730). Some IAMs adopt asimplified approach to consequential changes related to variable renewable energy. For example,WITCH and MESSAGE adopt flexibility and capacity constraints that weight the dispatchability ofdifferent generator types [81].
The LCA literature emphasises the use of standard guidelines and the primacy of datasets basedon ‘complete and verifiable documentation’ [82]. The limitation of standard guidelines is that studies arethen restricted to answering the research questions to which that methodology is suited. In the context ofelectricity system transitions, considerations relating to the engineering-systems perspective of electricitysupply are generally treated as lying outside the domain of conventional life-cycle research.
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In the case of solar photovoltaics for instance, the choice of goal definition, methodology andboundaries can alter the EROI by an order of magnitude [21]. Since the LCA literature has tended toconverge towards a standard suite of guidelines, study results emphasise differences between studies,rather than a high level of completeness. The use of attributional, process-based LCAs may be unsuitedto the task of assessing large-scale energy transitions, unless they are combined with some form ofconsequential approach.
3.4. Steel and Cement
IAMs mostly ignore material cycles and recycling [83], although some material flows areconsidered. In the AR5 literature, life-cycle analysis is identified (e.g., Sathaye et al. [75], p. 730)but is not broadly adopted in IAMs. Similarly, material scarcity associated with low-emission energysources is identified ([29], p. 549), but not explicitly modelled in IAMs.
Some IAMs include material flows that have a significant emissions footprint, such as steel andcement; these comprise 28% and 27% of direct industrial CO2 emissions, respectively ([84], Figure 2.22).However, no IAMs fully integrate material flows into the physical system [83]. For example, IMAGE isone of the most comprehensive models for the steel cycle and its emissions, but steel demand is modelledas a function of per-capita GDP ([85], Figure 1). A more detailed approach would have been to link steelproduction to the main steel consuming sub-sectors: buildings and transportation ([83] p. 17).
Furthermore, the embodied material and energy of energy supply technologies are not modelled.Fossil fuel-derived electricity is significantly more lifecycle greenhouse intensive than renewablesources due to direct combustion ([29], Figure 7.6); [86]. On the other hand, renewable and nuclearelectricity possess a higher embodied material and energy content [86,87]. Wind, ocean and CSPrequire more steel and cement than fossil fuel plants, per unit of electricity generated ([29], p. 549).
3.5. Biofuels
AR5 devoted a chapter to bioenergy ([88], Chapter 11), which covered life-cycle emissions ofbiofuels. The life-cycle emissions are a proxy for embodied energy, but land use changes significantlyincrease life-cycle emissions relative to embodied energy ([88], Figure 11.24). Most of the biofuel-relatedLCA literature applies carbon accounting rather than energy accounting, but arrives at similarconclusions to Hall et al. [89]; for example, DeCicco et al. [90] found that U.S. corn biofuel onlyoffsets CO2 emissions by around a third of that required to achieve carbon neutrality. Much of thestudied bioenergy is marginal from a net-energy perspective ([91], Figure 2), ([92], Figure 8), but someproduction systems (e.g., Brazilian ethanol) may be favourable in some contexts [93].
Virtually all bottom-up energy scenario models that include biofuels adopt gross energy flows(e.g., Elliston et al. [94]). In IAMs, projected costs are modelled, but implicitly assume that the energyintensity of energy supply technologies is commensurate with incumbent sources. Where the EROI ofa biofuel production system is significantly less than conventional fuels, the net-energy may be muchless than assumed, thereby overstating the extent of fuel substitution.
The use of biomass energy with carbon dioxide capture and storage (BECCS), will reduce the EROIfurther since CCS is estimated to consume 25–35% of gross output ([95], p. 338). In a review of IAMs,Bruckner et al. [29] (p. 559) found that a carbon price of USD 100 per tonne CO2eq would be sufficientto drive large-scale deployment of BECCS. Yet, from a biophysical perspective, energy sources withan EROI in the range of 0.8–3:1 cannot support an advanced society [12], irrespective of carbon price.AR5 identifies several limiting factors for BECCS, including land availability, a sustainable supply ofbiomass and storage capacity ([1], p. 485), but does not identify EROI or net-energy as a constraint.
3.6. EROI Constraints
The theoretical global potential of RE sources is substantially higher than global energydemand ([96], p. 10). Studies have typically focused on economic costs, land availability and kinetic or
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radiative energy constraints. However, AR5 identified the issue of whether some of the ‘bottom up’estimates are consistent with real physical limits ([29], pp. 525–526).
One of those additional constraints is EROI. When EROI constraints are applied, the practicallimits can reduce by an order of magnitude. For example, estimates of global wind power potentialgenerally report that wind power could conceivably supply a large proportion of global energysupply [97,98]. Lu et al.’s (2009) estimate of 840 EJ annual global wind power potential, with a 20%capacity factor constraint, exceeds the 400–800 EJ global final energy use of baseline scenarios for 2050from Clarke et al. [1] (Figure 6.18). For a reference, global wind power generation in 2016 equalled3.5 EJ [99]. Conversely, models that also include an EROI constraint generally report an estimate thatis an order of magnitude lower (e.g., [100,101]). Dupont et al. [101] calculated a global wind powerpotential of 709, 536, 322 and 99 EJ/year, with an EROI constraint of 5, 8, 10 and 12, respectively,although Kubiszewski et al.’s (2010) [102] meta-analysis showed an average EROI of 20–25:1. Similarly,in an assessment of solar photovoltaics, wind power and storage, Palmer [103] found that there ismarked diminishing return to EROI at higher grid penetration.
3.7. Fossil Fuel Resource Availability
IAMs adopt differing resource availability estimates based on SRES storylines ([2], pp. 207–211).Since there is large uncertainty in resources, a range of values reflecting so-called ‘optimistic versuspessimistic’ forecasts is considered ([2], p. 134; [104], p. 435). Although baseline scenarios cover a broadrange of annual GHG emissions, Edenhofer et al. [26] (Figure SPM.4) shows the median baseline lyingroughly midway between RCP6.0 and RCP8.5, with the upper range of baseline scenarios extendingup to and beyond RCP8.5. The surplus availability of fossil fuels through the Twenty-First Century,including unconventional resources, is reflected in the SRES, which stated ‘It is evident that, in theabsence of climate policies, none of the SRES scenarios depicts a premature end to the fossil-fuelage.’ ([2], p. 208). Similarly, in the EMF27 inter-model comparison, McCollum et al. [105] concludedthat fossil fuel resource constraints are unlikely to limit GHG emissions this century. The ‘resourceoptimist’ approach is also reflected in the Global Energy Assessment (GEA) [104], which is often usedas a basis for IPCC estimates (e.g., Bruckner et al. [29], Table 7.2).
Estimates of fossil fuel availability have been drawn from several sources, including Rogner [106],which proposed the theory of learning-by-extracting (LBE). The LBE theory was based on theobservation that historically, the reserves-to-production (R-P) ratio of coal, oil and gas has beendynamic. A static concept of present technology and cost will not properly reflect the replenishmentof reserves by resources: the availability of economically-available reserves at any time relieson the dynamic interplay between geological assurance, technological possibilities and economicfeasibility ([74], p. 379).
The basis for the bias towards the upper end of RCPs reflects an assumption of a so-called ‘returnto coal’ [107], which has by far the largest resource base ([104], Table 7.1). Historically, the R-P of coalhas been expressed in hundreds of years, but more recent reviews suggest a resource-constrainedsupply peak in the first half of this century (e.g., Mohr et al. [108], Rutledge [109]). The high-coalconclusion can be traced back to scenario modelling through the 1970s, which assumed coal-to-liquidsas a backstop liquid energy supply [107]. Other ‘resource pessimists’ argue that there is greateruncertainty and significantly lower economically-recoverable resources than often assumed in IAMscenarios [108–113].
The BPE perspective is less about resource availability per se, but about the increasing workto locate, upgrade and refine lower-quality and difficult to access resources [10]. Difficult to access,or lower quality resources, lower the EROI and lead to a higher energy expenditure cost share.This would result in lower productivity and economic growth than assumed in high-resourceavailability scenarios.
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4. Discussion and Recommendations
Pauliuk et al. [83] proposed several strategies to increase the robustness of IAM scenarios, basedon an industrial ecology (IE) perspective. These included: higher levels of detail regarding materialstocks, flows and physical linkages; the explicit physical description of products, processes, transportand infrastructure; improving the link between fixed capital and material stocks; vintage tracking; andthe development of a ‘standard model’ of society’s biophysical basis.
Although not related to the IAM literature, Daly et al. [76] adopted an environmentally-extendedmulti-region input-output (EE-MRIO) model, combined with an energy system optimisation model(ESOM) to estimate the indirect CO2 emissions of current and future energy technologies for the U.K.
The broad aim of both of those approaches was to better connect the conventional economicapproaches with a biophysical perspective. The approach of this study more closely relates to themodelling approach of Daly et al., except that the metric of interest is ‘energy industry own use’ (EIOU),with a focus on how this relates to economic growth.
4.1. A Proposed Net-Energy Feedback Model
Net-energy analysis requires estimation of both the direct and indirect energy of energy supply toderive a systematically-complete EIOU for the EROI denominator (see Equation (2)). Where the EROIis sufficiently high (>~20:1) or it is assumed that future energy systems will possess an EROI at least ashigh as incumbent energy systems, a net-energy approach may not contribute additional informationto that already available in IAMs.
A provisional national estimate for EIOU can be derived from the IEA energy balance statistics.The IEA documentation states that ‘Energy industry own use contains the primary and secondaryenergy consumed by transformation industries for heating, pumping, traction, and lighting purposes,(including) for example, own use of energy in coal mines, own consumption in power plants andenergy used for oil and gas extraction.’ Some studies have used the EIOU to calculate EROI at anational level (e.g., Brand-Correa et al. [114], King et al. [115]), but the IEA metric only partly reflectsthe boundaries usually adopted for the EROI denominator [116]. In 2015, the IEA reported EIOU as8.9% of total final global consumption ([117], p. 47).
A systematically-complete method of deriving EIOU is environmentally-extended input-outputanalysis (EEIOA). One technique is to combine the national monetary use-table with the nationalenergy account [116]. EEOIA permits disaggregation of primary fuels from EIOU fuels and finaldemand and identification of indirect energy pathways. The main weakness of EEIOA is homogeneity,or the assumption that each sector of the economy produces a single, homogeneous good orservice. Depending on the dependence of imported fuels and electricity supply capital equipment,a multi-regional model may be necessary.
For scenario analysis based on an IAM, it is necessary to convert the IAM aggregate energy andeconomic system into a detailed energy model and input-output table. From this, an updated EEIOAis evaluated to calculate a system-EROI (see Figure 1).
From Section 3.1, a significant fraction of total factor productivity growth can be attributed toenergy. The proposed model requires a transfer function that links EROI (and energy expenditures’share of GDP) to growth in GDP. The transfer function is underpinned by Bashmakov’s [118]‘Three Laws of Energy Transitions’, which was the observation that the energy cost to income ratiotends to converge towards a stable long-term ratio. When the energy costs to GDP ratio is below agiven threshold, which Bashmakov defines as less than 11% in OECD countries, energy exhibits amoderate price elasticity. However, when the threshold is exceeded, price reactions to small changesin demand are much higher, and economic growth is hampered. Similarly, Fizaine and Court [119]found that the energy cost share in the United States must be less than 11% for the national economyto exhibit a positive economic growth rate. Fizaine and Court [119] explicitly linked the energy costshare to EROI. Whereas neoclassical economics makes the a priori assumption that output elasticities
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will mirror cost shares in the economy [120], the BPE perspective is that the output elasticity of energyis much larger than the factor share, and for labour, much smaller [33,121].
A further way to model the transfer function is to adopt an aggregate production functionapproach. In the macroeconomic module of IAMs, output is determined by an aggregate productionfunction, typically of a Cobb–Douglas, or constant elasticity of substitution (CES) form. For example,MACRO’s production function is a nested CES, comprising capital, labour force, electricity demandand non-electric energy demand ([122], Equation (2)).
However, the IAM choice of production function does not resolve the ‘factor share’ problem.The asymmetry of energy’s output elasticity with respect to factor share is consistent with Bashmakov’sasymmetry of economic growth with respect to energy expenditure cost share. In a recent contributionto addressing the problem, Keen and Ayres [56] developed an energy-augmented production function,including a proof that production (output) and distribution (cost shares) are no longer congruent,arguing that ‘something other than marginal products’ must determine the distribution of income.
Referring to Figure 1, finally, the ‘calculated GDP’ that has been determined by the transferfunction is compared with the ‘scenario GDP’, giving an error term, which is used as a basis torevise the energy system. We envisage an iterative process of testing IAMs and revising the energysystem accordingly.
Integrated assessmentmodel
Detailed energysystem model
Modelled input-output tables
Scenario-basedEEIO Model
EROI andenergy cost share
Aggregateenergy system
GDP growth = f EROI
ScenarioGDP
CalculatedGDP
-+
Aggregateeconomic system
Energy technologyLCA
Revise energysystem
error
Transfer function
Figure 1. Proposed biophysical economic feedback model.
4.2. Future Work
The proposed feedback model as depicted here is intended as a preliminary conceptual model.The model as described is technically demanding. IAMs operate at a regional level, but EEIOA isusually modelled at a national level. Multi-regional input output models, such as the AustralianIndustrial Ecology Virtual Laboratory (IELab) [123], facilitate regional energy-economic modellingand would provide a basis for EEIOA. The transfer function as described requires further work todemonstrate the linkages between energy supply and the economy.
5. Conclusions
Analysis of the costs and benefits of climate mitigation involve extrapolation into thefuture. However, the long time horizons of interest extend well beyond the range of standard
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economic-development scenarios. In order to guide policy makers, ‘transformation pathways’ arederived from integrated Assessment Models (IAMs). These models represent interactions betweenhuman and natural systems, including energy, agriculture, the carbon cycle and economic systems.
IAMs adopt simplified, stylised and numerical approaches to complex systems. Since the futureevolution of demography, socio-economic development and technology are highly uncertain, scenarioshave been developed that describe plausible alternative pathways for the key socio-economic driversof greenhouse gas (GHG) emissions. The drivers can be described by the Kaya identity. The Kayaidentity includes population, per-capita income, energy intensity of the economy and carbon intensityof energy. Since the identity is multiplicative, the component growth rates are additive. Therefore,growth of per-capita income is compatible with a decline of GHG emissions, provided energy andcarbon intensity decline sufficiently rapidly.
A hypothesis of biophysical economics (BPE) is that per-capita income growth is in fact anemergent parameter from the biophysical-economic system. Rather than being independent variables,the Kaya parameters are interlinked in complex ways. If future energy systems were to possess worsebiophysical performance characteristics, we would expect lower productivity and economic growth,and therefore, the price of reaching emission targets may be significantly costlier than projected.With the long time horizons of IAMs, the nature of compound growth means that relatively smalldifferences in economic growth result in a significant divergence in outcomes.
We propose that per-capita income growth is included as a feedback loop in IAMs. One approachwould be to use environmentally-extended input-output analysis (EEIOA) to link the biophysicalproperties of the modelled energy system with projected economic growth. The EEIOA would bebased on a detailed energy system model that is constructed from the aggregate model described bythe IAM. A transfer function would link the calculated energy cost share, derived from the EEIOAmodel, to economic growth. Depending on the difference between the exogenous and calculated GDP,the IAM energy system would be revised. The proposed feedback model is intended as a preliminaryconceptual model. Further work is required to build a framework that can supplement and supportIAM analysis.
Acknowledgments: Thanks to the three anonymous reviewers who have added much to the quality of this articlethrough their insightful comments. Thanks to Igor Bashmakov and the other anonymous reviewers for commentson an earlier version of the draft and to Josh Floyd for discussions and feedback. Thanks also to my supervisorsRobert Crawford and Roger Dargaville.
Conflicts of Interest: The author declares no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AR5 IPCC Fifth Assessment ReportCO2eq Carbon dioxide equivalentCSP Concentrated solar thermal powerDNE21+ Dynamic New Earth 21 modelEEIOA Environmentally-extended input-output analysisEIOU Energy industry own useEMF27 Stanford Energy Modeling Forum Study 27EROI Energy return on investmentEV Electric vehicleGEA Global Energy AssessmentGDP Gross domestic productIAM Integrated Assessment Model
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IE Industrial EcologyIEA International Energy AgencyIMAGE Integrated Model to Assess the Global EnvironmentIPCC Intergovernmental Panel on Climate ChangeISO International Organization for StandardizationLCA Life-cycle assessment or analysisMACRO IIASA macroeconomic modelMESSAGE Model for energy supply strategy alternatives and their general environmental impactMESSAGE-MACRO Linked energy supply model (MESSAGE) and macroeconomic model (MACRO)OECD Organisation for Economic Co-operation and DevelopmentOPEC Organization of the Petroleum Exporting CountriesPOLES Prospective outlook on long-term energy systemsRCP Representative Concentration PathwaysRE Renewable energySRES Special Report on Emissions ScenariosWEO IEA World Energy OutlookWITCH World induced technical change hybrid
References
1. Clarke, L.; Jiang, K.; Akimoto, K.; Babiker, M.; Blanford, G.; Fisher-Vanden, K.; Hourcade, J.; Krey, V.;Kriegler, E.; Loschel, A.; et al. Assessing Transformation Pathways. In Climate Change 2014: Mitigation ofClimate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel onClimate Change; Cambridge University Press: New York, NY, USA, 2014.
2. Nakicenovic, N.; Alcamo, J.; Davis, G.; De Vries, B.; Fenhann, J.; Gaffin, S.; Gregory, K.; Griibler, A.; Jung, T.Y.;Kram, T. Special Report on Emissions Scenarios; Intergovernmental Panel on Climate Change: Geneva,Switzerland, 2000.
3. Gupta, S.; Harnisch, J.; Barua, D.C.; Chingambo, L.; Frankel, P.; Jorge, R.; Vázquez, G.; Gomez Echeverri, L.;Haites, E.; Huang, Y. Cross-cutting investment and finance issues. In Climate Change 2014: Mitigation ofClimate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel onClimate Change; Cambridge University Press: New York, NY, USA, 2014.
4. Krey, V.; Masera, O. Annex II: Metrics and Methodology. In Climate Change 2014: Mitigation of Climate Change.Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change;Cambridge University Press: New York, NY, USA, 2013.
5. Stern, N. The structure of economic modeling of the potential impacts of climate change: Grafting grossunderestimation of risk onto already narrow science models. J. Econ. Lit. 2013, 51, 838–859.
6. Moyer, E.J.; Woolley, M.D.; Matteson, N.J.; Glotter, M.J.; Weisbach, D.A. Climate impacts on economicgrowth as drivers of uncertainty in the social cost of carbon. J. Legal Stud. 2014, 43, 401–425.
7. Smith, K. Discounting, Risk and Uncertainty in Economic Appraisals of Climate Change Policy: ComparingNordhaus, Garnaut and Stern. Available online: http://www.garnautreview.org.au/update-2011/commissioned-work/smith-discounting-risk-uncertainty-comparing-nordhaus-garnaut-stern.pdf (accessedon 20 June 2017).
8. Pindyck, R.S. The use and misuse of models for climate policy. Rev. Environ. Econ. Policy 2017, 11, 100–114.9. Chevallerau, F.X. What Is Biophysical Economics?. Available online: https://biophyseco.org (accessed on
20 November 2017).10. Hall, C.A.; Klitgaard, K.A. Energy and the Wealth of Nations: Understanding the Biophysical Economy;
Springer Science & Business Media: New York, NY, USA, 2011.11. Hall, C.A. Energy Return on Investment: A Unifying Principle for Biology, Economics, and Sustainability; Springer:
Berlin, Germany, 2016.12. Lambert, J.G.; Hall, C.A.; Balogh, S.; Gupta, A.; Arnold, M. Energy, EROI and quality of life. Energy Policy
2014, 64, 153–167.13. King, C.W. Comparing world economic and net energy metrics, Part 3: Macroeconomic Historical and
Future Perspectives. Energies 2015, 8, 12997–13020.
132
Energies 2018, 11, 839 13 of 17
14. Brandt, A.R.; Dale, M. A general mathematical framework for calculating systems-scale efficiency of energyextraction and conversion: Energy return on investment (EROI) and other energy return ratios. Energies2011, 4, 1211–1245.
15. Murphy, D.J.; Hall, C.A. Energy return on investment, peak oil, and the end of economic growth. Ann. N. Y.Acad. Sci. 2011, 1219, 52–72.
16. Murphy, D.J.; Hall, C.A.; Dale, M.; Cleveland, C. Order from chaos: A preliminary protocol for determiningthe EROI of fuels. Sustainability 2011, 3, 1888–1907.
17. King, C.W.; Maxwell, J.P.; Donovan, A. Comparing World Economic and Net Energy Metrics, Part 1: SingleTechnology and Commodity Perspective. Energies 2015, 8, 12949–12974.
18. King, C.W.; Hall, C.A. Relating financial and energy return on investment. Sustainability 2011, 3, 1810–1832.19. Heun, M.K.; de Wit, M. Energy return on (energy) invested (EROI), oil prices, and energy transitions.
Energy Policy 2012, 40, 147–158.20. Jacks, D.S. From Boom to Bust: A Typology of Real Commodity Prices in the Long Run; Working Paper No. 18874;
The National Bureau of Economic Research (NBER): Cambridge, MA, USA, 2013.21. Palmer, G.; Floyd, J. An Exploration of Divergence in EPBT and EROI for Solar Photovoltaics. BioPhys. Econ.
Resour. Qual. 2017, 2, 15.22. Von Hippel, F.; Bunn, M.; Diakov, A.; Ding, M.; Goldston, R.; Katsuta, T.; Ramana, M.; Suzuki, T.; Yu, S.
Nuclear Energy. In Global Energy Assessment; Cambridge University Press: New York, NY, USA, 2012.23. Warner, E.S.; Heath, G.A. Life cycle greenhouse gas emissions of nuclear electricity generation. J. Ind. Ecol.
2012, 16, S73–S92.24. Stocker, T.; Qin, D.; Plattner, G.K.; Alexander, L.; Allen, S.; Bindoff, N.; Bréon, F.M.; Church, J.; Cubasch, U.;
Emori, S. Technical summary. In Climate Change 2013: The Physical Science Basis. Contribution of WorkingGroup I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge UniversityPress: New York, NY, USA, 2013.
25. International Institute for Applied Systems Analysis (IIASA). AR5 Scenario Database; IIASA: Laxenburg,Austria, 2014.
26. Edenhofer, O.; Pichs-Madruga, R.; Sokona, Y.; Farahani, E. Summary for Policymakers. In Climate Change2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of theIntergovernmental Panel on Climate Change; Cambridge University Press: New York, NY, USA, 2014.
27. Tol, R.S. On the optimal control of carbon dioxide emissions: An application of FUND. Environ. Model. Assess.1997, 2, 151–163.
28. Nordhaus, W.D.; Boyer, J. Warming the World: Economic Models of Global Warming; MIT Press: Cambridge,MA, USA, 2000.
29. Bruckner, T.; Bashmakov, I.; Mulugetta, Y.; Chum, H.; De la Vega Navarro, A.; Edmonds, J.; Faaij, A.;Fungtammasan, B.; Garg, A.; Hertwich, E. Energy systems. In Climate Change 2014: Mitigation of ClimateChange. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on ClimateChange; Cambridge University Press: New York, NY, USA, 2014.
30. Iyer, G.; Hultman, N.; Eom, J.; McJeon, H.; Patel, P.; Clarke, L. Diffusion of low-carbon technologies and thefeasibility of long-term climate targets. Technol. Forecast. Soc. Chang. 2015, 90, 103–118.
31. Mankiw, N.G. Macroeconomics; Worth Publishers: New York, NY, USA, 2009.32. Solow, R.M. Technical change and the aggregate production function. Rev. Econ. Stat. 1957, 39, 312–320.33. Ayres, R.U.; Warr, B. The Economic Growth Engine: How Energy and Work Drive Material Prosperity;
Edward Elgar Publishing Limited: Cheltenham, UK, 2010.34. Humphrey, T.M. Algebraic production functions and their uses before Cobb-Douglas. FRB Richmond Econ. Q.
1997, 83, 51–83.35. Grubb, M. Planetary Economics: Energy, Climate Change and the Three Domains of Sustainable Development;
Routledge: London, UK, 2014.36. Krugman, P.R. The Age of Diminished Expectations: US Economic Policy in the 1990s; MIT Press: Cambridge,
MA, USA, 1997.37. Romer, P. Endogenous Technological Change; University of Chicago: Chicago, IL, USA, 1990.38. Buonanno, P.; Carraro, C.; Galeotti, M. Endogenous induced technical change and the costs of Kyoto.
Resour. Energy Econ. 2003, 25, 11–34.
133
Energies 2018, 11, 839 14 of 17
39. Gillingham, K.; Newell, R.G.; Pizer, W.A. Modeling endogenous technological change for climate policyanalysis. Energy Econ. 2008, 30, 2734–2753.
40. Azar, C.; Dowlatabadi, H. A review of technical change in assessment of climate policy. Annu. Rev.Energy Environ. 1999, 24, 513–544.
41. Organization for Economic Cooperation and Development (OECD). OECD Compendium of ProductivityIndicators 2017; Report; OECD: Paris, France, 2017.
42. Gordon, R.J. The Rise and Fall of American Growth: The US Standard of Living Since the Civil War; PrincetonUniversity Press: Princeton, NJ, USA, 2016.
43. Cowen, T. The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, andWill (Eventually) Feel Better: A Penguin eSpecial from Dutton; Penguin: New York, NY, USA, 2011.
44. Summers, L.H. US economic prospects: Secular stagnation, hysteresis, and the zero lower bound. Bus. Econ.2014, 49, 65–73.
45. Edenhofer, O.; Lessmann, K.; Bauer, N. Mitigation strategies and costs of climate protection: The effects ofETC in the hybrid model MIND. Energy J. 2006, 27, pp. 207–222.
46. Rao, S.; Keppo, I.; Riahi, K. Importance of technological change and spillovers in long-term climate policy.Energy J. 2006, 27, 123–139.
47. Sano, F.; Akimoto, K.; Homma, T.; Tomoda, T. Analysis of Technological Portfolios for CO2 Stabilizationsand Effects of Technological Changes. Energy J. 2006, 27, 141–161.
48. Löschel, A. Technological change in economic models of environmental policy: A survey. Ecol. Econ. 2002,43, 105–126.
49. Ayres, R. Energy, Complexity and Wealth Maximization; Springer: Berlin, Germany, 2016.50. Garrett, T.J. Long-run evolution of the global economy: 1. Physical basis. Earth’s Future 2014, 2, 127–151.51. Lindenberger, D.; Kümmel, R. Energy and the state of nations. Energy 2011, 36, 6010–6018.52. Kumhof, M.; Muir, D. Oil and the world economy: Some possible futures. Philos. Trans. R. Soc. Lond. A
Math. Phys. Eng. Sci. 2014, 372, doi:10.1098/rsta.2012.0327, 1–26.53. Kümmel, R.; Lindenberger, D.; Weiser, F. The economic power of energy and the need to integrate it with
energy policy. Energy Policy 2015, 86, 833–843.54. Kümmel, R. Why energy’s economic weight is much larger than its cost share. Environ. Innov. Soc. Transit.
2013, 9, 33–37.55. Giraud, G.; Kahraman, Z. How Dependent Is Growth from Primary Energy? The Dependency Ratio of Energy
in 33 Countries (1970–2011); Documents de Travail du Centre d’Economie de la Sorbonne; Maison des SciencesÉconomiques: Paris, France, 2014.
56. Keen, S.; Ayres, R. A Note on the Role of Energy in Production. Ecol. Econ. 2017, submitted.57. Barnett, W. Dimensions and economics: some problems. Q. J. Aust. Econ. 2004, 7, 95–104.58. US Energy Information Adminstration (EIA). Global Energy Intensity Continues to Decline; EIA: Washington,
DC, USA, 2016.59. Lightfoot, H.D. Understand the three different scales for measuring primary energy and avoid errors. Energy
2007, 32, 1478–1483.60. US Energy Information Adminstration (EIA). International Energy Outlook; Report; US Energy Information
Adminstration: Washington, DC, USA, 2016.61. Loftus, P.J.; Cohen, A.M.; Long, J.; Jenkins, J.D. A critical review of global decarbonization scenarios: What
do they tell us about feasibility? Wiley Interdiscip. Rev. Clim. Chang. 2015, 6, 93–112.62. Teske, S. Energy [R]evolution: A Sustainable World Energy Outlook, 3rd ed.; Greenpeace International, European
Renewable Energy Council: Amsterdam, The Netherlands; Brussels, Belgium, 2010.63. Jacobson, M.Z.; Delucchi, M.A. Providing all global energy with wind, water, and solar power, Part I:
Technologies, energy resources, quantities and areas of infrastructure, and materials. Energy Policy 2011,39, 1154–1169.
64. Hertwich, E.G.; Peters, G.P. Carbon footprint of nations: A global, trade-linked analysis. Environ. Sci. Technol.2009, 43, 6414–6420.
65. Crawford, R. Life Cycle Assessment in the Built Environment; Spon Press: Oxfordshire, UK, 2011.66. Hawkins, T.R.; Singh, B.; Majeau-Bettez, G.; Strømman, A.H. Comparative environmental life cycle
assessment of conventional and electric vehicles. J. Ind. Ecol. 2013, 17, 53–64.
134
Energies 2018, 11, 839 15 of 17
67. Crawford, R.; Stephan, A. The Significance of Embodied Energy in Certified Passive Houses. World Acad.Sci. Eng. Technol. 2013, 78, 589–595.
68. Alcott, B. Jevons’ paradox. Ecol. Econ. 2005, 54, 9–21.69. Greening, L.A.; Greene, D.L.; Difiglio, C. Energy efficiency and consumption—The rebound effect—A
survey. Energy Policy 2000, 28, 389–401.70. Brockway, P.E.; Saunders, H.; Heun, M.K.; Foxon, T.J.; Steinberger, J.K.; Barrett, J.R.; Sorrell, S. Energy
rebound as a potential threat to a low-carbon future: Findings from a new exergy-based national-levelrebound approach. Energies 2017, 10, 51.
71. Cullen, J.M.; Allwood, J.M. The efficient use of energy: Tracing the global flow of energy from fuel to service.Energy Policy 2010, 38, 75–81.
72. Sorrell, S. Reducing energy demand: A review of issues, challenges and approaches. Renew. Sustain.Energy Rev. 2015, 47, 74–82.
73. Kolstad, C.; Urama, K.; Broome, J.; Bruvoll, A.; Olvera, M.; Fullerton, D.; Gollier, C.; Hanemann, W.;Hassan, R.; Jotzo, F.; et al. Social, Economic, and Ethical Concepts and Methods. In Climate Change2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of theIntergovernmental Panel on Climate Change; Cambridge University Press: New York, NY, USA, 2014.
74. Blanco, G.; Gerlagh, R.; Suh, S.; Barrett, J.; de Coninck, H.; Morejon, C.; Mathur, R.; Nakicenovic, N.;Ahenkorah, A.; Pan, J.; et al. Drivers, Trends and Mitigation. In Climate Change 2014: Mitigation of ClimateChange. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on ClimateChange; Cambridge University Press: New York, NY, USA, 2014.
75. Sathaye, J.; Lucon, O.; Rahman, A.; Christensen, J.; Denton, F.; Fujino, J.; Heath, G.; Mirza, M.; Rudnick,H.; Schlaepfer, A. Renewable eNergy in the Context of Sustainable Development; Intergovernmental Panel onClimate Change (IPCC): Geneva, Switzerland, 2012
76. Daly, H.E.; Scott, K.; Strachan, N.; Barrett, J. Indirect CO2 emission implications of energy system pathways:Linking IO and TIMES models for the UK. Environm. Sci. Technol. 2015, 49, 10701–10709.
77. Lenzen, M. Errors in conventional and Input Output based Life Cycle inventories. J. Ind. Ecol. 2000, 4, 127–148.78. ISO. ISO 14041—Environmental Management—Life Cycle Assessment—Goal and Scope Definition and Inventory
Analysis; Report; International Organization for Standardization: Geneva, Switzerland, 1998.79. ISO. ISO 14040—Environmental Management—Life Cycle Assessment—Principles and Framework; Report;
International Organization for Standardization: Geneva, Switzerland, 2006.80. Jones, C.; Gilbert, P.; Raugei, M.; Mander, S.; Leccisi, E. An approach to prospective consequential life cycle
assessment and net energy analysis of distributed electricity generation. Energy Policy 2016, 100, 350–358.81. Sullivan, P.; Krey, V.; Riahi, K. Impacts of considering electric sector variability and reliability in the
MESSAGE model. Energy Strategy Rev. 2013, 1, 157–163.82. Sonnemann, G.; Vigon, B.; Rack, M.; Valdivia, S. Global guidance principles for life cycle assessment
databases: Development of training material and other implementation activities on the publication. Int. J.Life Cycle Assess. 2013, 18, 1169–1172.
83. Pauliuk, S.; Arvesen, A.; Stadler, K.; Hertwich, E.G. Industrial ecology in integrated assessment models.Nat. Clim. Chang. 2017, 7, 13–20.
84. IEA. Energy Technology Perspectives; Report; International Energy Agency: Paris, France, 2017.85. Van Ruijven, B.J.; Van Vuuren, D.P.; Boskaljon, W.; Neelis, M.L.; Saygin, D.; Patel, M.K. Long-term
model-based projections of energy use and CO2 emissions from the global steel and cement industries.Resour. Conserv. Recycl. 2016, 112, 15–36.
86. Hertwich, E.G.; Gibon, T.; Bouman, E.A.; Arvesen, A.; Suh, S.; Heath, G.A.; Bergesen, J.D.; Ramirez, A.;Vega, M.I.; Shi, L. Integrated life-cycle assessment of electricity-supply scenarios confirms globalenvironmental benefit of low-carbon technologies. Proc. Natl. Acad. Sci. USA 2015, 112, 6277–6282.
87. Hall, C.A.; Lambert, J.G.; Balogh, S.B. EROI of different fuels and the implications for society. Energy Policy2014, 64, 141–152.
88. Smith, P.; Bustamante, M.; Ahammad, H.; Clark, H.; Dong, H.; Elsiddig, E.; Haberl, H.; Harper, R.; House, J.;Jafari, M.; et al. Agriculture, forestry and other land use (AFOLU). In Climate Change 2014: Mitigation ofClimate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel onClimate Change; Cambridge University Press: New York, NY, USA, 2014.
135
Energies 2018, 11, 839 16 of 17
89. Hall, C.A.; Dale, B.E.; Pimentel, D. Seeking to understand the reasons for different energy return oninvestment (EROI) estimates for biofuels. Sustainability 2011, 3, 2413–2432.
90. DeCicco, J.M.; Liu, D.Y.; Heo, J.; Krishnan, R.; Kurthen, A.; Wang, L. Carbon balance effects of US biofuelproduction and use. Clim. Chang. 2016, 138, 667–680.
91. Ketzer, F.; Skarka, J.; Rösch, C. Critical Review of Microalgae LCA Studies for Bioenergy Production.BioEnergy Res. 2017, 11, 95–105.
92. Carneiro, M.L.N.; Pradelle, F.; Braga, S.L.; Gomes, M.S.P.; Martins, A.R.F.; Turkovics, F.; Pradelle, R.N.Potential of biofuels from algae: Comparison with fossil fuels, ethanol and biodiesel in Europe and Brazilthrough life cycle assessment (LCA). Renew. Sustain. Energy Rev. 2017, 73, 632–653.
93. Agostinho, F.; Ortega, E. Energetic-environmental assessment of a scenario for Brazilian cellulosic ethanol.J. Clean. Prod. 2013, 47, 474–489.
94. Elliston, B.; Diesendorf, M.; MacGill, I. Simulations of scenarios with 100 percent renewable electricity in theAustralian National Electricity Market. Energy Policy 2012, 45, 606–613.
95. ASEA Brown Boveri (ABB). Power Generation—Energy Efficient Design of Auxiliary Systems in Fossil-Fuel PowerPlants; Report; ABB: Zurich, Switzerland, 2009.
96. Edenhofer, O.; Pichs-Madruga, R.; Sokona, Y.; Seyboth, K.; Matschoss, P.; Kadner, S.; Zwickel, T.; Eickemeier, P.;Hansen, G.; Schlömer, S.; et al. Summary for Policymakers. In IPCC Special Report on Renewable Energy Sourcesand Climate Change Mitigation; Cambridge University Press: New York, NY, USA, 2011.
97. Lu, X.; McElroy, M.B.; Kiviluoma, J. Global potential for wind-generated electricity. Proc. Natl. Acad.Sci. USA 2009, 106, 10933–10938.
98. Miller, L.M.; Brunsell, N.A.; Mechem, D.B.; Gans, F.; Monaghan, A.J.; Vautard, R.; Keith, D.W.; Kleidon, A.Two methods for estimating limits to large-scale wind power generation. Proc. Natl. Acad. Sci. USA 2015,112, 11169–11174.
99. BP. Statistical Review of World Energy 2017; Report; BP: London, UK, 2017.100. Moriarty, P.; Honnery, D. Can renewable energy power the future? Energy Policy 2016, 93, 3–7.101. Dupont, E.; Koppelaar, R.; Jeanmart, H. Global available wind energy with physical and energy return on
investment constraints. Appl. Energy 2017, 209, 322–338, doi:10.1016/j.apenergy.2017.09.085.102. Kubiszewski, I.; Cleveland, C.J.; Endres, P.K. Meta-analysis of net energy return for wind power systems.
Renew. Energy 2010, 35, 218–225.103. Palmer, G. A Framework for Incorporating EROI into Electrical Storage. BioPhys. Econ. Resour. Qual. 2017, 2,
doi:10.1007/s41247-017-0022-3.104. Rogner, H.H.; Aguilera, R.F.; Bertani, R.; Bhattacharya, S.C.; Dusseault, M.B.; Gagnon, L.; Haberl, H.;
Hoogwijk, M.; Johnson, A.; Rogner, M.L.; et al. Energy Resources and Potentials. In Global EnergyAssessment—Toward a Sustainable Future; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2012.
105. McCollum, D.; Bauer, N.; Calvin, K.; Kitous, A.; Riahi, K. Fossil resource and energy security dynamics inconventional and carbon-constrained worlds. Clim. Chang. 2014, 123, 413–426.
106. Rogner, H.H. An assessment of world hydrocarbon resources. Annu. Rev. Energy Environ. 1997, 22, 217–262.107. Ritchie, J.; Dowlatabadi, H. Why do climate change scenarios return to coal? Energy 2017, 140, 1276–1291.108. Mohr, S.; Wang, J.; Ellem, G.; Ward, J.; Giurco, D. Projection of world fossil fuels by country. Fuel 2015,
141, 120–135.109. Rutledge, D. Estimating long-term world coal production with logit and probit transforms. Int. J. Coal Geol.
2011, 85, 23–33.110. Capellán-Pérez, I.N.; Arto, I.N.; Polanco-Martínez, J.M.; González-Eguino, M.; Neumann, M.B. Likelihood
of climate change pathways under uncertainty on fossil fuel resource availability. Energy Environ. Sci. 2016,9, 2482–2496.
111. Mohr, S.H.; Evans, G.M. Forecasting coal production until 2100. Fuel 2009, 88, 2059–2067.112. Murray, J.W. Limitations of Oil Production to the IPCC Scenarios: The New Realities of US and Global Oil
Production. BioPhys. Econ. Resour. Qual. 2016, 1, 13.113. Turner, G.M. A comparison of the Limits to Growth with 30 years of reality. Glob. Environ. Chang. 2008,
18, 397–411.114. Brand-Correa, L.; Brockway, P.; Carter, C.; Foxon, T.; Owen, A.; Taylor, P. Developing an Input-Output based
method to estimate a national-level EROI (energy return on investment). Energies 2017, 10, 534.
136
Energies 2018, 11, 839 17 of 17
115. King, C.W.; Maxwell, J.P.; Donovan, A. Comparing World Economic and Net Energy Metrics, Part 2:Total Economy Expenditure Perspective. Energies 2015, 8, 12975–12996.
116. Palmer, G. An input-output based net-energy assessment of an electricity supply industry. Energy 2017,141, 1504–1516.
117. IEA. Key World Energy Statistics—2017; Report; International Energy Agency: Paris, France, 2017.118. Bashmakov, I. Three laws of energy transitions. Energy Policy 2007, 35, 3583–3594.119. Fizaine, F.; Court, V. Energy expenditure, economic growth, and the minimum EROI of society. Energy Policy
2016, 95, 172–186.120. Heun, M.K.; Santos, J.; Brockway, P.E.; Pruim, R.; Domingos, T.; Sakai, M. From theory to econometrics to
energy policy: Cautionary tales for policymaking using aggregate production functions. Energies 2017, 10, 203.121. Kümmel, R.; Ayres, R.U.; Lindenberger, D. Thermodynamic laws, economic methods and the productive
power of energy. J. Non-Equilib. Thermodyn. 2010, 35, 145–179.122. Messner, S.; Schrattenholzer, L. MESSAGE–MACRO: Linking an energy supply model with a macroeconomic
module and solving it iteratively. Energy 2000, 25, 267–282.123. Lenzen, M.; Geschke, A.; Wiedmann, T.; Lane, J.; Anderson, N.; Baynes, T.; Boland, J.; Daniels, P.;
Dey, C.; Fry, J. Compiling and using input–output frameworks through collabourative virtual labouratories.Sci. Total Environ. 2014, 485, 241–251.
c© 2018 by the author. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Chapter 8
Conclusions and future work
8.1 Conclusions
The motivation for this thesis was to better understand the challenge of decarbonising electricity grids
in biophysical terms. Biophysical economics (BPE) uses the tools of net-energy analysis (NEA), of
which the energy return on investment (EROI) is the most common. Proponents of NEA believe that
it offers insights into the energy-economic nexus that may not be obvious from a price-based approach
alone. NEA is a tool for exploring questions about sustainability of energy systems, viability of
alternatives and feasibility of transition pathways. It is not intended to replace conventional price-
based analysis, but rather, supplement economic, environmental, and other types of analysis. The
EROI ratio provides an energetic valuation of electricity generation technologies, which may, or may
not, correlate with the conventional economic valuation, discussed in chapters 1 and 2.
Ideally, NEA should provide a means to inform and shape energy transition analysis. However,
a lack of methodological consistency has led to contestation of NEA’s relevance to energy transition
feasibility assessment. The aim of this thesis was to address the methodological inconsistency of
electricity-based NEA.
A major weakness in the literature is the failure to adequately incorporate the systems-engineering
properties of electricity supply. There are several reasons for this. Firstly, electricity generation EROI
studies were historically based on attributional, process-based life-cycle assessment (LCA) studies,
with a functional unit of 1 kWh of electricity delivered to the grid. Attributional refers to ‘attribut-
ing’ environmental impacts to the production of the generation technology, and process-based refers to
a method of evaluating the impacts by tracking the production of the generation technology through
a process tree. The conventional study goal was ascertaining whether an electricity generation tech-
nology, considered independently of the grid, ‘paid back’ the energy invested in its production, over
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the lifetime of the device. The simple payback concept implied that an EROI of 1:1 was sufficient for
establishing energetic viability. The expression ‘energy payback time’ evolved out of this conceptual
framing. Implicit in this framing was that instances of the technology would be in ‘energy debt’
for some, or most of their lifetime, and that the incumbent energy system would energetically fund
transitions.
A requirement to ‘pay back’ the energy debt is necessary, but not sufficient for establishing whether
a technology can support the energy demands of advanced economies. Furthermore, a process-based
analysis is systematically incomplete, and truncates typically one third to one half of the embodied
energy.
The contemporary configuration of society requires a system-wide EROI of around 10:1 or greater.
Historically, societies have flourished and grown with a lower EROI, but exhibited a much simpler
degree of cultural and industrial complexity. The hierarchy of human wants and needs has evolved
markedly since the beginning of industrialisation. Earlier configurations exhibited much higher pro-
portions of primary industries, including agriculture and mining, and much less of discretionary and
service sectors. An energy supply technology that is greater than 1:1 but less than roughly 10:1 is not
energetically deficient in itself, but inadequate for supporting the energy demands of contemporary
developed economies.
Since the publication of the first EROI studies in the 1970s, climate emission targets and the asso-
ciated mitigation and scenario literature has progressed significantly. Contemporary studies examine
the efficacy of energy transitions, often including high-penetration variable renewable energy (VRE).
As scenario analysis has evolved, the implicit goal and scope of NEA has shifted, discussed in chapter
6. However, the evolving goal and scope hasn’t been explicitly identified or formalized in the NEA
literature. This has become particularly problematic for electricity-based EROI analyses because elec-
tricity supply is delivered by complex real-time systems. Furthermore, the performance characteristics
of many low-emission substitutes is markedly different to conventional generators. Evaluating a par-
ticular component of electricity supply based on lifetime electricity generation is far too coarse-grained
if the goal is transition feasibility assessment.
An EROI analysis needs to establish the impact on the overall system EROI, in order to establish
the energetic viability of a transition pathway. Such an approach is termed a consequential analysis.
For non-dispatchable electricity sources, the difference between the isolated-case and the system-case
is much wider than for dispatchable sources. An example of a wide divergence is solar photovoltaics
(PV). Based on an implicit isolated-case, some studies calculate an EROI above 20:1. However, due
to the daily and seasonally dominated supply curve, the contribution of PV to system-EROI can be
reduced to below 5:1.
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In some cases, an ad-hoc approach has been taken to address the weaknesses of the attributional
approach. But paradoxically, many studies that correctly identified weaknesses of an attributional
approach introduced other shortcomings because the study goals and boundaries departed from es-
tablished conventions.
There has been recent progress towards the adoption of consequential analysis. However, since the
systems-engineering of electricity supply lies outside the domain of life-cycle research, it has tended
to be insufficiently described.
The research method to addressing these issues is to approach the methodological problem from
two ‘directions’, based on the conceptual idea of hybrid-LCA analysis. Firstly, using a top-down
approach for systematic completeness; and a bottom-up approach for a more detailed technology-
specific analysis.
Chapter 4 comprises the top-down approach. It comprises an environmentally extended input-
output analysis (EEIOA) of the Australian electricity supply industry. This is the first study to
calculate the EROI of electricity at a national level, with a calculated EROI of 40:1. The result confirms
the common assumption that EROI is not a major factor with respect to the current configuration
of the Australian electricity supply industry. Yet the study also validates the biophysical economists’
intuition that a shift away from the incumbent generation suite risks significantly increasing the
energetic costs of the system, and consequently lowering the system EROI below an energetically
viable threshold.
The bottom-up approach includes two technology-focused studies. Chapter 5 introduces a frame-
work for implementing a consequential EROI analysis of variable renewable energy (VRE) and storage.
Since the underlying function of electricity grids is to ensure supply meets demand at a prescribed
reliability level, a power based functional unit of 1 kW was adopted to supplement the conventional 1
kWh of energy delivered to the grid. This is the first EROI study to undertake a power-based analysis,
and provides a framework for comparing the energetic costs of VRE and electrical storage, with the
system value that storage provides.
Chapter 6 is an evaluation of the factors that contribute to a divergence in the renewables EROI
literature. This is the first comprehensive evaluation of these factors, and establishes a framework for
incorporating process-based LCA into a consequential EROI assessment.
The most significant factors are ‘primary energy equivalence’; the role of storage; the choice of LCA
methodology; and differences between assumed values for key performance parameters and real-world
performance. The first factor has been inadequately settled in the electricity-based EROI literature.
It is related to energy reporting conventions, sometimes known as the ‘IEA’ and ‘EIA’ reporting
conventions. Since combustion fuels have historically dominated commercial energy supply, by con-
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vention, primary energy has been expressed in thermal equivalence. Since electricity is a secondary
energy carrier, statistical accounting of primary fuels requires standard reporting conventions. Ther-
mal equivalence provides a consistent metric for comparing combustion fuels, but is less meaningful
for non-thermal fuels, such as hydro, solar PV and wind. Since nuclear power and concentrated solar
thermal power include a thermodynamic cycle, with a thermal efficiency of 30 to 40%, but use pri-
mary energy without combustion, different approaches to reporting convention can result in around
a three-fold difference in primary energy consumption.
Furthermore, the LCA literature adopts a further reporting convention: the cumulative energy
demand (CED). CED accounts for all forms of energy extracted from nature, and is more diverse
than the thermal equivalence adopted by energy reporting agencies. The LCA literature differentiates
between the ‘energy harvested’ and the ‘energy harvestable’ concepts. For example, in the case of solar
PV, the former evaluates the CED as equivalent to the electricity generated, while the latter evaluates
the CED as the total solar radiation that is incident on a panel. With a panel solar conversion efficiency
of 14%, the latter evaluates CED at around seven times the former. The choice and application of
conventions, between both the energy investments of manufacturing and constructing the generation
assets, and the electricity generated during the operational phase, can significant alter the results of
an EROI study. In the case of the ‘IEA versus EIA’ conventions, the choice can alter the result by a
factor of three. The choice of convention, and the contexts in which each applies, differs depending
on study goal. The common approach to accounting for the electricity generated has been to assume
that a unit of electricity, from any source, is considered equivalent to the primary energy required to
produce a unit of electricity from the overall mix of sources for the grid in question. The so-called
‘replacement logic’ makes sense from an energy payback perspective, but is less meaningful where the
study goal is transition feasibility. In the future, a relative decline in combustion-based generation will
reduce the appropriateness of thermal equivalence. In such cases, a more comprehensive and dynamic
approach is required.
The second factor relates to the appropriate magnitude of ‘buffering’ for VRE. There is no single
best answer to the appropriate quantity of storage, if any. The contribution of storage to EROI
should be assessed at a system level in order to capture system-level effects, such as geographic and
technology diversity, curtailment, demand management, and additional transmission. A weakness
in the literature has been the adoption of ad hoc renewable overbuild and storage requirements. A
contribution of this thesis is to establish a framework for evaluating the role of storage in a consistent
manner, described in chapter 5.
The choice of LCA methodology and boundaries significantly alters the result. The LCA litera-
ture emphases the importance of standardization, and complete and verifiable documentation. The
141
consistent treatment of system boundaries is important for comparing findings of studies with similar
goals. However, where the context for NEA is feasibility assessment for large scale energy transition,
providing a comprehensive account from a net-energy perspective may be a higher priority than data
precision, especially if precision necessarily comes at the cost of narrowing the focus for data collection.
A further issue is the necessary adoption of key performance indicators. Meta-analyses typically
adopt standard values for performance indicators, such as assumed lifetime, capacity factor, and
performance degradation. These ensure a standard set of conditions for inter-study comparison.
In the case of rapidly evolving technologies, there is uncertainty of long-term performance due to
insufficient operational time to establish empirical data. Furthermore, there is often a divergence
between assumed values and the average real-world operating conditions. Studies tend to assume that
renewable technologies will be constructed in locations with favourable natural resources. However,
the availability of natural resources is only one of many factors that contribute to the geographic
location of renewable installations. Important factors include population and per-capita income,
the availability of transmission infrastructure, alternative investment options, and cultural-political
support and subsidies.
The key conclusion of this thesis is that a technology-specific EROI analysis needs to establish
what the impact on the overall system EROI will be, in order to establish the energetic viability of a
transition pathway.
This thesis provides a foundation for incorporating net-energy analysis into integrated and scenario
modelling. In order to place the research within a policy-relevant context, chapter 7 is an analysis of
the IPCC Fifth Assessment Report (AR5), specifically in relation to Integrated Assessment Models
(IAMs). The chapter concludes with a modelling proposal to supplement and support IAMs.
In summary, this thesis has shown that EROI is critical for determining technology choices in the
future, and establishing the energetic viability of low-emission electricity transition pathways.
8.2 Electrical storage
One of the important contributions of this thesis is a re-appraisal of the role of electrical storage in
the context of energy transitions. Chapter 5 introduces the surplus energy-storage synergy hypothesis
as a general principle for exploring the role of electrical storage. The value of storage is nearly always
framed in terms of a financial analysis within the particularly market or tariff regime that the storage
operates in. On the other hand, the NEA approach is less about the value to particular economic
agents, but the contribution of storage to the system as a whole, with an emphasis on an energetic
valuation.
Since electrical storage time-shifts power rather than converting primary energy, by definition, it
142
must lower the system EROI. Furthermore, some forms of electrical storage, especially electrochemical
batteries, possess a high embodied energy. At modest scale, there may be worthwhile energetic trade-
offs with the adoption of storage. For example, with sufficiently high-EROI renewable power, it may
be preferable to store and re-use a portion of surplus power rather than curtail. However, there is
a marked diminishing return to VRE and storage, such that beyond a given penetration, scenarios
with a sufficiently high storage are energetically infeasible. ‘Infeasible’ refers to a system-EROI that
is significantly less than 10:1. A low EROI does not imply technical infeasibility – there are many
successful examples of remote and small island grids that operate reliably. However, these examples
are inevitably small scale and subsidised, both economically and energetically.
8.3 What would a low-emission scenario of Australian elec-
tricity look like if it was optimised for EROI?
A pending question of this research is – what would a low-emission scenario of Australian electricity
look like if it was optimised for EROI? A full analysis is beyond the scope of the present study.
However, the highest EROI of the scalable renewable options, and the most biophysically feasible VRE
is wind power. Furthermore, since long distance transmission has a low embodied energy relative to
the transmitted energy, an EROI-optimal solution would likely include geographically diversified wind
power to supply a large proportion of system energy.
In a high-VRE scenario, load balancing would be provided by some combination of fossil fuel gen-
eration, hydro power and nuclear power, subject to other constraints, especially emissions target, cost,
social licence, resource availability, and practical utilisation of dispatchable generators. Synchronous
generators would also provide other essential system functions, such as inertia and reactive power.
Since electrical storage lowers the system EROI, an optimal system would likely possess modest or
no storage for bulk generation1. The three technologies above essentially provide storage services
in the form of: ‘stored sunlight’; potential energy behind a dam wall; and stored nucleosynthesis,
respectively. Some Australian low-emission scenarios include biofuels to provide an energy storage
role for load balancing. Although the modest use of biofuels may provide a useful role measured by
market value, the EROI of biofuels is too low to supply a large fraction of generation. The addition of
carbon capture to fossil fuel generation impairs the EROI, however the EROI of coal-fired generation
is sufficiently high that coal with CCS may still be energetically viable.
Wind and solar PV continue to show an upward trend of EROI. Ongoing technological advances in
manufacturing and construction are likely to continue this trend, but may be offset by an expansion
1 Storage can also be effectively used for network support as a substitute for network augmentation.
143
into regions with less favourable wind or solar resources. In general, global oil and gas show a long-run
downward trend. The global average EROI for coal remains high, but is likely to decline through the
twenty first century.
Since Australia has a surplus of natural resources, both renewable and non-renewable, there is likely
a multitude of near optimal solutions that are biophysically feasible. On the other hand, in many
parts of the world, such as high latitude regions, and without geographically diverse and favourable
wind resources, high-penetration renewable options would be far more limited.
8.4 Future work
1. Nearly all low-emission policy prescriptions focus on, or include price as a key driver. The main
IPCC integrated models are driven by optimising costs. Carbon pricing is broadly considered an
essential element of climate mitigation. Since the period of competition reform and liberalization
of electricity systems, markets and prices have become the principle mechanism for public policy.
However, if a central hypothesis of this thesis is accepted – that the price system alone is
insufficient for understanding energy transitions – then other questions are opened up. What
is the role of energy and carbon markets? Can markets drive the optimal trajectory? What if
markets push towards energetic bottlenecks? Should there be a greater role for command-and-
control of scenario pathways?
2. The adoption of EROI constraints to energy system optimisation modelling (ESOM) would
improve their biophysical rigour. This could take the form of an additional technology-based
constraint, subject to geographic location or other parameters.
3. Alternatively, ESOM models could be run as an EROI-maxima optimisation to contrast with
cost-minima model runs.
4. A challenge of NEA is improving the linkages between energy and economic metrics. The
question of whether EROI contributes more as a metric that stands separately to conventional
economic metrics, or whether EROI can be better incorporated into economic metrics, is an
open question. The role of energy generally, and electricity specifically, in national economic
productivity is an important question.
5. This thesis focused on electricity, for which there are multiple technology substitutes. A greater
substitution challenge is transport fuels. There are currently no scalable substitutes for liquid
fuels for air travel, oceanic shipping, and heavy freight. Electricity-based light transport util-
ising battery and hydrogen-based options are generally more embodied energy intensive than
144
petroleum fuelled vehicles, and therefore require greater attention to NEA. Expanding electricity-
based EROI boundaries to a ‘well-to-wheels’ vehicles analysis would reveal potential constraints
in electrifying transport.
6. A proposal for future work is detailed in chapter 7. It addresses a significant weakness of
integrated assessment models (IAMs), which is that GDP and productivity growth are given
as exogenous. It is based on deriving energy-industry-own-use (EIOU) with environmentally
extended input-output analysis (EEIOA). The EIOU provides a basis for estimating economic
growth constraints and provides feedback mechanism for IAMs.
145
References
ABB (2009), Power Generation - Energy Efficient Design of Auxiliary Systems in Fossil-Fuel Power
Plants, Report, ABB, Zurich, Switzerland.
Abdulla, K., Wirth, A., Halgamuge, S. K. & Steer, K. C. (2014), Selecting an optimal combination
of storage & transmission assets with a non-dispatchable electricity supply, in ‘Information and
Automation for Sustainability (ICIAfS), 2014 7th International Conference on, ’, IEEE, pp. 1–6.
ABS (1920), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1940), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1954), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1955), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1960), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1962), Census of Motor Vehicles, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1965), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1970), Year Book of Australia, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1976), Census of Motor Vehicles, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1988), Census of Motor Vehicles, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (1999), Census of Motor Vehicles, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (2001), Electricity Industry - Industry restructing and the effects on statistics, Report, Australian
Bureau of Statistics, Canberra, Australia.
ABS (2004), ‘4648.0.55.001 - Detailed Energy Statistics, Australia, 2001-02’.
URL: http://www.abs.gov.au/ausstats/[email protected]/mf/4648.0.55.001
146
ABS (2012), ‘5206.0 - Australian national accounts: national income, expenditure and product’.
URL: http://www.abs.gov.au/ausstats/[email protected]/mf/5206.0
ABS (2013a), ‘8752.0 - Building Activity, Australia, Featured Article: Average floor area of new
residential dwellings’.
URL: http://www.abs.gov.au/AUSSTATS/[email protected]/Previousproducts/8752.0Feature%20Article1Jun%202013
ABS (2013b), Labour Statistics: Concepts, Sources and Methods - Employee Remuneration, Report,
Australian Bureau of Statistics, Canberra, Australia.
ABS (2013c), ‘Measures of Australia’s Progress - Is life in Australia getting better?’.
URL: http://www.abs.gov.au/ausstats/[email protected]/mf/1370.0
ABS (2014a), 3105 - Australian Historical Population Statistics, Report, Australian Bureau of Statis-
tics, Canberra, Australia.
ABS (2014b), 9223.0 - Road Freight Movements, Report, Australian Bureau of Statistics, Canberra,
Australia.
ABS (2015a), 5206.0 - Australian National Accounts: National Income, Expenditure and Product -
series A2304334J, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (2015b), Census of Motor Vehicles, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (2016a), ‘5209.0.55.001 - Australian National Accounts: Input-Output Tables, 2013-14 ’.
URL: http://www.abs.gov.au/ausstats/[email protected]/mf/5215.0.55.001
ABS (2016b), 6401.0 - Consumer Price Index - series A2328141J - Electricity, Report, Australian
Bureau of Statistics, Canberra, Australia.
ABS (2017a), 5206.0 - Australian National Accounts: National Income, Expenditure and Product -
series A2304334J, Report, Australian Bureau of Statistics, Canberra, Australia.
ABS (2017b), ‘Personal communication: Electricity consumption by the electricity supply industry’.
ACIL Tasman (2009), Fuel resource, new entry and generation costs in the NEM, 0419-0035, Report,
ACIL Tasman, Melbourne, Australia.
Aebischer, B. & Hilty, L. M. (2015), The energy demand of ICT: a historical perspective and current
methodological challenges, in ‘ICT Innovations for Sustainability’, Springer, pp. 71–103.
AEMO (2012a), ‘2012 Planning Studies Input Tables’.
URL: https://www.aemo.com.au/media/Files/Other/planning/Modelling Assumptions and Data 2012 v3.xls
147
AEMO (2012b), Rooftop PV Information Paper, Report, Australian Energy Market Operator, Mel-
bourne, Australia.
AEMO (2013a), 100% renewables study - modelling outcomes, Report, Australian Energy Market
Operator, Melbourne, Australia.
AEMO (2013b), Power System Adequacy for the National Electricity Market, Report, Australian
Energy Market Operator, Melbourne, Australia.
AEMO (2016a), ‘NEMWEB archived dispatch data’.
URL: http://www.nemweb.com.au/REPORTS/ARCHIVE/Dispatch SCADA/
AEMO (2016b), Regional Boundaries for the National Electricity Market, Report, Australian Energy
Market Operator, Melbourne, Australia.
Aghahosseini, A., Bogdanov, D. & Breyer, C. (2016), 100 % Renewable Energy in North America and
the role of Solar Photovoltaics, in ‘EU-PVSEC conference June 20-24, 7DV.4.8, Munich, Germany
’.
Agostinho, F. & Ortega, E. (2013), ‘Energetic-environmental assessment of a scenario for Brazilian
cellulosic ethanol’, Journal of Cleaner Production 47, 474–489.
Ahmed, N. M. (2016), Failing states, collapsing systems: biophysical triggers of political violence,
Springer, Berlin, Germany.
Alcott, B. (2005), ‘Jevons’ paradox’, Ecological Economics 54(1), 9–21.
Allday, A. (2015a), ‘IBISWorld Industry Report D2630 Electricity Distribution in Australia’, IBIS
World, Melbourne, Australia .
Allday, A. (2015b), ‘IBISWorld Industry Report D2640 Electricity Retailing in Australia’, IBIS World,
Melbourne, Australia .
Allen, R. C. (2012), ‘Backward into the future: The shift to coal and implications for the next energy
transition’, Energy Policy 50, 17–23.
Alsema, E. (2000), ‘Energy pay-back time and CO2 emissions of PV systems’, Progress in photo-
voltaics: research and applications 8(1), 17–25.
Andreessen, M. (2011), ‘Why Software Is Eating The World’’, Wall Street Journal 20 Aug 2011.
Archer, C. & Jacobson, M. (2007), ‘Supplying baseload power and reducing transmission requirements
by interconnecting wind farms’, Journal of Applied Meteorology and Climatology 46(11), 1701–1717.
148
Arvesen, A. & Hertwich, E. G. (2015), ‘More caution is needed when using life cycle assessment to
determine energy return on investment (EROI)’, Energy Policy 76, 1–6.
Arvidsson, R., Fransson, K., Froling, M., Svanstrom, M. & Molander, S. (2012), ‘Energy use indicators
in energy and life cycle assessments of biofuels: review and recommendations’, Journal of Cleaner
Production 31, 54–61.
Australian Department of Industry (2013a), Energy Efficiency Opportunities Program - The First 5
Years: 2006-2011 - The Mining Sector, Report, Department of Industry, Canberra, Australia.
Australian Department of Industry (2013b), Energy Efficiency Opportunities Program - The First 5
Years: 2006-2011 - The Oil & Gas Sector, Report, Department of Industry, Canberra, Australia.
Australian Department of the Environment (2015), National Greenhouse Accounts Factors, Report,
Department of the Environment, Canberra, Australia.
Australian Productivity Commission (2013), Electricity Network Regulatory Frameworks Report, Re-
port, Productivity Commission, Canberra, Australia.
Ayres, R. (2016), Energy, Complexity and Wealth Maximization, Springer, Berlin, Germany.
Ayres, R. U. (2001), ‘The minimum complexity of endogenous growth models:: the role of physical
resource flows’, Energy 26(9), 817–838.
Ayres, R. U. & Nair, I. (1984), ‘Thermodynamics and economics’, Physics Today 37, 62–71.
Ayres, R. U. & Warr, B. (2010), The economic growth engine: how energy and work drive material
prosperity, Edward Elgar Publishing Limited, Cheltenham, UK.
Azar, C. & Dowlatabadi, H. (1999), ‘A review of technical change in assessment of climate policy’,
Annual review of energy and the environment 24(1), 513–544.
Baldwin, K. (2017), ‘Is coal still cheaper than renewables as an energy source?’.
URL: http://theconversation.com/factcheck-qanda-is-coal-still-cheaper-than-renewables-as-an-
energy-source-81263
Ball, A., Bernie, K., Feng, A., McCluskey, C., Stanwix, G., Willcock, T. & Wokker, N. (2014), ‘Energy
in Australia 2014’, Bureau of Resources and Energy Economics .
Barnes, F. S. & Levine, J. G. (2011), Large energy storage systems handbook, CRC Press, Boca Raton,
FL, USA.
149
Barnett, W. (2004), ‘Dimensions and economics: some problems’, Quarterly Journal of Austrian
Economics 7(1), 95–104.
Barnhart, C., Carbajales-Dale, M. & Benson, S. M. (2015), ‘Flexible Power Grid Resources - an NEA
Analysis’.
URL: http://gcep.stanford.edu/pdfs/events/workshops/Barnhart NEA%20Stanford%20April%202015.pdf
Barnhart, C. J. & Benson, S. M. (2013), ‘On the importance of reducing the energetic and material
demands of electrical energy storage’, Energy & Environmental Science 6(4), 1083–1092.
Barnhart, C. J., Dale, M., Brandt, A. R. & Benson, S. M. (2013), ‘The energetic implications of
curtailing versus storing solar-and wind-generated electricity’, Energy & Environmental Science
6(10), 2804–2810.
Bashmakov, I. (2007), ‘Three laws of energy transitions’, Energy Policy 35(7), 3583–3594.
Bashmakov, I., Bruckner, T., Mulugetta, Y., Chum, H., Navarro, A. & Edmonds, J. (2014), Energy
systems, in O. Edenhofer, R. Pichs-Madruga & Y. Sokona, eds, ‘Climate Change 2014: Mitigation
of Climate Change’, Cambridge University Press, New York, NY, USA.
Beaudreau, B. C. (1999), Energy and the rise and fall of political economy, Greenwood Press.
Beaudreau, B. C. (2005), ‘Engineering and economic growth’, Structural Change and Economic Dy-
namics 16(2), 211–220.
Bhandari, K. P., Collier, J. M., Ellingson, R. J. & Apul, D. S. (2015), ‘Energy payback time (EPBT)
and energy return on energy invested (EROI) of solar photovoltaic systems: A systematic review
and meta-analysis’, Renewable and Sustainable Energy Reviews 47, 133–141.
Billinton, R. & Allan, R. N. (1996), Reliability evaluation of power systems, 2nd edn, Springer Sci-
ence+Business Media, New York, USA.
Bithas, K. & Kalimeris, P. (2013), ‘Re-estimating the decoupling effect: Is there an actual transition
towards a less energy-intensive economy?’, Energy 51, 78–84.
Blakers, A., Lu, B. & Stocks, M. (2017), ‘100% renewable electricity in Australia’, Energy 133, 471–
482.
Blanco, G., Gerlagh, R., Suh, S., Barrett, J., de Coninck, H., Morejon, C., Mathur, R., Nakicenovic,
N., Ahenkorah, A., Pan, J., Pathak, H., Rice, J., Richels, R., Smith, S., Stern, D., Toth, F. & Zhou,
P. (2014), Drivers, Trends and Mitigation, in ‘Climate Change 2014: Mitigation of Climate Change.
150
Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change’, Cambridge University Press, New York, NY, USA.
Bogdanov, D. & Breyer, C. (2016), ‘North-East Asian Super Grid for 100% renewable energy supply:
Optimal mix of energy technologies for electricity, gas and heat supply options’, Energy Conversion
and Management 112, 176–190.
Bollard, A. (2016), A few hares to chase: the economic life and times of Bill Phillips, Oxford University
Press.
Boyle, G. (2016), ‘The Economic Implications of The Maximum Power Principle For a Sustainable
Society’.
URL: http://www.isecoeco.org/wp-content/uploads/2016/09/Boyle-Gavin-economic.pdf
BP (2017), Statistical Review of World Energy 2017, Report, BP, London, UK.
Brady, F. (1996), ‘Contribution on Australia: A Dictionary on Electricity’, The International Con-
ference on Large High Voltage Electrical Systems .
Brand-Correa, L., Brockway, P., Carter, C., Foxon, T., Owen, A. & Taylor, P. (2017), ‘Developing
an Input-Output based method to estimate a national-level EROI (energy return on investment)’,
Energies 10(4).
Brandt, A. R. (2017), ‘How Does Energy Resource Depletion Affect Prosperity? Mathematics of a
Minimum Energy Return on Investment (EROI)’, BioPhysical Economics and Resource Quality
2(1), 2.
Brandt, A. R. & Dale, M. (2011), ‘A general mathematical framework for calculating systems-scale
efficiency of energy extraction and conversion: Energy return on investment (EROI) and other
energy return ratios’, Energies 4(8), 1211–1245.
Brandt, A. R., Dale, M. & Barnhart, C. J. (2013), ‘Calculating systems-scale energy efficiency and
net energy returns: A bottom-up matrix-based approach’, Energy 62, 235–247.
BREE (2013), Energy in Australia 2013, Report, Bureau of Resources and Energy Economics, Can-
berra, ACT, Australia.
BREE (2014a), Australian Energy Projections to 2049-50, Report, Bureau of Resources and Energy
Economics, Canberra, ACT, Australia.
BREE (2014b), Energy in Australia 2014, Report, Bureau of Resources and Energy Economics, Can-
berra, ACT, Australia.
151
BREE (2015), Energy in Australia 2015, Report, Bureau of Resources and Energy Economics, Can-
berra, ACT, Australia.
Breyer, C. & Schmid, J. (2010), Population Density and Area weighted Solar Irradiation: global
Overview on Solar Resource Conditions for fixed tilted, 1-axis and 2-axes PV Systems, in ‘25th
European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photo-
voltaic Energy Conversion, 6-10 September 2010, Valencia, Spain ’, pp. 6–10.
Brockway, P. E., Barrett, J. R., Foxon, T. J. & Steinberger, J. K. (2014), ‘Divergence of trends in US
and UK aggregate exergy efficiencies 1960–2010’, Environmental science & technology 48(16), 9874–
9881.
Brockway, P. E., Saunders, H., Heun, M. K., Foxon, T. J., Steinberger, J. K., Barrett, J. R. &
Sorrell, S. (2017), ‘Energy rebound as a potential threat to a low-carbon future: findings from a
new exergy-based national-level rebound approach’, Energies 10(1), 51.
Brown, J. H., Burnside, W. R., Davidson, A. D., DeLong, J. P., Dunn, W. C., Hamilton, M. J.,
Mercado-Silva, N., Nekola, J. C., Okie, J. G. & Woodruff, W. H. (2011), ‘Energetic limits to
economic growth’, BioScience 61(1), 19–26.
Brown, T. & Faruqui, A. (2014), ‘Structure of electricity distribution network tariffs: recovery of
residual costs’, Prepared for the Australian Energy Market Commission. The Brattle Group .
Bruckner, T., Bashmakov, I., Mulugetta, Y., Chum, H., De la Vega Navarro, A., Edmonds, J., Faaij,
A., Fungtammasan, B., Garg, A. & Hertwich, E. (2014), Energy systems, in ‘Climate Change 2014:
Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change.’, Cambridge University Press, New York, NY,
USA.
Brynjolfsson, E. & Hitt, L. M. (2000), ‘Beyond computation: Information technology, organizational
transformation and business performance’, The Journal of Economic Perspectives 14(4), 23–48.
Brynjolfsson, E. & McAfee, A. (2014), The second machine age: work, progress, and prosperity in a
time of brilliant technologies, WW Norton & Company, New York, NY, USA.
Budischak, C., Sewell, D., Thomson, H., Mach, L., Veron, D. E. & Kempton, W. (2012), ‘Cost-
minimized combinations of wind power, solar power and electrochemical storage, powering the grid
up to 99.9 % of the time’, Journal of Power Sources 225, 60–74.
Buenstorf, G. (2004), The economics of energy and the production process: an evolutionary approach,
Edward Elgar Publishing, Cheltenham, UK.
152
Bullard, C. W. (1975), ‘Energy costs, benefits, and net energy’, CAC document; no. 174 .
Bullard, C. W. & Herendeen, R. A. (1975), ‘The energy cost of goods and services’, Energy policy
3(4), 268–278.
Bullard, C. W., Penner, P. S. & Pilati, D. A. (1978), ‘Net energy analysis: Handbook for combining
process and input-output analysis’, Resources and energy 1(3), 267–313.
Buonanno, P., Carraro, C. & Galeotti, M. (2003), ‘Endogenous induced technical change and the costs
of Kyoto’, Resource and Energy economics 25(1), 11–34.
Bureau of Infrastructure Transport and Regional Ecnomics (2016), Traffic and congestion cost trends
for Australian capital cities, Report, Australian Department of Infrastructure and Regional Devel-
opment, Canberra, Australia.
Butlin, N. G. (1962), Australian domestic product, investment and foreign borrowing, 1861-1938/39,
Cambridge University Press.
Buxton, M. (2006), ‘Urban form and urban efficiency’.
URL: http://www.environment.gov.au/node/22564
Byrne, D., Fernald, J. & Reinsdorf, M. (2016), ‘Does the United States have a productivity slowdown
or a measurement problem?’, Brookings papers on economic activity .
Capellan-Perez, I. n., Arto, I. n., Polanco-Martınez, J. M., Gonzalez-Eguino, M. & Neumann, M. B.
(2016), ‘Likelihood of climate change pathways under uncertainty on fossil fuel resource availability’,
Energy & Environmental Science 9(8), 2482–2496.
Carbajales-Dale, M., Barnhart, C. J. & Benson, S. M. (2014), ‘Can we afford storage? A dynamic
net energy analysis of renewable electricity generation supported by energy storage’, Energy &
Environmental Science 7(5), 1538–1544.
Carbajales-Dale, M., Raugei, M., Fthenakis, V. & Barnhart, C. (2015), ‘Energy return on investment
(EROI) of solar PV: an attempt at reconciliation’, Proceedings of the IEEE 103(7), 995–999.
Carneiro, M. L. N., Pradelle, F., Braga, S. L., Gomes, M. S. P., Martins, A. R. F., Turkovics, F. &
Pradelle, R. N. (2017), ‘Potential of biofuels from algae: Comparison with fossil fuels, ethanol and
biodiesel in Europe and Brazil through life cycle assessment (LCA)’, Renewable and Sustainable
Energy Reviews 73, 632–653.
Carnot, S. (1825), Reflections on the motive power of heat and on machines fitted to develop this
power, Annales scientifiques de l’Ecole normale, Paris, France.
153
Chaisson, E. J. (2011), ‘Energy rate density as a complexity metric and evolutionary driver’, Com-
plexity 16(3), 27–40.
Chapman, P. (1975a), ‘Energy analysis of nuclear power stations’, Energy Policy 3(4), 285–298.
Chapman, P. (1975b), ‘For and against nuclear power’, New Scientist (London) 68(971), 142–143.
Chen, P.-Y., Chen, S.-T. & Chen, C.-C. (2012), ‘Energy consumption and economic growth—New
evidence from meta analysis’, Energy Policy 44, 245–255.
Chevallerau, F.-X. (2017), ‘What is Biophysical Economics?’.
URL: https://biophyseco.org/biophysical-economics/what-is-biophysical-economics/
Clarke, L., Jiang, K., Akimoto, K., Babiker, M., Blanford, G., Fisher-Vanden, K., Hourcade, J., Krey,
V., Kriegler, E., Loschel, A., McCollum, D., Paltsev, S., Rose, S., Shukla, P., Tavoni, M., van der
Zwaan, B. & van Vuuren, D. (2014), Assessing Transformation Pathways, in ‘ Climate Change 2014:
Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report
of the Intergovernmental Panel on Climate Change’, Cambridge University Press, New York, NY,
USA.
Clausius, R. (1867), The mechanical theory of heat: with its applications to the steam-engine and to
the physical properties of bodies, J. van Voorst.
Cleveland, C. J. (1987), ‘Biophysical economics: historical perspective and current research trends’,
Ecological Modelling 38(1), 47–73.
Cleveland, C. J., Costanza, R., Hall, C. A. & Kaufman, R. (1984), ‘Energy and the U.S. Economy: A
Biophysical Perspective’, Science 225, 890–897.
Cleveland, C. J., Kaufmann, R. K. & Stern, D. I. (2000), ‘Aggregation and the role of energy in the
economy’, Ecological Economics 32(2), 301–317.
Common, M. S. & Salma, U. (1992), ‘Accounting for changes in Australian carbon dioxide emissions’,
Energy Economics 14(3), 217–225.
Commoner, B. (1971), The closing circle: nature, man, and technology, Alfred A. Knopf Inc., New
York.
Commsec (2016), ‘US overtakes Australia to build biggest homes - CommSec Home Size Trends
Report’.
URL: https://www.commsec.com.au/content/dam/EN/ResearchNews/Eco Insights31.10 US overtakes Australia to build biggest homes..pdf
154
Corcoran, P. & Andrae, A. (2013), ‘Emerging trends in electricity consumption for consumer ICT’,
NUI Galway, Ireland .
Cottrell, F. (1955), Energy and society: the relation between energy, social changes, and economic
development, Vol. 13209, McGraw-Hill.
Coulter, J. E. (2017), ‘A physical economic model of ecosystem services’, Ecosystem Services 25, 195–
200.
Court, V. & Fizaine, F. (2017), ‘Long-Term Estimates of the Energy-Return-on-Investment (EROI)
of Coal, Oil, and Gas Global Productions’, Ecological Economics 138, 145–159.
Cowen, R. (2005), ‘Exploiting the Earth’.
URL: http://mygeologypage.ucdavis.edu/cowen/ gel115/salt.html
Cowen, T. (2011), The great stagnation: How America ate all the low-hanging fruit of modern history,
got sick, and will (eventually) feel better: A Penguin eSpecial from Dutton, Penguin, New York,
NY, USA.
Cramer, J. & Krueger, A. B. (2015), ‘Disruptive Change in the Taxi Business: The Case of Uber’,
National Bureau of Economic Research (22083).
Crawford, R. (2009), ‘Life cycle energy and greenhouse emissions analysis of wind turbines and the
effect of size on energy yield’, Renewable and Sustainable Energy Reviews 13(9), 2653–2660.
Crawford, R. (2011), Life cycle assessment in the built environment, Spon Press, Oxfordshire, UK.
Crawford, R. & Stephan, A. (2013), ‘The Significance of Embodied Energy in Certified Passive Houses’,
World Academy of Science, Engineering and Technology 78.
Crawford, R., Treloar, G. J., Fuller, R. & Bazilian, M. (2006), ‘Life-cycle energy analysis of building
integrated photovoltaic systems (BiPVs) with heat recovery unit’, Renewable and sustainable energy
reviews 10(6), 559–575.
Cullen, J. M. & Allwood, J. M. (2010), ‘The efficient use of energy: Tracing the global flow of energy
from fuel to service’, Energy Policy 38(1), 75–81.
Cunningham, N. (2015), ‘The World’s Top Five Energy MegaStructures’.
URL: https://oilprice.com/Energy/Energy-General/The-Worlds-Top-Five-Energy-
MegaStructures.html
Curry, D. (2016), ‘Court records reveal how much revenue and profit Google has made from Android’.
URL: http://www.digitaltrends.com/mobile/google-android-revenue-revealed/
155
Dale, M. & Benson, S. M. (2013), ‘Energy balance of the global photovoltaic (PV) industry-is the PV
industry a net electricity producer?’, Environmental science & technology 47(7), 3482–3489.
Dale, M., Krumdieck, S. & Bodger, P. (2012), ‘Global energy modelling—A biophysical approach
(GEMBA) part 1: An overview of biophysical economics’, Ecological Economics 73, 152–157.
Daly, H. E. (1972), ‘In Defense of a Steady-State Economy’, American Journal of Agricultural Eco-
nomics 54(5), 945–954.
Daly, H. E. (1986), ‘Thermodynamic and economic concepts as related to resource-use policies: com-
ment’, Land Economics 62(3), 319–322.
Daly, H. E., Scott, K., Strachan, N. & Barrett, J. (2015), ‘Indirect CO2 emission implications of
energy system pathways: linking IO and TIMES models for the UK’, Environmental science &
technology 49(17), 10701–10709.
Day, J. W., D’Elia, C. F., Wiegman, A. R., Rutherford, J. S., Hall, C. A., Lane, R. R. & Dismukes,
D. E. (2018), ‘The Energy Pillars of Society: Perverse Interactions of Human Resource Use, the
Economy, and Environmental Degradation’, BioPhysical Economics and Resource Quality 3(1), 2.
de Wild-Scholten, M. M. (2013), ‘Energy payback time and carbon footprint of commercial photo-
voltaic systems’, Solar Energy Materials and Solar Cells 119, 296–305.
DeCicco, J. M., Liu, D. Y., Heo, J., Krishnan, R., Kurthen, A. & Wang, L. (2016), ‘Carbon balance
effects of US biofuel production and use’, Climatic Change 138(3-4), 667–680.
Deloitte (2016), ‘Economic effects of ridesharing in Australia’.
URL: https://www2.deloitte.com/content/dam/Deloitte/au/Documents/Economics/deloitte-au-
economic-effects-ridesharing-australia-010216.pdf
Deng, Y. (2011), Ancient Chinese Inventions, Cambridge University Press, Cambridge, UK.
Denholm, P. & Hand, M. (2011), ‘Grid flexibility and storage required to achieve very high penetration
of variable renewable electricity’, Energy Policy 39(3), 1817–1830.
Denholm, P. & Kulcinski, G. L. (2004), ‘Life cycle energy requirements and greenhouse gas emissions
from large scale energy storage systems’, Energy Conversion and Management 45(13), 2153–2172.
Denholm, P., O’Connell, M., Brinkman, G. & Jorgenson, J. (2015), ‘Overgeneration from Solar Energy
in California: A Field Guide to the Duck Chart’, NREL (National Renewable Energy Laboratory
(NREL), Golden, CO (United States)) .
156
Department of Energy England (1975), Nuclear safety and the environment - evidence by the De-
partment of Energy to the Royal Commission on Environment. , Report, Department of Energy
England, London, UK.
Diamond, J. M. (2005), Guns, germs, and steel, Vintage, London, UK.
Dodd, N. (2016), The social life of money, Princeton University Press, Princeton, NJ, USA.
Dupont, E., Koppelaar, R. & Jeanmart, H. (2017), ‘Global available wind energy with physical and
energy return on investment constraints’, Applied Energy 209, 322–338.
Dyster, B. & Meredith, D. (1990), Australia in the International Economy: in the twentieth century,
Cambridge University Press, Australia.
Edenhofer, O., Lessmann, K. & Bauer, N. (2006), ‘Mitigation strategies and costs of climate protection:
The effects of ETC in the hybrid model MIND’, The Energy Journal pp. 207–222.
Edenhofer, O., Pichs-Madruga, R., Sokona, Y. & Farahani, E. (2014), Summary for Policymakers, in
‘Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change’, Cambridge University
Press, New York, NY, USA.
Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T.,
Eickemeier, P., Hansen, G., Schlomer, S. & von Stechow, C. (2011), Summary for Policymakers, in
‘IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation’, Cambridge
University Press, New York, NY, USA.
Education, P. (2016), ‘Average Solar Radiation’.
URL: http://www.pveducation.org/pvcdrom/average-solar-radiation
Edwards, J. B., McKinnon, A. C. & Cullinane, S. L. (2010), ‘Comparative analysis of the carbon
footprints of conventional and online retailing. A “last mile” perspective’, International Journal of
Physical Distribution & Logistics Management 40(1/2), 103 – 123.
EIA (2015a), ‘Electricity - Form EIA-923 detailed data’.
URL: http://www.eia.gov/electricity/data/eia923/index.html
EIA (2015b), ‘Passenger travel accounts for most of world transportation energy use’.
URL: https://www.eia.gov/todayinenergy/detail.php?id=23832
EIA (2016a), ‘Global energy intensity continues to decline’.
URL: https://www.eia.gov/todayinenergy/detail.php?id=27032
157
EIA (2016b), International Energy Outlook, Report, US Energy Information Administration, Wash-
ington DC, USA.
Ellingsen, L. A., Majeau-Bettez, G., Singh, B., Srivastava, A. K., Valøen, L. O. & Strømman, A. H.
(2014), ‘Life Cycle Assessment of a Lithium-Ion Battery Vehicle Pack’, Journal of Industrial Ecology
18(1), 113–124.
Elliston, B., Diesendorf, M. & MacGill, I. (2012), ‘Simulations of scenarios with 100 percent renewable
electricity in the Australian National Electricity Market’, Energy Policy 45, 606–613.
Elliston, B., MacGill, I. & Diesendorf, M. (2014), ‘Comparing least cost scenarios for 100% renewable
electricity with low emission fossil fuel scenarios in the Australian National Electricity Market’,
Renewable Energy 66, 196–204.
Elliston, B. & Riesz, J. (2015), Future high renewable electricity scenarios – Insights from mapping
the diversity of near least cost portfolios, in ‘Power and Energy Engineering Conference (APPEEC),
2015 IEEE PES Asia-Pacific, ’, IEEE.
Enevoldsen, M. K., Ryelund, A. & Andersen, M. S. (2009), The impact of energy taxes on competi-
tiveness: A panel regression study of 56 European industry sectors, in M. S. Andersen & P. Ekins,
eds, ‘Carbon-Energy Taxation’, Oxford Univeristy Press, New York, USA.
ERAWA (2015), The Electricity Industry, Report, Economic Regulation Authority Western Australia,
Perth, WA.
European Commission (2017), Commission Implementing Regulation EU 2017/367 of 1 March 2017,
Report, European Commission, Brussels.
Fares, R. L. & King, C. W. (2017), ‘Trends in transmission, distribution, and administration costs for
US investor-owned electric utilities’, Energy Policy 105, 354–362.
Ferroni, F. & Hopkirk, R. J. (2016), ‘Energy Return on Energy Invested (ERoEI) for photovoltaic
solar systems in regions of moderate insolation’, Energy Policy 94, 336–344.
Finch, C. (2015), ‘IBISWorld Industry Report D2620, Electricity Transmission in Australia’, IBIS
World, Melbourne, Australia .
Finkel, A., Moses, K., Munro, C., Effeney, T. & O’Kane, M. (2017), ‘Independent Review into the
Future Security of the National Electricity Market’, Commonwealth of Australia .
Fizaine, F. & Court, V. (2016), ‘Energy expenditure, economic growth, and the minimum EROI of
society’, Energy Policy 95, 172–186.
158
Floyd, J. (2016), ‘Navigating the energy transition landscape: summary findings from a dynamic
systems view’.
URL: https://beyondthisbriefanomaly.org/2016/09/22/navigating-the-energy-transition-landscape-
summary-findings-from-a-dynamic-systems-view/
Fouquet, R. (2010), ‘The slow search for solutions: Lessons from historical energy transitions by sector
and service’, Energy Policy 38(11), 6586–6596.
Fouquet, R. (2011), ‘Divergences in long-run trends in the prices of energy and energy services’, Review
of Environmental Economics and Policy 5(2), 196–218.
Fouquet, R. (2014), ‘Long-run demand for energy services: income and price elasticities over two
hundred years’, Review of Environmental Economics and Policy 8(2), 186–207.
Frischknecht, R., Althaus, H.-J., Dones, R., Hischier, R., Jungbluth, N., Nemecek, T., Primas, A. &
Wernet, G. (2007), Renewable Energy Assessment within the Cumulative Energy Demand Concept:
Challenges and Solutions, in ‘proceedings from: SETAC Europe 14th LCA case study symposium:
Energy in LCA-LCA of Energy, 3-4 Dec 2007, Gothenburg ’.
Frischknecht, R., Heath, G., Raugei, M., Sinha, M., de Wild-Scholten, M., Fthenakis, V., Kim, H.,
Alsema, E. & Held, M. (2016), ‘Methodology Guidelines on Life Cycle Assessment of Photovoltaic
Electricity, 3rd edition, IEA PVPS Task 12’, Report IEA-PVPS T12-08:2016.
Frischknecht, R., Itten, R., Sinha, M., de Wild-Scholten, M., Zhang, V., Fthenakis, V., Kim, H.,
Raugei, M. & Stucki, M. (2015), ‘Life Cycle Inventories and Life Cycle Assessment of Photovoltaic
Systems’, Report T12-04:2015.
Frischknecht, R., Jungbluth, N., Althaus, H.-J., Bauer, C., Doka, G., Dones, R., Hischier, R., Hellweg,
S., Humbert, S., Kollner, T., Loerincik, Y., Margni, M. & Nemecek, T. (2007), ‘Implementation of
Life Cycle Impact Assessment Methods, Data v2.0, ecoinvent Report No. 3’, ecoinvent .
Frischknecht, R., Wyss, F., Knopfel, S. B., Lutzkendorf, T. & Balouktsi, M. (2015), ‘Cumulative
energy demand in LCA: the energy harvested approach’, The International Journal of Life Cycle
Assessment 20(7), 957–969.
Frost, L. & Dingle, T. (1995), Sustaining Suburbia: an historical perspective on Australia’s Urban
Growth, in P. N. Troy, ed., ‘Australian Cities: Issues, strategies and policies for urban Australia in
the 1990s’, Cambridge University Press, Cambridge, UK.
Fthenakis, V. & Kim, H. C. (2011), ‘Photovoltaics: Life-cycle analyses’, Solar Energy 85(8), 1609–
1628.
159
Fu, Y., Liu, X. & Yuan, Z. (2015), ‘Life-cycle assessment of multi-crystalline photovoltaic (PV) systems
in China’, Journal of Cleaner Production 86, 180–190.
Fuller, R. B. (1973), Nine chains to the moon, Cape, London.
fur Wirtschaft und Technologie, B. (2015), ‘Generating capacity, gross electricity generation and
gross consumption Germany’.
URL: http://www.bmwi.de/BMWi/Redaktion/Binaer/Energiedaten/energietraeger10-
stromerzeugungskapazitaeten-bruttostromerzeugung,property=blob,bereich=bmwi2012,sprache=de,rwb=true.xls
Gaines, L. (2014), ‘The future of automotive lithium-ion battery recycling: Charting a sustainable
course’, Sustainable Materials and Technologies 1, 2–7.
Garrett, T. J. (2014), ‘Long-run evolution of the global economy: 1. Physical basis’, Earth’s Future
2(3), 127–151.
Georgescu-Roegen, N. (1972), ‘The entropy law and the economic process’, Philosophy of Science
39(3).
Giampietro, M. & Sorman, A. H. (2012), ‘Are energy statistics useful for making energy scenarios?’,
Energy 37(1), 5–17.
Gillingham, K., Newell, R. G. & Pizer, W. A. (2008), ‘Modeling endogenous technological change for
climate policy analysis’, Energy Economics 30(6), 2734–2753.
Giraud, G. & Kahraman, Z. (2014), ‘How Dependent is Growth from Primary Energy? The Depen-
dency ratio of Energy in 33 Countries (1970-2011)’, Documents de travail du Centre d’Economie de
la Sorbonne .
Glaub, M. & Hall, C. A. (2017), ‘Evolutionary Implications of Persistence Hunting: An Examination
of Energy Return on Investment for Kung Hunting’, Human Ecology 45(3), 393–401.
Glockner, R. & de Wild-Scholten, M. (2012), Energy payback time and carbon footprint of Elkem
Solar Silicon, in ‘27th EUPVSEC, Frankfurt, Germany, 24-28 September 2012, ’.
Gordon, R. J. (2016), The rise and fall of American growth: The US standard of living since the civil
war, Princeton University Press, Princeton, NY, USA.
Gotz, M., Lefebvre, J., Mors, F., Koch, A. M., Graf, F., Bajohr, S., Reimert, R. & Kolb, T. (2016),
‘Renewable Power-to-Gas: A technological and economic review’, Renewable Energy 85, 1371–1390.
Graeber, D. (2001), Toward an anthropological theory of value: The false coin of our own dreams,
Springer, Berlin, Germany.
160
Greening, L. A., Greene, D. L. & Difiglio, C. (2000), ‘Energy efficiency and consumption—the rebound
effect—a survey’, Energy policy 28(6-7), 389–401.
Gross, R., Heptonstall, P., Anderson, D., Green, T., Leach, M. & Skea, J. (2006), ‘The Costs and
Impacts of Intermittency: An assessment of the evidence on the costs and impacts of intermittent
generation on the British electricity network’, UK Energy Research Centre .
Grossmann, W., Grossmann, I. & Steininger, K. W. (2015), ‘Solar electricity supply isolines of gener-
ation capacity and storage’, Proceedings of the National Academy of Sciences 112(12), 3663–3668.
Grubb, M. (2014), Planetary Economics: Energy, Climate Change and the Three Domains of Sus-
tainable Development, Routledge, Abingdon, Oxon, UK.
Grubb, M. (2017), From macro to micro (and back again): new evidence and behavioural interpre-
tations on the Bashmakov-Newbery constant of energy expenditure, in ‘1st Workshop “Economic
Theories and Low-carbon Transformation Policies”, Cambridge University, UK, Cambridge Univer-
sity, UK ’.
Grubler, A., Johansson, T. B., Muncada, L., Nakicenovic, N., Pachauri, S., Riahi, K., Rogner, H.-H. &
Strupeit, L. (2012), Energy primer, in ‘Global Energy Assessment - Toward a Sustainable Future’,
Cambridge University Press, Cambridge, UK.
Gupta, S., Harnisch, J., Barua, D. C., Chingambo, L., Frankel, P., Jorge, R., Vazquez, G.,
Gomez Echeverri, L., Haites, E. & Huang, Y. (2014), Cross-cutting investment and finance is-
sues, in ‘Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III
to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change’, Cambridge
University Press, New York, NY, USA.
Gulen, S. C. (2011), ‘Importance of Auxiliary Power Consumption for Combined Cycle Performance’,
Journal of Engineering for Gas Turbines and Power 133(4), 041801.
Hall, C. A. (1972), ‘Migration and metabolism in a temperate stream ecosystem’, Ecology 53(4), 585–
604.
Hall, C. A. (2004), ‘The continuing importance of maximum power’, Ecological modelling 178(1), 107–
113.
Hall, C. A. (2011), ‘Introduction to special issue on new studies in EROI (Energy Return on Invest-
ment)’, Sustainability 3(10), 1773–1777.
161
Hall, C. A. (2016), Energy Return on Investment: A Unifying Principle for Biology, Economics, and
Sustainability, Springer, New York, NY, USA.
Hall, C. A. & Cleveland, C. J. (1981), ‘Petroleum drilling and production in the United States: Yield
per effort and net energy analysis’, Science 211(4482), 576–579.
Hall, C. A., Dale, B. E. & Pimentel, D. (2011), ‘Seeking to understand the reasons for different energy
return on investment (EROI) estimates for biofuels’, Sustainability 3(12), 2413–2432.
Hall, C. A. & Klitgaard, K. A. (2011), Energy and the wealth of nations: understanding the biophysical
economy, Springer Science & Business Media, New York, NY, USA.
Hall, C. A., Lambert, J. G. & Balogh, S. B. (2014), ‘EROI of different fuels and the implications for
society’, Energy Policy 64, 141–152.
Hall, C. A., Powers, R. & Schoenberg, W. (2008), Peak oil, EROI, investments and the economy in
an uncertain future, in ‘Biofuels, solar and wind as renewable energy systems’, Springer, Berlin,
Germany.
Hall, C., Balogh, S. & Murphy, D. (2009), ‘What is the minimum EROI that a sustainable society
must have?’, Energies 2(1), 25–47.
Hall, Douglas and Lee, Randy (2014), Assessment of Opportunities for New United States Pumped
Storage Hydroelectric Plants Using Existing Water Features as Auxiliary Reservoirs, Report, Idaho
National Laboratory (INL), Idaho Falls, ID, USA.
Hamilton, J. D. (1983), ‘Oil and the macroeconomy since World War II’, Journal of political economy
91(2), 228–248.
Hamilton, J. D. (2009), ‘Causes and Consequences of the Oil Shock of 2007-08’, National Bureau of
Economic Research 15002.
Hammerschlag, R. (2006), ‘Ethanol’s energy return on investment: A survey of the literature 1990-
Present’, Environmental Science & Technology 40(6), 1744–1750.
Hart, E. K., Stoutenburg, E. D. & Jacobson, M. Z. (2012), ‘The potential of intermittent renewables
to meet electric power demand: Current methods and emerging analytical techniques’, Proceedings
of the IEEE 100(2), 322–334.
Hau, J. L. & Bakshi, B. R. (2004), ‘Promise and problems of emergy analysis’, Ecological Modelling
178(1), 215–225.
162
Hawkins, T. R., Singh, B., Majeau-Bettez, G. & Strømman, A. H. (2013), ‘Comparative environmental
life cycle assessment of conventional and electric vehicles’, Journal of Industrial Ecology 17(1), 53–
64.
Henning, H.-M. & Palzer, A. (2014), ‘A comprehensive model for the German electricity and heat
sector in a future energy system with a dominant contribution from renewable energy technolo-
gies—Part I: Methodology’, Renewable and Sustainable Energy Reviews 30, 1003–1018.
Heptonstall, P., Gross, R. & Steiner, F. (2017), ‘The costs and impacts of intermittency – 2016 update’,
UK Energy Research Centre .
Hertwich, E. G., Gibon, T., Bouman, E. A., Arvesen, A., Suh, S., Heath, G. A., Bergesen, J. D.,
Ramirez, A., Vega, M. I. & Shi, L. (2015), ‘Integrated life-cycle assessment of electricity-supply sce-
narios confirms global environmental benefit of low-carbon technologies’, Proceedings of the National
Academy of Sciences 112(20), 6277–6282.
Hertwich, E. G. & Peters, G. P. (2009), ‘Carbon footprint of nations: A global, trade-linked analysis’,
Environmental science & technology 43(16), 6414–6420.
Heun, M. K. & de Wit, M. (2012), ‘Energy return on (energy) invested (EROI), oil prices, and energy
transitions’, Energy Policy 40, 147–158.
Heun, M. K., Santos, J., Brockway, P. E., Pruim, R., Domingos, T. & Sakai, M. (2017), ‘From theory
to econometrics to energy policy: Cautionary tales for policymaking using aggregate production
functions’, Energies 10(2), 203.
Hibbs, D. A. & Olsson, O. (2004), ‘Geography, biogeography, and why some countries are rich and
others are poor’, Proceedings of the national academy of sciences of the United States of America
101(10), 3715–3720.
Hopkinson, J. (1892), ”On the Cost of Electricity Supply”, Presidential Address to the Junior Engi-
neering Society, November 4th, 1892 (From the Transactions of the Junior Engineering Society, Vol.
III, Part I, pp. 1-14), in B. Hopkinson, ed., ‘Original Papers by the Late John Hopkinson: Technical
papers’, Vol. 1, University Press, Cambridge.
Hughes, J. D. (2018), Shale Reality Check: Drilling Into the U.S. Government’s Rosy Projections for
Shale Gas Tight Oil Production Through 2050, Report, Post Carbon Institute, Sebastopol, CA,
USA.
Humphrey, T. M. (1997), ‘Algebraic production functions and their uses before Cobb-Douglas’, FRB
Richmond Economic Quarterly 83(1), 51–83.
163
Huva, R., Dargaville, R. & Rayner, P. (2016), ‘Optimising the deployment of renewable resources
for the Australian NEM (National Electricity Market) and the effect of atmospheric length scales’,
Energy 96, 468–473.
IEA (2014), ‘Technology Roadmap - Concentrating Solar Power’.
URL: https://www.iea.org/publications/freepublications/publication/TechnologyRoadmapSolarThermalElectricity 2014edition.pdf
IEA (2016a), ‘Energy Technology Perspectives - Data Visualisation’.
URL: https://www.iea.org/etp/explore/
IEA (2016b), IEA-PVPS Trends in Photovoltaic applications - Survey report of selected IEA countries
between 1992 and 2015, Report, IEA Photovoltaic Power Systems Programme, Paris, France.
IEA (2016c), Key world energy statistics - 2016, Report, International Energy Agency, Paris, France.
IEA (2017a), ‘Commentary: Understanding and using the Energy Balance’.
URL: http://www.iea.org/newsroom/news/2017/september/commentary-understanding-and-
using-the-energy-balance.html
IEA (2017b), Energy Balance - Australia, Report, International Energy Agency, Paris, France.
IEA (2017c), Energy Efficiency, Report, International Energy Agency, Paris, France.
IEA (2017d), Energy Technology Perspectives, Report, International Energy Agency, Paris, France.
IEA (2017e), Key world energy statistics - 2017, Report, International Energy Agency, Paris, France.
IEEE (2007), IEEE Std 762-2006 - IEEE Standard Definitions for Use in Reporting Electric Generating
Unit Reliability, Availability, and Productivity, Report, IEEE Power Engineering Society, New York,
NY, USA.
IIASA (2014), ‘AR5 Scenario Database’.
URL: https://tntcat.iiasa.ac.at/AR5DB/dsd?Action=htmlpage&page=about
IRENA (2012), Concentrating Solar Power, Report, International Renewable Energy Agency, Abu
Dhabi, United Arab Emirates.
ISO (1998), ISO 14041 - Environmental management — Life cycle assessment — Goal and scope
definition and inventory analysis, Report, International Organization for Standardization.
ISO (2006), ISO 14040 - Environmental management - Life cycle assessment - Principles and frame-
work, Report, International Organization for Standardization.
164
Ito, M., Kato, K., Komoto, K., Kichimi, T. & Kurokawa, K. (2005), Analysis of transmission losses of
Very Large-Scale Photovoltaic power generation systems (VLS-PV) in world desert, in ‘Photovoltaic
Specialists Conference, 2005. Conference Record of the Thirty-first IEEE, ’, IEEE, pp. 1706–1709.
Ito, M., Kato, K., Komoto, K., Kichimi, T. & Kurokawa, K. (2008), ‘A comparative study on cost
and life-cycle analysis for 100 MW very large-scale PV (VLS-PV) systems in deserts using m-Si,
a-Si, CdTe, and CIS modules’, Progress in Photovoltaics: research and applications 16(1), 17–30.
Ito, M., Kato, K., Sugihara, H., Kichimi, T., Song, J. & Kurokawa, K. (2003), ‘A preliminary study
on potential for very large-scale photovoltaic power generation (VLS-PV) system in the Gobi desert
from economic and environmental viewpoints’, Solar Energy Materials and Solar Cells 75(3), 507–
517.
Ito, M., Lespinats, S., Merten, J., Malbranche, P. & Kurokawa, K. (2016), ‘Life cycle assessment
and cost analysis of very large-scale PV systems and suitable locations in the world’, Progress in
Photovoltaics: Research and Applications 24(2), 159–174.
Iyer, G., Hultman, N., Eom, J., McJeon, H., Patel, P. & Clarke, L. (2015), ‘Diffusion of low-carbon
technologies and the feasibility of long-term climate targets’, Technological Forecasting and Social
Change 90, 103–118.
Jacks, D. S. (2013), ‘From boom to bust: A typology of real commodity prices in the long run’, NBER
Working Paper No. 18874.
Jacobs, W. (1998), The British Strategic Air Offensive Against Germany in World War II, in R. C.
Hall, ed., ‘Case Studies in Strategic Bombardment’, US Government Printing Office, Washington
DC, pp. 91–182.
Jacobson, M. Z. & Delucchi, M. A. (2011), ‘Providing all global energy with wind, water, and solar
power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials’,
Energy Policy 39(3), 1154–1169.
Jacobson, M. Z., Delucchi, M. A., Cameron, M. A. & Frew, B. A. (2015), ‘Low-cost solution to the
grid reliability problem with 100 percent penetration of intermittent wind, water, and solar for all
purposes’, Proceedings of the National Academy of Sciences 112(49).
Jeppesen, M., Brear, M., Chattopadhyay, D., Manzie, C., Dargaville, R. & Alpcan, T. (2016), ‘Least
cost, utility scale abatement from Australia’s NEM (National Electricity Market). Part 1: Problem
formulation and modelling’, Energy 101, 606–620.
Jevons, W. S. (1871), The principles of political economy, London: Macmillan & co.
165
Joas, F., Pahle, M., Flachsland, C. & Joas, A. (2016), ‘Which goals are driving the Energiewende?
Making sense of the German Energy Transformation’, Energy Policy 95, 42–51.
Johnston, W. (2017), ‘How long will your solar panels last, and how well will they perform?’, Re-
NewEconomy .
Johnston, W. & Egan, R. (2015), ‘National Survey Report of PV Power Applications in Australia
2015’, Australian PV Institute .
Jones, C., Gilbert, P., Raugei, M., Mander, S. & Leccisi, E. (2016), ‘An approach to prospective
consequential life cycle assessment and net energy analysis of distributed electricity generation’,
Energy Policy 100, 350–358.
Jordan, D. C., Kurtz, S. R., VanSant, K. & Newmiller, J. (2016), ‘Compendium of photovoltaic
degradation rates’, Progress in Photovoltaics: Research and Applications 24:978–989.
Jorgenson, D. W. (1984), ‘The role of energy in productivity growth’, The Energy Journal 5(3), 11–26.
Kalimeris, P., Richardson, C. & Bithas, K. (2014), ‘A meta-analysis investigation of the direction
of the energy-GDP causal relationship: implications for the growth-degrowth dialogue’, Journal of
Cleaner Production 67, 1–13.
Kannan, R., Leong, K., Osman, R., Ho, H. & Tso, C. (2006), ‘Life cycle assessment study of solar
PV systems: an example of a 2.7 kW p distributed solar PV system in Singapore’, Solar energy
80(5), 555–563.
Keen, S. & Ayres, R. (2017), ‘A Note on the Role of Energy in Production’, Ecological Economics
submitted.
Kelvin, W. T. (1852), ‘On a Universal Tendency in Nature to the Dissipation of Mechanical Energy’,
Mathematical and Physical Papers i(59), 511.
Ketzer, F., Skarka, J. & Rosch, C. (2017), ‘Critical Review of Microalgae LCA Studies for Bioenergy
Production’, BioEnergy Research pp. 1–11.
Kilian, L. (2006), ‘Not all oil price shocks are alike: Disentangling demand and supply shocks in the
crude oil market’, American Economic Review 99(3), 1053–69.
Kilian, L. (2008), ‘The economic effects of energy price shocks’, Journal of Economic Literature
46(4), 871–909.
King, C. (2015a), ‘The Rising Cost of Resources and Global Indicators of Change’, American Scientist
103, 410–417.
166
King, C. W. (2014), ‘Matrix method for comparing system and individual energy return ratios when
considering an energy transition’, Energy 72, 254–265.
King, C. W. (2015b), ‘Comparing world economic and net energy metrics, Part 3: Macroeconomic
Historical and Future Perspectives’, Energies 8(11), 12997–13020.
King, C. W. (2015c), Net Energy in Context of Macroeconomic Trends and Indicators, in
‘GCEP/Stanford Net Energy Analysis Workshop, March 31- April 1, 2015, Stanford University,
’.
King, C. W. (2016), ‘Information Theory to Assess Relations Between Energy and Structure of the
US Economy Over Time’, BioPhysical Economics and Resource Quality 1(2), 10.
King, C. W. & Hall, C. A. (2011), ‘Relating financial and energy return on investment’, Sustainability
3(10), 1810–1832.
King, C. W., Maxwell, J. P. & Donovan, A. (2015a), ‘Comparing World Economic and Net Energy
Metrics, Part 1: Single Technology and Commodity Perspective’, Energies 8(11), 12949–12974.
King, C. W., Maxwell, J. P. & Donovan, A. (2015b), ‘Comparing World Economic and Net Energy
Metrics, Part 2: Total Economy Expenditure Perspective’, Energies 8(11), 12975–12996.
King, C. W. & Rhodes, J. D. (2018), ‘A Lack of Systematic Thinking Keeps America from Staying
Great’, The University of Texas at Austin Energy Institute .
Kirby, B., Milligan, M., Makarov, Y., Hawkins, D., Jackson, K. & Shiu, H. (2003), ‘California Renew-
ables Portfolio Standard Renewable Generation Integration Cost Analysis’, The California Energy
Commission and The California Public Utilities Commission .
Kitzes, J. (2013), ‘An introduction to environmentally-extended input-output analysis’, Resources
2(4), 489–503.
Kolstad, C., Urama, K., Broome, J., Bruvoll, A., Olvera, M., Fullerton, D., Gollier, C., Hanemann, W.,
Hassan, R., Jotzo, F., Khan, M. R., Meyer, L. & Mundaca, L. (2014), Social, Economic, and Ethical
Concepts and Methods, in ‘Climate Change 2014: Mitigation of Climate Change. Contribution of
Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change’, Cambridge University Press, New York, NY, USA.
Koppelaar, R. (2016), ‘Solar-PV energy payback and net energy: Meta-assessment of study quality,
reproducibility, and results harmonization’, Renewable and Sustainable Energy Reviews 72, 1241–
1255.
167
Krause, F., Bossel, H. & Muller-Reißmann, K.-F. (1981), Energie-Wende: Wachstum und Wohlstand
ohne Erdol und Uran, S. Fischer.
Krey, V. & Clarke, L. (2011), ‘Role of renewable energy in climate mitigation: a synthesis of recent
scenarios’, Climate Policy 11(4), 1131–1158.
Krey, V. & Masera, O. (2013), Annex II: Metrics and Methodology, in ‘Climate Change 2014: Mitiga-
tion of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change’, Cambridge University Press, New York, NY, USA.
Krugman, P. R. (1997), The age of diminished expectations: US economic policy in the 1990s, MIT
press, Cambridge, MA, USA.
Kubiszewski, I., Cleveland, C. J. & Endres, P. K. (2010), ‘Meta-analysis of net energy return for wind
power systems’, Renewable energy 35(1), 218–225.
Kumhof, M. & Muir, D. (2014), ‘Oil and the world economy: some possible futures’, Philosophical
Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences
372(2006).
Kummel, R. (2011), The second law of economics: Energy, entropy, and the origins of wealth, Springer
Science & Business Media, New York, NY, USA.
Kummel, R. (2013), ‘Why energy’s economic weight is much larger than its cost share’, Environmental
Innovation and Societal Transitions 9, 33–37.
Kummel, R., Ayres, R. U. & Lindenberger, D. (2010), ‘Thermodynamic laws, economic methods and
the productive power of energy’, Journal of Non-Equilibrium Thermodynamics 35(2), 145–179.
Kummel, R., Lindenberger, D. & Weiser, F. (2015), ‘The economic power of energy and the need to
integrate it with energy policy’, Energy Policy 86, 833–843.
Kummu, M. & Varis, O. (2011), ‘The world by latitudes: a global analysis of human population,
development level and environment across the north–south axis over the past half century’, Applied
geography 31(2), 495–507.
Kurzweil, R. (1999), The age of spiritual machines: When computers exceed human intelligence,
Penguin, New York, NY, USA.
Lambert, J. G., Hall, C. A., Balogh, S., Gupta, A. & Arnold, M. (2014), ‘Energy, EROI and quality
of life’, Energy Policy 64, 153–167.
168
Lang, P. A. (2017), ‘Nuclear Power Learning and Deployment Rates; Disruption and Global Benefits
Forgone’, Energies 10(12), 2169.
Laudan, R. (2015), Cuisine and Empire: Cooking in World History, University of California Press,
CA, USA.
Leccisi, E., Raugei, M. & Fthenakis, V. (2016), ‘The Energy and environmental performance of
ground-mounted photovoltaic systems—A timely update’, Energies 9(8), 622.
Ledovskikh, A. (2016), ‘IBISWorld Industry Report OD4042 Solar Panel Installation in Australia’,
IBIS World .
Lee, R. B. (1969), !Kung Bushman Subsistence: An input output analysis, in A. P. Vayda, ed.,
‘Environment and cultural behavior: Ecological studies in cultural anthropology’, University of
Texas Press, Austin, TX, USA.
Lee, T. (2011), ‘The Great Ephemeralization’.
URL: http://timothyblee.com/2011/04/26/the-great-ephemeralization/
Lenzen, M. (1998), ‘Primary energy and greenhouse gases embodied in Australian final consumption:
an input–output analysis’, Energy policy 26(6), 495–506.
Lenzen, M. (2000), ‘Errors in conventional and Input Output based Life Cycle inventories’, Journal
of Industrial Ecology 4(4), 127–148.
Lenzen, M., Geschke, A., Wiedmann, T., Lane, J., Anderson, N., Baynes, T., Boland, J., Daniels,
P., Dey, C. & Fry, J. (2014), ‘Compiling and using input–output frameworks through collaborative
virtual laboratories’, Science of the Total Environment 485, 241–251.
Lenzen, M., McBain, B., Trainer, T., Jutte, S., Rey-Lescure, O. & Huang, J. (2016), ‘Simulating
low-carbon electricity supply for Australia’, Applied Energy 179, 553–564.
Lenzen, M. & Wachsmann, U. (2004), ‘Wind turbines in Brazil and Germany: an example of geo-
graphical variability in life-cycle assessment’, Applied energy 77(2), 119–130.
Leontief, W. (1966), Input-output economics, University Press, New York, NY, USA.
Lightfoot, H. D. (2007), ‘Understand the three different scales for measuring primary energy and avoid
errors’, Energy 32(8), 1478–1483.
Lindenberger, D. & Kummel, R. (2011), ‘Energy and the state of nations’, Energy 36(10), 6010–6018.
169
Lindenberger, D., Weiser, F., Winkler, T. & Kummel, R. (2017), ‘Economic Growth in the USA and
Germany 1960–2013: The Underestimated Role of Energy’, Biophysical Economics and Resource
Quality 2(3), 10.
Loftus, P. J., Cohen, A. M., Long, J. & Jenkins, J. D. (2015), ‘A critical review of global decarboniza-
tion scenarios: what do they tell us about feasibility?’, Wiley Interdisciplinary Reviews: Climate
Change 6(1), 93–112.
Loschel, A. (2002), ‘Technological change in economic models of environmental policy: a survey’,
Ecological economics 43(2-3), 105–126.
Lotka, A. J. (1922), ‘Contribution to the energetics of evolution’, Proceedings of the National Academy
of Sciences of the United States of America 8(6), 147.
Louwen, A., van Sark, W. G., Faaij, A. P. & Schropp, R. E. (2016), ‘Re-assessment of net energy
production and greenhouse gas emissions avoidance after 40 years of photovoltaics development’,
Nature Communications 7, 13728.
Lu, X., McElroy, M. B. & Kiviluoma, J. (2009), ‘Global potential for wind-generated electricity’,
Proceedings of the National Academy of Sciences 106(27), 10933–10938.
Luo, X., Wang, J., Dooner, M. & Clarke, J. (2015), ‘Overview of current development in electrical
energy storage technologies and the application potential in power system operation’, Applied Energy
137, 511–536.
MacDonald, A. E., Clack, C. T., Alexander, A., Dunbar, A., Wilczak, J. & Xie, Y. (2016a), ‘Future
cost-competitive electricity systems and their impact on US CO2 emissions’, Nature Climate Change
6, 526–531.
MacDonald, A. E., Clack, C. T., Alexander, A., Dunbar, A., Wilczak, J. & Xie, Y. (2016b), ‘Sup-
plementary information: Future cost-competitive electricity systems and their impact on US CO2
emissions’, Nature Climate Change 6, 526–531.
MacKay, D. (2008), Sustainable Energy-without the hot air, UIT Cambridge, Cambridge, England.
Magner, L. (2016a), ‘Fast Fashion in Australia’, IBIS World, Melbourne, Australia .
Magner, L. (2016b), ‘Online Women’s Clothing Sales in Australia’, IBIS World, Melbourne, Australia
.
Mankiw, N. G. (2009), Macroeconomics, Worth Publishers, New York, NY, USA.
170
Marks, R. E. (1986), ‘Energy issues and policies in Australia’, Annual review of energy 11(1), 47–75.
Maubach, K.-D. (2014), Energiewende: Wege zu einer bezahlbaren Energieversorgung, Springer-Verlag.
McCollum, D., Bauer, N., Calvin, K., Kitous, A. & Riahi, K. (2014), ‘Fossil resource and energy
security dynamics in conventional and carbon-constrained worlds’, Climatic change 123(3-4), 413–
426.
McConnell, D., Forcey, T. & Sandiford, M. (2015), ‘Estimating the value of electricity storage in an
energy-only wholesale market’, Applied Energy 159, 422–432.
Meadows, D. H., Meadows, D. L., Randers, J. & Behrens, W. W. (1972), ‘The limits to growth’, New
York 102.
Mearns, E. (2008), ‘The global energy crises and its role in the pending collapse of the global economy’.
URL: http://www.theoildrum.com/node/4712
Melcher, A. G., Maddox, K., Prien, C., Nevens, T., Yesavage, V., Dickson, P., Fuller, J., Loehr,
W., Baldwin, R. & Bain, R. (1976), ‘Net energy analysis: An energy balance study of fossil fuel
resources’, Colorado Energy Research Institute .
Menger, C. (1871), Principles of economics, New York: New York University Press.
Messner, S. & Schrattenholzer, L. (2000), ‘MESSAGE–MACRO: linking an energy supply model with
a macroeconomic module and solving it iteratively’, Energy 25(3), 267–282.
Meydbray, J. & Dross, F. (2016), ‘PV Module Reliability Scorecard Report 2016’, DNV-GL .
Miller, K. (2001), ‘How Important was Oil in World War II?’, History New Networks .
Miller, L. M., Brunsell, N. A., Mechem, D. B., Gans, F., Monaghan, A. J., Vautard, R., Keith, D. W.
& Kleidon, A. (2015), ‘Two methods for estimating limits to large-scale wind power generation’,
Proceedings of the National Academy of Sciences 112(36), 11169–11174.
Mitchell, T. (2009), ‘Carbon democracy’, Economy and Society 38(3), 399–432.
Modahl, I. S., Raadal, H. L., Gagnon, L. & Bakken, T. H. (2013), ‘How methodological issues affect the
energy indicator results for different electricity generation technologies’, Energy Policy 63, 283–299.
Moeller, D. & Murphy, D. (2016), ‘Net Energy Analysis of Gas Production from the Marcellus Shale’,
BioPhysical Economics and Resource Quality 1(1), 1–13.
Mohr, S. H. & Evans, G. M. (2009), ‘Forecasting coal production until 2100’, Fuel 88(11), 2059–2067.
171
Mohr, S., Wang, J., Ellem, G., Ward, J. & Giurco, D. (2015), ‘Projection of world fossil fuels by
country’, Fuel 141, 120–135.
Mokhtarian, P. (2009), ‘If telecommunication is such a good substitute for travel, why does congestion
continue to get worse?’, Transportation Letters 1(1), 1–17.
Moody’s (2016), ‘Continued hotel construction a greater threat to US lodging sector CMBS than
Airbnb’.
URL: https://www.moodys.com/research/Moodys-Continued-hotel-construction-a-greater-threat-
to-US-lodging–PR 345701
Morgan, J. (2014), ‘The catch-22 of energy storage’, Chemistry in Australia 2014(Aug 2014), 22.
Moriarty, P. & Honnery, D. (2016), ‘Can renewable energy power the future?’, Energy Policy 93, 3–7.
Morris, C. & Jungjohann, A. (2016), Energy Democracy: Germany’s Energiewende to Renewables,
Springer, Berlin, Germany.
Morris, I. (2015), Foragers, farmers, and fossil fuels: How human values evolve, Princeton University
Press, Princeton, NY, USA.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P.,
Carter, T. R., Emori, S., Kainuma, M. & Kram, T. (2010), ‘The next generation of scenarios for
climate change research and assessment’, Nature 463(7282), 747–756.
Moyer, E. J., Woolley, M. D., Matteson, N. J., Glotter, M. J. & Weisbach, D. A. (2014), ‘Climate
impacts on economic growth as drivers of uncertainty in the social cost of carbon’, The Journal of
Legal Studies 43(2), 401–425.
Murphy, A. E. (1993), ‘John Law and Richard Cantillon on the circular flow of income’, Journal of
the History of Economic Thought 1(1), 47–62.
Murphy, D. J. & Hall, C. A. (2010), ‘Year in review - EROI or energy return on (energy) invested’,
Annals of the New York Academy of Sciences 1185(1), 102–118.
Murphy, D. J. & Hall, C. A. (2011), ‘Energy return on investment, peak oil, and the end of economic
growth’, Annals of the New York Academy of Sciences 1219(1), 52–72.
Murphy, D. J., Hall, C. A., Dale, M. & Cleveland, C. (2011), ‘Order from chaos: A preliminary
protocol for determining the EROI of fuels’, Sustainability 3(10), 1888–1907.
Murray, J. W. (2016), ‘Limitations of Oil Production to the IPCC Scenarios: The New Realities of
US and Global Oil Production’, BioPhysical Economics and Resource Quality 1(2), 13.
172
MWH (2009), ‘Technical Analysis of Pumped Storage and Integration with Wind Power in the Pacific
Northwest : MWH-HDC-T12’.
URL: http://www.hydro.org/wp-content/uploads/2011/07/PS-Wind-Integration-Final-Report-
without-Exhibits-MWH-3.pdf
Nakicenovic, N., Alcamo, J., Davis, G., De Vries, B., Fenhann, J., Gaffin, S., Gregory, K., Griibler,
A., Jung, T. Y. & Kram, T. (2000), Special Report on Emissions scenarios: A Special Report of
Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, UK.
Nakicenovic, N., Grubler, A., Ishitani, H., Johansson, T., Marland, G., Moreira, J. & Rogner, H.-H.
(1996), Energy primer, in R. T. Watson, M. C. Zinyowera & R. H. Moss, eds, ‘Climate Change
1995 - Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses’,
Cambridge University Press, New York, NY, USA.
NASA (2017), ‘NASA Surface meteorology and Solar Energy: Global Data Sets’.
URL: https://eosweb.larc.nasa.gov/cgi-bin/sse/global.cgi
NERC (2011), ‘Planning Resource Adequacy Analysis, Assessment and Documentation, BAL-502-
RFC-02’, North American Electric Reliability Corporation .
Neubauer, J. S., Pesaran, A., Williams, B., Ferry, M. & Eyer, J. (2012), ‘A techno-economic analysis
of PEV battery second use: repurposed-battery selling price and commercial and industrial end-user
value’.
URL: http://papers.sae.org/2012-01-0349/
Nordhaus, W. D. (1979), The efficient use of energy resources, Yale University Press.
Nordhaus, W. D. & Boyer, J. (2000), Warming the world: economic models of global warming, MIT
Press.
Norgaard, R. (1990), ‘Economic Indicators of Resource Scarcity: A Critical Essay’, Journal of Envi-
ronmental Economics and Management 19.
Nugent, D. & Sovacool, B. K. (2014), ‘Assessing the lifecycle greenhouse gas emissions from solar PV
and wind energy: A critical meta-survey’, Energy Policy 65, 229–244.
Nykvist, B. & Nilsson, M. (2015), ‘Rapidly falling costs of battery packs for electric vehicles’, Nature
Climate Change 5(4), 329–332.
Odum, H. T. (1973), ‘Energy, ecology, and economics’, Ambio 2(6), 220–227.
173
Odum, H. T. (1996), Environmental accounting: emergy and environmental decision making, John
Wiley & Sons Inc., New York, NY, USA.
Odum, H. T. & Pinkerton, R. C. (1955), ‘Time’s speed regulator: the optimum efficiency for maximum
power output in physical and biological systems’, American Scientist pp. 331–343.
OECD (2017), OECD Compendium of Productivity Indicators 2017, Report, OECD, Paris, France.
OECDiLibrary (2015), Dataset: OECD - Electricity/heat supply and consumption, Report, OECD.
Office of the Chief Economist (2015a), Table B1 - Australia population, GDP and energy consumption,
Report, Department of Industry, Innovation and Science, Canberra, ACT, Australia.
Office of the Chief Economist (2015b), Table F1 - Australian energy consumption, by industry and
fuel type, energy units, Report, Department of Industry, Innovation and Science, Canberra, ACT,
Australia.
Office of the Chief Economist (2015c), Table O1 - Australian electricity generation, by fuel type,
physical units, Report, Department of Industry, Innovation and Science, Canberra, ACT, Australia.
Office of the Chief Economist (2017), Table F1 - Australian energy consumption, by industry and
fuel type, energy units, Report, Department of Industry, Innovation and Science, Canberra, ACT,
Australia.
OFGEM (2013), Electricity Capacity Assessment Report 2013, Report, OFGEM, London, UK.
Ostwald, W. (1909), Energetische grundlagen der kulturwissenschaft, Vol. 16, Klinkhardt.
Oswald, J., Raine, M. & Ashraf-Ball, H. (2008), ‘Will British weather provide reliable electricity?’,
Energy Policy 36(8), 3212–3225.
Ozturk, I. (2010), ‘A literature survey on energy–growth nexus’, Energy policy 38(1), 340–349.
Palmer, G. (2012), ‘Does Energy Efficiency Reduce Emissions and Peak Demand? A Case Study of
50 Years of Space Heating in Melbourne’, Sustainability 4(7), 1525–1560.
Palmer, G. (2013), ‘Household Solar Photovoltaics: Supplier of Marginal Abatement, or Primary
Source of Low-Emission Power?’, Sustainability 5(4), 1406–1442.
Palmer, G. (2017a), ‘A Framework for Incorporating EROI into Electrical Storage’, BioPhysical Eco-
nomics and Resource Quality 2(2), 6.
Palmer, G. (2017b), ‘An input-output based net-energy assessment of an electricity supply industry’,
Energy 141, 1504–1516.
174
Palmer, G. (2017c), ‘Energetic Implications of a Post-industrial Information Economy: The Case
Study of Australia’, BioPhysical Economics and Resource Quality 2(2), 5.
Palmer, G. & Floyd, J. (2017), ‘An Exploration of Divergence in EPBT and EROI for Solar Photo-
voltaics’, BioPhysical Economics and Resource Quality 2(4), 15.
Palzer, A. & Henning, H.-M. (2014), ‘A comprehensive model for the German electricity and heat sec-
tor in a future energy system with a dominant contribution from renewable energy technologies–Part
II: Results’, Renewable and Sustainable Energy Reviews 30, 1019–1034.
Pauliuk, S., Arvesen, A., Stadler, K. & Hertwich, E. G. (2017), ‘Industrial ecology in integrated
assessment models’, Nature Climate Change 7(1), 13–20.
Payne, J. E. (2010), ‘Survey of the international evidence on the causal relationship between energy
consumption and growth’, Journal of Economic Studies 37(1), 53–95.
Pearl, J. (2009), Causality: models, reasoning, and inference: Second Edition, Cambridge University
Press, New York, NY, USA.
Peet, J. (1992), Energy and the ecological economics of sustainability, Island Press, Washington DC,
USA.
Phylipsen, G. J. M. & Alsema, E. A. (1995), Environmental life-cycle assessment of multicrystalline
silicon solar cell modules, Department of Science, Technology and Society, Utrecht University.
Pickard, W. F. (2012), ‘The history, present state, and future prospects of underground pumped hydro
for massive energy storage’, Proceedings of the IEEE 100(2), 473–483.
Pickard, W. F. (2014a), ‘Energy return on energy invested (eroi): a quintessential but possibly inad-
equate metric for sustainability in a solar-powered world?[point of view]’, Proceedings of the IEEE
102(8), 1118–1122.
Pickard, W. F. (2014b), ‘Smart Grids Versus the Achilles’ Heel of Renewable Energy: Can the Needed
Storage Infrastructure Be Constructed Before the Fossil Fuel Runs Out?’, Proceedings of the IEEE
102(7), 1094–1105.
Pimentel, D. (1991), ‘Ethanol fuels: Energy security, economics, and the environment’, Journal of
agricultural and environmental ethics 4(1), 1–13.
Pindyck, R. S. (2017), ‘The use and misuse of models for climate policy’, Review of Environmental
Economics and Policy 11(1), 100–114.
175
Pirker, O and Argyrakis, I and Babkin, V and Chudy, M and Crosnier, G and Dahlback, N and
Gianatti, R and Gomez Martin, P and Gudnason, E.G. and Hellsten, K and Kreiss, G and Nikolov,
I and Oesch, P and o‘Mahony, B and Pala, R and Reinig, L and Kreikenbaum, D and Polak, D and
Romer, N and Saturka, Z and Stanojevic, V and Stettler, A and Marin, C and Freitas, J.C.T and
Lobacz, J and Weisrock, G and Jenko, J and SEELOS, K and Timm, M (2011), Hydro in Europe:
Powering Renewables, Report, Eurelectric, Brussels, Belgium.
Podolinsky, S. (1883), ‘Menschliche arbeit und einheit der kraft’, Die Neue Zeit 1(9), 413–424.
Poser, H., Altman, J., ab Egg, F., Granata, A. & Board, R. (2014), ‘Development and integration of
renewable energy: Lessons learned from Germany’, Finadvice, FAA Financial Advisory, Soodstrasse
55.
PowerWater (2017), Northern Territory Electricity Network, Report, Darwin, Australia.
Preston, E. G., Grady, W. M. & Baughman, M. L. (1997), ‘A new planning model for assessing
the effects of transmission capacity constraints on the reliability of generation supply for large
nonequivalenced electric networks’, IEEE transactions on power systems 12(3), 1367–1373.
Preston, G. (2015a), ‘100% solar and wind power simulation for ERCOT’.
URL: http://egpreston.com/100percentrenewables.pdf
Preston, G. (2015b), ‘A Simple Calculation Procedure for LOLE, LOLH, and EUE, Calculation of
Probabilistic Transmission Line Flows, and Study Results for Extreme Renewables in ERCOT’.
URL: http://egpreston.com/Presentation3.pdf
Preston, G. (2016a), ‘Direct calculation example convolving ten randomly outaged generators’.
URL: http://egpreston.com/DC.txt
Preston, G. (2016b), ‘Microgrids Can Play An Important Role In Reducing ERCOT’s Fossil Fuel
Dependency’, Renewable Energy Law Conference .
Prieto, P. & Hall, C. (2013), Spain’s Photovoltaic Revolution: The Energy return on Investment,
Springer, New York, NY, USA.
Prigogine, I., Nicolis, G. & Babloyantz, A. (1972), ‘Thermodynamics of evolution’, Physics Today
25(11), 23.
Pulsford, Sandy (2016), PV Module Quality - Challenges for the Australian Market, Report, Australia.
Rao, S., Keppo, I. & Riahi, K. (2006), ‘Importance of technological change and spillovers in long-term
climate policy’, The Energy Journal pp. 123–139.
176
Raugei, M. (2013), ‘Comments on ”Energy intensities, EROIs (energy returned on invested), and
energy payback times of electricity generating power plants” : making clear of quite some confusion’,
Energy (59), 781.
Raugei, M., Carbajales-Dale, M., Barnhart, C. J. & Fthenakis, V. (2015), ‘Rebuttal: “Comments on
‘Energy intensities, EROIs (energy returned on invested), and energy payback times of electricity
generating power plants’: Making clear of quite some confusion”’, Energy 82, 1088–1091.
Raugei, M., Frischknecht, R., Olson, C., Sinha, P. & Heath, G. (2016), ‘Methodological guidelines
on Net Energy Analysis of Photovoltaic Electricity, IEA-PVPS Task 12, Report T12- 07:2016’,
IEA-PVPS .
Raugei, M., Fullana-i Palmer, P. & Fthenakis, V. (2012), ‘The energy return on energy investment
(EROI) of photovoltaics: Methodology and comparisons with fossil fuel life cycles’, Energy Policy
45, 576–582.
Raugei, M. & Leccisi, E. (2016), ‘A comprehensive assessment of the energy performance of the
full range of electricity generation technologies deployed in the United Kingdom’, Energy Policy
90, 46–59.
Raworth, K. (2017), Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist,
Chelsea Green Publishing, White River Junction, VT, USA.
Riahi, K., Grubler, A. & Nakicenovic, N. (2007), ‘Scenarios of long-term socio-economic and envi-
ronmental development under climate stabilization’, Technological Forecasting and Social Change
74(7), 887–935.
Ricardo, D. (1891), Principles of political economy and taxation, G. Bell and sons.
Richa, K., Babbitt, C. W., Nenadic, N. G. & Gaustad, G. (2015), ‘Environmental trade-offs across
cascading lithium-ion battery life cycles’, The International Journal of Life Cycle Assessment
22(1), 66–81.
Ritchie, J. & Dowlatabadi, H. (2017), ‘Why do climate change scenarios return to coal?’, Energy
140, 1276–1291.
Rocco, M. V. (2016), Primary Exergy Cost of Goods and Services: An Input-Ouput Approach,
Springer, Italy.
Rockstrom, J., Steffen, W., Noone, K., Persson, , Chapin III, F. S., Lambin, E. F., Lenton, T. M.,
Scheffer, M., Folke, C. & Schellnhuber, H. J. (2009), ‘A safe operating space for humanity’, nature
461(7263), 472.
177
Rogner, H.-H. (1997), ‘An assessment of world hydrocarbon resources’, Annual review of energy and
the environment 22(1), 217–262.
Rogner, H.-H., Aguilera, R. F., Bertani, R., Bhattacharya, S. C., Dusseault, M. B., Gagnon, L.,
Haberl, H., Hoogwijk, M., Johnson, A., Rogner, M. L., Wagner, H. & Yakushev, V. (2012), Energy
Resources and Potentials, in ‘Global energy assessment - toward a sustainable future’, Cambridge
University Press, New York, NY, USA.
Romer, P. (1990), ‘Endogenous technological change’, Journal of Political Economy 98(5), S71–S102.
Rosenberg, N. (1994), Exploring the black box: Technology, economics, and history, Cambridge Uni-
versity Press, New York, NY, USA.
Rugani, B., Panasiuk, D. & Benetto, E. (2012), ‘An input-output based framework to evaluate human
labour in life cycle assessment’, The International Journal of Life Cycle Assessment 17(6), 795–812.
Ruthven, P. (2012), ‘A Snapshot of Australia’s Digital Future to 2050’, IBIS World, Melbourne,
Australia .
Ruthven, P. (2013), ‘The Phenomenon of Industry Cycles’, IBIS World, Melbourne, Australia .
Rutledge, D. (2011), ‘Estimating long-term world coal production with logit and probit transforms’,
International Journal of Coal Geology 85(1), 23–33.
Rydh, C. J. & Sanden, B. A. (2005), ‘Energy analysis of batteries in photovoltaic systems. Part I:
Performance and energy requirements’, Energy Conversion and Management 46(11), 1957–1979.
Salles, M., Aziz, M. & Hogan, W. (2016), Potential Arbitrage Revenue of Energy Storage Systems in
PJM during 2014, in ‘In Proceedings of the 2016 IEEE PES General Meeting (PIPGS16), Boston,
MA, USA ’.
Samuelson, P. & Nordhaus, W. D. (2010), Macroeconomics, 19th edn, McGraw-Hill, Boston, USA.
Sano, F., Akimoto, K., Homma, T. & Tomoda, T. (2006), ‘Analysis of Technological Portfolios for
CO Stabilizations and Effects of Technological Changes’, The Energy Journal pp. 141–161.
Sathaye, J., Lucon, O., Rahman, A., Christensen, J., Denton, F., Fujino, J., Heath, G., Mirza, M.,
Rudnick, H. & Schlaepfer, A. (2011), ‘Renewable energy in the context of sustainable development’,
Renewable Energy Sources and Climate Change Mitigation .
Sayeef, S., Heslop, S., Cornforth, D., Moore, T., Percy, S., Ward, J. K., Berry, A. & Rowe, D.
(2012), ‘Solar intermittency: Australia’s clean energy challenge: Characterising the effect of high
penetration solar intermittency on Australian electricity networks’.
178
Schiebahn, S., Grube, T., Robinius, M., Tietze, V., Kumar, B. & Stolten, D. (2015), ‘Power to gas:
Technological overview, systems analysis and economic assessment for a case study in Germany’,
International journal of hydrogen energy 40(12), 4285–4294.
Schneider, E. D. & Kay, J. J. (1994), ‘Life as a manifestation of the second law of thermodynamics’,
Mathematical and computer modelling 19(6), 25–48.
Schneider, E. & Sagan, D. (2005), Into the Cool - Energy flow, thermodynamics and life, University
of Chicago Press, Chicago, USA.
Schor, J. (2016), ‘Debating the sharing economy’, Journal of Self-Governance and Management Eco-
nomics 4(3), 7–22.
Schramski, J. R., Gattie, D. K. & Brown, J. H. (2015), ‘Human domination of the biosphere: Rapid
discharge of the earth-space battery foretells the future of humankind’, Proceedings of the National
Academy of Sciences 112(31), 9511–9517.
Schreiber, M., Wainstein, M. E., Hochloff, P. & Dargaville, R. (2015), ‘Flexible electricity tariffs:
Power and energy price signals designed for a smarter grid’, Energy 93, 2568–2581.
Schurr, S. H. (1990), Electricity in the American economy: Agent of technological progress, Greenwood
Publishing Group, Westport, CT, USA.
Scott, R. (1994), IEA, the First 20 Years: Origins and structure, Vol. 1, OECD/IEA, Paris, France.
Sgouridis, S. (2014), ‘Defusing the energy trap: the potential of energy-denominated currencies to
facilitate a sustainable energy transition’, Frontiers in Energy Research 2(8), 1–12.
Simon, J. L. (1981), The ultimate resource, Princeton University Press, Princeton, NY, USA.
Sims, R. E., Schock, R. N., Adegbululgbe, A., Fenhann, J. V., Konstantinaviciute, I., Moomaw,
W., Nimir, H. B., Schlamadinger, B., Torres-Martınez, J. & Turner, C. (2007), Energy supply, in
‘Climate change 2007: Mitigation. Contribution of Working Group III to the fourth assessment
report of the Intergovernmental Panel on Climate Change’, Cambridge University Press, New York,
NY, USA.
Sims, R., Mercado, P., Krewitt, W., Bhuyan, G., Flynn, D., Holttinen, H., Jannuzzi, G., Khennas,
S., Liu, Y. & Nilsson, L. J. (2011), Integration of renewable energy into present and future energy
systems, in ‘IPCC special report on renewable energy sources and climate change mitigation’,
Cambridge University Press, New York, NY, USA.
179
Sims, R., Schaeffer, R., Creutzig, F., Cruz-Nunez, X., D’Agosto, M., Dimitriu, D., Figueroa Meza, M.,
Fulton, L., Kobayashi, S., Lah, O., McKinnon, A., Newman, P., Ouyang, M., Schauer, J., Sperling,
D. & Tiwari, G. (2014), Transport, in ‘Climate Change 2014: Mitigation of Climate Change.
Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change’, Cambridge University Press, New York, NY, USA.
Sinclair, M., Thompson, M. & America, O. (2001), Cuba, Going Against the Grain: Agricultural Crisis
and Transformation, Oxfam America.
Smil, V. (2008), Energy in Nature and Society: General Energetics of Complex Systems, The MIT
Press, Cambridge, MA, USA.
Smil, V. (2013), Making the modern world: materials and dematerialization, John Wiley & Sons, West
Sussex, UK.
Smith, A. (1776), An Inquiry Into the Nature and Causes of the Wealth of Nations, T. Nelson and
Sons.
Smith, K. (2011), ‘Discounting, risk and uncertainty in economic appraisals of climate change policy:
comparing Nordhaus, Garnaut and Stern’, Garnaut Review .
Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E., Haberl, H., Harper, R.,
House, J., Jafari, M., Masera, O., Mbow, C., Ravindranath, N., Rice, W., Abad, C., Romanovskaya,
A., Sperling, F. & Tubiello, F. (2014), Agriculture, forestry and other land use (AFOLU), in
‘Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change’, Cambridge University
Press, New York, NY, USA.
Soddy, F. (1933), Wealth, virtual wealth and debt: The solution of the economic paradox, Britons
Publishing Company, London, England.
Solar Business Services (2014), Solar Businesses in Australia, Report, Rec Agents Association, Aus-
tralia.
Solomon, B. D., Barnes, J. R. & Halvorsen, K. E. (2007), ‘Grain and cellulosic ethanol: History,
economics, and energy policy’, Biomass and Bioenergy 31(6), 416–425.
Solow, R. M. (1957), ‘Technical change and the aggregate production function’, The review of Eco-
nomics and Statistics 39(3), 312–320.
180
Solow, R. M. (1974), ‘The economics of resources or the resources of economics’, The American
Economic Review 64(2), 1–14.
Sonnemann, G., Vigon, B., Rack, M. & Valdivia, S. (2013), ‘Global guidance principles for life cycle
assessment databases: development of training material and other implementation activities on the
publication’, The International Journal of Life Cycle Assessment 18(5), 1169–1172.
Sorrell, S. (2015), ‘Reducing energy demand: A review of issues, challenges and approaches’, Renewable
and Sustainable Energy Reviews 47, 74–82.
Stanwix, G., Pham, P. & Ball, A. (2015), ‘End-use energy intensity in Australia’, Office of the Chief
Economist, Canberra, ACT, Australia .
Stern, D., Csereklyei, Z. & Rubio, M. (2014), Energy and Economic Growth: The Stylized Facts, in
‘Energy & the Economy, 37th IAEE International Conference, June 15-18, 2014, ’, International
Association for Energy Economics.
Stern, D. I. (1993), ‘Energy and economic growth in the USA: a multivariate approach’, Energy
Economics 15(2), 137–150.
Stern, D. I. (1997), ‘Limits to substitution and irreversibility in production and consumption: a
neoclassical interpretation of ecological economics’, Ecological Economics 21(3), 197–215.
Stern, D. I. (2011), ‘The role of energy in economic growth’, Annals of the New York Academy of
Sciences 1219(1), 26–51.
Stern, D. I. & Enflo, K. (2013), ‘Causality between energy and output in the long-run’, Energy
economics 39, 135–146.
Stern, D. I. & Kander, A. (2012), ‘The role of energy in the industrial revolution and modern economic
growth’, The Energy Journal pp. 125–152.
Stern, N. (2013), ‘The structure of economic modeling of the potential impacts of climate change:
grafting gross underestimation of risk onto already narrow science models’, Journal of Economic
Literature 51(3), 838–859.
Sterner, M. (2009), Bioenergy and renewable power methane in integrated 100% renewable energy
systems: Limiting global warming by transforming energy systems, Vol. 14, Universitat Kassel,
Kassel, Germany.
Stiglitz, J. (1974), ‘Growth with exhaustible natural resources: efficient and optimal growth paths’,
The review of economic studies 41, 123–137.
181
Stocker, T., Qin, D., Plattner, G.-K., Alexander, L., Allen, S., Bindoff, N., Breon, F.-M., Church,
J., Cubasch, U. & Emori, S. (2013), Technical summary, in ‘Climate Change 2013: The Physical
Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergov-
ernmental Panel on Climate Change’, Cambridge University Press, New York, NY, USA.
Strathern, M. (1992), Qualified value: the perspective of gift exchange, in C. Humphrey & S. Hugh-
Jones, eds, ‘Barter, exchange and value: an anthropological approach’, Cambridge University Press,
Cambridge, pp. 169–91.
Suh, S. & Huppes, G. (2002), ‘Missing inventory estimation tool using extended input-output analysis’,
The International Journal of Life Cycle Assessment 7(3), 134–140.
Suh, S. & Huppes, G. (2005), ‘Methods for life cycle inventory of a product’, Journal of Cleaner
Production 13(7), 687–697.
Sullivan, P., Krey, V. & Riahi, K. (2013), ‘Impacts of considering electric sector variability and
reliability in the MESSAGE model’, Energy Strategy Reviews 1(3), 157–163.
Summers, L. H. (2014), ‘US economic prospects: Secular stagnation, hysteresis, and the zero lower
bound’, Business Economics 49(2), 65–73.
Summers, L. H. (2016), ‘The Age of Secular Stagnation’, Foreign Affairs (March/April 2016).
Sumner, S. B. (2015), The Midas paradox: financial markets, government policy shocks, and the great
depression, Independent Institute.
Tainter, J. (1990), The collapse of complex societies, Cambridge University Press, New York, NY,
USA.
Tainter, J. A. (2011), ‘Resources and cultural complexity: Implications for sustainability’, Critical
Reviews in Plant Sciences 30(1-2), 24–34.
Tainter, J. A. & Patzek, T. W. (2011), Drilling Down: The Gulf Oil Debacle and Our Energy Dilemma,
Springer, Berlin, Germany.
Taylor, P. J. (1988), ‘Technocratic optimism, HT Odum, and the partial transformation of ecological
metaphor after World War II’, Journal of the History of Biology 21(2), 213–244.
Taylor, T. G. & Tainter, J. A. (2016), ‘The Nexus of Population, Energy, Innovation, and Complexity’,
American Journal of Economics and Sociology 75(4), 1005–1043.
Teske, S. (2010), ‘Energy [R]evolution: A sustainable world energy outlook, 3rd edition’, Greenpeace
International, European Renewable Energy Council .
182
The University of Texas at Austin (2016), Texas Solar Radiation Database, Report, The University
of Texas.
Thiess (2017), ‘Yallourn Mine Alliance’.
URL: https://www.thiess.com/projects/yallourn-mine-alliance/detail
Tillman, A.-M. (2000), ‘Significance of decision-making for LCA methodology’, Environmental Impact
Assessment Review 20(1), 113–123.
Tol, R. S. (1997), ‘On the optimal control of carbon dioxide emissions: an application of FUND’,
Environmental Modeling & Assessment 2(3), 151–163.
Train, K. E. (1991), Optimal regulation: the economic theory of natural monopoly, MIT Press Books,
Cambridge, MA, USA.
Treloar, G. J. (1997), ‘Extracting embodied energy paths from input–output tables: towards an
input–output-based hybrid energy analysis method’, Economic Systems Research 9(4), 375–391.
Treloar, G. J., Love, P. E. & Holt, G. D. (2001), ‘Using national input/output data for embodied energy
analysis of individual residential buildings’, Construction Management and Economics 19(1), 49–61.
Turner, G. M. (2008), ‘A comparison of The Limits to Growth with 30 years of reality’, Global
Environmental Change 18(3), 397–411.
UN (2012), World Population Prospects, Report, United Nations DENA, New York, NY, USA.
UNEP/SETAC (2011), ‘Global Guidance Principles for Life Cycle Assessment Databases-A Basis
for Greener Processes and Products’, UNEP/SETAC Life Cycle Initiative, Paris, United Nations
Environment Programme .
US DOE (2016), DOE Global Energy Storage Database, Report, United States Department of Energy.
US EPA (1974), US Federal Non-nuclear Energy Research and Development Act of 1974 (Public Law
93–577), Report, United States Environmental Protection Agency, Washington DC, USA.
Vamplew, W. (1987), Australians: a historical library. v10: Australians: historical statistics, Vol. 10,
Fairfax, Syme & Weldon.
van der Burg, L. & Pickard, S. (2015), ‘G20 subsidies to oil, gas and coal production: Germany’, Oil
Change International .
183
Van Ruijven, B. J., Van Vuuren, D. P., Boskaljon, W., Neelis, M. L., Saygin, D. & Patel, M. K.
(2016), ‘Long-term model-based projections of energy use and CO2 emissions from the global steel
and cement industries’, Resources, Conservation and Recycling 112, 15–36.
Van Wee, B., Geurs, K. & Chorus, C. (2013), ‘Information, communication, travel behavior and
accessibility’, The Journal of Transport and Land Use 6(3).
von Hippel, F., Bunn, M., Diakov, A., Ding, M., Goldston, R., Katsuta, T., Ramana, M., Suzuki, T.
& Yu, S. (2012), Nuclear Energy, in ‘Global Energy Assessment’, Cambridge University Press, New
York, NY, USA.
Walker, J. (2010), ‘Privatized Transit and (or vs.) The Public Good’.
URL: http://humantransit.org/2010/02/privatized-transit-and-or-vs-the-public-good.html
Walker, J. (2011), Human transit: How clearer thinking about public transit can enrich our commu-
nities and our lives, Island Press, Washington DC, USA.
Walras, L. (1896), Elements d’economie politique pure, ou, Theorie de la richesse sociale, F. Rouge.
Wang, K., Vredenburg, H., Wang, J., Xiong, Y. & Feng, L. (2017), ‘Energy Return on Investment of
Canadian Oil Sands Extraction from 2009 to 2015’, Energies 10(5).
Warner, E. S. & Heath, G. A. (2012), ‘Life cycle greenhouse gas emissions of nuclear electricity
generation’, Journal of Industrial Ecology 16(S1), S73–S92.
Weißbach, D., Ruprecht, G., Huke, A., Czerski, K., Gottlieb, S. & Hussein, A. (2013), ‘Energy
intensities, EROIs (energy returned on invested), and energy payback times of electricity generating
power plants’, Energy 52, 210–221.
Weißbach, D., Ruprecht, G., Huke, A., Czerski, K., Gottlieb, S. & Hussein, A. (2014), ‘Reply on
“Comments on ‘Energy intensities, EROIs (energy returned on invested), and energy payback times
of electricity generating power plants’–Making clear of quite some confusion”’, Energy 68, 1004–
1006.
Weitzel, E. M. & Codding, B. F. (2016), ‘Population growth as a driver of initial domestication in
Eastern North America’, Royal Society Open Science 3(8), 160319.
White, L. A. (1943), ‘Energy and the evolution of culture’, American Anthropologist 45(3), 335–356.
White, S. W. & Kulcinski, G. L. (2000), ‘Birth to death analysis of the energy payback ratio and
CO2 gas emission rates from coal, fission, wind, and DT-fusion electrical power plants’, Fusion
Engineering and Design 48(3), 473–481.
184
Wiedmann, T. O., Schandl, H., Lenzen, M., Moran, D., Suh, S., West, J. & Kanemoto, K. (2015), ‘The
material footprint of nations’, Proceedings of the National Academy of Sciences 112(20), 6271–6276.
Wiser, R., Jenni, K., Seel, J., Baker, E., Hand, M., Lantz, E. & Smith, A. (2016), ‘Expert elicitation
survey on future wind energy costs’, Nature Energy 1, 16135.
Wolfram, P., Wiedmann, T. & Diesendorf, M. (2016), ‘Carbon footprint scenarios for renewable
electricity in Australia’, Journal of Cleaner Production 124, 236–245.
World Bank (2014), Electric power transmission and distribution losses, Report, World Bank, Wash-
ington DC, USA.
Wrigley, E. A. (2010), Energy and the English industrial revolution, Cambridge University Press, New
York, NY, USA.
Wu, X., Xia, X., Chen, G., Wu, X. & Chen, B. (2016), ‘Embodied energy analysis for coal-based power
generation system-highlighting the role of indirect energy cost’, Applied Energy 184, 936–950.
Yang, C.-J. & Jackson, R. B. (2011), ‘Opportunities and barriers to pumped-hydro energy storage in
the United States’, Renewable and Sustainable Energy Reviews 15(1), 839–844.
Yao, Y., Chang, Y. & Masanet, E. (2014), ‘A hybrid life-cycle inventory for multi-crystalline silicon
PV module manufacturing in China’, Environmental Research Letters 9(11), 114001.
Yergin, D. (2011), The prize: The epic quest for oil, money & power, Simon and Schuster, New York,
NY, USA.
Zackrisson, M., Avellan, L. & Orlenius, J. (2010), ‘Life cycle assessment of lithium-ion batteries for
plug-in hybrid electric vehicles–Critical issues’, Journal of Cleaner Production 18(15), 1519–1529.
Zhai, P. & Williams, E. D. (2010), ‘Dynamic hybrid life cycle assessment of energy and carbon of
multicrystalline silicon photovoltaic systems’, Environmental science & technology 44(20), 7950–
7955.
Zhou, C. & Robb, K. (2016), ‘Shopping centre redevelopments are putting suburbs back on the map’.
URL: https://www.domain.com.au/news/shopping-centre-redevelopments-are-putting-suburbs-
back-on-the-map-20161025-gsa64f/
185