Masterarbeit 170226 final - uni-graz.at

A REGIONAL ECONOMIC ANALYSIS OF AUSTRIA’S CLIMATE AND ENERGY MODEL REGIONS

Master's thesis

to be awarded the degree

Master of Science

in Environmental System Sciences – Economics

at the University of Graz, Austria

submitted by

Rafael Bramreiter

Supervisor: Assoc. Prof. Dr. Birgit Bednar-Friedl

Department of Economics

Wegener Center for Climate and Global Change

Graz, 2017

Author’s Declaration

Unless otherwise indicated in the text or references, or acknowledged above, this thesis is

entirely the product of my own scholarly work. Any inaccuracies of fact or faults in reasoning

are my own and accordingly I take full responsibility. This thesis has not been submitted either

in whole or part, for a degree at this or any other university or institution. This is to certify that

the printed version is equivalent to the submitted electronic one.

Acknowledgement

This master’s thesis was written within the research project “Linking climate change mitigation,

energy security and regional development in climate and energy model regions in Austria”

(LINKS). The LINKS project is funded by the Austrian Climate and Energy Fund within its

seventh call of the Austrian Climate Research Program (ACRP) (project number:

KR14AC7K11935). My contribution to LINKS encompasses the creation and editing of the

LINKS working papers 1.1 (Bramreiter et al. 2016) and 2.1 (Truger et al. 2016) together with

my colleague Barbara Truger. In this regard, Barbara Truger was responsible for the energy

related areas of our economic analysis in LINKS, while my responsibility in LINKS

encompasses the regional areas.

I would first like to thank my thesis supervisor Birgit Bednar-Friedl and the co-supervisor

Thomas Schinko, who gave me the opportunity to be part of the LINKS project, and who took

always the time to make comments and suggestions when I needed their advice and support.

At the same time, both set an example of scientific work for me and I am grateful that they

shared parts of their knowledge and experience with me. I would also like to thank Barbara,

my colleague in the last months, for the excellent cooperation in LINKS and all the others, who

supported me during the writing of my thesis with scientific advice at the Wegener Center.

I would also like to thank Julia and Stephan for their advice and for giving me motivation in

difficult times to finish this thesis.

Finally, I want to thank my parents, who always supported me in my decisions and who were

and are always by my side.

Abstract

The Climate and Energy Model Region (CEM) approach is an instrument to achieve Austria’s

climate and energy goal of 34% energy from Renewable Energy Sources (RES) by 2020, while

at the same time pursuing regional development. The CEM approach supports Austrian

regions distributed throughout Austria in fostering RES and in becoming energy self-sufficient.

Regional development by fostering RES technologies has been proved to be an appropriate

strategy in literature and has shown positive economic effects, but also has been criticized as

overly optimistic regarding unrecognized macro-economic feedback effects. Hence, the

research question of this thesis is twofold: First, which economic framework conditions affect

the CEM’s feasibility to achieve energy self-sufficiency and how should a future CEM look like,

to achieve the highest possible environmental and economic benefits by limited financial

resources. Second, how does an increased RES deployment of CEMs affect not only the

different CEMs but also the overall Austrian economy.

By using a cluster analysis, based on empirical economic data, three homogenous CEM

clusters can be identified. The suburban cluster is characterized by the highest population

density and Gross Value Added (GVA) per capita, the largest share of employees in the tertiary

sector, and the smallest heat and electricity self-sufficiency potentials. The semi-rural and rural

clusters are quite similar, but differ regarding smaller heat-self-sufficiency potentials and

smaller primary sector employment shares in the semi-rural cluster.

The macroeconomic effects in the three CEM clusters are investigated with a spatial multi-

sectoral Computable General Equilibrium (CGE), which is deployed for two different policy

scenarios, an ambitious one with 100% RES electricity in CEMs (Scenario 1), and a less

ambitious one, at least 50% RES electricity scenario (Scenario 2), compared to Business as

Usual (BAU) in 2020. We find an increase in Gross Domestic Product (GDP) and a reduction

in Austrian aggregate output in both scenarios, while employment increases in Scenario 2.

Regarding aggregate production, the rural CEM cluster and the Agriculture, Forestry, and

Fishery sector are the regional and sectoral winner, while the Gas, Mining and Electricity

sectors and the Rest of Austria model region are the sectoral and regional losers.

The results show the CEM approaches’ ability for fostering rural development, as GDP and

energy from RES can be increased, while there are trade-offs involved. Hence, we identify

rural, agricultural, and forestry dominated regions as most suitable for the CEM approach,

which should be selected as new CEMs in the future. In addition, the CEM approach as no-

regret strategy would require a faster technological change or a focus on more economically

competitive RES technologies.

Zusammenfassung

Der Klima- und Energiemodellregionen (CEM) Ansatz ist ein Programm, welches zur

Erreichung des österreichischen Klima- und Energiezieles (34% Energie aus erneuerbaren

Energieträgern (RES) bis 2020) beiträgt. Der CEM Ansatz unterstützt österreichische

Regionen dabei energieunabhängig zu werden. Regionale Entwicklungsstrategien, die auf der

Förderung von RES beruhen, zeigen in der Literatur positive ökonomische Effekte, werden

aber oft, auf Grund von unberücksichtigten makroökonomischen Feedbackeffekten, als zu

optimistisch kritisiert. Die Forschungsfrage dieser Masterarbeit ist zweigeteilt: Welche

wirtschaftlichen Rahmenbedingungen beeinflussen CEMs dabei energieunabhängig zu

werden, und wie sollte eine zukünftige CEM aussehen, um mit knappen finanziellen

Ressourcen den größtmöglichen ökonomischen und ökologischen Nutzen zu erzielen? Wie

beeinflusst eine erhöhte Energieautarkie in CEMs Österreich im Allgemeinen und die CEMs

selbst im Speziellen?

Mit Hilfe einer Clusteranalyse konnten drei homogene CEM Cluster identifiziert werden. Der

suburbane Cluster ist durch seine hohe Bevölkerungsdichte und Bruttowertschöpfung (GVA),

seinen großen Anteil an Beschäftigten im Tertiärsektor und den geringsten Potentialen zur

Energieunabhängigkeit charakterisiert. Der semi-rurale und rurale Cluster sind sich relativ

ähnlich, jedoch weißt der semi-rurale Cluster geringere Potentiale zur Wärmeunabhängigkeit

und geringeren Anteilen an Beschäftigen im Primärsektor auf.

Die makroökomischen Effekte durch energieunabhängige CEM Cluster wurden mit einem

räumlich multisektoralem Computable General Equilibrium (CGE) Modell untersucht. Dabei

werden zwei Szenarien, ein ambitioniertes Szenario (100% Elektrizität in CEMs aus RES;

Szenario 1) und ein weniger ambitioniertes Szenario (mindestens 50% Elektrizität in CEMs

aus RES; Szenario 2) mit einem Business as Usual (BAU) Szenario in 2020 verglichen. Dabei

zeigt sich ein Anstieg des Bruttoinlandsproduktes (GDP) und ein Rückgang der aggregierten

Produktion in beiden Szenarien sowie ein Beschäftigungsanstieg in Szenario 2. Die Gewinner

sind ländliche CEMs und der Landwirtschafts-, Forst- und Fischereisektor. Die Verlierer sind

der Rest von Österreich und die Sektoren Gas, Bergbau und Elektrizität.

Die Resultate zeigen, dass der CEM Ansatz zur Förderung der ländlichen Entwicklung

geeignet ist, da sowohl GDP als auch RES gesteigert werden können. Außerdem sollten in

Zukunft rurale, land- und forstwirtschaftlich geprägte Regionen als CEM ausgewählt werden.

Aufgrund von regionalen und sektoralen Zielkonflikten kann der CEM Ansatz aber nicht als

„no-regret“ Strategie betrachtet werden, wofür es entweder eine schnellere technologische

Entwicklung oder einen Fokus auf die wirtschaftlichsten RES Technologien benötigen würde.

Table of Contents

1 Introduction ..................................................................................................................... 12 Sub-National Energy Transition – The Austrian CEM Approach ............................... 6

2.1 The Austrian CEM Program and its Goals ........................................................................ 62.1.1 The CEM History ................................................................................................................... 62.1.2 The CEM Process, Funding and Monitoring ......................................................................... 92.1.3 The Goal and Definition of Energy Autarky: Balanced Energy Autarky .............................. 11

2.2 Review of International and Austrian Energy Transition Approaches ........................ 132.3 Conclusion ......................................................................................................................... 14

3 Economic Characteristics and CEM Clustering ........................................................ 163.1 Economic Structure of the Austrian CEMs ..................................................................... 163.1.1 Data Basis and Limitations .................................................................................................. 173.1.2 Methodology for Economic Data Processing ...................................................................... 183.1.3 Results of Economic Data Processing ................................................................................ 19

3.2 Cluster Analysis ................................................................................................................ 223.3 Conclusion ......................................................................................................................... 29

4 Methodological Background: Sub-national CGE Analysis ....................................... 314.1 Macro-Economic Policy Analysis Techniques ............................................................... 324.2 Historical Development of CGE Modeling ...................................................................... 334.2.1 Emergence of CGE Analysis in Literature ........................................................................... 344.2.2 The Arrow-Debreu Model .................................................................................................... 35

4.3 National Scale CGE Modeling .......................................................................................... 364.3.1 Circular Flows of a Closed Economy .................................................................................. 364.3.2 Market Clearance, Zero Profits Conditions and Income Balance ....................................... 384.3.3 Inclusion of a SAM .............................................................................................................. 384.3.4 Benchmark Solution and Counterfactual Scenarios ............................................................ 394.3.5 Production and Demand Functions in Combination with Nesting Structures ...................... 404.3.6 Expansion to a Simple Small Open Economy CGE Model ................................................. 41

4.4 Sub-National Scale CGE Modeling .................................................................................. 434.4.1 Treatment of Regions in Sub-National CGE Models ........................................................... 444.4.2 Sub-National Modeling Challenges and Requirements ...................................................... 464.4.3 Existing, Sub-National CGE Studies Concerning Regional Renewable Energy Strategies 53

4.5 Conclusion ......................................................................................................................... 565 Analysis of the CEM Approach in a Sub-National CGE Model ................................. 58

5.1 Implementation of National and Regional Energy Goals in a Sub-National CGE Approach ........................................................................................................................................... 585.2 Methodology – CGE Model Specification ....................................................................... 595.2.1 CGE Model Classification and Fundamental Assumptions ................................................. 605.2.2 The Basic CGE Model Structure ......................................................................................... 625.2.3 Regional and Domestic Production ..................................................................................... 665.2.4 International Trade .............................................................................................................. 705.2.5 Regional Household and Government Demand ................................................................. 71

5.3 Sub-National Economic Data ........................................................................................... 725.4 Scenario Description ........................................................................................................ 785.5 Results: Economic Consequences of the CEM Energy Transition Approach ............ 825.6 Discussion of CGE Model Results .................................................................................. 94

6 Summary and Conclusion ......................................................................................... 100References .......................................................................................................................... 105

List of Figures

Figure 1: Annual change of participating CEMs ..................................................................................... 7Figure 2: The active municipalities of the Austrian CEMs for the years 2010 to 2016 ........................... 8Figure 3: The municipalities covered by the 82 Austrian CEMs analyzed in this thesis ......................... 9Figure 4: The CEM process timeline .................................................................................................... 10Figure 5: Mapping of the CEM clusters ................................................................................................ 24Figure 6: Population density in the CEM clusters ................................................................................. 25Figure 7: GVA per capita in the CEM clusters ...................................................................................... 26Figure 8: Economic structure of the CEM clusters ............................................................................... 26Figure 9: Energy consumption and potentials of the CEM clusters ...................................................... 28Figure 10: The circular flow of the economy ........................................................................................ 37Figure 11: Nesting of the domestic production sectors (Xi) .................................................................. 41Figure 12: Flowchart of a static CGE model ......................................................................................... 42Figure 13: Flowchart of the bottom-up sub-national multi-sectoral CGE model of Austria ................... 64Figure 14: Nesting structure of regional conventional electricity generation, transmission, distribution

and trade, regional renewable electricity generation, and regional production sectors other than electricity and coke manufacturing .............................................................................................. 68

Figure 15: Nesting structure of regional coke manufacturing production ............................................. 68Figure 16: Nesting structure of regional electricity production ............................................................. 70Figure 17: Nesting structure of regional private household consumption ............................................ 72Figure 18: Nesting structure of domestic government consumption .................................................... 72Figure 19: Structure of the national SAM of Austria ............................................................................. 75Figure 20: Regional breakdown of the national SAM of Austria ........................................................... 76Figure 21: Regional effects (without relative price changes) on electricity generation compared to BAU

2020 in mio € ............................................................................................................................... 83Figure 22: Regional effects (without relative price changes) on electricity generation compared to BAU

2020 in % ..................................................................................................................................... 85Figure 23: Effects on sectoral output quantities at the national level compared to BAU 2020 in mio € and

in % .............................................................................................................................................. 86Figure 24: Regional effects on total output quantity in the model regions compared to BAU 2020 in %

..................................................................................................................................................... 88Figure 25: Effects on sectoral output quantities at the regional level compared to BAU 2020 in mio € 89Figure 26: National effects on GDP, unemployment and aggregate output compared to BAU 2020 in %

..................................................................................................................................................... 90Figure 27: National effects on government income and spending compared to BAU 2020 in mio € and

in % .............................................................................................................................................. 92Figure 28: Effects on regional welfare in % of Hicksian equivalent variation relative to BAU 2020 ..... 93

List of Tables

Table 1: CEMs – Population and employment ..................................................................................... 19Table 2: CEM – Degree of urbanization and GVA ............................................................................... 21Table 3: Variables for cluster analysis .................................................................................................. 23Table 4: Results of cluster analysis ...................................................................................................... 24Table 5: Economic structure of the CEM clusters – all ÖNACE 2008 section sectors ......................... 27Table 6: Economic structure of the CEM clusters – the ten most important ÖNACE 2008 section sectors

..................................................................................................................................................... 28Table 7: A SAM for a closed economy ................................................................................................. 39Table 8: Economic characteristics of the model regions in 2011 ......................................................... 61Table 9: Sectoral and regional set indices ........................................................................................... 63Table 10: Sectoral restructuring of the Austrian SAM of the year 2011 ............................................... 66Table 11: Sector- and agent specific elasticities .................................................................................. 69Table 12: Electricity in Austria and the model regions: status quo and potentials ............................... 79Table 13: Electricity production technologies and producer prices (€/MWh) in 2020 (selected

intermediate inputs) ..................................................................................................................... 80Table 14: Exogenous renewable electricity production target in the CEM model regions ................... 81

List of Abbreviations

ACRP Austrian Climate Research Program

BAU Business as Usual

BMLFUW Federal Ministry of Agriculture, Forestry, Environment, and Water Management

CEM Climate and Energy Model Region

CES Constant Elasticity of Substitution

CGE Computable General Equilibrium

CO2 Carbon Dioxide

EU European Union

GDP Gross Domestic Product

GHG Green House Gas

GTAP Global Trade Analysis Project

GVA Gross Value Added

GWh Gigawatt Hours

ha Hectare

IO Input-Output

ISTAT Italian National Institute of Statistics

KLIEN Climate and Energy Fund

LINKS Linking Climate Change Mitigation, Energy Security, and Regional

Development in Climate and Energy Model Regions in Austria

mio Million

MWh Megawatt Hour

NACE Statistical Classification of Economic Activities in the European Communities

NBS National Bureau of Statistics

NST Standard Goods Classification for Transport Statistics

NUTS Nomenclature of Territorial Units for Statistics

ÖNACE Austrian Version of Statistical Classification of Economic Activities in the

European Communities

PV Photovoltaic

RES Renewable Energy Source

SAM Social Accounting Matrix

USA United States of America

1

1 Introduction

In 2009, the Austrian government passed its climate and energy package in accordance with

the European Union’s (EU) climate and energy targets 2020. The Austrian government

specified a target of Renewable Energy Source (RES) deployment of 34% until 2020,

according to the EC-directive 2009/28/EG (European Parliament 2009). In this regard, the

overall Austrian energy goal aims to foster RES technologies, to reduce overall energy

consumption, and to reduce Carbon Dioxide (CO2) emissions (European Parliament 2009;

Bundesministerium für Wissenschaft, Forschung und Wirtschaft 2017). Fostering RES

deployment will unavoidably initiate a transition of the energy market, since RES technologies

differ in their input and cost structures from the present fossil fuel dominated RES mix in Austria

(Energy Economics Group 2016; Statistics Austria 2016).

To achieve its RES target, the Austrian government has implemented diverse actions and

measures. One measure was the establishment of The Austrian Climate and Energy Fund

(KLIEN) in 2007, which took place before the Austrian climate and energy package was passed

(Climate and Energy Fund 2014). Amongst other programs, the KLIEN established the

Austrian Climate and Energy Model Region (CEM) program in 2009. The CEM program aims

to support rural and structurally weak regions on maximal Nomenclature of Territorial Units for

Statistics (NUTS)1 3 level or at a lower regional level in becoming independent of fossil fuels

and is a driver of rural development. CEMs should deploy own RES potentials and increase

efficiency in all energy sectors, as electricity, heat, and mobility. In addition to some limited

basic funding provided by KLIEN, CEMs need individual co-funding (Climate and Energy Fund

2015b). Overall, the CEM approach implies that individual CEMs should achieve energy

autarky, recently more often referred as energy self-sufficiency, which means RES production

should equal RES consumption over a certain period (Climate and Energy Fund 2014).

The individual bottom-up actions of CEMs distributed throughout Austria, to become energy

self-sufficient by utilizing individual strengths and regional potentials, are seen as key success

factors within the CEM approach (Climate and Energy Fund 2015b). The sub-national energy

1 The NUTS classification is introduced to organize the economic territory in the EU in a hierarchical system from

major socio-economic regions (NUTS 1) to small regions for specific diagnoses (NUTS 3) (Eurostat 2017b).

2

transition of the CEM approach includes heterogeneous CEMs, characterized by different

socioeconomic and energy related characteristics. The different economic and energy related

characteristics imply different RES potentials and different economically structured regions,

ranging from regions characterized as agricultural or industrial dominated towards regions

characterized as service dominated. These characteristics in turn influence the feasibility of a

CEM to become energy self-sufficient. By looking at the 82 CEMs active in November 2015,

which provide an implementation concept on the CEM homepage

(klimaundenergiemodellregionen.at, accessed 3 December 2015), the differences and

heterogeneity between the CEMs become obvious.

Bottom-up energy transition approaches, as the Austrian CEM approach, are not new.

Especially in Austria, different bottom-up approaches on low scale regional level have been

established recently, as the e5, klimaaktiv, leader, and e-mobility model region program. Müller

et al. (2011) is dealing with low scale level regions striving to become energy autarkic or energy

self-sufficient, including the Austrian CEMs, but also international examples as the German

bio energy village and Swiss energy regions.

In contrast, Stanzer et al. (2010) investigate the feasibility of all Austrian districts to become

heat and electricity self-sufficient until 2012 and 2020. While Stanzer et al. (2010) reveal the

possibility for heat and electricity self-sufficiency for individual districts until 2020, for whole

Austria until 2020 only electricity self-sufficiency is achievable. Streicher et al. (2010)

investigate the transition towards a low carbon Austrian society until 2050 and find that with

enormous energy efficiency of 50% compared to 2008 and an intelligent energy use, energy

self-sufficiency from RES is possible.

Two studies, which explicitly deal with economic effects arising from the CEM approach, are

Kettner et al. (2012) and the follow-up study of Kettner, Köppl, and Streicher (2015). While

both studies investigate the economic effects arising from projected CEM measures on federal

state level, Kettner et al. (2012) use a Computable General Equilibrium (CGE) model and

found an increase in Gross Domestic Product (GDP) and employment. Kettner, Köppl, and

Streicher (2015) use an Input-Output (IO) model and found that, presupposing large

investments and a change in behavior, energy savings, increased labor force, and increased

Gross Value Added (GVA) is possible.

The simultaneous achievements of RES potentials in different CEMs, which are distributed

throughout the country, will have effects on whole Austria. The directly affected energy sectors

of the individual CEMs are connected by trade flows of intermediate, factor, and final demand

3

with other Austrian regions and sectors. These economic links within the Austrian economy

have cross-sectoral economic spillover and macro-economic feedback effects on whole

Austria, which differ on sub-national level due to different economic characteristics of different

sub-national regions.

National distribution, simultaneous action, individual funding, and diverse economic structures

and energy potentials are the major characteristics of the CEM approach. These

characteristics require an appropriate regional economic policy modeling technique, to conduct

an economic analysis of the CEM approach. Partridge and Rickman (2010) and Allan (2015)

identified econometric, IO, Social Accounting Matrix (SAM), and CGE models as suitable for

regional economic policy analysis. Partridge and Rickman (2010) and Allan (2015) mention

that CGE modeling is the most appropriate regional economic modeling technique to account

for cross-sectoral economic spillover and macro-economic feedback effects in an economic

analysis, while econometric, IO, and SAM models are restricted due to data and

methodological limitation.

CGE modeling is characterized by the combination of the Walrasian general equilibrium

structure (Arrow and Debreu 1954; Arrow and Hahn 1971) and Leontief´s IO accounting

system (Leontief 1937; Leontief 1951). Based on this pioneering work, a multi-sectoral sub-

national top-down CGE model was established by Dixon et al. (1982). Further contribution

work of Shoven and Whalley (1984; 1992) and Pyatt and Round (1985), as well as improved

computer power and software improvements, enabled the implementation of the global, multi-

regional, multi-sectoral Global Trade Analysis Project (GTAP) model of Hertel (1997). While in

the following years programming simplifications (Rutherford 1999) and CGE model code

sharing (Rutherford and Paltsev 1999) enabled a further distribution of CGE models on a global

and national scale, sub-national CGE models remain rare due to insufficient data availability

and methodology limitations (Partridge and Rickman 1998; Partridge and Rickman 2010).

Although sub-national CGE models are rare, work is in progress. Rodriguez (2007) did an

overview of existing CGE models and identified three groups of sub-national CGE model,

which can be classed as region-specific (Horridge 1999; Cansino et al. 2014), bottom-up

(Horridge, Madden, and Wittwer 2005; Standardi, Bosello, and Eboli 2014), and “partial”2

2 “Partial” sub-national CGE models should not be confused with partial equilibrium models.

4

(Dixon et al. 1982; Filho and Horridge 2005; Clements, Jung, and Gupta 2007; Schinko et al.

2013). Beside the study of Trink et al. (2010), which is no classical sub-national CGE model,

only a few CGE models dealing with a bottom-up energy transition of one or more regions on

NUTS 3 level or at least below NUTS 2 level are available in literature. In contrast, the number

of CGE models dealing with sub-national or national energy transition on a federal state level

(Zhang et al. 2013; Wu et al. 2016) or on EU’s NUTS 2 level (Kettner et al. 2012; Cansino et

al. 2014) in literature is higher.

Sub-national CGE models are identified by Partridge and Rickman (2010) as most appropriate

to do an macro-economic analysis of the CEM approach, but they are still rare. While a sub-

national CGE analysis of the CEM approach was done by Kettner et al. (2012), they do not

investigate the Austrian energy sector and energy potentials, characteristics, and

heterogeneity of CEMs in detail. Additionally, they ignore budget effects and government

subsidies, and scale-up individual targets on federal state only. To contribute to the

deployment of sub-national CGE models, we employ a bottom-up sub-national multi-sectoral

CGE model of Austria including a detailed representation of the Austrian energy sector and

the spatial economic and energy related characteristics of the CEM approach. To show the

present differences between individual CEMs, we use homogenous CEM clusters.

This master thesis poses three major research questions, which concern the CEMs feasibility

of becoming energy self-sufficient, the identification of most suitable regions to become energy

self-sufficient, and the associated cross-sectoral and macro-economic effects of these energy

self-sufficiencies: First, which economic framework conditions affect the CEM’s feasibility to

achieve energy self-sufficiency? Second, how should a CEM be characterized regarding

economic and energy related properties to achieve the highest possible environmental and

economic benefits by limited financial resources? Third, how affect an increased RES

deployment of CEMs not only the different CEMs but also the overall Austrian economy

concerning GDP, unemployment, sectoral production, and household welfare?

To answer our research questions, chapter 2 of this thesis provides an overview of the Austrian

sub-national energy transition CEM approach, including an investigation of the CEM programs’

goals and its historical development, and a literature review about other Austrian and

international energy transition approaches. Afterwards, we identify in chapter 3 the CEMs

economic characteristics and the economic framework conditions of the CEMs feasibility to

achieve energy self-sufficiency. Based on these economic characteristics and economic

5

framework conditions, we additionally implement a cluster analysis to attain the heterogeneous

CEMs to homogenous CEM clusters.

In chapter 4, we justify why a sub-national CGE analysis is the most appropriate economic

modeling technique to analyze the CEM approach. We first discuss the available macro-

economic police analysis approaches, to demonstrate why the CGE approach is the most

appropriate. In the second section, we show the historical development of CGE modeling to

become a commonly used macro-economic policy analysis method. Third, we discuss the

theoretical background of CGE modeling by showing how the circular flows of a closed

economy can be developed to a simple, small, and open economy CGE model. Fourth, we

show which sub-national CGE modeling classes are available in literature and the main

challenges of sub-national CGE modeling. Finally, we discuss which class of sub-national CGE

modeling is the most suitable to identify the socio-economic effects arising from the CEM

approach, its special characteristics, and the associated spillover and feedback effects.

In Chapter 5, we carry out the sub-national CGE model analysis of the CEM approach, where

we first provide an overview about the Austrian energy goals and the identified achievable

energy potentials. In the second and third section, we present and discuss the developed CGE

model and its data basis, while the fourth section describes the analyzed scenarios. In the last

two sections of the fifth chapter, we show the results and provide a discussion. In chapter 6,

we summarize the thesis, answer the research questions, and draw a conclusion.

6

2 Sub-National Energy Transition – The Austrian CEM Approach3

In 2009, the Austrian CEM approach started. The aim of this approach was to achieve the

Austrian climate and energy goals by a regional bottom-up approach. While the initial objective

of the CEM approach was the establishment of “energy autarkic” rural regions within Austria,

this objective has evolved since then. This chapter answers the questions how the objectives

of the CEM approach and the number of participating CEMs have changed since its

establishment, what is required from the participating CEMs and how the CEMs deal with the

concept of energy autarky. To deal with these questions, the CEM program and its goals are

discussed in section 2.1, while a review of international and Austrian energy transition

approaches is done in section 2.2.

2.1 The Austrian CEM Program and its Goals

In the following sections, we first discuss the historical development of the CEM approach

since its establishment in 2009 until January 2016 (section 2.1.1). Afterwards, the process of

becoming a CEM and the funding and monitoring system are reviewed (section 2.1.2). Finally,

we deal with the question what is meant by “energy autarkic” in the context of the CEM

approach (section 2.1.3).

2.1.1 The CEM History

The KLIEN was founded in 2007 (Climate and Energy Fund 2014). In the context of the KLIEN

establishment, the Austrian CEMs were instituted via a top-down initiative since 2009 as an

instrument, to foster the achievement of Austria’s climate and energy goals. These CEMs are

3 This chapter contains adapted sections of the “Linking climate change mitigation, energy security and regional

development in climate and energy model regions in Austria” (LINKS) working paper 2.1 by Truger et al. (2016) in

which the author of the current thesis is also one of the co-authors. Section 2.1.1 of this thesis (section 2.1 of Truger

et al. (2016)), section 2.1.3 of this thesis (section 2.2 of Truger et al. (2016)), and section 2.2 of this thesis (section

2.1 of Truger et al. (2016)) are based on adapted parts of Truger et al. (2016) written by the Author of this thesis,

while section 2.1.2 of this thesis (section 2.1 of Truger et al. (2016)) are based on adapted parts of Truger et al.

(2016) written by the Author of this thesis together with Barbara Truger.

7

groups of municipalities, which are selected to take a leadership role in reaching these goals

(Climate and Energy Fund 2015b). The climate and energy goals require, amongst others, that

by 2020 34% of the gross final energy consumption in Austria should be covered by RES

(European Parliament 2009). In this context, the aim of the CEMs is to support the ambitious

goal by striving to become independent of fossil fuels, based on a regional bottom-up

approach. The bottom-up approach comprises of each CEM aiming to meet this target by

setting its own goals and by implementing different, regionally tailored projects. The projects

are based on the pillars of sustainability, economic and environmental efficiency, and should

lead to an accomplishment of fossil fuel independency by exploiting regional RES potentials

and by fostering civil participation. These projects comprise measures of energy efficiency and

RES development in all energy related areas, covering electricity, heat, and mobility (Climate

and Energy Fund 2014).

As shown in Figure 1, until February 12, 2016, 138 CEMs joined the CEM approach in total.

29 CEMs have left the CEM program between 2010 and 2016. The municipalities covered by

two CEMs switched to another CEM over this period, and further two CEMs have not started

their work until February 2015. 107 CEMs are still operational in 2016, despite a reduction in

the annual number of CEMs joining the CEM program, respectively an increase in the number

of CEMs leaving the CEM program, can be identified. The absolute number of active CEMs

has been relatively stable since 2013. The municipalities, which have been active in CEMs

from 2010 until 2016, can be seen in the maps of Figure 2.

Figure 1: Annual change of participating CEMs

Source: Truger et al. (2016)

0

20

40

60

80

100

120

2010 2011 2012 2013 2014 2015 2016

Numbero

fCEM

s

CEMsjoining CEMsleaving ActiveCEMs

8

Figure 2: The active municipalities of the Austrian CEMs for the years 2010 to 2016


2010 2011

2012 2013

2014 2015

2016

9

At the time, we started the analysis of the CEM program in 2015, we employed the latest

available data from November 1, 2015. At this date, 87 CEMs were operational in Austria and

82 of those CEMs stated an official implementation concept. These 82 CEMs cover about 2.2

million inhabitants and 26% of Austria’s population (Climate and Energy Fund 2014), which

corresponds to 37% of Austria’s population living in intermediate density and thinly populated

area according to European Commission’s definition of the degree of urbanization of local

administrative units level 2 or municipality level (Climate and Energy Fund 2014; European

Commission and Statistics Austria 2016). Additionally, these 87 CEMs, which can be seen in

Figure 3, cover 42% of Austria’s territory.

Figure 3: The municipalities covered by the 82 Austrian CEMs analyzed in this thesis


2.1.2 The CEM Process, Funding and Monitoring

The process of becoming a CEM has changed in 2015 and starts since then with an application

of a group of municipalities. Before this amendment, single municipalities or consulting

agencies were possible contractual partners as well. New CEMs should ideally be rural and

structurally weak regions. New CEMs should consist of at least two municipalities with a

minimum number of 3,000 and a maximum number of around 60,000 inhabitants per region,

in special cases this number can be exceeded or fallen below. Being selected as a CEM by

the KLIEN, the new CEM has to develop an implementation concept and has to install a CEM

manager with at least a twenty-hour contract within the first year of the first phase of the CEM

10

process. The second and third year of the first phase constitute the two-year implementation

phase of the concept, as shown in Figure 4 (Climate and Energy Fund 2015b).

Figure 4: The CEM process timeline

Source: Truger et al. (2016) based on Climate and Energy Fund (2015b)

On January 1, 2015, 94 implementation concepts were available on the CEM homepage

(klimaundenergiemodellregionen.at, accessed 3 December 2015), while only 82 of those

CEMs with concepts were listed as active CEMs at the cut-off date November 1, 2015. The

implementation concepts have to contain data regarding the current energy situation, RES

potentials, information about initial public participation, public relations, and the acceptance

within the municipalities, as well as the definition of ten concrete work packages, which have

to be implemented during the implementation phase (Climate and Energy Fund 2015b). The

implementation concepts vary greatly regarding the energy data, both in collection and detail.

There are also great differences in terms of length, content, and structure of these concepts,

which can be partly explained by changing guidelines, since the available concepts are from

the years 2010 to 2014. Finally, 64 of the 82 analyzed concepts provide comprehensive data

regarding their energy demand, while the remaining 18 regions do not distinguish between

electricity, heat, and mobility, state contradicting data or provide no quantitative data at all.

During the implementation phase, a special focus rests on the introduction of stakeholder

networks and an awareness increase within the population in the municipalities (Climate and

Energy Fund 2015b). The first implementation phase is funded by the KLIEN with a maximum

of € 145,000 and requires a 25% co-financing by the municipality for the whole phase

(Wolfsegger 2015).

11

For the operationalization of the bottom-up approach, a CEM manager is installed in each

region, who is connected to the managers of the other regions within a network. The CEM

manager has a key role for the success of each CEM and the whole CEM approach. The task

of the CEM managers is to identify strengths of the regions in becoming fossil fuel independent

and to define and implement work packages regarding energy efficiency and increased RES

development (Climate and Energy Fund 2015b).

In the following three-year continuation phase, which requires a new application by the region,

the CEM manager has to identify and implement ten work packages. This continuation phase

can be applied several times. The continuation phase is funded by the KLIEN for the whole

period with a maximum of € 200,000 and requires again a 25% co-funding of the municipalities

(Wolfsegger 2015). There are further tasks required for a CEM to be eligible for the

continuation phase. For each CEM, a concluding quality management report based on the e5

methodology (e5 2016), is needed after the implementation phase and each continuation

phase (Climate and Energy Fund 2015b), as shown in Figure 4.

For 2015, there is a total budget of € 10,000,000 available for the whole CEM approach. The

budget provides € 1,000,000 for sample refurbishments of public buildings and € 500,000 for

charging stations. Next to the overall financing of the CEMs, the remaining budget can be used

for financing lead projects and for investment support of Photovoltaic (PV) plants, biomass

heating systems, thermal solar systems, sample refurbishments, and charging stations for e-

vehicles, public buildings, and the general public. These investments are funded with €

1,750,000 per year by the Austrian program of rural development, by funds of the EU, and by

the Austrian Federal Ministry of Agriculture, Forestry, Environment, and Water Management

(BMLFUW) (Climate and Energy Fund 2015b). However, as a large part of the budget is used

up for financing the implementation and continuation phases, it is also necessary to find

external investors for lead projects and further investments (Wolfsegger 2015).

2.1.3 The Goal and Definition of Energy Autarky: Balanced Energy Autarky

The RES goals of the Austrian government and the goal of some CEMs to become

independent of fossil fuels require a definition of energy autarky (Kettner et al. 2010; Climate

and Energy Fund 2014). To that end, the CEM managers are instructed by the KLIEN to use

the definition of Jamek et al. (2014). This definition of energy autarky includes the sectors

electricity, heat, and mobility and strives for the largest possible independences of the regions

12

from fossil fuels in regional energy production and from energy imports. Jamek et al. (2014)

state that this should not be translated into a state of isolation from international markets. Its

aim should rather be to develop the RES potentials in each region and to improve energy

efficiency. Another aim should be the creation of a network between different regions, to

produce energy where potentials are available. Energy should be exploited not only in an

economically efficient, but also in an ecologically compatible and sustainable way.

Hence, energy autarky, by the definition of Jamek et al. (2014), should be balanced across a

certain region, as Austria or a CEM, over a certain period. This definition does not imply that

whole Austria has to serve its energy demand at each point in time by itself; rather net exports

should be zero or positive across a certain period. The definition of balanced energy autarky

is especially sensible when fossil fuel imports of the mobility sector are considered, at least in

the medium term. In the earlier stages towards energy autarky (on a net basis), net energy

imports should be possible for whole Austria. Nevertheless, energy imports should be

minimized.

Such definitions of balanced energy autarky are also used in other studies. Streicher et al.

(2010) used a similar definition regarding energy autarky achievement by the year 2050. As

energy autarky needs time for adjustment, their definition requires that Austria can produce its

whole energy demand on its own in 2050. However, Streicher et al. (2010) point out that their

definition does not imply that energy demand is completely met by domestic production. They

also include energy imports and exports, which should be balanced over the whole period.

Another similar definition of energy autarky is stated by Müller et al. (2011), who define energy

autarky as a situation, where a majority of energy is produced by local resources. They also

argue that a region is an open system with exchange of people and resources. This definition

should therefore be understood as a transition towards a more sustainable decentralized

society, which increases energy efficiency and uses endogenous potentials, instead of isolated

regions.

The term energy autarky was especially used in Austria in regard with the previously stated

definitions. Despite that, the term energy autarky is recently avoided in Austria’s energy policy

domain. A turning point was the change from Dipl.-Ing. Nikolaus Berlakovic to Dipl.-Ing. Andrä

Rupprechter as Austrian minister of the BMLFUW in 2013. Since then, the concept of energy

autarky has been replaced in the political discourse by concepts such as energy transition,

energy self-sufficiency or by the more general terms energy efficiency and RES development.

One reason for the change in wording was that energy autarky could have been confused with

13

energy isolation, which has, however, never been a goal communicated by the KLIEN for its

CEMs (Stanzer et al. 2010; Wolfsegger 2015).

2.2 Review of International and Austrian Energy Transition Approaches

There are already some studies on the CEM approach and the feasibility of a transition of the

Austrian energy sector towards a higher share of RES and increased energy efficiency. Apart

from the Austrian example of the CEM program, other examples of energy autarky and energy

transition both in Austria and from abroad can be found.

Another example of an energy transition program is the e-mobility model region approach in

Austria, which was initiated by the KLIEN in collaboration with the BMLFUW. In August 2015,

the program included seven foremost urban but also some rural regions. The aim of the

program is the collection of information about future potentials in different living spaces

(Climate and Energy Fund 2015a). In the context of energy transition, there are also some

other approaches implemented in Austria, such as the e5, klimaaktiv and leader programs.

Next to the Austrian CEMs, international examples of energy model regions exist, such as the

German bio energy villages and the Swiss energy regions. The German bio energy village

program has a similar objective as the CEM program. The aim is to meet, if possible, the

largest part of electricity and heat demand of the different regions by biomass technology and

to simultaneously reduce the dependency on scarce resources such as fossil fuels (Ruppert

et al. 2010). The second approach, initialized by the Swiss Federal Offices for Spatial

Development, Federal Offices for Energy, Federal Offices for Agriculture, and the State

Secretariat for Economic Affairs, is based on the Austrian and German approaches. It has a

broader objective, as it allows for different strategies in the scope of energy efficiency and RES

development, which goes from simply increased energy self-sufficiency to energy export-

regions. The Swiss approach understands energy autarky as a long-term adjustment towards

energy self-sufficiency (Ribi et al. 2012). Müller et al. (2011) summarize the different programs

in Austria, Germany, and Switzerland, and give a broader overview about the existing

structures in 2011.

Kettner et al. (2012) investigated energy transition in Austria based on five case study CEM

implementation concepts. The study by Kettner et al. (2012) employs a CGE model, which

investigates the effects of different CEM measures stated in the implementation concepts,

projected on a federal state level. They conclude that under their assumptions, a national

14

increase in GDP and employment is possible, but there are huge differences between the

federal states, which lead to negative outcomes for some Austrian regions.

In a follow-up study, Kettner, Köppl, and Streicher (2015) extend the number of case study

CEM implementation concepts to 22, which are then used for the projection on federal state

level. The study accounts for the potential effects of different measures on the Austrian

economy. For their approach, they use an IO model of Austria, and include two different

scenarios, which cover the differences in ambitiousness of measures in the different regions.

They reveal that large energy savings are possible in both scenarios, which lead to an

increased labor force and Gross Value Added (GVA). However, these positive effects require

large investments and a change in behavior.

Other studies investigate the technical and economic feasibility of energy transition in Austria

on different regional levels. Stanzer et al. (2010) did a feasibility study of Austria’s RES

potentials at the district level for the base year 2007 and calculated two different scenarios of

possible RES implementations until 2012 and 2020. They note that, in an optimistic scenario

starting from 2007, electricity autarky could be possible in 2020 for most districts, while only a

60% self-sufficiency in the heat sector could be reached.

The study of Streicher et al. (2010) on the contrary, did not analyze the accessible degree of

self-sufficiency in a certain year, but rather how a transition towards a low carbon society of

maximum 20% of the Green House Gas (GHG) emissions of the year 1990 can look like.

Streicher et al. (2010) conclude that such a transition could be possible for 2050 under the

anticipated technological progresses and energy demand reductions.

2.3 Conclusion

We have shown in this chapter how the Austrian CEM approach has evolved since its

establishment in 2009. We find that the absolute number of CEMs is slightly increasing and

reached a maximum of 107 participating CEMs on January 1, 2016, which are evenly

distributed over whole Austria. The demands on the participating CEMs evolved recently and

the whole CEM approach has become more organized and structured. Contractual partners

are now only municipalities. The size of CEMs regarding their number of inhabitants is set out

precisely and a CEM has to consist of at least two municipalities, which excludes cities with

inhabitants above this number by definition. Each CEM has to employ a manager, by a twenty-

hour contract and has to submit a concept in each participating phase.

15

Additionally, this chapter critically discusses the definition of the term “energy autarky”, which

means that energy production should become equal to energy consumption in a certain region

while allowing for energy trade. Recently, the term “energy autarky” has disappeared in the

Austrian energy policy discourse and has been replaced by energy self-sufficiency, energy

transition, or RES development.

Concerning the literature on sub-national bottom-up energy transition, recent studies for

Austria found energy potentials of electricity and heat on district level for 2012 and 2020

(Stanzer et al. 2010) and show how energy transition can look like until 2050 (Streicher et al.

2010). Other studies, which investigated the CEM approach, found potential overall positive

effects on GDP and employment, while regionally negative effects are possible (Kettner et al.

2012). Additionally, energy savings are possible with simultaneously increased labor force and

GVA, if investments can be increased and behavior can be changed (Kettner, Köppl, and

Streicher 2015).

16

3 Economic Characteristics and CEM Clustering4

By considering the existing literature, official documents, and various energy and economic

datasets, this chapter sets out to identify the existing economic related framework conditions

of 82 Austrian CEMs. The questions we set out to answer are diverse. First, how do these

CEMs differ in size of population, area, and employees? Second, how do CEMs differ in

economic structure regarding GVA and employed persons in primary, secondary, and tertiary

sector? Third, are CEMs more urban or rural? Fourth, what are the economic and energy

related characteristics of the current CEMs? Finally, how can CEMs be clustered in

accordance to their economic and energy characteristics? We do this clustering in the context

of a cluster analysis to obtain representative CEM clusters, which are needed for our CGE

analysis in chapter 5. By answering these questions, we identify the economic framework

conditions of the CEMs, which may influence the feasibility of achieving CEMs’ climate and

energy goals. Thus, this chapter first shows the economic structure of the Austrian CEMs in

section 3.1, and a cluster analysis of the 82 Austrian CEMs in section 3.2, which is based on

the CEMs economic structure. In section 3.3, we draw a conclusion.

3.1 Economic Structure of the Austrian CEMs

This section presents the current economic situation of the 82 CEMs that were part of the CEM

program as of November 1, 2015, and have published an official implementation concept

before this date. A survey of economic characteristics is done to determine the specific

economic framework conditions in the different CEMs in 2011, the year of investigation. The

determination of the economic framework conditions requires a sufficient economic database.

We create such an economic database by combining different economic datasets on different

regional levels. This is done for the reason, that sufficient economic data is neither available

4 This chapter contains adapted sections of the LINKS working paper 1.1 by Bramreiter et al. (2016) in which the

author of the current thesis is also one of the authors. Section 3.1 of this thesis (chapter 3 of Bramreiter et al.

(2016)), are based on adapted parts of Bramreiter et al. (2016) written by the Author of this thesis, while section 3.2

of this thesis (section 4.1 of Truger et al. (2016)) are based on adapted parts of Bramreiter et al. (2016) written by

the Author of this thesis together with Barbara Truger.

17

in the CEM implementation concepts, nor in an interrelated database on municipality, district

or at least Nomenclature of Territorial Units for Statistics (NUTS) 3 level.

3.1.1 Data Basis and Limitations

For the economic assessment of the CEMs, economic data at the smallest regional level

(municipality) is needed as CEMs can even consist of municipalities from more than one

federal states. The national census of Austria from the year 2011 provides data on population,

employed persons, and commuters at municipality level (Statistics Austria 2013b). In addition,

the census of employment for the year 2012 is used to expand the database by employment

data, which is not available in the census of 2011 (Statistics Austria 2014a). For the year 2011,

employment data on district level is available, which distinguishes between the primary,

secondary, and tertiary sector, and at a more detailed sectoral level between the sectors of

the Austrian Version of Statistical Classification of Economic Activities in the European

Communities (ÖNACE) 2008 section classification5 (Statistics Austria 2008; STATcube 2015).

Additionally to this dataset, there is GVA data for Austria’s NUTS 3 regions in 2011 available

for primary, secondary, and tertiary sectors (Statistics Austria 2014b).

To classify the Austrian municipalities into rural or urban, the degree of urbanization of the

European Union is used (European Commission and Statistics Austria 2016). The advantage

of this approach is that EUs’ local administrative unit level 2 regions6 can be classified into

three groups of urbanization due to their number of population, population density, and

contiguity of the region. The contiguity of a region is measured by using a harmonized size of

grid cells of one square kilometer. Each square kilometer is divided into rural grid cells (if the

population density is smaller than 300 inhabitants per square kilometer or population of the

contiguous area is smaller than 5,000 inhabitants), into urban clusters (if both values are equal

or above this value threshold) or into high-density clusters (if the grid cell has a population

density of at least 1,500 inhabitants and the overall population of this contiguous area is at

5 The ÖNACE 2008 classification is the Austrian version of Statistical Classification of Economic Activities in the

European Communities (NACE). ÖNACE 2008 section sectors are subcategories of the primary, secondary and

tertiary sectors (Statistics Austria 2008).

6 The local administrative unit level 2 equals the Austrian municipality level.

18

least 50,000 inhabitants). Based on this classification approach, in a next step of our data

processing exercise each municipality is mapped to a certain class of urbanization. The

municipality is classified as a densely populated area, if at least 50% of the population lives in

high-density clusters. It is classified as an intermediate density area, if less than 50% of the

population lives in high-density clusters but also less than 50% of the population lives in rural

grid cells”. Furthermore, it is classified as a thinly populated are, if more than 50% of the

population lives in rural grid cells (Eurostat 2011).

3.1.2 Methodology for Economic Data Processing

In order to obtain sufficient data for each CEM regarding their economic structures in 2011, we

need to disaggregate the data discussed in section 3.1.1 to municipal and CEM level. The

GVA on NUTS 3 level, which distinguishes the primary, secondary, and tertiary sector (j), is

disaggregated to the ÖNACE 2008 sectors from section A to S (i). A disaggregation is also

needed at the regional level, from NUTS 3 level (n) to district level (d) and to municipality level

(m). For this double disaggregation, equation 1 is used:

𝐺𝑉𝐴$,& = ()*+,,-+,,

∗ -/-0∗ 𝐸$,2 (1)

In equation 1 the GVA of each ÖNACE 2008 sector (i) in each municipality (m) equals the GVA

in the respective primary, secondary, or tertiary sector (j) and NUTS 3 region (n) divided by

the employment (E) in the respective primary, secondary, or tertiary sector and NUTS 3 region,

times the total employment in the respective municipality7 divided by the total employment in

the respective district (d)8, times the employment in the respective ÖNACE 2008 sector and

district.

If the GVAi,m is summed up over each ÖNACE 2008 sector and each municipality, the whole

GVA of Austria (nat) (GVAnat) is obtained, as it is shown in equation 2:

𝐺𝑉𝐴789 = 𝐺𝑉𝐴$,&&$ (2)

7 The total employment in the respective municipality is only available for the year 2012, but the change from 2011

to 2012 can be assumed as negligible.

8 The total employment in the respective district is again taken from 2012 for consistency.

19

A last step comprises the aggregation of GVA to the CEM level. On that account, the GVA of

each ÖNACE 2008 sector for each CEM (mc) can be calculated with equation 3:

𝐺𝑉𝐴$,; = 𝐺𝑉𝐴$,&;&; (3)

It has to be noted that each municipality belongs to a certain district (md), a certain CEM (mc),

and a certain NUTS 3 region (mn), which is not true for higher levels, as not every district

belongs to a certain CEM (c) or NUTS 3 region (n).

3.1.3 Results of Economic Data Processing

We have analyzed the economic data of 82 CEMs. In this regard, Table 1 presents the

population and employment data of these 82 CEMs. It is shown that the 82 CEMs cover 25.9%

of the total Austrian population, namely 2,174,289 inhabitants. Regarding the population size

of the CEMs, the data observes a heterogeneity between the CEMs, as the population varies

from 1,269 to 81,268 inhabitants. A change in the CEM guidelines in 2015, for example

concerning a minimum of two municipalities per CEM or a minimum of 3,000 and a maximum

of 60,000 inhabitants, might lead to a reduction of the gap for new CEMs in the future (Climate

and Energy Fund 2015b). The average population in the CEMs amounts to 26,516 inhabitants,

while most regions are below this value, indicated by the median of 19,370.

Table 1: CEMs – Population and employment

Popu

latio

n

Area

in h

a

Employment

Empl

oym

ent

Empl

oym

ent /

Po

pula

tion

Shar

e pr

imar

y se

ctor

Shar

e se

cond

ary

sect

or

Shar

e te

rtiar

y se

ctor

Sum CEM 2,174,289 3,531,505 956,923 44.0% 8.0% 29.0% 63.1%

Sum Austria 8,401,940 8,387,899 4,167,164 49.6% 4.6% 24.1% 71.4%

Percentage share 25.9% 42.1% 23.0%

Median 19,370 26,376 8,082 38.5% 9.2% 28.7% 60.3%

Average 26,516 43,067 11,670 41.7% 9.1% 29.4% 61.5%

Maximum value 81,268 201,929 60,146 124.9% 20.2% 39.8% 83.3%

Minimum value 1,269 1,047 151 11.9% 0.2% 16.5% 50.0%

Source: Bramreiter et al. (2016) based on data by Statistics Austria (2013b; 2014a; 2014b)

20

The heterogeneous characteristics apply for the size of area of the CEMs as well; while the

largest region has more than 200,000 Hectare (ha), the smallest region has only 1,047 ha. The

median of all CEMs is 26,376 ha, while the mean of 43,067 ha is nearly 20,000 ha larger. As

already mentioned, CEMs are mostly rural and structurally weak regions, which is confirmed

by the fact that with 42.1% of the Austrian territory, the share of the Austrian area is

considerably larger than the share of the Austrian inhabitants (25.9%) covered by CEMs.

In contrast to the population share of the CEMs, the total employment of 956,923 within the

CEMs relates only to a share of 23% of the total Austrian employment, which is lower than the

respective population share. The most employees in an individual CEM are 60,146 employees

in “K&E Modellregionen - Ausbau und Erhaltung der Erneuerbaren Energie”, a CEM including

the City of St. Pölten. On the other side, the CEM with the lowest number of employees is the

single-municipality-CEM “K&E Modellregion - EnergieGemeindeTrins Nachhaltige

Modellgemeinde” with only 151 employees. The average number of employees in the CEMs

is 11,670; the median (8,082) is again below this value. The relation of employees to the

population highlights on the one hand the higher share in whole Austrian compared to the part

of Austria covered by the CEM approach, and on the other hand the heterogeneity of the

CEMs, ranging from a minimum of 11.9% to a maximum of 124.9%, as well as an average of

41.7% and a median of 38.5%. While the CEM with the lowest relation value of employees to

the population is again “K&E Modellregion - EnergieGemeindeTrins Nachhaltige

Modellgemeinde”, the CEM with the highest relation value is “K&E Modellregionen - Energy

Shopping Vösendorf”, a single-municipality-CEM with a large shopping center and therefore a

high share of commuters working in the municipality.

Regarding the proportion of employees in the different sectors, we find that the proportion in

the primary and secondary sectors are larger for all CEMs compared to the Austrian average,

while the proportion in the tertiary sector is smaller, which is in line with the KLIEN definition of

the CEMs as mostly rural areas. However, we ascertained also considerable differences

between the CEMs, with some CEMs having a proportion in primary sector above 20%, while

others are below 0.2%. This heterogeneity is also visible in the secondary and tertiary sectors,

where the range goes from 16.5% to 39.8% for the secondary sector and from 50% to 83.3%

for the tertiary sector.

Table 2 contains data of the 82 CEMs’ degree of urbanization and their GVA. For the degree

of urbanization, the data shows that none of the 920 CEM municipalities is classified as densely

populated area, which means that larger cities are not part of the CEM program, which is again

21

in accordance with the definition of CEMs as rural and structurally weak regions. Concerning

intermediate density area and thinly populated area, our analysis shows that only 11% of the

municipalities are classified as intermediate density area, while the other 89% are classified

as thinly populated or rural area. The median of 0% for the CEMs indicates that in more than

50% of the analyzed CEMs no municipality is classified as intermediate density area. Again,

for the degree of urbanization, the heterogeneity between the CEMs is shown, as there are,

despite the small number of intermediate density municipalities, CEMs with 100% intermediate

density municipalities. These small suburban CEMs include the CEMs with the highest shares

of employees in the tertiary sector.

Table 2: CEM – Degree of urbanization and GVA

Degree of urbanization GVA

Shar

e of

in

term

edia

te d

ensi

ty

area

mun

icip

aliti

es

Shar

e of

thin

ly-

popu

late

d ar

ea

mun

icip

aliti

es

GVA

in m

illion

€

GVA

per

cap

ita

Prim

ary

sect

or in

m

illion

€

Seco

ndar

y se

ctor

in

milli

on €

Terti

ary

sect

or in

m

illion

€

Sum CEM 101 819 58,309.10 26,817.55 1,890.50 20,926.80 35,491.80

Sum Austria 274,897.00 32,718.28 4,424.00 78,465.00 192,008.00

Percentage share 11.0% 89.0% 21.2% 42.7% 26.7% 18.5%

Median 0.0% 100.0% 479.59 22,360.71 17.92 156.15 290.77

Average 15.2% 84.8% 711.09 25,290.24 23.05 255.20 432.83

Maximum value 100.0% 100.0% 3,588.68 89,539.01 84.32 1,177.38 2,796.81

Minimum value 0.0% 0.0% 9.69 7,633.40 0.10 3.66 5.93

Source: Bramreiter et al. (2016) based on data by Statistics Austria (2013b; 2014a; 2014b); STATcube (2015); European Commission and Statistics Austria (2016)

Comparing Table 1 to Table 2 indicates a certain dependency between employment and GVA.

In general, the populous CEMs with high employment in relation to the population have the

highest absolute GVA. The GVA per capita, which ranges from € 7,633 to € 89,539,

emphasizes the heterogeneity between the CEMs again, as the highest GVA per capita is

more than ten times higher than the lowest value. The two single-municipality-CEMs Vösendorf

(maximum) and Trins (minimum) are those with the extreme values regarding GVA per capita.

For the GVA per capita, the results indicate that the CEMs with intermediate density area are

those with the higher values on average. This is also valid for the share of the tertiary sector

22

in relation to the other sectors, which means that those regions with relatively more

intermediate density municipalities, have a higher per capita GVA and a higher share of GVA

generated in the tertiary sector. In general, the results show that 42.7% of GVA generated in

the Austrian primary sector are produced in the CEMs, while only 26.7% are produced in the

secondary and 18.5% in the tertiary sector.

3.2 Cluster Analysis

Next to the highlighted differences in size, GVA, and economic structure between CEMs,

CEMs differ regarding the current energy consumption and the energy potentials. The energy

consumption data is taken from the published CEM implementation concepts. Since data

provided by the implementation concepts is not sufficient for energy potentials, this data is

taken from the previously mentioned study of Stanzer et al. (2010) (see section 2.2).

Additionally, data regarding energy consumption of mobility is not stated in many

implementation concepts and Stanzer et al. (2010) provides no potentials for mobility,

wherefore we decided to skip mobility and focus on electricity and heat consumption and

potentials. For the determination of CEMs’ economic conditions of viability regarding energy

autarky and energy self-sufficiency and the identification of the CEMs differences, the CEMs

are grouped to CEM clusters by means of a cluster analysis in this chapter. These CEM

clusters are preferably homogenous, but among each other heterogeneous.

A cluster analysis is used to group the heterogeneous CEMs and to better assess their

characteristics and differences. It is based on economic data presented in the previous

sections, energy data from Stanzer et al. (2010), and the CEM implementation concepts. The

variables used for the cluster analysis are listed in Table 3. All variables are given in relative

numbers to enable the comparison of CEMs with different sizes, such as the energy

consumption in Megawatt Hour (MWh) per capita. The cluster analysis uses standardized

values, so that variables with different ranges are treated equally.

23

Table 3: Variables for cluster analysis

Variables Units Source

Population density inhabitants/ha Statistics Austria (2013b; 2015b)

GVA per capita €/capita Statistics Austria (2014a; 2014b); STATcube (2015)

Employees primary sector % Statistics Austria (2014a; 2014b)

Employees secondary sector % Statistics Austria (2014a; 2014b)

Employees tertiary sector % Statistics Austria (2014a; 2014b)

Energy consumption MWh/capita CEM implementation concepts

Potential electricity self-sufficiency % Stanzer et al. (2010)

Potential heat self-sufficiency % Stanzer et al. (2010)

Source: Bramreiter et al. (2016)

Due to the heterogeneous data in the implementation concepts, only the CEMs’ current energy

consumption is taken from there. An inclusion of the potentials for heat and electricity self-

sufficiency from the investigated implementation concepts would lead to the omission of 25%

of the CEMs in the cluster analysis because of data gaps. To avoid this loss of cases

considered in the clustered CEMs, we use data of Stanzer et al. (2010) instead. Stanzer et al.

(2010) give information on RES potentials and hence self-sufficiency by 2020 of all Austrian

districts for three scenarios. The district potentials of the “Maxi” scenario are used for all the

districts’ municipalities, which are subsequently used to calculate the potential of the respective

CEM according to the share of area. With this data, 78 of the 82 CEMs can be assigned to a

cluster; the missing 4 CEMs do not state their energy demand in the implementation concepts.

All economic and population data employed in the cluster analysis are derived from Statistics

Austria (Table 3).

The cluster analysis is based on the hierarchical Ward method using squared Euclidean

distances, which are minimized between the CEMs in one cluster. The respective mean values

of the Ward clusters are taken to perform a K-means cluster analysis in the next step. The K-

means cluster analysis is based on the existing mean values of a cluster and assigns all CEMs

to a cluster by comparing the CEMs’ variables with the respective mean values. In this analysis,

six CEMs switched between clusters. The new clusters are more homogenous according to

mean and median values, and have greater differences between each other. Therefore, we

decided to use the results from the K-means method for the following analysis.

The three final clusters contain the 78 CEMs. The clusters are labeled as “suburban”, “semi-

rural”, and “rural” cluster. The CEM clusters distribution throughout Austria is shown in Figure

24

5. The average values, the total population, the GVA, and the number of CEMs in each cluster

are given in Table 4. The suburban cluster is the smallest cluster regarding the number of

comprising CEMs, with only six of the 78 CEMs (8%). Its high population density, however,

assigns a share of 12% of the CEM population to the suburban cluster. The GVA per capita is

found to be highest in the suburban cluster, yielding a share of 20% of the total GVA of the 78

CEMs. The semi-rural and rural clusters are more similar to each other, with the highest

population in the rural cluster and a slightly larger GVA in the semi-rural cluster.

Figure 5: Mapping of the CEM clusters


Table 4: Results of cluster analysis

Suburban Semi-rural Rural

Average values

Population density (inhabitants/ha) 5.2 0.8 0.7 GVA per capita (€/capita) 51,062 25,103 21,493 Employees in primary sector (%) 1.8 6.8 12.8 Employees in secondary sector (%) 19.7 30.3 29.6 Employees in tertiary sector (%) 78.4 62.9 57.7 Energy consumption (MWh/capita) 36.0 28.6 30.4 Potential electricity self-sufficiency (%) 77.6 128.3 125.3 Potential heat self-sufficiency (%) 29.4 48.7 83.5

Sum

Number of CEMs 6 37 35 Total population 239,531 909,308 920,262 Total GVA (million €) 11,209 23,339 21,397


25

The population densities of the clusters in Figure 6 reveal the higher population density of the

suburban cluster. The suburban CEMs have an average population density of 5.2

inhabitants/ha, while the population density of the semi-rural and rural clusters is both below

one inhabitant/ha. Since the suburban cluster is the smallest, its total population is well below

the population of the others, shown in Table 4.

Figure 6: Population density in the CEM clusters


Considerable differences between the clusters regarding GVA per capita are illustrated in

Figure 7. The suburban cluster dominates the GVA per capita, with a value of over 50,000

€/capita. The semi-rural and rural clusters are both below half of the suburban value. The rural

cluster has with slightly over 21,000 €/capita the lowest GVA per capita. Due to the high GVA

per capita of the suburban cluster, the total GVA of the small suburban cluster accounts for

20% of the total GVA of all clustered CEMs.

0

1

2

3

4

5

6


Inha

bitants/ha

26

Figure 7: GVA per capita in the CEM clusters


Figure 8 identifies the differences in the economic structure across the three CEM clusters.

The suburban cluster is dominated by the tertiary sector, while the employment shares of the

primary and secondary sectors are relatively small. This distribution is different in the semi-

rural cluster, where both the primary and secondary sector gain in importance. In the rural

cluster, the share of the secondary sector is nearly as high as in the semi-rural cluster, while

the share of the primary sector is almost doubled. Moreover, the rural cluster is the cluster with

the lowest employment shares in the tertiary sector.

Figure 8: Economic structure of the CEM clusters


0

10000

20000

30000

40000

50000

60000


€/capita

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Employees tertiarysector

Employees secondarysector

Employees primarysector

27

The sectoral differences of employment in the ÖNACE 2008 sectors between the CEM clusters

are presented in Table 5, where the share of each sector is shown in percent for each cluster

and for the 78 CEMs in total. Table 5 shows the differences in employment especially for the

sectors A (Agriculture, forestry and fishing), C (Manufacturing), N (Administrative and support

service activities), and P (Education).

Table 5: Economic structure of the CEM clusters – all ÖNACE 2008 section sectors

Sector ÖNACE 2008 section sector Index Cluster

All CEMs Suburban Semi-rural Rural

Primary Agriculture, forestry and fishing A 1.9% 6.7% 11.7% 7.8%

Secondary

Mining and quarrying B 0.1% 0.4% 0.2% 0.2% Manufacturing C 10.9% 19.3% 21.5% 18.6% Electricity, gas, steam and air conditioning supply D 0.2% 0.7% 0.6% 0.5%

Water supply; sewerage, waste management and remediation activities E 0.5% 0.6% 0.5% 0.6%

Construction F 6.5% 9.5% 9.6% 8.9% Wholesale and retail trade; repair of motor vehicles and motorcycles G 16.5% 15.7% 14.2% 15.2%

Tertiary

Transportation and storage H 7.8% 4.7% 3.8% 4.9% Accommodation and food service activities I 3.8% 7.7% 6.0% 6.2% Information and communication J 1.5% 0.9% 0.9% 1.0% Financial and insurance activities K 2.9% 2.5% 2.4% 2.5% Real estate activities L 1.5% 1.4% 1.1% 1.3% Professional, scientific and technical activities M 5.2% 4.6% 3.5% 4.3% Administrative and support service activities N 8.2% 2.9% 2.7% 3.8% Public administration and defense; compulsory social security O 9.5% 4.0% 4.1% 5.1%

Education P 10.3% 6.8% 6.1% 7.2% Human health and social work activities Q 7.4% 7.6% 7.5% 7.5% Arts, entertainment and recreation R 1.4% 1.0% 0.7% 1.0% Other service activities S 3.9% 3.0% 2.8% 3.1%

100.0% 100.0% 100.0% 100.0%


In Table 6 the ten most important ÖNACE 2008 section sectors of each cluster are ranked,

which makes the importance of sector I (Accommodation and food service activities) in the

semi-rural cluster more obvious.

28

Table 6: Economic structure of the CEM clusters – the ten most important ÖNACE 2008 section sectors

Ranking Sector Suburban Sector Semi-rural Sector Rural 1 G 16.5% C 19.3% C 21.5% 2 C 10.9% G 15.7% G 14.2% 3 P 10.3% F 9.5% A 11.7% 4 O 9.5% I 7.7% F 9.6% 5 N 8.2% Q 7.6% Q 7.5% 6 H 7.8% P 6.8% P 6.1% 7 Q 7.4% A 6.7% I 6.0% 8 F 6.5% H 4.7% O 4.1% 9 M 5.2% M 4.6% H 3.8% 10 S 3.9% O 4.0% M 3.5%


Figure 9 shows the energy consumption per capita and the potential degrees of self-sufficiency

of the three clusters. Regarding the current energy demand based on the CEM implementation

concepts, the suburban cluster has the highest value, followed by the rural cluster. The semi-

rural cluster has the lowest current energy demand. The energy potentials for heat and

electricity from (Stanzer et al. 2010) show that the semi-rural and rural cluster have the highest

potentials to become self-sufficient. Electricity potentials are generally higher than heat

potentials. According to these numbers, not even rural CEMs, nor suburban or semi-rural

CEMs, have the potential to become heat self-sufficient. In contrast, rural and semi-rural CEMs

could become electricity exporters in the future. Suburban CEMs have low potentials to cover

their energy demand, which correlates with a higher absolute demand.

Figure 9: Energy consumption and potentials of the CEM clusters


29

3.3 Conclusion

In this chapter, we set out to analyze the Austrian CEMs regarding their economic

characteristics. Additionally, we identify which conditions allow CEMs to achieve their climate

and energy goals. As shown in Table 1 and Table 2, the size of population, area, and employed

persons differ between CEMs, as the ranges go from 1,269 to 81,268 inhabitants (population),

from 1,047 to 201,929 ha (area), and from 151 to 60,146 employed persons. This

heterogeneity is also shown in the economic structures of the CEMs regarding their shares of

employed persons in the primary, secondary, and tertiary sector, which differ obviously

between the individual CEMs shown in the minimum and maximum values.

Due to the heterogeneity between individual CEMs, a cluster analysis is well suited to highlight

the differences between CEMs by grouping them as homogenous entities. The cluster analysis

has shown that most CEMs are assigned to the semi-rural and rural clusters, while only a few

(six out of 78) are allocated to the suburban cluster (see Table 4). Out of the three clusters,

the suburban cluster shows the most deviations, as it is characterized by a higher population

density and a higher GVA per capita compared to the other two clusters. The suburban cluster

is furthermore characterized by a relatively low employment level in the primary sector and a

dominance of the tertiary sector. Energy consumption per capita is relatively high for the

suburban cluster, while the potential degrees of energy self-sufficiency are for the suburban

cluster the lowest among the three clusters.

The semi-rural and rural clusters, on the other hand, share many similarities. They both have

lower population densities compared to the suburban cluster and only around half of the

suburban cluster’s GVA per capita. Furthermore, energy consumption and potential electricity

self-sufficiency are similar between the semi-rural and rural CEM clusters. The differences

between the semi-rural and rural clusters are mainly to be found in their economic structures

and their heat potentials. The share of employees in the primary sector in the rural cluster is

nearly twice as high as the share in the semi-rural cluster. The shares of employees in the

secondary sector are almost equal in both clusters, while the semi-rural cluster has a higher

share in the tertiary sector. The potential for self-sufficiency in heat is by far the highest in the

rural cluster.

Additionally, we find, based on the cluster analysis of 78 CEMs, that mainly rural and semi-

rural Austrian regions have the theoretical potential to become energy self-sufficient. Their high

levels of potential electricity and heat self-sufficiency are not only driven by the availability of

renewable energy resources, but also by the socioeconomic structures of these regions. The

30

socioeconomic structures of the semi-rural and rural clusters are characterized, in contrast to

the suburban cluster, by lower population densities, lower GVA, higher shares of employment

in the primary and secondary sector, lower shares of employment in the tertiary sector, and

lower levels of energy consumption.

31

4 Methodological Background: Sub-national CGE Analysis

In section 2.1, the Austrian CEM approach as part of the Austrian energy transition strategy

was discussed. The aim of the CEM approach is to play an important part in reducing Austria’s

GHG emissions by a bottom-up exploitation of the existing RES potentials in the CEMs. In

section 2.2, it was shown and discussed, which national and international examples of sub-

national bottom-up energy transition approaches, like the CEM approach, exist and in which

manner they have already been investigated. In section 3.1, we analyzed the economic

characteristics of the 82 CEMs, which were identified as operational in 2015 and which have

published an implementation concept until November 1, 2015. The analysis of the economic

characteristics revealed substantial differences between the different CEMs, whereby we

assigned these 82 CEMs to three CEM clusters that are homogenous within themselves but

heterogeneous to each other. The methodology and results of the cluster analysis are shown

in section 3.2. The three clusters include the 78 CEMs, which are possible to assign due to

economic and energy related characteristics.

Sub-national bottom-up energy self-sufficiency transition approaches, as the CEM approach,

face similar challenges, such as the coverage of energy demand by own CEM-based RES

potentials, which often lead to efficiency losses from economic uncompetitive technologies.

The simultaneous achievements of these uncompetitive RES potentials in the different CEMs

will have consequences on Austria’s economy and the different CEMs. Associated

consequences, as demand and price changes on the energy market, have cross-sectoral

spillover effects. An approach, which is well suited to identify these associated consequences,

is the CGE method, as it is widely used, well established, and adapted to different economic

questions at sub-national, national, and global scale.

First, this chapter answers the question why the CGE approach is an appropriate methodology

to analyze macro-economic effects from an RES achievement in the Austrian CEMs. Second,

we discuss the question how a sub-national CGE model should look like to consider

additionally the diverse cross-sectoral spillover effects arising from simultaneous action in

different CEMs.

The first part of this chapter (section 4.1) discusses available alternatives to analyze the CEM

approach and the arising cross-sectoral spillover effects sufficiently, next to the CGE

framework. The second part of this chapter (section 4.2) reviews how the CGE framework

emerged and which theoretical background it is based on. In section 4.3, we present how a

standard national CGE model looks like. In this context, the circular flows of a closed economy,

32

its equilibrium conditions, the inclusion of real economic data, its benchmark solution,

functional forms, and finally the expansion to a small-open economy CGE model is discussed.

Based on the cluster analysis (section 3.2), we decide to use the three obtained CEM clusters

and the rest of Austria as our four CGE model regions. Therefore, the fourth part of this chapter

(section 4.4) discusses how regions can be included in CGE models, which challenges arise

for CGE models investigating low scale regions as the clustered CEMs, and which modeling

tasks these low scale regional, sub-national CGE models require. Finally, section 4.5

discusses CGE modeling regarding our CEM-based approach and identifies an appropriate

sub-national CGE model class.

4.1 Macro-Economic Policy Analysis Techniques

The joint achievement of regional RES potentials within different CEMs lead to simultaneous

cross-sectoral economic spillover and macro-economic feedback effects on other sub-national

regions within Austria, the CEMs themselves and at the national level. For our investigation of

possible economic consequences on Austria and different regions within Austria, we need a

suitable macro-economic policy analysis technique. This technique must be able to measure

economic inefficiencies and diverse feedback effects of changes in the energy sectors’

production structure by RES achievement in CEMs, which result in energy price and quantity

effects.

Models which are in general capable for regional economic policy analysis are econometric

models, multi-sectoral fixed-price models as IO models and SAM models, and multi-sectoral

flex-price models as CGE models (Partridge and Rickman 2010). These models are, next to

different methodological limitation, faced by data limitations on sub-national, low scale regional

level.

In the context of low scale regional models, such as sub-national models, especially

econometric models perform insufficiently, as econometric models need a huge amount of

data (time-series) for every variable of the model, which are often not available on sub-national

level or do still not exist (Partridge and Rickman 2010; Allan 2015). Due to data limitations on

sub-national level, IO models were often the only methodological option for policy maker’s up

to the early 2000s (West 1995; Partridge and Rickman 2010). Miller and Blair (2009) illustrate

that IO models work well to show feedback effects arising from increased (decreased)

exogenous final demands in a certain sector, which pass on through intermediate demands to

other sectors. These spillover effects are called multiplier effects, and end up the economy in

33

an equilibrium, where aggregated production changed by more than the initial increase

(decrease) of exogenous final demand. Based on this mechanism, SAM models, which

evolved from IO models, additionally show changes on income from all sources within an

economy and not only production related factor income (Allan 2015).

However, IO and SAM models have their deficiencies, as they do neither consider feedback

effects from changed output to exogenous final demand, nor a flexibility in proportion of

sectoral production technology and consumer demand preferences (Allan 2015). IO models

do not include displacement effects and do not consider restricted excess supply, which means

that the fixed-price assumption overestimates demand increases, since multiplier effects are

not slowed down by opposing price effects (Partridge and Rickman 2010). Another deficiency

of fixed-price models is that implicit perfect elastic intermediate input supply and especially

implicit perfect factor mobility can lead in short to medium term to overestimations of policy

potentials in sub-national, low scale regional models (Koh, Schreiner, and Shin 1993). In the

long term IO and SAM models are more appropriate because factors are assumed to be fully

mobile (Allan 2015).

The multi-sectoral flex-price CGE models are more appropriate to show diverse feedback

effects on low regional scale and become more common recently. The achievements of sub-

national RES potentials, which lead to a change in intermediate and factor demand, result in

feedback effects on output quantities and prices of sub-national electricity production in a CGE

model. Partridge and Rickman (2010) emphasis in this regard the ability of CGE models to

incorporate simultaneously positive multiplier and negative displacement effects in one

approach. Additionally, CGE models are less depended on historic data requirements than

econometric models and can be calibrated to SAMs on year-based IO-tables (Allan 2015).

Nevertheless, CGE models are faced by methodological challenges, especially on a sub-

national level (Partridge and Rickman 2010). These methodological challenges are reviewed

after the discussion of the historical development of CGE modeling in section 4.2, on national

scale in section 4.3, and on sub-national scale in section 4.4.

4.2 Historical Development of CGE Modeling

The CGE approach allows the analysis of consequences of diverse feedback effects triggered

by policy shocks. Such a policy shock, for instance a change in production technology of a

certain sector, will have effects on connected suppliers and customers in the CGE model due

to changed input demand and changed output prices and quantities. CGE modeling allows

34

investigating shocks on different regional levels, such as the sub-national, national, and global

levels. CGE modeling builds on the theoretical foundation of the Walrasian general equilibrium

structure and Leontief´s IO accounting system. Therefore, this section shows how the CGE

approach emerged in literature (section 4.2.1) and which theoretical background a standard

national CGE model is based on (section 4.2.2).

4.2.1 Emergence of CGE Analysis in Literature

The Walrasian general equilibrium structure was one cornerstone that contributed greatly to

the emergence of today’s CGE models, which is described by the Arrow-Debreu model (Arrow

and Debreu 1954; Arrow and Hahn 1971). This general equilibrium structure can be

characterized as an equilibrium situation of demand and supply in all interconnected

commodities of an economy (Shoven and Whalley 1984; Rutherford and Paltsev 1999; Sue

Wing 2004). The Arrow-Debreu model is well-suited to identify winners and losers in the case

of policy changes or policy shocks, especially regarding resource allocation (Shoven and

Whalley 1984).

The second fundamental cornerstone was the work done by Leontief, as he was the first, who

created an accounting system of the economy in USA9. He developed a balanced system of

consumption and production of the whole economy in USA, by considering sectors as

agriculture, industry, and transportation. Johansen (1960) developed his framework of multi-

sectoral analysis of economic growth, based on Leontief’s accounting system and other

Leontief based studies, as Chenery and Clark (1959), who refined Leontief´s IO accounting

system by including new behavioral functions and demand systems. Johansen's (1960) study

was therefore identified by Mitra-Kahn (2008) as the first CGE model, as it first combined

national IO accounts with macro-economic balancing equations. However, Johansen's (1960)

work does still not take into account the Walrasian general equilibrium structure described in

the Arrow-Debreu model.

The combination of the micro-consistent framework of the Walrasian general equilibrium

structure of Arrow and Hahn (1971) into the macro framework of Johansen (1960) only occurs

9 See Leontief (1937) for the years 1919 till 1929 and Leontief (1951) for the years 1919 till 1939.

35

years later and benefits from some influential circumstances and additional contributing

studies. The most important circumstances and studies were increasing computer power, the

increasing efforts and findings in gathering and processing data as the helpful work of Pyatt

and Round (1985) on a SAM, and the work of Scarf (1967) and Shoven and Whalley (1984;

1992). Scarf (1967) developed a computer algorithm to determine numerically the Walrasian

general equilibrium in an Arrow-Debreu model. In contrast, Shoven and Whalley (1984; 1992)

based their work on Leontief, Johansen, and Scarf by converting “(…) the Walrasian general

equilibrium structure (formalized in the 1950s by Kenneth Arrow, Gerard Debreu, and others)

from an abstract representation of an economy into realistic models of actual economies”

(Shoven and Whalley 1984, p. 1007). Shoven and Whalley benefit in this regard from

increasing computer power to work on greater dimensions.

Finally, the combination of the work of Shoven and Whalley (1992) and Pyatt and Round (1985)

was done among others by Rutherford and Paltsev (1999). They refined the work of Shoven

and Whalley (1984; 1992), as they based their CGE model on the Shoven and Whalley (1992)

Arrow-Debreu framework combined with a SAM of real economic data. While Shoven and

Whalley (1984; 1992) have analyzed the economic effects of a simple two consumer and two

good economy, Rutherford and Paltsev (1999) calibrated their CGE model to a nine-sectors

IO-table of Russia from the year 1995 as “the analysis of economic policy in a micro-consistent

framework demands both theory and data” (Rutherford and Paltsev 1999, p. 2).

4.2.2 The Arrow-Debreu Model

A Walrasian general equilibrium, such as the equilibrium of the Arrow-Debreu model, occurs

when demand equals supply on all interconnected markets of an economy, including labor and

capital markets. An Arrow-Debreu model and thereon based standard CGE models contain a

specified number of consumers, owning an initial endowment of commodities. Each consumer

has a set of preferences, represented by a demand function for each commodity. The sum of

all consumer demands equals the market demand. Rutherford and Paltsev (1999) and Sue

Wing (2004) demonstrate that a representative consumer can be introduced, which is endowed

by the sum of all consumer’s endowment and covers the market demand. The market demand

depends simultaneously on the price of each commodity and meets Walras´s Law. Walras´s

Law implies that consumers spend their total income in commodities they demand according

to their preferences at a given set of prices, as they want to maximize utility. Commodities are

produced by firms, which can be summarized to a representative firm for each commodity in a

36

standard CGE model (Sue Wing 2004). The producers face constant returns to scale

production functions and perfectly competitive markets and maximize their profits by

considering a standard CGE model. In equilibrium, interactions of consumer’s and producer’s

behavior lead to an equalization of demand and supply driven by the price mechanism, which

finally results in a unique optimal level of prices and an appropriate quantity of production for

each commodity.

4.3 National Scale CGE Modeling

A standard national CGE model is based on the theory of the Walrasian general equilibrium

as used in the Arrow-Debreu (see section 4.2.2). For this reason, the following sections discuss

how such a standard national CGE model works and how it can be developed. Therefore,

starting from the circular flows of a closed economy (section 4.3.1), we discuss the equilibrium

conditions of market clearance, zero profits conditions and income balance (4.3.2), the

inclusion of real economic data from a SAM (4.3.3), the calibration of this data to a benchmark

solution and thereon based scenarios (4.3.4), the explicit modeling of production and demand

functions with the help of elasticities and nesting structures (4.3.5), and finally end up with the

expansion to a simple small open economy CGE model (4.3.6).

4.3.1 Circular Flows of a Closed Economy

The Arrow-Debreu model consists, in its basic formulation, of different production sectors

(commodities produced by producers) and two agents (consumers and producers). The

version used for standard CGE models usually contains additionally the factors of production,

labor and capital, but can be further enlarged by its number of sectors, factors (as land and

natural resources), and agents (as the government). Following Sue Wing (2004), three

different agents: a representative consumer, a representative producer for each sector and a

government; two production factors: labor and capital; and a specified number of sectors can

be identified in their representation of the circular flows of a closed economy, see Figure 10.

This joint interaction of agents, according to their behavior, finally ends up in an equilibrium

situation.

37

Figure 10: The circular flow of the economy

Source: Sue Wing (2004, p. 29)

The explanation of the circular flows in Figure 10 can be started with the supply of production

factors by households. The firms use these factors as primary input in production of goods and

services, which are afterwards provided back to the households. Therefore, the households

are faced by expenditures for goods and services to meet their demands. To compensate the

firms, the consumers use their factor income, in turn received from the firms. In this example,

the government simply collects taxes, which are then returned to the consumer and producer

as goods and services.

The circular flows of an economy, in a Walrasian general equilibrium of the Arrow-Debreu

model, are determined by the model actors and their behavior, described by their utility and

profit maximizing functions (Rutherford and Paltsev 1999). As mentioned above, firms

maximize their profits due to their constant returns to scale production functions on perfectly

competitive markets. On the contrary, consumers in a standard CGE model usually

government and households spend their factor income on goods and services to maximize

their utility subject to their preferences. Profit maximization and utility maximization lead to a

single optimal level of production at a single optimal price level (Sue Wing 2004).

38

4.3.2 Market Clearance, Zero Profits Conditions and Income Balance

When circular flows of an economy are considered, it is important to mention that neither

factors, taxes, commodity, and goods, nor income and expenditures can appear or disappear.

The economy is bounded to the conservation of product and value (Sue Wing 2004).

Additionally, Mathiesen (1985) shows that the interactions in the Walrasian general equilibrium

can be formulated and solved as a complementarity problem, which must satisfy three

equilibrium conditions: market clearance, zero profit conditions, and income balance.

First, the conservation of product means that the production of a good is equal to the total

consumption of this good. This consumption can be demanded by consumers or the

government, but also by firms as intermediate input. Hence, the conservation of product

satisfies the principle of material balance and indicates market clearance. Second, the

conservation of value complies the principle of budgetary balance for agents. This means for

producers that their revenue of production is offset by costs for intermediate inputs, factor

inputs, and tax expenditures. Therefore, constant returns to scale and perfect competition lead

to zero profit conditions for producers. For consumers on the contrary, budgetary balance

means that their consumption (and their savings, by allowance of savings) must be equal to

their income from production factors, such as labor and capital, as value cannot disappear.

This indicates that each consuming agent adheres to an income balance (Sue Wing 2004).

4.3.3 Inclusion of a SAM

The data of CGE models are in general provided from national accounts of a certain year or

an average of several years. Often, such national accounts are developed to an IO-table.

Another possibility is to present the data of the national account in a balanced SAM, which is

an expansion of the IO-table by interrelations of sectors, factors, and agents, as established

by Pyatt and Round (1985). A SAM contains information of the flows in an economy, such as

the values of sector outputs and consumption, in matrix form but also about the

interconnectedness of the economy. Bergman (2003, p. 1) states, “the quality of the CGE

model and the results it produces depends on the quality of the data on which it is based.”

Table 7 shows a SAM of a closed economy, based on a simple example. It includes two sectors

(1 and 2), one factor, and two consumers (household and government). The rows represent

income of agents, where households receive income from providing factor endowment, while

government receives income from tax revenues. The columns represent sectoral production

39

and agent’s consumption. As explained above, the Walrasian general equilibrium of a CGE

model must satisfy market clearance, zero profit conditions, and income balance. Table 7 fulfils

all three conditions. Inputs of sectors and consumption of agents (sector 1: 20+15+20+5=60)

equal production (sector 1: 60), which comply with market clearance, costs of inputs in

production (sector 1: 20+5+25+10=60) equal the revenue of output (sector 1: 60), which

indicates zero profit conditions, and income from providing factor endowment (factor input:

25+10=35) and from tax revenues (tax: 10+10=20) equal the consumption of goods and

services by household (household: 20+15=35) and government (government: 5+15=20),

which determines the income balance.

Table 7: A SAM for a closed economy

Source: Own extended version based on Bergman (2003, p. 2)

4.3.4 Benchmark Solution and Counterfactual Scenarios

A benchmark solution, which is the calibrated equilibrium of an economy at a certain point in

time, results in a balanced SAM characterized by rows and columns corresponding exactly to

each other. The closed economy benchmark solution, as it is shown in Table 7, is an

equilibrium determined by a set of prices and an appropriate quantity of production for each

commodity. Sue Wing (2004) mentions that in standard CGE model, like in other neoclassical

economic models, an explicit modeling of money is not necessary, since the expenditure of a

sector or a consumer in a certain cell equals the income of another sector or another consumer.

A CGE model represents an equilibrium in a benchmark solution, in which the circular flows

are shown by quantities, while prices for each commodity are equal to one. This means, in the

SAM the value of each flow is indicated by a given quantity times the price (in benchmark equal

to one).

CGE models should not only represent the benchmark solution. Therefore, changes from

benchmark equilibrium to another equilibrium are obtained in CGE models by a counterfactual

scenario. Counterfactuals comprise one or more policy shocks, such as changes of taxes, an

introduction of tariffs, or changes of production technologies. Faced by a policy shock, an

Sector1 Sector2 Household Government Sales/IncomeSector1 20 15 20 5 60Sector2 5 5 15 15 40FactorInput 25 10 35Tax 10 10 20

Production/Consumption 60 40 35 20

40

economy becomes imbalanced and prices adjust, as the assumed rational agents will adopt.

Consequently, the economy is carried forward to a new equilibrium after the policy shock of

the counterfactual scenario and price and quantity effects occur. While quantity effects can be

obtained directly, to measure price changes from benchmark to counterfactual solution the

price of a unique commodity or good is fixed to one when calculating a counterfactual scenario

in CGE models. This means that prices of other goods change in relation to this fixed-price-

good, which is called numeraire good. Hence, the associated changes between the benchmark

and counterfactual equilibrium are measured as relative price changes in relation to the

numeraire.

4.3.5 Production and Demand Functions in Combination with Nesting Structures

In the initial benchmark equilibrium of a CGE model, consumers have no incentive to change

their consumption behavior, determined by preferences, and producers have no incentive to

change their proportion of inputs, determined by production technologies. However, policy

shocks lead to imbalances in an economy, which cause agents to react in accordance with

their functional form of preferences and technologies. These functional forms arise from

elasticities of transformation and substitution and reflect, together with the corresponding

nesting structure, the flexibility of a CGE model.

Therefore, the different production and demand functions can be used to represent production

technologies and preferences by elasticities of substitution and transformation. Domestic

production faces several inputs, such as intermediate inputs of different sectors and factor

inputs, such as labor (LAB) and capital (CAP). In general, there are three possibilities to model

these production technologies. First, the Leontief production function, which indicates an

elasticity of substitution of zero. A Leontief production does not allow for a change in proportion

between two inputs, it is common for the trade-off between factor, such as labor and capital,

and intermediate inputs (Rutherford and Paltsev 1999). Second, the constant elasticity of

substitution (CES) implies an imperfect elasticity of substitution greater than zero. Third, the

Cobb-Douglas production function, which is a special type of the CES production function,

contains an elasticity of substitution equal to one.

Finally, as mentioned previously, the form of a nesting structure is important for the flexibility

of a CGE model. Figure 11 shows how the production technology of a domestic production

sector, Xi can look like. Rutherford and Paltsev (1999) mention that factors of production at the

41

highest nesting level, trade-off as a Leontief aggregate, which does not allow for substitution

of factor inputs, as capital (CAP) and labor (LAB) inputs are assumed to be not substitutable

with primary products (G) in production. At the second nesting level, the aggregate of the input

factors capital and labor (KL) trade-off by a Cobb-Douglas production function. Also at the

second nesting level, the intermediate inputs of the Armingtion aggregate (Gi-n), modeled again

as a Leontief production function, are not allowed to substitute against each other. Such a

nesting structure of domestic production, Xi is especially reasonable, if there is only a low

sectoral aggregation of the production sectors. On the contrary, if the production sectors are

modeled at a high aggregation sector with diverse sectors, a modeling as done by Bachner et

al. (2015) would be more appropriate. In this regard, Bachner et al. (2015) use CES production

functions with sector specific elasticities, greater than zero, of top, kl and int, at the different

nesting levels, which allow for substitution for each input, as shown in Figure 11. More

information and an algebraic formulation of these functional forms can be found in Rutherford

and Paltsev (1999).

Figure 11: Nesting of the domestic production sectors (Xi)

Source: Own extended version based on Rutherford and Paltsev (1999, p. 12) and Bachner et al. (2015, p. 110)

4.3.6 Expansion to a Simple Small Open Economy CGE Model

The expansion of the Arrow-Debreu model to a simple closed economy CGE model presents

the integrated feedback effects in an economy (see section 4.3.2). The driving force of these

feedback effects is the price mechanism, subject to behavioral functions, by affecting demand

and supply of goods and services as well as factor and tax income. According to Rutherford

and Paltsev (1999), the interconnectedness of national economies with each other have been

empirically proven, which suggests that a CGE model must meet the requirements of trade.

42

Figure 12 shows the flows in a standard small open economy, static single country CGE model.

This single country is connected by imports and exports with the rest of the world under a small

open economy assumption. This assumption implies that a price change due to a policy in a

small economy has no price effects on the world markets, as it is assumed that the economy

is too small to affect the rest of the world. This means, if the price of a good decreases at

home, exports of this good increase, but world prices do not change.

Figure 12: Flowchart of a static CGE model


The starting point of the flows in the static CGE model illustrated in Figure 12 is the domestic

production, Xi of a certain sector. It is assumed that each sector produces only one single

good. The domestic production supplies goods and services to two different markets, the rest

of the world as exports, EXi, and the domestic supply, Di as inputs. When a good is provided

to different markets, it is common to use a constant elasticity of transformation, which indicates

an imperfect elasticity of transformation greater than zero. The Armington aggregate Gi reflects

the transformation of domestic goods, from domestic supply and from abroad produced

imports, IMi, into one single good. In accordance with the Armington assumption, these two

goods are not perfect substitutes (Armington 1969). For this reason, the Armington aggregate

usually trades-off its inputs by a CES production function in a standard CGE model, to show

the imperfect substitutability of domestic and foreign goods. Therefore, it is assumed that

preferences of the consumers are different for domestic and foreign products. Apart from the

43

place of production, domestic and foreign products are equal. The Armington aggregate is

demanded as intermediate demand by the different production sectors and as final demand

by the representative private household, PrivHH and the government, GOV. The domestic

production sectors, Xi in turn, use intermediate inputs of the Armington aggregate together with

factor inputs of labor, LAB and capital, CAP, which are provided by the representative private

household, to produce their outputs and finally close the circular flows of this small open

economy.

4.4 Sub-National Scale CGE Modeling

In recent years, CGE models on global and national levels have become more and more

important in general economic analyses, but also in sub-national economic analyses (Partridge

and Rickman 2010). In this regard, CGE models do not only vary on their regional scale, but

differ also in their complexity, design, and purpose. First, global CGE models are often used

to answer economic questions regarding consequences of changed trade policies or

environmental issues. These global CGE models, which are modeled for instance as multi-

regional CGE models with a country or a group of countries as smallest regional unit, have

become common in the last years. Regions within these global multi-regional CGE models are

typically connected to each other by global trade. A frequently mentioned example of a global

multi-regional CGE model, which deploys countries and groups of countries as regions and

acts as a precursor for many following CGE models, is GTAP model of Hertel (1997), which

covers the whole global economy linked by global trade flows. Second, national single-region

CGE models try to investigate national policy implication or policy options. An example of an

entire national study of Austria, the country of investigation in this study, is the work by Bachner

et al. (2015). They investigated various environmental questions within a national single-region

CGE model, which is connected to the rest of the world and based on the small open economy

assumption with Armingtion trade. Third, sub-national CGE models, such as Horridge,

Madden, and Wittwer (2005), Schinko et al. (2013), and Standardi, Bosello, and Eboli (2014),

take a special focus on spatial and economic characteristics of different regions on different

regional scales located within a country. Sub-national CGE models are often modeled as multi-

regional CGE models.

While the purpose of national single-region CGE models is to investigate national issues, sub-

national and global CGE models can be used in this regard as well. Hence, as there are

different regional CGE models on different regional scales available, it is important to define

44

how these different regions are specified. Rodriguez (2007) mentions that regions are in

general defined in two different ways. First, regions are defined as connected units, usually as

a country or a group of countries, trading within a multi-regional CGE model on a global market.

Hence, these kinds of regions are used for global and national CGE models. The CGE models

by Hertel (1997) and Hertel, Tyner, and Birur (2008) can be identified as such global multi-

regional CGE models. Second, sub-national CGE models deal with regions located within a

country, like federal states (Kettner et al. 2012) or aggregated groups of municipalities

(Horridge, Madden, and Wittwer 2005; Horridge and Wittwer 2008a; Horridge and Wittwer

2008b; Wittwer and Horridge 2010). Sub-national CGE models define regions as a sub-

national unit within a group of countries like a NUTS 3 region (Jean and Laborde 2004), which

is connected to a higher territorial unit. For sub-national CGE models it is also possible to

divide the region of investigation into rural and urban regions (Kilkenny 1993; Kilkenny 1999;

Clements, Jung, and Gupta 2007).

However, while the global CGE model of Hertel (1997) and the national CGE model of Bachner

et al. (2015) use common global and national CGE approaches, which have become quite

popular concerning general economic policy analysis, sub-national CGE models are versatile

and are used for different issues on different sub-national levels. Sub-national CGE models,

especially CGE models on low territorial or administrative units as municipality level, are still

rather rare, due to methodological and data related constraints (Rodriguez 2007; Partridge and

Rickman 2010). Since our study is primarily interested and focused on these sub-national CGE

models, this section provides an overview of data and methodology related requirements,

modeling options, and existing CGE models in the context of sub-national CGE modeling.

4.4.1 Treatment of Regions in Sub-National CGE Models

As mentioned in the previous section, the focus of this study is on sub-national CGE models.

Beside the classification of regions into regions of multi-regional global trade CGE models and

into regions of sub-national CGE models located within a higher territorial unit, it is possible to

subdivide the second category. In this context, Rodriguez (2007) did a general discussion and

classification of existing sub-national CGE models into three categories. However, CGE

models within these categories can still be heterogeneous concerning number of regions,

sectors, factors, and agents and concerning the region-size of area and population.

45

The first category of sub-national CGE models is the region-specific CGE model category.

Such CGE models are similar to a national CGE model and are based on a specific region

within a certain country, while treating the rest of the country as part of the rest of the world.

Region-specific CGE models are more appropriate for investigations on local policies than

national CGE models, as demonstrated by Horridge (1999), who analyzed the energy use by

urban transports in the City of Melbourne, Australia. While Horridge's (1999) CGE model,

which should rather be seen as city-specific CGE model, do not comply with standard region-

specific CGE models which are similar to national CGE models, other region-specific CGE

models as Cansino et al. (2014) are more in line with this definition by Rodriguez (2007). Next

to investigations on local policies, region-specific CGE models can show effects of national

policies on the specific region. Region-specific CGE models are often used together with

national CGE models by applying national results as data inputs in the region-specific sub-

national CGE model. A caveat of a region-specific CGE model is the low availability of sub-

national data. Another weakness is the low or not existing spatial linkage of these CGE models,

as international trade is often excluded or over-simplistically illustrated (Rodriguez 2007).

The second category of sub-national CGE models is identified by Rodriguez (2007) as bottom-

up multi-regional CGE model. Such a multi-regional CGE model divides the country into

regions with own economic sectors and factors. CGE model agents can be modeled as

regional or national agent. In the case of regional modeled agents, one alternative is to include

an additional national government. It is also common for bottom-up CGE models to connect

all regions and the rest of the world by bilateral trade flows as it was done by Horridge, Madden,

and Wittwer (2005) and Standardi, Bosello, and Eboli (2014). Rodriguez (2007) identifies this

spatial connectedness of regions by trade as an advantage of bottom-up CGE models

compared to region-specific CGE models, as national shocks or shocks from other regions

generate spillover and feedback effects, which can be identified and investigated. Another

advantage of bottom-up CGE models is that imperfect factor mobility can be implemented.

This means that increased labor demand can be covered by native and outside workers, who

are not perfectly substitutable (Horridge, Madden, and Wittwer 2005). The downside of bottom-

up CGE models is their high demand for data, which is higher than for region-specific CGE

models. For bottom-up CGE models all data must be available for each region, including

international and inter-regional trade flows (Rodriguez 2007).

The third category is defined as a “partial” sub-national CGE model by Rodriguez (2007) and

can be subdivided into three subcategories. This CGE model category, as indicated by its

name, is characterized by a partial disaggregation of the higher territorial unit, which is usually

46

a country. CGE Models are characterized as “partial” sub-national CGE models, if CGE models

do not meet both requirements, inter-regional trade and explicitly specified household and

sectoral production behavior at the regional level. Such CGE models are often modeled as

top-down CGE models. Not all sectors, factors, and agents are modeled regionally, which

implies reduced feedback effects compared to a bottom-up CGE model. The term “partial” is,

however, slightly unfavorable as it can easily lead to confusions with partial equilibrium models.

The first subcategory of “partial” sub-national CGE models regionalizes only the production. A

well-known example is the ORANI model by Dixon et al. (1982), which is inspired by Leontief

et al. (1965) and shocks the CGE model on national level. Further, as the output share of each

region is known and assumed to be constant, new regional output is calculated from those

regional output shares and the new national sectoral outputs is obtained by the national shock.

Another possibility is to simply split up certain sectors of the economy into sectors representing

different regions, as it is done in the top-down CGE model by Schinko et al. (2013), which

divides the tourism sector in different sectors representing regional production. The second

subcategory of “partial” sub-national CGE models is characterized by a regionalization of

consumers, who are characterized by individual incomes and preferences. Such CGE models

are of importance regarding research on poverty and social exclusion, as the disaggregation

of the national consumers can be done due to other characteristics than geographical location.

An example, which analyzed a reduction in petroleum subsidies in Indonesia, is Clements,

Jung, and Gupta (2007). Clements, Jung, and Gupta (2007) included different household

groups in their investigation and revealed, negative economic effects on household

consumption and poverty from decreased petroleum subsidies. The third subcategory of

“partial” sub-national CGE models is the combination of regionalized production and

consumption, which is not done for the same regions, as by Filho and Horridge (2005) for

Brazil. However, “partial” sub-national CGE models contain weaker inter-regional feedback

effects compared to bottom-up CGE models, as inter-regional trade of commodities and factors

is not established. On the contrary, the amount of data is considerably reduced compared to

bottom-up CGE models, which is the reason, why Rodriguez (2007) identifies “partial” sub-

national CGE models as well-suited to investigate national issues.

4.4.2 Sub-National Modeling Challenges and Requirements

CGE models can be used to analyze direct and indirect economic effects of different policies

on different regional levels by the interaction of economic agents. The investigation of various

47

economic issues and the resulting feedback effects are major advantages of CGE models.

Another advantage of CGE models is the combination with real economic data, which

represents especially in multi-regional CGE models the strengths and weakness in terms of

economic characteristics of different countries or sub-national regions. CGE models allow at

different time steps to measure and display economic variables such as employment, GDP,

and GVA, but also land use and GHG emissions. Nevertheless, CGE models can only show a

possible impact, for instance of the introduction of a policy, on the investigated economies

under the given circumstances and the defined assumptions.

Partridge and Rickman (2010) show the existing limitations of all three classes of sub-national

CGE models in their survey. They identify five major methodological limitations, which reduce

the usefulness of sub-national CGE models in regional economic development analysis. First,

they mention that regional CGE models are often based on external elasticities of substitution

or transformation, which are usually developed and used for their national counterparts.

Second, sub-national CGE models often have a weak representation of economic interactions

in low scale regions, as their structure is in general based on global CGE models and do not

cover the stronger specialization of regions within a certain country compared to different

countries. Third, regional interconnected labor market effects, which are more relevant within

than between countries, are often missing or modeled insufficiently. Fourth, Partridge and

Rickman (2010) claim that regional CGE models need a time element to find the optimal timing

for different policies. Fifth, in many regional CGE models spatial linkages are missing, whereby

the inter-regional trade of commodities and factors is not modeled or modeled insufficiently.

However, Partridge and Rickman (2010) consider that these deficiencies of earlier CGE

models should not be a checklist, which must be processed, but should be kept in mind when

developing a new CGE model. More important for a newly developed CGE model is the

respective parameterization and the respective economic setting regarding the purpose of the

investigation. Therefore, sub-national CGE modeling shall consider data inconsistency,

mobility of production factors, spatial linkages, and possible implications of modeling a static

or dynamic CGE model. The remaining part of this section discusses how scholars deal with

these major issues of sub-national CGE models.

Inconsistent Data Base on Sub-National Level

As CGE models often investigate currently existing economic policy shocks as changed

subsidies or effect of climatic conditions, CGE models usually depend on real economic data.

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This is especially true for bottom-up sub-national CGE models, which are the most data

demanding, but also for the other two categories of sub-national CGE models. This real

economic data is in general provided on national level by one-year IO accounts from national

institutes of statistics, as the Istituto Nazionale di Statistica (Italian National Institute of

Statistics, ISTAT), Statistics Austria, or the Australian National Bureau of Statistics (NBS), and

on global level by multi-region IO databases as the GTAP database10. Next to the GTAP

database, which is identified as the most often used database for multi-region IO analysis by

Wiedmann (2009), there are also other multi-region IO database as AIIOT, EXIOPOL, Eora,

and WIOD available (Wiedmann et al. 2011). Although data based on time series would be the

preferred option for CGE models, they are often unavailable, especially on a sub-national level

(Allan 2015). One-year IO-tables reduce the amount of data needed compared to a CGE model

based on time series, but these tables can be affected by unusual events and can be distorted.

Therefore, some modelers overcome this problem by predictions, based on two points in the

past, which decrease the amount of data compared to time series (Partridge and Rickman

2010). Other CGE modelers, such as Lofgren and Robinson (1999; 2002), use simple

prototype sample datasets without connection to real economic data.

Data limitation is the reason, why many CGE modelers work with GTAP or other multi-region

IO databases. The advantage of these multi-regional IO databases is their consistency due do

data processing. For example, the GTAP 9 database combines data from 140 regions into a

global consistent, sufficiently adapted, and balanced SAM structure database, which includes

international trade flows. An additional advantage of the GTAP 9 database is the supply of its

adequate number of factors, such as land, natural resources, capital, and unskilled and skilled

labor. Thus, there are many examples of sub-national CGE models, which use the different

versions of the GTAP database, such as Trink et al. (2010), Bednar-Friedl et al. (2013), and

Standardi, Bosello, and Eboli (2014). A deficiency of the GTAP database concerning sub-

national CGE modeling is the missing sub-national detail, especially the missing inter-regional

10 The GTAP database, which was initially created to satisfy the data demand for the GTAP model of Hertel (1997),

was improved several times since its creation and is now available in its ninth version. GTAP 9 includes 140 regions

(countries or groups of countries) and 57 sectors for three different reference years: 2004, 2007, and 2011. For

details see Aguiar, Narayanan, and McDougall (2016).

49

and international trade flows between different sub-national regions within and between

countries.

If a sub-national issue is investigated, in most cases sub-national SAMs, including international

and inter-regional trade, must be created separately. This creation must be based on different

assumptions, heuristics, and secondary data, which can reduce the accuracy of these SAMs.

However, while the task of creating and balancing a consistent sub-national database is time-

consuming, it has the advantage that the created database can be built on special

requirements of the CGE model. This advantage allows meeting the needs of the modeling

purpose and satisfying data requirements in more detail in areas of interest (Lofgren, Harris,

and Robinson 2002).

Standardi, Bosello, and Eboli (2014) mention that the implementation of inter-regional trade is

the major challenge of creating a sub-national database. They tried to overcome this challenge

by the combination of national transport data and economic production data of Italy, while

increasing the consistency of these two datasets by deploying the RAS method11. Horridge,

Madden, and Wittwer (2005) focus also on inter-regional trade flows with their creation of The

Enormous Regional Model (TERM) of Australia and their sub-national TERM database. The

TERM database is further developed since then, as shown in Horridge and Wittwer (2008a)

and Wittwer and Horridge (2010), and is applied to other regions as China (Horridge and

Wittwer 2008b). In general, they based their inter-regional trade flows on the gravity

assumption, as sufficient inter-regional trade statistics are not available for Australia. The

gravity assumptions imply that trade flows of a specific sector decrease by distance, while they

increase by the value of production in the exporting region.

Concerning inter-regional trade, data for Austria shows especially for sectors such as

agricultural, forestry, or industrial products that the volume traded is higher within than between

countries (Statistics Austria 2015c). This becomes more obvious if services are seen as

haircuts, where the share of inter-regional exports gets larger the smaller a region is (Partridge

and Rickman 2010). The so-called border effect, which shows that on the same distances,

11 The RAS method is a SAM balancing method as discussed in Deming and Stephan (1940), Bacharach (1970)

and Trinh and Phong (2013).2/27/17 12:11:00 AM

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there is more trade within than between countries (McCallum 1995), supports the given data.

For this reason, trade elasticities must be higher within than between countries.

In contrast, Bilgic et al. (2002) found in their study of estimates for trade elasticities of

substitution of regional commodities in USA that the more specialized a commodity, the smaller

the elasticity of substitution should be. This is especially true on a regional level, where the

specialization is larger. However, as shown above in section 4.2.2, standard CGE models and

sub-national CGE models use representative firms for each sector, which means that product

specialization is often not analyzed in sub-national CGE models. Therefore, in sub-national

CGE models each sector usually includes a huge number of different branches and industries,

which include a huge number of different firms as well.

As the flexibility of a CGE model depends on both the nesting structure and the elasticities of

substitution and transformation (see section 4.3.6), CGE models in general, but also sub-

national CGE models, are sensitive to these parameters. CGE models comprise several

production and demand blocks and each of them needs a well-chosen nesting structure and

thereon adjusted elasticities of substitution and transformation, which requires an appropriate

sensitivity analysis (Partridge and Rickman 2010). Nesting structures and elasticities are

mostly based on external studies, such as econometric studies, or taken from other databases

as GTAP, which are often not fully consistent with the regions and sectors of investigation

(Okagawa and Ban 2008). This is especially the case for sub-national CGE models, which is

the reason why sub-national CGE models particularly calls for sensitivity analysis (Partridge

and Rickman 2010).

Production Factors on Sub-National Level

A major challenge of sub-national CGE models is the question how to deal with the mobility of

the factors of production. In this context, most national and global CGE models assume factors

of production, such as labor and capital, as immobile between but mobile within a country.

However, assuming perfectly mobile production factors probably overestimates regional

growth effects, as, especially in the short run, costs of moving are underestimated. Costs of

moving are also the reason why mobility should be more elastic within than between countries,

as these costs are lower within than between countries (Allan 2015).

For sub-national CGE models, both perfectly mobile and immobile production factors are too

simplifying assumptions, which do not go far enough in the case of connected geographical

51

areas. Therefore, the assumption of perfectly mobile labor within and perfectly immobile labor

between sub-national regions can also overestimate growth effects, as a certain amount of

increased labor demand is always covered by commuters (Partridge and Rickman 2010).

Flows of commuters are also existent between municipalities, which can be identified for

Austria (Statistics Austria 2013a). An advantage of sub-national bottom-up CGE models is the

possibility to model these commuter and migrant flows between different regions. However,

sub-national bottom-up CGE models are very sensitive to the handling of commuter flows and

to the degree of labor mobility (Horridge, Madden, and Wittwer 2005).

In this regard, if a certain region is experiencing economic growth, which in turn leads to

increased demand for labor, parts of this economic growth will be absorbed by commuters

from other regions, who work in the growing region but consume at home. While these

commuters will contribute to an additional positive feedback effect in their home region, they

could weaken economic growth in the region of their workplace (McGregor, Swales, and Yin

1999). In general, three different groups shall be mentioned in sub-national bottom-up CGE

models, which will cover labor demand. First, the additional demand on workforce is covered

by unemployed persons or by natural labor supply growth of residents in the region. Second,

the demand for workers is met by commuters, who live outside a certain region. The final group

consists of migrants, who increase the labor endowment in the region of increased jobs and

decrease the labor endowment in their native region. Additionally, CGE model results are also

sensitive to the place where the incomes of this residents, commuters, or migrants are

consumed (Partridge and Rickman 2010).

The development of a sub-national CGE model needs to deal with the question how

unemployment benefits, in a case without full employment assumption, are modeled. One

feasible solution could be to model unemployment benefits endogenously, which would imply

that a decreasing number of unemployed persons in a region should also decrease the

transfers for unemployment benefits (Partridge and Rickman 2010).

Next to the production factors capital and labor, which are included in most CGE models, a

disaggregation of labor into skilled and unskilled, as well as the inclusion of the production

factor land, has become common for all classes of sub-national CGE models recently. Land is

a sensitive factor, as it is not tradable and therefore immobile. Its availability is limited to its

endowment, wherefore it is a limiting factor within a CGE model, which is especially of

importance the smaller a region is. In CGE models investigating area consuming activities, as

52

agriculture, forestry, biomass, or biofuels, land as a production sector would be advisable

(Kretschmer and Peterson 2010; Allan 2015).

Spatial Linkage on Sub-National Level

Sub-national regions within a country differ regarding their economic capacity, performance,

and characteristics. Policy shocks will therefore lead to different effects on different regions,

which also differ significantly from the national average. Another important economic aspect is

the geographical location of a region, which includes space and geographic closeness to other

economically strong regions. The simplification of economic interdependences, such as

international and inter-regional trade or space, can lead to consequences on sub-national CGE

model results. Many CGE models consider international or inter-regional linkages as trade or

spatial conditions insufficiently. This insufficient consideration of international and inter-

regional linkages influences assumptions on economic aspects connected to space and

geographic closeness, such as transport costs (Lofgren and Robinson 2002).

Regarding trade, sub-national regions are often assumed as too small to have economic

effects on other regions outside the CGE model. The reason for this assumption is in most

cases the lack of data or too little computer power, which is also the reason why sub-national

CGE models usually include only a small number of sectors and regions (Horridge, Madden,

and Wittwer 2005). There are also CGE models, which reduce spatial linkage to higher or other

administrative units and treat them as the rest of the country or the rest of the world. Such

CGE models reduce or ignore the feedback effects arising by flows of labor (Partridge and

Rickman 2010).

Lofgren and Robinson (2002) criticize the rare integration of space and transport costs in

common CGE models. In most CGE models goods from different locations are treated as

imperfect substitutes and make use of Armington elasticities (Armington 1969). By using the

Armington approach, it should be mentioned that initial trade flows could not disappear

completely. The Armington approach has its legitimacy in the case of highly aggregated

sectors, which are common in global, national, and sub-national CGE models. However,

Lofgren and Robinson (2002) claim its usage in the case of heterogeneous products, which

occur with higher spatial resolution, where regions contain only a small number of businesses,

which produce a small number of different products. Lofgren and Robinson (2002) mention

that a two-way trade cannot be found empirically in the case of heterogeneous agricultural

goods. To overcome this problem of two-way trade of heterogeneous goods, Lofgren and

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Robinson (2002) created a national spatial-network CGE model with included transport costs

and limited trade within different national regions and the rest of the world.

When spatial linkages are modeled and global and local trade flows are assumed, initial

starting values are needed to establish trade. If these initial starting values do not exist, trade

cannot occur between two regions. In the case of missing data, Kretschmer and Peterson

(2010) state two possible solutions. First, they mention that it is possible to create a new sector,

which contains no initial trade starting value, as a perfect substitute to another sector. This

practice is reasonable if a new technology, such as the RES technology, is introduced in a

CGE model with an initial single conventional energy sector. The second option is to specify

an initial starting value, based on reasonable assumptions. Such assumptions are often based

on secondary data.

Static versus Dynamic on Sub-National Level

CGE models can further be categorized into static and dynamic CGE models. On the one

hand, static CGE models show how the equilibrium of an economy has changed compared to

the baseline. Hence, if a CGE model is calibrated to a twenty-year horizon, the solution after

these twenty years is shown without the depiction of the path between the initial and final

equilibrium. On the other hand, dynamic CGE models can show the path between the initial

and final equilibrium. However, they cannot show a certain point in time, only the individual

equilibria an economy passes from time step to time step on the way to a long-run equilibrium

at the end of the period. Additionally, dynamic CGE models can include forward-looking

agents, stock changes of capital, and labor and technological changes (Allan 2015).

Additionally, dynamic CGE models can be subcategorized into recursive dynamic and “fully”

dynamic CGE models. “Fully” dynamic CGE models include actors with perfect foresight, while

recursive dynamic CGE models pass on certain variable from equilibrium to equilibrium.

4.4.3 Existing, Sub-National CGE Studies Concerning Regional Renewable Energy Strategies

The previous sections of this chapter have discussed why CGE is the preferred method to

meet the needs of analyzing the CEM approach (section 4.1), how the CGE approach has

emerged historically (section 4.2), and how a standard national CGE model looks like (section

4.3). Finally, this section has shown how individual heterogeneous regions are treated in sub-

54

national CGE models (section 4.4.1) and which modeling challenges and requirements should

be considered when generating a sub-national CGE model (4.4.2). Therefore, we now present

a small number of existing sub-national CGE models dealing with regional renewable energy

strategies, such as energy transition approaches or biomass expansion.

One study which deals with energy reduction in the transport sector and combating urban

sprawl is the study of Horridge (1999). This region-specific or city-specific CGE model of

Melbourne, the second largest city of Australia, divides the city into nine zones with different

attributes, such as land availability and employment opportunities, while spatial linkage to

regions outside the City is missing. Additionally, the CGE model differentiates households by

attributes, for instance by income level or lifestyle preferences. To analyze the changes in

energy use of the transport sector, the urban development, and the population growth, three

different simulations are investigated. Horridge (1999) found an increase of transport energy

of about 4% in the case of a 30% increase in population. In contrast, he found a decline of

transport of 9.6% from a tax on transport compared to a case without such a tax. Additionally,

he found urban consolidation policies as unsuitable to reduce urban transport occurrence.

The work by Cansino et al. (2014) represents another region-specific sub-national CGE model.

This CGE model analyzes the socio-economic impacts of increased electricity production from

PV in solar parks of Andalusia, Spain. Trade is only established to the rest of the world, which

includes the rest of Spain. Cansino et al. (2014) analyze the impacts of a PV installation of 400

MW in 2013. As Cansino et al. (2014) find positive economic impacts on employment,

disposable income, tax revenues, and GDP, in addition to a decrease of GHG emissions in

Andalusian electricity production, Cansino et al. (2014) recommend the deployment of the

solar park.

In contrast, Zhang et al. (2013) investigate the impacts of energy transition and compares the

environmental and economic effects of provincial and national CO2 reduction targets in the

same amount in a sub-national bottom-up CGE model of China. The CGE model comprises

30 of the 31 provinces of China12 in a multi-regional CGE model with integrated inter-regional

trade. The rest of the world is represented by three country aggregates, including the United

States, the European Union and other European countries, and the rest of the world. In their

12 The region Tibet is missing in this study due to lack of data.

55

two scenarios, they compared changes on the Chinese economy and CO2 emissions arising

from the implementation of CO2 targets defined in the Twelfth Chinese Five-Year Plan (2011-

2015) on provincial level, without emission trading, and on national level, including trading of

emission allowances allocated by auctions. Zhang et al. (2013) found that using national

targets, with included emission trading, results in a 20% reduction of welfare losses in China

compared to provincial targets.

In a similar approach, Wu et al. (2016) investigate energy transition in a sub-national bottom

CGE model of China. They compared the effects arising from implementing two different feed-

in tariff schemes. For their study, Wu et al. (2016) adopted the CE3MS model of Wu, Fan, and

Xia (2016), which includes 30 regions, 17 sectors, and trade. Trade of sectoral output is

established between the 30 regions and to the rest of the world. Wu et al. (2016) found that

feed-in tariffs financed by a tax on conventional electricity, are preferable compared to feed-in

tariffs financed by fiscal revenues of local governments, since they reduce CO2 emission more

effectively. In contrast to the differences in CO2 emissions, Wu et al. (2016) found only small

differences in the GDP of the two feed-in tariff schemes.

A CGE analysis to assess possible economic feedback effects on the Austrian economy

caused by the CEM energy transition approach, was done in the study of Kettner et al. (2012).

Kettner et al. (2012) by employing the theoretical CGE approach of Sindelar 10 (Haddad and

Hewings 2005) and created a sub-national bottom-up CGE model of Austria by disaggregating

Austria into its nine federal states connected by inter-regional trade. While Kettner et al. (2012)

neither take into account the specific economic characteristics of the different CEMs (see

section 3.1 and section 3.2) nor their different specific RES potentials (see section 3.2), it is

focused on a projection of the defined objectives of five case study CEMs on the Austrian

economy. As Vienna is excluded by definition from the CEM approach, Kettner et al. (2012)

assume policy shocks to the eight other federal states. While a direct projection of the

objectives on the five investigated CEMs to their federal states was done based on the

respective case study region, targets for the other three federal states were calculated based

on the five considered case study CEMs. Kettner et al. (2012) found different regional effects,

due to different deployed RES technologies in different federal states, while overall GDP and

employment effects are positive among Austria.

Finally, Trink et al. (2010) modeled a global multi-regional CGE model, which includes a sub-

national perspective of East Styria, Austria. In this regard, Trink et al. (2010) developed a two-

plus-ten region CGE model, which means that the primary region of investigation, Styria, is

56

divided into two sub-national regions with a special trade-related connection to Austria and the

world market. On a global market, all ten regions (the rest of Austria and nine other country-

aggregates) are connected by trade. Within this two-plus-ten region CGE model, Trink et al.

(2010) did a full cost analysis of agricultural- and forestry-based biomass technologies and

thermal insulation. While they found increased net employment effects from forestry-based

biomass technologies, international competition on the agriculture market offset positive

effects of agriculture-based biomass technologies.

4.5 Conclusion

For the analysis of a simultaneous RES achievement in different CEMs, we were looking for

the most appropriate modeling technique to investigate macro-economic feedback effects and

cross-sectoral spillover effects. We identified some modeling techniques such as econometric

models, which are vulnerable to low data availability, or IO and SAM models, which do not

provide flexibility in production and do not consider price effects, as insufficient for our

research. Since our CGE model should be able to be employed on a low regional scale, which

implies in our case restricted data availability and different RES targets in different CEMs, as

well as including some economically uncompetitive RES technologies, we identified the CGE

approach as the most appropriate modeling technique.

The CGE approach has experienced continuous development over the last decades, starting

from Leontief’s IO account system and the Arrow-Debreu model, which makes use of the

Walrasian general equilibrium. This Walrasian general equilibrium is characterized by the joint

interaction of profit maximization of firms and utility maximizing consumers, which lead to a

unique equilibrium with an optimal level of prices and output quantities. A Walrasian general

equilibrium can be solved as a complementarity problem by employing real economic data

provided by a balanced SAM, which satisfies market clearance, zero profit conditions, and

income balance. Together, theory and data can be used in a CGE model to find possible paths

of the economy under different model shocks.

The previous sections have shown the difficulties arising from sub-national CGE modeling.

Therefore, it is important to select an appropriate model for the present research question

instead of adjusting the research question to an available model. This is especially true when

it comes to the selection of an appropriate CGE model to analyze the Austrian CEM approach.

The CEM approach is characterized by a simultaneous development of diverse, ideally rural,

and structurally weak CEMs, which are geographically distributed over whole Austria. The

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existing CEMs have shown to be heterogeneously both regarding their geographical location

in Austria and their economic and energy related characteristics. In the course of the current

study and in contrast to Kettner et al. (2012), a CGE model should be developed, which

explicitly represents these economic and energy related characteristics of certain CEMs,

especially regarding its RES potentials, and which can analyze the macro-economic

implications of increased RES deployment in the CEMs.

Based on the cluster analysis presented in section 3.2, we identified four model regions, which

are the three CEM clusters and a fourth model region “Rest of Austria”. These model regions

satisfy a homogenous representation of the Austrian CEMs regarding economic and energy

related characteristics. However, the selection of model regions in accordance with their

economic and energy related characteristics, instead of their geographic location, implies a

missing geographical linkage between different CEMs or other municipalities within a certain

model region. Missing geographical linkages between certain model regions are the reason

why we decide to investigate our research question based on a bottom-up sub-national multi-

sectoral CGE model of the CEM approach in Austria.

We modeled our CGE model bottom-up by including a spatial disaggregation of sectoral

production and household consumption. Additionally, we decided to create a sub-national

database for our requirements based on national statistics and regional secondary data, while

we leave deeper sensitivity analysis for further analysis of the same CGE model and only test

different RES targets within this study. We deal with production factors on sub-national level

in that way that only labor and capital are included. Due to the distributed CEM locations and

data limitations, we model two factors, labor and capital, which are both fully mobile across

Austria but immobile to the rest of the world. For the reason of the distributed CEM locations,

we also decided to do not mention space or geographical closeness and inter-regional trade

and to make use of the small-open economy assumption with Armington trade. Finally, our

CGE model is static for simplicity.

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5 Analysis of the CEM Approach in a Sub-National CGE Model

The previous chapters have presented the Austrian CEM approach (chapter 2), the economic

structure of the selected 82 CEMs as well as the economic and energy related characteristics

of 78 CEMs, which were assigned to three representative CEM clusters by a cluster analysis

(chapter 3), and the CGE approach and its requirements in terms of sub-national CGE models

(chapter 4). Building on this background, we provide in this section an analysis of the Austrian

CEM approach by means of a sub-national CGE model, within a comparative static scenario

approach.

The aim of our CGE model is to analyze how the achievement of a renewable electricity

production target for CEMs not only affect the different regions, but also the overall Austrian

economy concerning GDP, unemployment, sectoral output, and household consumption

compared to a Business as Usual (BAU) scenario in 2020. Additionally, we investigate the

effects of fostering ambitious RES targets on the public sector, for instance government

revenues and expenditures, and the different results by deploying a less ambitious RES target

in CEMs with lower RES potentials. A further propose of this thesis is to identify, how future

CEMs should be characterized regarding their economic structure.

For this purpose, we first recapitulate the Austrian and regional energy goals and potentials

(section 5.1). In the second part of this chapter, we deal with the theoretical and practical

implementation of the CEM approach within the CGE framework, including the modeling tasks

regarding production, trade, and demand (section 5.2). In section 5.3, we discuss the database

development and in section 5.4 the employed scenarios. While we show the economic

consequences of the CEM energy transition approach on regional and national scale in the

fifth part (section 5.5), we provide a discussion of our approach, especially concerning our

assumptions and results in the last part of this chapter (section 5.6).

5.1 Implementation of National and Regional Energy Goals in a Sub-National CGE Approach

The implementation of the CEM approach within a CGE analysis requires the consideration of

national and regional energy goals, but also the overall goals of the KLIEN concerning the

CEM approach. As discussed in detail in chapter 2, Austria has set a renewable energy goal

of 34% gross final energy consumption covered by RES until 2020. This RES-share of gross

final energy consumption, according to the EC-directive 2009/28/EG (European Parliament

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2009), was increased from 23.9% in 2005 to 30.5% in 2011 (Statistics Austria 2016). While

this is a considerable increase from 2005 to 2011, by considering that the financial crisis

occurred in that period, it should be kept in mind that continued efforts will be necessary until

2020 to fulfil the RES objective. On this account, different implementation and support

measures were established recently. The CEM approach, introduced by the KLIEN, is one of

these instruments, which aim at supporting an increase of the RES potentials until 2020.

The bottom-up CEM approach aims to encourage participating CEMs to increase their shares

of RES by setting own goals defined by own implementation concepts, as discussed in section

2.1.2. However, the different implementation concepts vary greatly in content, structure, and

detail of data. Additionally, the self-defined renewable energy goals of the CEMs differ greatly.

Since the data provided from the 82 available and investigated implementation concepts is not

sufficient in detail and quality, we decided to refer on the overall objective of the CEM

approach, which induce the CEMs to establish a sustainable and independent energy supply

by using regional RES potentials optimally (Climate and Energy Fund 2015b).

The CEM approach includes energy transition in all its aspects, comprising electricity, heat,

and mobility. Thus, we decide to use the consistent feasibility study of Austria’s electricity and

heat potentials at the district level by Stanzer et al. (2010) for the year 2020 instead of data

from the CEM implementation concepts. As data about mobility is not available in Stanzer et

al. (2010), and other studies do not provide an appropriate low regional level, we focus on

electricity and heat. Stanzer et al. (2010) found that most districts and Austria in its entity could

become at least electricity but not heat self-sufficient by 2020. Because disaggregating

different technologies of electricity and heat from the initial Austrian SAM for different regions

is very time consuming, we decided to focus on data processing of the electricity sector and

leave the heat sector for future analysis. However, our economic CGE model should also allow

for a simple expansion by heat for these future approaches. The overarching goal of this study

is to analyze the economic consequences and macro-economic feedback effects of achieving

an exogenously specified renewable electricity production target in Austria´s CEMs in 2020

within a sub-national, multi-regional, and multi-sectoral CGE model.

5.2 Methodology – CGE Model Specification

Based on the available data on sub-national level concerning economic characteristics,

geographical location as well as energy goals and potentials, this section discusses the thereof

resulting aspects relevant for sub-national CGE modeling. At the beginning, the fundamental

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CGE model assumptions derived from special characteristics of the CEM approach and the

CGE model classifications are presented (section 5.2.1). In the next sections, the basic CGE

model structure (5.2.2) and the modeling of production (section 5.2.3), trade (section 5.2.4),

and demand (section 5.2.5) are shown.

5.2.1 CGE Model Classification and Fundamental Assumptions

As mentioned above, the goal of this study is to analyze the economic consequences and

macro-economic feedback effects of achieving an exogenously specified renewable electricity

production target in Austria´s CEMs in 2020. To that end, we develop a sub-national multi-

regional and multi-sectoral CGE model for Austria. We decided to investigate an exogenously

specified RES target based on an existing RES potential scenario analysis for Austria (Stanzer

et al. 2010) and technology cost estimates from a detailed bottom-up electricity sector model

in order to describe a most realistic picture of the Austrian RES system. CGE models are only

simplified version of the reality with limited capability to represent real world regional RES

supply constraints and hence to identify an economically optimal as well as technologically

feasible future RES mix.

In contrast to other examples of sub-national CGE models, discussed in section 4.4, the

nationwide distribution of CEMs requires an appropriate CGE model setup. In this context, it

should also be mentioned that the Austrian CEMs are heterogeneous regarding their economic

structure and their energy potentials. This heterogeneity is the reason for our cluster analysis

in chapter 3.

Based on the cluster analysis, three CEM clusters, suburban, semi-rural, and rural (see Table

4), are identified. As CGE models are restricted in their size of regions and sectors by software

and data limitations, as discussed in section 4.4.2, and to keep the number of model regions

to a minimum by providing sufficiently required detail, our CGE model includes four sub-

national model regions located within Austria. These four model regions are the three

homogenous CEM clusters and a fourth “Rest of Austria” model region, which includes all

municipalities of Austria that could not be allocated by the cluster analysis to a certain cluster.

However, while the three CEM cluster model regions derived from the cluster analysis are

homogenous within themselves, they are still different among each other and the fourth model

region Rest of Austria. The model regions are different in size, but also according to economic

characteristics (shown in Table 8), such as number of inhabitants, employed and unemployed

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persons, household income, and production. Rest of Austria is the largest CGE model region,

with 75% of Austria’s population and 78.9% of Austria’s production in Euro. A relatively large

share of unemployed persons compared to its population share additionally characterizes Rest

of Austria. A similar picture can be drawn for suburban CEMs, which have a larger share of

production than population and relative to their population a large share of unemployed

persons. In contrast, semi-rural and rural CEMs have a large share of population compared to

their share of consumption and production. On the contrary, semi-rural and rural CEMs’ share

of employed persons is relatively large and their share of unemployed persons is relatively low.

Table 8: Economic characteristics of the model regions in 2011

Suburban Semi-rural Rural Rest of Austria

Sum of Austria

Absolute values in 2011 Population 239,531 909,308 920,262 6,332,839 8,401,940 Employed persons 109,368 439,358 447,437 2,961,107 3,957,270 Unemployed persons 11,010 28,778 25,877 274,200 339,864 Household income (in Million €) 5,622 17,434 17,139 134,142 174,338 Production (in Million €) 21,753 51,501 48,499 456,606 578,360 GDP (in Million €) 308,647

Relative shares in 2011 Population (in %) 2.9% 10.8% 11.0% 75.4% Employed persons (in %) 2.8% 11.1% 11.3% 74.8% Unemployed persons (in %) 3.2% 8.5% 7.6% 80.7% Household income (in Million €) 3.2% 10.0% 9.8% 76.9% Production (in %) 3.8% 8.9% 8.4% 78.9%

Source: Own processed data based on Statistics Austria (2013b) and Statistics Austria (2015a)

This selection of model regions has major implications on our CGE model structure. Since the

cluster analysis is done based on economic and energy characteristics and ignores the

geographical vicinity between municipalities, the different CEM clusters are distributed over

the country, as shown in Figure 5 above in section 3.2. This missing geographical vicinity is

especially of importance for our CGE model assumptions about the treatment of regions.

We started our investigation on early sub-national CGE models by taking the approach of Trink

et al. (2010) into account, who analyze the sub-national implications of East Styria embedded

within a global context. We particularly considered their modeling of inter-regional trade for our

own CGE model setup. In deviation from Trink et al. (2010), the CEM approaches

characteristics and the associated missing geographical vicinity within the CEM clusters,

without geographically linked areas, we decided to not model inter-regional trade. We have

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taken this decision, because we cannot assume larger trade volumes within a certain region

than between regions, as discussed in section 4.4.2, in a case without geographically linked

regions. Therefore, there is neither enough detailed data, nor a scientific basis to which regions

inter-regional trade flows go to. Regarding labor and capital we decided to model these factors

of production as mobile within Austria, but as immobile to the rest of the world. While it was

mentioned in section 4.4.2 that perfectly mobile factors are not realistic for sub-national CGE

models, under the given circumstances of non-linked geographical areas we decided to use

this simplifying assumption.

Therefore, the missing geographical linkages between different CEMs within certain model

regions are the reason why we decided for a bottom-up sub-national multi-sectoral CGE

model, which is discussed in detail in the following sections. While production and consumption

of the model regions are regionalized, there is no inter-regional trade and only one, a national,

government. We thereby use a similar approach as Bednar-Friedl et al. (2013) and Schinko et

al. (2013), with the major difference that we additionally regionalize the Austrian households.

The basic structure of our CGE model follows the preliminary comparative static studies of

Bachner et al. (2015) and Rutherford and Paltsev (1999), as shown in section 4.2, by deploying

the small open economy assumption with Armington trade. Finally, the energy related

modeling of production technologies is based on Bednar-Friedl et al. (2015).

5.2.2 The Basic CGE Model Structure

We develop and implement our CGE model by employing the programming software

GAMS/MPSGE (Rutherford 1999) within a comparative static scenario approach based on the

latest available Austrian IO-table of 2011, when we started modeling (Statistics Austria 2015a).

GAMS/MPSGE is compatible with set notation, which gives us different sectoral and regional

indices. Table 9 show the respective sectors’ sectoral and the respective regions’ regional set

assignment indicated by “x”. The flowchart for our bottom-up sub-national multi-sectoral CGE

model of Austria, shown in Figure 13, is similar to the CGE model of the simple small open

economy of Figure 12 in section 4.3.6. The solution process for finding a new economic

equilibrium after exogenous shocks occur is still driven by the price mechanism. The different

components of the bottom-up sub-national multi-sectoral CGE model are located on the sub-

national and national level, while the domestic economy is linked to the rest of the world by

trade flows. Additionally, the CGE model can be split into interacting components of production,

trade, and demand.

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Table 9: Sectoral and regional set indices

No. Model code es esne esnec rest con res

Regional electricity production 1 ELECTR x 1a EL_TDT x 1b EL_CON x 1c EL_HYD x 1d EL_PVL x 1e EL_PVS x 1f EL_WIN x 1g EL_BGS x 1h EL_BMS x Regional non-electricity production 2 MD_GAS x x x 3 D_HEAT x x x 4 AGRICU x x x x 5 MINING x x x 6 MANU_C x x 7 MANU_E x x x x 8 MANU_O x x x x 9 CO_WAT x x x x 10 FIN_TD x x x x 11 TRANSP x x x x 12 SERVIC x x x x

Model code Suburban Semi-rural Rural Rest of Austria

reg x x x x cems x x x nat es: economic sectors, esne: economic sectors without electricity, esnec: economic sectors without electricity and coke, rest: non-energy intensive sectors, con: conventional electricity sectors, res: renewable energy source electricity, reg: Austrian region, cems: CEM cluster, nat: whole Austria (no regional disaggregation)

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Figure 13: Flowchart of the bottom-up sub-national multi-sectoral CGE model of Austria


65

As illustrated in Figure 13, the four representative regional households (PrivHH (reg)) (where

reg indicates the four Austrian model regions) receive the factor income by providing their

endowment of capital (CAP (nat)) and labor (LAB (nat)) (nat stands for “national” and indicates

no sub-national disaggregation) to the homogenous firms producing regional production (X

(es, reg)) (es indicates the economic sectors), subject to CES or Leontief production functions.

In respect to the factor endowment of capital and labor, it should be mentioned that we

modeled both factors as perfectly mobile within the country, wherefore it does not matter in the

regional production from which region the factor inputs come from.

Twelve sectors generate regional production (listed in shown in Table 10 – sectors 1-12), which

can be divided into regional non-electricity (esne) production (X (esne, reg)) and regional

electricity production (X (ELECTR, reg)). While the sectors of non-electricity production (X

(esne, reg)) receive factor inputs directly, electricity production (X (ELECTR, reg)) is an

aggregate sector of electricity and a composite of eight electricity sub-sectors (1a-1h),

wherefore it is receiving factor inputs indirectly. The eight electricity sub-sectors (1a-1h), which

also produce subject to CES production functions, are “regional electricity transmission,

distribution, and trade” (EL_TDT (reg)) (1a) and generation of electricity from different sources

(1b-1h). Hence, additional to the division into twelve regional production sectors, our CGE

model contains 19 regional sectors generating value added (1a-1h and 2-12).

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Table 10: Sectoral restructuring of the Austrian SAM of the year 2011

No. Activity / industry Model code ÖNACE 2008 Section

ÖNACE 2008 Division

Regional electricity production 1 Electricity ELECTR D 35 1a Electricity transmission, distribution, and trade EL_TDT D 35 1b Generation of electricity – conventional mix EL_CON D 35 1c Generation of electricity by small scale hydro EL_HYD D 35 1d Generation of electricity by large scale PV EL_PVL D 35 1e Generation of electricity by small scale PV EL_PVS D 35 1f Generation of electricity by wind power EL_WIN D 35 1g Generation of electricity by biogas EL_BGS D 35 1h Generation of electricity by biomass EL_BMS D 35 Regional non-electricity production 2 Manufacture and distribution of gas MD_GAS D 35 3 District heating D_HEAT D 35 4 Agriculture, forestry, and fishing AGRICU A 01-03 5 Mining and quarrying MINING B 05-09 6 Coke manufacturing MANU_C C 19 7 Energy intensive manufacturing MANU_E C 16-18; 20-25 8 Other manufacturing MANU_O C 10-15; 26-32 9 Construction and water supply CO_WAT E, F 36-39; 41-43 10 Financial, insurance, real estate, and trade activities FIN_TD G, K, L 45-47; 64-66; 68 11 Transportation and storage TRANSP H 49-53 12 Other service activities SERVIC I, J, M-T 55-56; 58-63; 69-99

As shown in Figure 13, the households receive transfers, unemployment benefits, and other

transfers from the national government (GOV), which in turn collects tax income from different

sources (capital, labor, production, and others) to ensure a balanced budget. The households

and the government spend their income to maximize their utility subject to their preferences,

represented by a nested CES consumption function of final demand, on domestic supply goods

(D (es, nat)).

5.2.3 Regional and Domestic Production

As mentioned above and shown in Figure 13, the regional production takes place in twelve

different regional production sectors (sectors 1-12). While electricity production (1) does not

directly contribute to Austria’s value added and is only an aggregated sector and a composite

of the eight, value added generating, regional electricity sub-sectors (1a-1h) produced in the

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respective region, the other eleven regional non-electricity production sectors (2-12) contribute

directly to Austria’s value added. The 19 regional value added generating sectors (2-12) and

sub-sectors (1a-1h), can be roughly divided into four sets (see Table 9 for details of sectoral

and regional sets), which are the single sector 1a, produced in all four model regions, “regional

electricity transmission, distribution, and trade” (EL_TDT (reg)), the single sector 1b “regional

generation of electricity – conventional mix” (EL_CON (reg)), the renewable electricity set (res)

of the sectors 1c-1h, produced only in the three CEM clusters (cems), “regional renewable

electricity production” (ELPR (res, cems)), and the set of non-electricity production sectors 2

to 12 “regional non-electricity production” (X (esne, reg)).

Figure 14 and Figure 15 show the nesting structure of the regional value generating sectors

(1a-1h and 2-12). As we use nested CES or Leontief production functions, we have sector-

and agent specific elasticities indicated by individual indices, as top, int, kle, kl, eii and e in

Figure 14. These individual indices are shown and parameterized in Table 11. In this regard,

Figure 14 shows the nesting of regional production sectors without electricity and coke

manufacturing (esnec; sectors 2-5 and 7-12) produced in all four model regions (X (esnec,

reg)); regional conventional electricity generation, transmission, distribution, and trade (con,

sectors 1a and 1b) in all four model regions (ELP (con, reg)), and production of regional

renewable electricity generation (sectors 1c-1h) produced in the three CEM clusters only

(ELPR (res, cems)). All these sectors, 1a-1h, 2-5, and 7-12, are represented within the CGE

model by nested CES production functions with several nesting levels13, which specify the

substitution possibilities between national primary inputs of capital (CAP (nat)) and labor (LAB

(nat)) as well as national intermediate inputs of domestic supply goods from the sectors 1-12

(D (es, nat)). Starting at nesting level 1, a composite of non-energy intensive (rest) intermediate

inputs (D (rest, nat)), excluding the sectors ELECTR (nat), MD_GAS (nat), D_HEAT (nat),

MANU_C (nat) and MINING (nat), trade-off against a composite of capital, labor and energy

intensive sectors (KLE (nat)), at a sector specific constant elasticity of substitution top. On

nesting level 2, the composite primary inputs (KL (nat)) and energy intensive intermediate

inputs (EII (nat)) are specified as two different nests by the sector specific constant elasticity

13 Elasticities are adopted from Okagawa and Ban (2008), as well as Bednar-Friedl, Kulmer, and Schinko (2012),

Bednar-Friedl, Schinko, and Steininger (2012), and Bednar-Friedl et al. (2015), which used data of Narayanan and

Walmsley (2008), Okagawa and Ban (2008), and Beckman and Hertel (2009).

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of substitution kle. On nesting level 3, a composite of energy inputs (E (nat)) and the sectors

MANU_C (nat) and MINING (nat) can be substituted with the elasticity eii. Figure 15 shows

the slightly different nesting of the regional coke manufacturing production (sector 6) in the

four model regions (X (MANU_C, reg)).

Figure 14: Nesting structure of regional conventional electricity generation, transmission, distribution and trade, regional renewable electricity generation, and regional production sectors other than

electricity and coke manufacturing

Figure 15: Nesting structure of regional coke manufacturing production

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Table 11: Sector- and agent specific elasticities

Model code v top int ext kle kl eii e hea In Figure

Regional electricity production EL_TDT 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_CON 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_HYD 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_PVL 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_PVS 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_WIN 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_BGS 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 EL_BMS 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 Regional non-electricity production MD_GAS 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 D_HEAT 0.000 0.391 0.256 0.460 0.160 0.070 Figure 14 AGRICU 0.392 0.000 0.516 0.023 0.160 0.070 Figure 14 MINING 0.729 0.309 0.553 0.139 0.160 0.070 Figure 14 MANU_C 0.000 0.848 0.082 0.070 0.000 0.334 1.000 Figure 15 MANU_E 0.406 0.309 0.529 0.046 0.160 0.070 Figure 14 MANU_O 0.130 0.459 0.292 0.295 0.160 0.070 Figure 14 CO_WAT 0.632 0.196 0.392 0.263 0.160 0.070 Figure 14 FIN_TD 0.629 0.044 0.475 0.281 0.160 0.070 Figure 14 TRANSP 0.352 0.331 0.281 0.310 0.160 0.070 Figure 14 SERVIC 0.874 0.196 0.754 0.322 0.160 0.070 Figure 14 Agents Demand PrivHH 0.200 1.000 0.500 0.070 Figure 17 GOV 0.200 1.000 0.500 0.070 0.050 Figure 18 v: nesting between non-energy intensive, capital, labor, and energy intensive (TOP(nat)) and extraction (EXT(nat)) inputs, top: nesting between non-energy intensive (INT(nat)) and capital, labor, and energy intensive (KLE(nat)) inputs, int: nesting between non-energy intensive (INT(nat)) inputs, ext: nesting between extraction (EXT(nat)) inputs, kle: nesting between capital and labor (KL(nat)) and energy intensive (EII(nat)) inputs, kl: nesting between capital (CAP(nat)) and labor (LAB(nat)) inputs, eii: nesting between different energy intensive (EII(nat)) inputs, e: nesting between different energy (E(nat)) inputs hea: nesting between different heat (HEA(nat)) inputs

Source: Own processed data based on Okagawa and Ban (2008), as well as Bednar-Friedl, Kulmer, and Schinko (2012), Bednar-Friedl, Schinko, and Steininger (2012), and Bednar-Friedl et al. (2015), which used data of

Narayanan and Walmsley (2008), Okagawa and Ban (2008), and Beckman and Hertel (2009).

Regional electricity production (sector 1) is a sector aggregate and a composite of the

electricity sub-sectors 1a-1h produced in all regions (X (ELECTR, reg)), as shown in Figure

16. The regional electricity production uses only goods produced in their own region. In all

model regions “electricity transmission, distribution, and trade” (EL_TDT) is combined with

“production of electricity – conventional mix” (EL_CON) by a Leontief production function. In

the three CEM cluster model regions “electricity transmission, distribution, and trade”

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(EL_TDT) is additionally combined with a composite of electricity production technologies from

different sources, including the conventional mix and RES, by a Leontief production function

at the lowest nesting level.

Figure 16: Nesting structure of regional electricity production

After combining the production of sector 1a-1h to a single regional electricity good (X

(ELECTR, reg)), we finally end up with twelve regional production sectors, which are provided

to the national market (1-12). As shown in Figure 13, the twelve regional production sectors,

produced in every model region (X (es, reg)), are used as inputs in the domestic production

and are combined by a Cobb-Douglas production function to twelve domestic production

sectors (Z (es, nat)) in the next step, which provides a single national consumer price for each

good.

5.2.4 International Trade

After aggregating regional production to one national good for each sector, trade with the rest

of the world is represented by making use of the small open economy assumption with

Armington trade. As our CGE model should allow for re-exporting of imports and due to the

structure of the Austrian IO-table, which is used to create the sub-national SAMs, imports from

the rest of the world are combined with the domestic production good (Z) as imperfect

substitutes with sector specific Armington elasticities of substitution, adopted from Bachner et

al. (2015), to a single Armington good for each sector (G (es, nat)). This Armington good is

taken as input for the domestic supply to produce a national domestic supply good (D (es,

nat)), which is used as domestic intermediate and final demand, and to produce national

exports, sold to the rest of the world, by using sector specific Armington elasticities of

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transformation. The exports to the rest of the world generate foreign exchange reserves,

measured at a single world price, which are subsequently used to finance imports. As already

mentioned, these imports are re-exportable and go as an input into the production of the

Armington good (G (es, nat)).

5.2.5 Regional Household and Government Demand

As shown in Figure 13, the domestic supply good (D (es, nat)), which is a composite of

domestic production and imports, is used to cover final and intermediate demand. While

intermediate demand is used in the regional primary production, the single national

government (GOV (nat)) and four regional representative private households (PrivHH (reg))

consume domestic supply goods (D (es, nat)) in respect of maximizing their utility subject to

their preferences.

These preferences are represented by the nested CES consumption functions of four regional

representative private households (PrivHH (reg)) in Figure 17 and the national government

(GOV (nat)) in Figure 18. On the highest nesting level of the consumption function in Figure

17, the composite of non-energy intensive intermediate inputs (D (rest, nat)), ELECTR (nat),

MD_GAS (nat), D_HEAT(nat), MANU_C (nat), and MINING (nat) can be substituted with the

composite of energy intensive sectors (EII (nat)) by constant elasticity of substitution top. At

the second nesting level, a composite of energy inputs (E (nat)) and the sectors MANU_C (nat)

and MINING (nat) trade-off against each other with the elasticity of eii. At the third nesting

level, the energy related sectors ELECTR (nat) and MD_GAS (nat) can be substituted with a

heat composite (HEA (nat)) with the elasticity of e. Finally, at the fourth nesting level, the sector

D_HEAT and local heating by private households (HEATHH, reg), a composite of the domestic

supply good (D (es, nat)) that is an upstream consumption sector including region specific

inputs, trade-off with the elasticity of hea.

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Figure 17: Nesting structure of regional private household consumption

Figure 18: Nesting structure of domestic government consumption

5.3 Sub-National Economic Data

As discussed in section 4.3.3, a CGE model is in general based on a SAM. Common CGE

models derive a SAM from an IO-table delivered by an official statistical office, or use a SAM

created and processed from a multi-region IO databases, such as the GTAP 9 database. The

GTAP 9 database is consistent within itself and covers international trade flows as well as

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different regions, sectors, and factors. On the contrary, this consistency, especially regarding

international trade flows, reduces accuracy in respect to a single region. Therefore, on a

national level the Austrian IO-table provided by Statistics Austria (2015a) is more accurate.

IO-tables are not often available at a level below nations. Therefore, the development of a sub-

national multi-regional CGE model often requires a sub-national multi-regional SAM of the

applied model regions (Table 8). Especially the sub-national bottom-up CGE models of

Horridge and Wittwer (2008a) and Wittwer and Horridge (2010) demonstrate how consistent

sub-national SAMs of all 150 Australian federal single-seat electoral districts can be created

based on small-region census data. For the breakdown of the Austrian SAM, we also decided

to use a census-based approach.

Our sub-national CGE model is calibrated to the year 2011, by using the Austrian IO-table of

2011 (Statistics Austria 2015a). The economic sectors of the Austrian IO-table are classified

according to the ÖNACE 2008 classification. In fact, the sectors are classified as “ÖNACE

2008 division” sectors that are subcategories of the “ÖNACE 2008 section” sectors, which in

turn are subcategories of the primary, secondary, and tertiary sectors (Statistics Austria 2008).

The initial national Austrian SAM contains 74 economic sectors, two agents (the representative

household and the Austrian government), intermediate inputs, primary inputs (labor and

capital), taxes, investments, savings and depreciation, public transfers, and international trade,

but no regional detail and no detail regarding the Austrian energy sector, which is covered by

a single economic sector.

The initial Austrian SAM is in a first step aggregated from ÖNACE 2008 division sectors to

ÖNACE 2008 section sectors from sector A to T by adding up the respective division sectors

(Statistics Austria 2008; Statistics Austria 2015a), because regional GVA data (processed in

section 3.1.2), also used for the regional breakdown, is only available for ÖNACE 2008 section

classification. In a second step ten economic sectors, including the ÖNACE 2008 section

sector “electricity, gas, steam, and air conditioning supply” (D) and 9 other ÖNACE 2008

section aggregates (sectors 4-12 of Table 10), are aggregated by adding up the respective

ÖNACE 2008 section sectors.

To reach the desired energy sector level detail of “electricity, gas, steam, and air conditioning

supply”, we use data provided from Eurostat (2016) concerning lower level sectoral production

shares for the sectoral disaggregation into four energy sectors by deploying the RAS method

(Deming and Stephan 1940; Bacharach 1970; Trinh and Phong 2013), a SAM balancing

procedure. These four conventional energy sectors are electricity transmission, distribution,

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and trade (EL_TDT) (1a), generation of electricity – conventional mix (EL_CON) (1b),

manufacture and distribution of gas (MD_GAS) (2), and district heating (D_HEAT) (3).

Additionally, six renewable electric power generation sectors are established (1c-1h), which

are restricted in production by the energy potentials of Stanzer et al. (2010) and based on

technology cost estimates from a detailed bottom-up electricity sector model from Energy

Economics Group (2016) and data from Statistics Austria (2016) regarding their production

technology. Respective taxes are derived from §§ 5-12 ÖSET-VO 2016, BGBl II 2015/459

(Federal Ministry of Science, Research and Economy 2016), and the Austrian IO-table

(Statistics Austria 2015a).

For the reason of software and data limitations, a CGE model with too much sectors and

regions cannot be solved by the CGE model software and too much sectors and regions

reduce the clarity and transparency of a CGE model, we reduced the number of economic

sectors. Hence, we end up with the final national SAM of Austria, including the Austrian

representative household and eleven non-electricity sectors (2-12), eight electricity sub-

sectors (1a-1h) and one electricity aggregate sector (1) (See Table 10). This national SAM of

Austria has a structural form as shown in Figure 19, where rows represent income of agents

or production of sectors and columns represent agent’s demand or sectoral sales, but this

national SAM still does not comprise any regional detail, which requires further regional

secondary data for a sub-national breakdown. Based on the cluster analysis (section 3.2) and

our bottom-up sub-national multi-sectoral CGE model structure (section 5.2), the national SAM

of Austrian is broken-down for each cell, to determine sub-national values of sub-national

production and sub-national household consumption for our four model regions.

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Figure 19: Structure of the national SAM of Austria

The regional breakdown of the Austrian SAM requires different regional secondary data, such

as data concerning population and employment (Statistics Austria 2013b), GVA (discussed in

section 3.1.2), household consumption (Statistics Austria 2011), and international and inter-

regional trade flows (Statistics Austria 2015c). These datasets are disaggregated to

municipality level for each of the six non-energy, non-manufacturing sectors (4, 5 and 9-12),

while for the eight electricity sub-sectors (1a-1h) and the gas and heat sectors (2 and 3) we

use data of the ÖNACE 2008 section sector “electricity, gas, steam, and air conditioning

supply” (D) and for the manufacturing sectors (6-8) we use data of the ÖNACE 2008 section

sector “manufacturing” (C). As it is known to which model region each Austrian municipality

belongs, the Austrian total of each sector and therefore the respective shares of each model

region can be calculated. These shares are used to regionalize each cell value or to calculate

it from cell values compiled in this way by multiplying the respective cell value with the

respective share of each model region. The sum over all regions of a certain regionalized cell

value must then yield to the Austrian cell value, as shown in Figure 20.

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Figure 20: Regional breakdown of the national SAM of Austria

In this way, we disaggregate each table element (TE) of Figure 19 (TE11-TE 87) and obtain a

SAM for each model region and in total four sub-national SAMs. For these four sub-national

SAMs it is assumed that for each model region the production technologies of a certain

economic sector (1-12 and 1a-1h), including intermediate inputs, factor inputs, subsidies, and

taxes, is equal to the technologies of the national SAM of Austria. This means that production

inputs of a certain sector are equal in the four sub-national SAMs, while the absolute values of

production differ in accordance to shares of GVA. The non-electricity sectoral (TE11, TE31,

TE41, and TE51) and electricity sub-sectoral (TE12, TE32, TE42, and TE 52) production inputs

depend on the share of GVA of a model region in a certain sector. In contrast, the intermediate

inputs of electricity aggregate (TE23) is the sum of the electricity sub-sectors’ production.

The government income (TE95) of a certain model region is obtained by the regions sum of all

taxes. While the government and investments are modeled on national level within the CGE

model, the demand of the government (TE15 and TE35), taxes payed by the government

(TE45), demand for investments (TE16 and TE36), and the taxes for investments (TE46) are

nevertheless regionalized by the share of GVA of a model region in a certain sector to achieve

balanced regional SAMs. Additionally, the GVA is used to calculate the capital endowment of

a certain model region (TE54), as it is assumed that capital used for production is provided by

the households of these regions. Therefore, the capital endowment equals the capital inputs

of production for each region. The second part of the factor endowment, the labor endowment

(also TE54), is broken-down by the model region’s shares of Austria’s employed persons

obtained by census data (Statistics Austria 2013b).

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In addition, the labor and capital endowment as well as the unemployment benefits (calculated

by the model region’s shares of the Austrian labor force) and other transfers (calculated by the

model region’s shares of population) (both TE64) determine the income of the model region’s

households (TE94). The model region’s shares of Austria’s household income (sub-national

income divided by sum of sub-national income) in turn is used to breakdown the savings and

depreciation (TE76). The regionalized unemployment and other transfer benefits (TE 64),

savings and depreciation (TE 76), and net exports (TE87), determine the respective values of

unemployment and other transfer expenditures (TE65), savings and depreciation

expenditures(TE75), and adjustments of the net exports (TE85), which are all financed by the

government income (TE95).

The total consumption of the national private household for each sector is given from

aggregated values of the IO table (Statistics Austria 2015a), while the total consumption, the

sum over all sectors, of the sub-national representative private households (TE14 and TE34)

and the associated taxes (TE44) are again determined from the households income (TE94).

In contrast, the consumption vector, the respective shares of each sector of sub-national

household consumption, is determined by consumer survey data of the period from 2009 to

2010 (Statistics Austria 2011). To achieve for the household demand (TE14 and TE34) of each

sector (1-12 and 1a-1h) that the sum of the sub-national values equal the national values, while

keeping the sub-national consumption on the level obtained by the income of the model

region’s households (TE94), we again deploy the RAS method (Deming and Stephan 1940;

Bacharach 1970; Trinh and Phong 2013).

Inter-regional trade and international trade (TE17 and TE37) of sub-national regions do not

exist within a national SAM, but is required if economic links between different regions should

be covered within sub-national SAMs. While inter-regional trade flows are skipped and

international trade flows are modeled on national level within our CGE model, they are

regionalized in the four sub-national SAMs and additionally used to balance the four sub-

national SAMs. Starting from data of the Austrian freight transport statistics (Statistics Austria

2015c), shares of international and unadjusted inter-regional exports and imports of each

sector and region are received by the help of GVA and demand without net exports. To balance

inter-regional trade, the inter-regional imports are adjusted to equal the inter-regional exports

over the sum of all clusters. Finally, to balance each sub-national SAM and the whole

database, the inter-regional exports and imports are increased or reduced by the same share

to ensure that the value of sectoral production and sales is equal within a cluster. This last

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balancing step has the additional function to fulfil the condition of equal inter-regional exports

and imports in a certain sector within whole Austria.

5.4 Scenario Description

As mentioned in section 2.1.3, by achieving energy autarky or self-sufficiency it is meant,

according to Jamek et al. (2014), that the CEMs should try to attain their different RES

potentials to become sustainably independent of fossil fuels by still allowing trade of electricity

in every direction. Therefore, we aim to investigate the achievement of regional electricity

potentials from two different exogenously specified renewable electricity production targets in

the three CEM clusters, which should represent one ambitious and one less ambitious RES

scenario, compared to the baseline scenario of BAU 2020. Since we investigate the economic

consequences in 2020, the CGE model is calibrated to the year 2011, based on the latest

available Austrian IO-table of 2011, when we started modeling (Statistics Austria 2015a). For

calculating the baseline scenario of BAU 2020, the CGE model is then up-scaled by an

assumed GDP increase of 0.94% per anno until 2020 based on a forecast by the International

Monetary Fund (2016). We chose exogenously specified renewable electricity production

targets, as identifying an ideal technology mix is not a strength of CGE models for the reason

of missing real world supply constraints. Additionally, endogenously optimized RES production

would raise the question to what RES is optimized, to GDP, welfare, or something else.

Our four model regions differ, next to the differences in size and economic characteristics, in

their energy related characteristics, which comprise the current electricity production (Statistics

Austria 2015a; Statistics Austria 2016), the renewable electricity potentials (Energy Economics

Group 2016), and the share of electricity self-sufficiency (Stanzer et al. 2010), as shown in

Table 12. In this context, the aggregate regional production shares in 2011 differ greatly

compared to the regional electricity production shares from the conventional mix in the same

year. While the semi-rural and rural CEMs are about the same size, they are about 2.5 times

larger than the suburban CEMs and ten times smaller than the Rest of Austria regarding their

aggregate production share. In contrast, electricity production is relatively more important in

the semi-rural CEMs and in Rest of Austria.

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Table 12: Electricity in Austria and the model regions: status quo and potentials

Suburban Semi-rural Rural Rest of Austria Sum of Austria Aggregate production share in 2011

In % 3.8% 8.9% 8.4% 78.9%

Conventional electricity production in 2011 In GWh 769.27 5,359.33 4,049.09 52,083.68 62,261.37 In % 1.2% 8.6% 6.5% 83.7% 100%

Potential of renewable electricity in 2020 in GWh Small scale hydro 677.28 7,984.61 6,368.12 32,879.50 47,909.50 Large scale PV 14.81 115.38 135.23 478.59 744.00 Small scale PV 14.81 115.38 135.23 478.59 744.00 Wind 737.21 1,355.13 1,856.16 4,771.00 8,719.50 Biogas 541.15 2,914.74 4,684.35 12,083.76 20,224.00 Biomass 281.28 4,592.62 7,465.57 17,012.23 29,351.70 Sum 2,266.53 17,077.85 20,644.66 67,703.66 107,692.70

Share of electricity self-sufficiency in 2020 in % Small scale hydro 88.0% 149.0% 157.3% 63.1% 76.9% Large scale PV 1.0% 1.1% 1.7% 0.5% 0.6% Small scale PV 1.0% 1.1% 1.7% 0.5% 0.6% Wind 95.8% 25.3% 45.8% 9.2% 14.0% Biogas 70.3% 54.4% 115.7% 23.2% 32.5% Biomass 36.6% 85.7% 184.4% 32.7% 47.1% Sum 292.7% 316.5% 506.5% 129.1% 171.8%

Source: Own processed data based on Statistics Austria (2013b; 2015a; 2016), Energy Economics Group (2016) and Stanzer et al. (2010)

Model regions also differ regarding RES potentials and shares of electricity self-sufficiency, in

terms of covering regional production from regional renewable potentials. The share of

electricity self-sufficiency is the largest in the rural CEMs and smallest in Rest of Austria. In

relation to sectoral potentials, shown in Table 12, the suburban CEMs have especially

potentials in small scale hydro, wind, and biogas. The semi-rural CEMs could fully cover their

electricity production by small scale hydro and almost completely by biomass. The rural CEMs

are in contrast characterized by large potentials of small scale hydro, biogas, and especially

biomass.

Table 13 illustrates how producer prices (€/MWh), including taxes arising during production,

and their components differ for each electricity production technology in 2020. Compared to

the conventional technology, which is a mix of the existing depreciated production capacities

on the Austrian market, the renewable electricity sources need a huge amount of capital, as

the power plants have not been built yet. RES technologies are also relatively capital-intensive

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compared to fossil technologies, as they have none or at least smaller fuel costs. Additionally,

renewable technologies receive product subsidies (feed-in tariffs) in respect to §§ 5-12 ÖSET-

VO 2016, BGBl II 2015/459 (Federal Ministry of Science, Research and Economy 2016), but

the arising differences between subsidies payed for different RES technologies are evident.

While the total intermediate inputs are smaller for most technologies compared to conventional

electricity production, factor inputs of both labor and capital are larger for the renewable

electricity technologies. The technologies biogas and biomass, which are characterized by the

largest intermediate inputs but also the largest subsidies, are especially noticeable. However,

these two technologies are still different. Compared to biogas, biomass has considerably large

agriculture inputs, but receive lower subsidies, which leads to the lowest overall price of biogas

and the largest overall price of biomass in comparison with all other electricity technologies.

Table 13: Electricity production technologies and producer prices (€/MWh) in 2020 (selected intermediate inputs)

Conventional mix

Small scale hydro

Small scale PV

Large scale PV Wind Biogas Biomass

Intermediate Inputs 29.51 5.33 12.80 12.80 6.67 42.97 109.82 AGRICU 0.04 28.57 100.00 MINING 13.49 MANU_O 0.60 5.33 12.80 12.80 6.67 14.40 9.82 ELECTR 5.11 MD_GAS 7.45 Factor Inputs 19.60 41.68 80.01 70.41 68.44 61.11 49.95 Labor 5.30 8.00 3.20 3.20 12.38 9.60 9.82 Capital 14.31 33.68 76.81 67.21 56.06 51.51 40.14 Taxes 6.79 5.53 -9.37 -10.92 -13.93 -88.37 -61.10 Labor Tax 3.90 5.89 2.36 2.36 9.12 7.07 7.23 Capital Tax 2.07 4.88 11.14 9.75 8.13 7.47 5.82 Product Tax 0.82 -5.24 -22.86 -23.02 -31.17 -102.91 -74.15 Price (€/MWh) 58.37 52.55 83.45 72.30 61.18 15.71 98.67

Source: Data based on Energy Economics Group (2016) and §§ 5-12 ÖSET-VO 2016, BGBl II 2015/459 (Federal Ministry of Science, Research and Economy 2016)

Table 12 show the different regional shares of electricity self-sufficiency in 2020 and the

associated diverse producer prices, while Table 13 shows the subsidies paid by the

government. By considering the technological differences shown in these tables, a complete

achievement of regional electricity potentials until 2020 in all three CEM clusters would be

difficult to implement. Especially, if considering, inter alia, a fivefold production of 2011

electricity in the rural CEM cluster only from RES. For that reason, we follow the overall

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KLIEN’s CEM objective of a sustainable and independent energy supply from RES (Climate

and Energy Fund 2015b). Based on this objective we investigate two different scenarios by

considering a transition of the CEMs towards energy self-sufficiency.

The two scenarios are based on the available electricity potentials stated by Stanzer et al.

(2010) shown in (Table 12). Since electricity should be produced from different RES and the

suburban CEMs could produce a maximum of 2,266.53 Gigawatt Hours (GWh) from RES in

2020, whereof 677.28 GWh come from small scale hydro as shown in Table 12, in the

suburban CEMs renewable electricity production should come at a share of 29.9% (677.28

GWh/2,266.53 GWh) from small scale hydro. Analogically, the remaining proportion of

renewable electricity production should come from other RES technologies. Based on that, in

Scenario 1 each of the three CEM clusters produce 100% of their electricity production from

RES, as illustrated in Table 14, based on an exogenously specified renewable electricity

production target.

Table 14: Exogenous renewable electricity production target in the CEM model regions

Suburban Semi-rural Rural Rest of Austria Share of electricity self-sufficiency in 2020

In % 292.7% 316.5% 506.5% 129.1%

Share of electricity production in 2020 in Scenario 1 Small scale hydro 29.9% 46.8% 30.8% 0% Large scale PV 0.7% 0.7% 0.7% 0% Small scale PV 0.7% 0.7% 0.7% 0% Wind 32.5% 7.9% 9.0% 0% Biogas 23.9% 17.1% 22.7% 0% Biomass 12.4% 26.9% 36.2% 0% Renewable 100.0% 100.0% 100.0% 0% Conventional 0% 0% 0% 100%

Share of electricity production in 2020 in Scenario 2 Small scale hydro 17.3% 29.2% 30.8% 0% Large scale PV 0.4% 0.4% 0.7% 0% Small scale PV 0.4% 0.4% 0.7% 0% Wind 18.8% 5.0% 9.0% 0% Biogas 13.8% 10.7% 22.7% 0% Biomass 7.2% 16.8% 36.2% 0% Renewable 57.8% 62.5% 100.0% 0% Conventional 42.2% 37.5% 0% 100%

Source: Own processed data based on Statistics Austria (2015a; 2016), Energy Economics Group (2016) and Stanzer et al. (2010)

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The transformation of the electricity supply within ten years towards a 100% electricity supply

produced from RES within the CEMs would be hardly implementable. Such a transformation

of the electricity supply requires substantial government subsidies (see Table 13), which could

lead to negative feedback effects of decreased government tax income. Decreased

government income, in turn could lead to lower government spending and therefore lower

transfers to households. Hence, too ambitious electricity targets could have negative overall

economic effects.

Therefore, we additionally implement a second, less ambitious scenario. In Scenario 2, total

electricity production from RES in CEMs is on the one hand reduced compared to Scenario 1,

but considers still present potentials on the other hand. Therefore, in Scenario 2 the electricity

production targets in the suburban and semi-rural CEMs are reduced in accordance to their

electricity potentials compared to the potentials of the rural CEMs. Hence, while the shares of

the different RES technologies compared to total electricity production from RES should stay

relatively equal in Scenario 2, which means that suburban CEMs should still produce 29.9%

of renewable electricity from small scale hydro, the total renewable electricity is down-scaled

to 57.8% (292.7%/506.5%) of electricity production from RES in the suburban CEMs and in

the same manner to 62.5% (316.5%/506.5%) in the semi-rural CEMs. The production shares

of each RES technology and of the total electricity from RES in relation to total electricity

production is shown in Table 14 for all four model regions and for both scenarios.

5.5 Results: Economic Consequences of the CEM Energy Transition Approach

For the analysis of our bottom-up sub-national CGE model, we posed three research

questions: First, how does the achievement of a renewable electricity production target for

CEMs affect not only the different regions, but also the overall Austrian economy concerning

GDP, unemployment, sectoral output, and household consumption compared to a BAU

scenario in 2020? Second, which effects arise from fostering ambitious RES targets on the

public sector, for instance government revenues and expenditures, and how do the results

change by deploying a less ambitious RES target in CEMs with lower RES potentials? Finally,

how should future CEMs be characterized regarding their economic structure? Hence, we

investigate the economic consequences and the macro-economic spillover effects arising from

the achievement of a renewable electricity production target for CEMs, represented by three

CEM cluster model regions, in the two scenarios compared to BAU 2020 as defined in section

5.4.

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Since CGE models calculate prices relative to a numeraire good (see section 4.3.4), results

obtained from a CGE model can depend on the chosen numeraire. If the quantity of a good is

multiplied with the new price, which is relative to the numeraire good, results can be misleading

if price and quantity effects are not disentangled and individually considered. For this reason,

we illustrate all our results in monetary production quantities, as regional electricity production

and domestic sectoral production, or relative to BAU 2020 in percent.

Figure 21 shows the regional change of electricity output (without considering relative price

effects) from different electricity sectors, conventional (EL_CON), aggregated renewable

electricity generation (ELPR), electricity transmission, distribution, and trade (EL_TDT), and

the regional electricity production composite of the eight electricity sub-sectors (ELECTR)

compared to BAU 2020 in absolute terms of million (mio) euro. As shown in Table 14, the

shares produced from each electricity technology are given exogenously from our RES

electricity targets (ELPR), which implicitly give a conventional electricity target (EL_CON), but

also, due to the chosen Leontief production structure (see Figure 16), the share of electricity

transmission, distribution, and trade compared to total regional electricity production

(EL_TDT). While this exogenously specifies the new regional electricity production technology

for each region, the relative price of electricity changes endogenously in Scenario 1 and 2

compared to BAU 2020. These endogenous price effects additionally change the overall

electricity output (ELECTR) compared to BAU 2020.

EL_CON: Generation of electricity – conventional mix (sector 1b); ELPR: Regional renewable electricity production (1c-1h); EL_TDT: Electricity transmission, distribution, and trade (1a); ELECTR: Electricity (1)

Figure 21: Regional effects (without relative price changes) on electricity generation compared to BAU 2020 in mio €

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As shown in Figure 21, due to the new electricity technologies in the three CEM cluster model

regions and the associated endogenous price effects, an increase of electricity output from

renewable electricity generation (ELPR) in the CEM cluster model regions (Scenario 1: +591

mio €, Scenario 2: +456 mio €), but also a decrease of conventional electricity generation

(EL_CON) in all four model regions (Scenario 1: -623 mio €, Scenario 2: -477 mio €) can be

observed in both scenarios compared to BAU 2020. Also in Rest of Austria, due to spillover

effects of the national electricity price, conventional electricity generation (EL_CON) (Scenario

1: -26 mio €, Scenario 2: -16 mio €) and electricity transmission, distribution, and trade

(EL_TDT) (Scenario 1: -89 mio €, Scenario 2: -57 mio €) in both scenarios are decreasing. For

whole Austria, the aggregated regional electricity production (ELECTR), which is a composite

of the eight electricity sub-sectors, decreases (Scenario 1: -142 mio €, Scenario 2: -93 mio €)

according to price effects occurring from the new region-specific electricity technologies.

Figure 22 shows the regional change of the regional electricity production composite of the

eight electricity sub-sectors (ELECTR) compared to BAU 2020 in relative terms. Due to the

new electricity technologies in the different model regions, the electricity price is changing

endogenously as well, which effects the regional electricity aggregate (ELECTR) outputs. The

rural CEM cluster model region is with a decline in electricity production of -1.45% in Scenario

1 and -1.16% in Scenario 2 losing the most compared to BAU 2020, because it is the model

region with the highest proportion of biomass (Table 14), which is the most expensive

technology (Table 13). In contrast, the suburban CEM cluster model region expands its

electricity production by +0.05% in Scenario 1 and is cutting back its electricity production in

Scenario 2 by only -0.03% compared to BAU 2020, since it has the lowest proportion of the

expansive biomass electricity of all CEM model regions. Rest of Austria, which deploy 100%

conventional in both scenarios is cutting back its electricity production by -0.84% in Scenario

1 and -0.53% in Scenario 2 since the conventional electricity generation technology is more

expansive than the electricity mix of the suburban CEM cluster model region, with cheaper

small scale hydro and the heavily subsidized biogas technology (Table 13).

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Figure 22: Regional effects (without relative price changes) on electricity generation compared to BAU 2020 in %

The changes in the relative shares of electricity technologies in the electricity mix of the CEMs

generate additional spillover effects to other regional non-electricity production sectors (X

(esne, reg)) and hence on the domestic non-electricity production sectors (D (esne, reg)).

Figure 23 illustrates the effects on domestic sectoral output (D (es, reg)) in Austria (quantity

effect only) compared to BAU 2020 in relative and absolute terms. Figure 23 presents

additionally the sectoral production share in BAU 2020 in percent on the x-axis. Due to the

changed electricity production structure, the intermediate inputs in electricity production are

changing, in particular inputs of agriculture, forestry, and fishing (AGRICU) and other

manufacturing (MANU_O) are increasing, while inputs of mining and quarrying (MINING) and

manufacture and distribution of gas (MD_GAS) are decreasing (see Table 13). This leads to

direct effects on these economic sectors’ output levels compared to BAU 2020, as indicated in

Figure 23.

-2%

-1%

0%

1%

2%

SuburbanCEMs Semi-ruralCEMs RuralCEMs RestofAustria

changeto

BAU

2020in%

Scenario1

Scenario2

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Figure 23: Effects on sectoral output quantities at the national level compared to BAU 2020 in mio € and in %

Due to these changes in the input structure of electricity and the fact that unit costs of

renewable electricity technologies are higher in the semi-rural and rural CEM cluster model

regions than those of the conventional mix (again see Table 13), which is leading to higher

national electricity market price (Z (ELECTR, nat)), we can identify further indirect effects on

other economic sectors. In Scenario 1, compared to BAU 2020, the sector agriculture, forestry,

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and fishing (AGRICU) is the only sectoral winner (+271 mio €, +2.52%), while all other sectors,

especially the largest sectors other service activities (SERVIC) (-299 mio €, -0.16%) and other

financial, insurance, real estate, and trade activities (FIN_TD) (-375 mio €, -0.28%), are losing.

In Scenario 1, total output declines by -1,359 mio € and -0.22% compared to BAU 2020. These

negative effects, but also the positive of agriculture, forestry, and fishing (AGRICU), are

reduced by choosing a less ambitious RES target in Scenario 2, leading to a decline of

aggregate national output compared to BAU 2020 of -319 mio € and -0.05%. In addition, the

output of individual sectors is reduced less in Scenario 2 compared to Scenario 1; the output

of other manufacturing (MANU_O) (Scenario 1: -180 mio €, Scenario 2: +4 mio €), the second

RES electricity intermediate input, for example is even increased due to the less increased

price of electricity (ELECTR). Compared to their size, the output of the sectors manufacture

and distribution of gas (MD_GAS) (Scenario 1: -2.22%, Scenario 2: -1.62%) and mining and

quarrying (MINING) (Scenario 1: -2.02%, Scenario 2: -1.99%), which are the most important

intermediate inputs in conventional electricity production (EL_CON), are declining relatively

the most in percent terms in both scenarios.

Since each model region has the same production technology for a certain sector, except

electricity production (ELECTR) for which each model region employs a different generation

mix, the relative change in sectoral production of a certain sector is the same in each region.

In contrast, the changes in absolute terms are different due to regional difference in sectoral

size, which leads to different changes of in the four model regions compared to BAU 2020, as

shown in relative terms of the aggregate regional output quantities in Figure 24. While the

negative effects on total production quantities are smaller in Scenario 2 (-319 mio €) than in

Scenario 1 (-1,359 mio €), total production quantities are negative in both scenarios (Figure

23). Due to the differences of sectoral production shares within the different CEM clusters,

production is decreased the most in Rest of Austria in both scenarios (Figure 24), by -0.23%

in Scenario 1 and by -0.06% in Scenario 2. The reason for these decreases are indicated in

Figure 25, which shows the effects on sectoral output quantities at the regional level compared

to BAU 2020 in absolute terms as well as the production share of sector and region in BAU

2020. Rest of Austria has, compared to the other model regions, a low share of the sector

agriculture, forestry, and fishing (AGRICU) of 1.3% in BAU 2020, which is gaining +160 mio €

in Scenario 1 and +124 mio € in Scenario 2 compared to BAU 2020, but a high share of the

sectors distribution of gas (MD_GAS), mining and quarrying (MINING), and electricity

production (ELECTR) which are losing compared to BAU 2020 (Figure 25). In contrast, the

rural CEMs, the regions with the largest sectoral production share of agriculture, forestry, and

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fishing (AGRICU), which is gaining in all model regions in both scenarios, of 5.1% in BAU 2020

are the model region with the relatively lowest total production decrease in both scenarios,

where total output declines by -0.13% in Scenario 1 but even increases in scenario 2 by

+0.02% (Figure 24 and Figure 25).

Figure 24: Regional effects on total output quantity in the model regions compared to BAU 2020 in %

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Figure 25: Effects on sectoral output quantities at the regional level compared to BAU 2020 in mio €

90

Figure 26 shows the change of Austria’s GDP, unemployment, and total production level

compared to BAU 2020 in relative terms. The ambitious RES production target of Scenario 1

has more negative effects than the less ambitious RES production target of Scenario 2,

because electricity is becoming relative more expensive in Scenario 1 compared to BAU 2020.

Next to the exogenously specified electricity targets, the endogenously resulting changes of

relative output prices in the CGE model lead to a change of sectoral inputs in electricity

production in the CEMs but also in Rest of Austria, which subsequently has economic

consequences for the whole economy. The decreases in total national electricity output and

total national aggregated output already suggest negative macro-economic effects. These

negative macro-economic effects are confirmed in Scenario 1 with a decrease of total

production quantity in Austria by -0.22%, which consequently leads to a growth of

unemployment in Austria by approximately -0.73%. However, the unemployment growth is

relatively larger than the decrease of production quantity, which is explained by a production

decrease of the labor-intensive sectors of financial, insurance, real estate, and trade activities

(FIN_TD) (-0.28%)and other service activities (SERVIC) (-0.16%). When considering the

negative effects of increased unemployment and decreased domestic production quantity, a

negative GDP effect compared to BAU 2020 might be expected. However, this negative GDP

(+0.04%) does not occur due to endogenously increasing relative prices.

Figure 26: National effects on GDP, unemployment and aggregate output compared to BAU 2020 in %

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The results in Scenario 2 are different, since the decrease of production quantity is smaller

than in Scenario 1, with -0.05% compared to BAU 2020. While the production decrease is still

negative in Scenario 2, we observe positive employment effects of approximately -2.12%

compared to BAU 2020. The unemployment decrease in Scenario 2 is explained by a higher

input share of labor in the electricity production technologies, which are different to Scenario 1

as shown in Table 14, compared to BAU 2020 and only a minor decrease in production of the

most labor-intensive sectors financial, insurance, real estate, and trade activities (FIN_TD) and

(-0.11%) other service activities (SERVIC) (-0.01%), as shown in Figure 23. In Scenario 2, due

to a smaller decrease of aggregate output than in Scenario 1 and the increase of employment

compared to BAU 2020, the GDP effect is with +0.5% larger than in Scenario 1.

Figure 27 shows the Austria-wide effects on government income and spending, which are

modeled to be equal in total to ensure a balanced budget, compared to BAU 2020 in absolute

and relative terms. In Scenario 1, the required increase in subsidies for renewable electricity

production (see Table 13) to realize the exogenous RES targets in the two policy scenarios

reduce the overall output tax revenue of the government (-553 mio €, -4.3%) compared to BAU

2020. On the other hand, more primary factor inputs in renewable electricity production (ELPR)

compared to conventional electricity generation (EL_CON) lead to more tax revenues from

capital (+91 mio €, +0.54%), while labor (-42 mio €) and other tax revenues (+3 mio €) are

quite small. In total, the decreased output tax revenue of the government is not offset by other

tax revenues and result therefore in an overall negative government revenue of -501 mio €

and -0.4% compared to BAU 2020. The government spending is fixed in our CGE model to

the BAU 2020 level, which allows the government to substitute between different sectors but

keeps the total consumption quantities to the level in BAU 2020. This was done to prevent the

CGE model from substituting governmental demand and transfers to households, which can

affect the results strongly. Nevertheless, government demand slightly increases due to relative

price changes, while the absolute increase of government spending and the smaller

government income result in a decrease of unemployment benefits (-16 mio €) even while

unemployment increased by -0.73% compared to BAU 2020, which indicates less

unemployment benefits per unemployed person. Other transfers additionally decrease due to

smaller government income (-603 mio €).

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Figure 27: National effects on government income and spending compared to BAU 2020 in mio € and in %

In Scenario 2, the less ambitious regional RES targets lead to a weaker output tax revenue

decrease, of -386 mio € and -3%, compared to BAU 2020, than in Scenario 1 and result in an

increase of all other tax revenues, and an overall government revenue increase of +89 mio €

and +0.07% compared to BAU 2020. The government demand increases due to price effects

in Scenario 2 is stronger than in Scenario 1, while government transfers only decrease slightly

to keep the national budget balanced. In Scenario 1 total unemployment benefits decrease by

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-16 mio € while the number of unemployed people increase. In contrast, in Scenario 2 the

unemployment benefits, which are in absolute terms more than ten times smaller than the

other transfers, decrease in monetary values (-96 mio €) more than the other transfers (-61

mio €), due to the decreased unemployment.

Figure 28 shows the effects on regional welfare in percent of Hicksian equivalent variation

relative to BAU 2020. In Scenario 1, the welfare of private households expressed in % of

Hicksian equivalent in all model regions decreases compared to BAU 2020 due to decreased

income from different sources (factor income and transfers). For the reason of different

preferences of the individual regional representative private households with different income

from different sources, the welfare decreases vary between -0.21% in the suburban CEM

cluster and -0.36% in the rural CEM cluster. The reason why the suburban CEM cluster is the

regional winner concerning changes in welfare is that it is the model region with the largest

proportion of income from capital endowment, the factor, which is used more intensively in the

two scenarios compared to BAU 2020. In contrast, the rural CEM cluster is the model region

with the smallest proportion of income from capital endowment. In Scenario 2, we obtain similar

results regarding regional winners and losers, but the results are less negative than in Scenario

1 compared to BAU 2020. The representative household in the suburban CEM cluster (regional

winner) can increase its welfare by +0.02%, while the representative household in the rural

CEM cluster can reduce its losses to -0.16% (regional loser).

Figure 28: Effects on regional welfare in % of Hicksian equivalent variation relative to BAU 2020

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5.6 Discussion of CGE Model Results

In section 5.5, we presented the resulting regional and macroeconomic consequences of the

two different scenarios with exogenously specified RES electricity targets in the three CEM

cluster model regions. These RES electricity targets represent the CEM energy transition

approach within a bottom-up sub-national multi-sectoral CGE model of Austria. In this regard,

we show how the achievement of RES electricity potentials in CEMs have implications on the

whole economy through cross-sectoral spillover and macro-economic feedback effects in two

policy scenarios compared to BAU 2020. Hence, we analyze how the achievement of an

exogenously renewable electricity production target for CEMs not only affect the different

regions but also the overall Austrian economy concerning GDP, unemployment, sectoral

output, government revenues, and household consumption compared to a BAU scenario in

2020. Moreover, we show the different results by deploying an ambitious RES targets or a less

ambitious RES target in CEMs with lower RES potentials. In addition, we identify which

economic and energy related characteristics and technologies are most appropriate in

expending the CEM approach to new CEMs.

The exogenously specified and changed electricity production technology (input structure) of

both scenarios (a high ambition 100% RES Scenario 1 and a lower ambition Scenario 2) leads

to a higher overall electricity price compared to BAU 2020 and to a decline in Austria’s total

electricity production. Moreover, the changed aggregate electricity production technology has

negative demand effects on other economic sectors, since the increased RES proportion of

electricity production implies increased production inputs from labor and capital, but a declined

intermediate input share of economic sectors in both scenarios. Additionally, the total electricity

production is decreasing in both scenarios, due to higher electricity prices and hence a

decreased demand for electricity. Together, these effects and the macro-economic feedback

effects lead to a reduction of domestic output quantities, with only one sectoral winner in

Scenario 1 (agriculture, forestry, and fishing (AGRICU)) and two sectoral winners in Scenario

2 (agriculture, forestry, and fishing (AGRICU) and other manufacturing (MANU_O)). All other

sectors, in particular the sectors manufacture and distribution of gas (MD_GAS), mining and

quarrying (MINING), and electricity (ELECTR), are faced by a reduction of production (quantity

effects) in relative terms compared to BAU 2020.

Although the results revealed decreases in aggregate output, we see increases of the Austrian

GDP in both scenarios, which is partly driven by relative price effects. The changed regional

electricity production technologies and the associated price effects result in an aggregate

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output reduction (-1,359 mio €, -0.22%) in Scenario 1, which is leading to an employment

reduction of -0.73% due to the absolute largest output reduction in the labor-intensivist sectors

financial, insurance, real estate, and trade activities (FIN_TD) (-299 mio €) and other service

activities (SERVIC) (-375 mio €). The positive GDP effects are driven by price effects and are

larger in Scenario 2 than in Scenario 1 compared to BAU 2020, which is caused by a smaller

decline of aggregate output (- 310 mio €, -0.05%) and in particular by a smaller decline of the

labor-intensive sectors financial, insurance, real estate, and trade activities (FIN_TD) (-24 mio

€) and other service activities (SERVIC) (-150 mio €). These smaller negative output effects

lead in Scenario 2 to an increase of employment of +2.12% compared to BAU 2020.

Despite reduced aggregate output in both scenarios, we face regional winners and losers. The

region with the smallest aggregate output reduction in Scenario 1 and the sole output

increasing region in Scenario 2 is the rural CEM cluster model region, which profits in both

scenario from its relatively large share of agriculture, forestry, and fishing (AGRICU) compared

to total production. In contrast, Rest of Austria, the region with the largest share of manufacture

and distribution of gas (MD_GAS), mining and quarrying (MINING), and electricity (ELECTR),

receives the largest decline of sectoral production in relation to BAU 2020. Concerning

changes on household welfare (Hicksian equivalent variation relative to BAU 2020), we identify

the households in the suburban CEM cluster model region as regional winners, which can

improve their welfare in Scenario 2 by +0.02% and reduces their welfare in Scenario 1

with -0.21% least compared to BAU. The rural CEM cluster households are the regional losers

in this regard, which are in both scenarios faced by the largest welfare decreases (Scenario

1: -0.36%, Scenario 2: -0.16%).

Investigating the public sector, government revenues decline in Scenario 1 (-0.4%), driven by

RES subsidy payments and negative output effects. The opposite effect occurs in Scenario 2,

the scenario with less RES subsidies due to less ambitious RES targets. The less ambitious

RES targets in Scenario 2 lead to increased total government revenues (+0.07%), while factor

tax revenues also increase due to an increased factor input in the regional electricity production

technologies of the CEM clusters. The overall increased government revenues in Scenario 2

allow an increase of government spending, which stimulate consumption and production.

In Scenario 2, the economy is faced by less negative effects of electricity and aggregate output

and more positive effects on employment and GDP, compared to BAU 2020, than in Scenario

1. Summarized, by considering the effects of an ambitious RES target (Scenario 1) compared

with a less ambitious RES target (Scenario 2), we find more positive effects in all parts of the

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Austrian economy in Scenario 2, since the less ambitious RES targets affect the relative price

of electricity to a lesser extent, which leads to smaller aggregate output reductions, positive

employment effects, and overall less negative cross-sectoral spillover and macro-economic

feedback effects than in Scenario 1 compared to BAU 2020. Thus, we recommend from an

economic perspective, under the used CGE model assumptions (which include the given

economic and political circumstances including the current RES subsidies) and without

detailed discussion of the scenarios weaknesses (which will follow in the below paragraphs),

the less ambitious RES targets of Scenario 2, while from an environmental perspective the

ambitious RES targets of Scenario 1 are preferable.

The CEM approach is primarily focused on rural and structurally weak regions (Climate and

Energy Fund 2015b), which best describes our rural CEM cluster. This rural CEM cluster model

region is identified by our CGE analysis as the only region which is increasing its regional

aggregate production in the case of a less ambitious RES target (Scenario 2) and which is

experiencing the smallest regional production decline in the case of an ambitious RES target

(Scenario 1). Concerning the regional aggregate production, the other two CEM clusters are

faced in both scenarios by worse negative effects in relative terms. Hence, by pursuing a

regional development strategy of fostering RES deployment in small scale regional units, under

the investigated circumstances, it would be most promising to choose this strategy in rural

regions with a large share of agriculture, forestry, and fishing (AGRICU) production. The

reason is that a large part of the Austrian RES potentials, in particular in the CEMs identified

as rural and semi-rural, are biogas and biomass, which require fuel inputs from the agriculture,

forestry, and fishing (AGRICU) sector. Thus, when including new regions within the CEM

approach, it would be most promising to choose rural regions with a large share of agriculture,

forestry, and fishing (AGRICU) production.

This thesis provides an approach to assess the economic effects of exogenous RES targets

aiming at a more environmentally friendly electricity production within the CEMs, but does not

choose the macro-economically most effective RES electricity mix endogenously. The

producer prices of the individual employed electricity production technologies reveal

substantial differences concerning output subsidies (feed-in tariffs), which is a competitive

advantage especially for biogas and biomass compared to other RES technologies. These

subsidies should be reviewed critically, in particular concerning the relatively large subsidies

of biomass compared to other RES technologies, which is despite these subsidies the most

expensive technology. Since biomass represents a large part of the RES electricity potentials

in the rural and semi-rural CEM cluster model regions, the model regions which reduce

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electricity production the most, the expensive biomass technology should be avoided for

electricity production and should be offset by more competitive RES technologies.

This study should not be seen as a forecast of the future. However, this study shows the

direction the Austrian economy can go under the specified circumstances, the current

subsidies, and the recent economic and political conditions of deploying the selected RES

technologies in CEMs. Hence, it should be mentioned that the presented results in this thesis

draw a picture of possible consequences of the CEM approach by a certain share of available

RES technologies, which depend on the RES potentials, within the CEMs only (Scenario 1 and

Scenario 2), including the strongly subsidized but still uncompetitive biomass technology.

Nevertheless, the results reveal negative output quantities by an achievement of RES

potentials compared to a BAU 2020 scenario, which can change, if the RES subsidies and the

increased use enhance technological advancement and considerable cost declines. In this

regard, further analysis should investigate the individual effects of different RES electricity

technologies, changed technology-mixes using less biomass, RES deployment in rural (CEM)

model regions of Austria only, but also the individual effects in a case of harmonized output

subsidies to different technologies.

In section 4.1, we have identified that CGE modeling is an appropriate economic modeling

technique to deal with regional economic policy analysis. We additionally identified

methodological and data related weaknesses of sub-national CGE models in section 4.4. CGE

models have the advantage of reflecting cross-sectoral spillover and macro-economic

feedback effects, which enable the identification of otherwise often overseen processes and

drivers. CGE models are less dependent on data requirements than econometric models. As

CGE models are calibrated to SAMs, based on IO-tables, data availability is especially a

limitation for sub-national CGE models as SAMs or IO-tables are often not available. In our

case, the availability of sufficient secondary data is given, although the accuracy of the data is

reduced by data processing based on secondary data compared to a consistent database. Our

bottom-up sub-national multi-sectoral CGE approach is determined by the CEM approaches’

characteristics, such as the geographic location of the CEMs distributed throughout Austria,

which restricts our approach. The geographic location of the CEMs speaks for a bottom-up

sub-national multi-sectoral CGE model of Austria, where inter-regional trade is excluded and

we decide for mobile labor and capital within Austria.

Concerning available RES technologies and the feasibility of Austria to become energy self-

sufficient, Stanzer et al. (2010) show that Austria has sufficient RES potentials at its disposal

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and Streicher et al. (2010) demonstrate that energy transition until 2050 is possible for Austria.

Although only a few low scale regional bottom-up energy transition approaches exist and

specialized studies are rare, other economic analyses of studies of the CEM approach are

available in literature. In this regard, the CGE analysis of Kettner et al. (2012) shows an

increase in GDP and employment in Austria, while there are substantial differences between

federal states, which can also be negative. The findings of Kettner, Köppl, and Streicher

(2015), who use an IO model, are similar and show an increase in labor force and GVA.

Sub-national bottom-up analyses and in particular sub-national bottom-up CGE analyses are

rare. However, sub-national CGE models are identified by Partridge and Rickman (2010) as

most appropriate to investigate cross-sectoral economic spillover effects from bottom-up

energy transition approaches. Sub-national CGE approaches are especially depended on the

respective CGE model assumptions, the representation of sectors and regions, and the spatial

linkages (Rodriguez 2007; Partridge and Rickman 2010). In this context, Kettner et al. (2012)

have only a weak representation of the CEMs characteristics including their strengths and

weaknesses regarding energy potentials and economic characteristics, as they do not model

the Austrian energy sector in detail and only scale-up individual targets to federal state level.

Concerning the regional and sectoral energy related detail of our CGE approach, including the

regional economic structure, the sectoral electricity disaggregation, and the individual RES

potentials, we set ourselves apart from Kettner et al. (2012). In addition, we particularly

consider budget effects from associated government subsidies on RES technologies.

While we differentiate strongly in detail from Kettner et al. (2012), the findings of Scenario 2

are similar to the findings of Kettner et al. (2012) regarding GDP and employment increase.

Since the potential employment, output, and GDP increase from fostering renewable electricity

technologies is by far not exhausted in Scenario 2, an optimization of subsidies on electricity

from RES can even lead to substantial increase of the respective results. An optimization of

subsidies toward subsidies on already more competitive technologies as small scale hydro,

wind, and large scale PV could be the most promising strategy. The obtained positive socio-

economic results of Kettner et al. (2012), Kettner, Köppl, and Streicher (2015), and our

Scenario 2 confirm the statement that the CEM approach can be a no-regret strategy. This can

become true if there is an appropriate focus, for instance on the right economic competitive

technologies and a level playing field between RES technologies and conventional fossil fuel

technologies, such as carbon pricing, can be achieved. Keeping this in mind, pursuing RES

deployment in structurally weak regions can lead to a regional increase of employment and

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aggregate output, which can potentially result in overall economic and environmental benefits

in Austria through multiplier effects.

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6 Summary and Conclusion

In this thesis, we posed three basic research questions to identify the CEMs feasibility to

become energy self-sufficient, the characteristics of suitable future CEMs, and the cross-

sectoral and macro-economic feedback effects arising by energy self-sufficient CEMs. First,

which economic framework conditions affect the CEM’s feasibility to achieve energy self-

sufficiency? Second, how should a CEM be characterized regarding economic and energy

related properties to achieve the highest possible environmental and economic benefits by

limited financial resources? Third, how does an increased RES deployment of CEMs affect not

only the different CEMs, but also the overall Austrian economy?

To investigate these research questions, we start our analysis after an introduction in chapter

1 with a literature review of the CEM program in chapter 2, including its goals, procedures, the

different regions, and other energy transition approaches in literature. While in January 2016

already 107 CEMs were active in Austria, in November 2015, when we started our analysis,

we identified 82 CEMs, which were active and published an implementation concept. The

literature review shows that CEMs are distributed throughout Austria. While the CEM approach

is further developed since 2009, its profile has been enhanced since then. It also becomes

obvious that CEM funding from public authorities is limited, which requires co-funding from

other entities. Additionally, we show that the term “energy autarky” disappears in connection

with the CEM approach, as it is replaced by “energy transition” or “energy self-sufficiency”.

In chapter 3, we identify the economic characteristics of the 82 analyzed CEMs. It becomes

obvious how heterogeneous the different CEMs are. This is not only true regarding their size

of area or number of inhabitants, but also regarding their shares of GVA and employed persons

in the primary, secondary, and tertiary sector. To complete our analysis in connection with our

first research question, we take the degree of urbanization and energy potentials of the CEMs

into account. In addition to the heterogeneity obtained from the geographic sizes and economic

structures of the CEMs, we find that the CEMs are also heterogeneous regarding their

definition as rural or suburban regions and that energy self-sufficiency is possible for electricity,

but not for heat until 2020 for all CEMs together.

Thus, as we are interested in different types of CEMs, which are homogenous within but

heterogeneous across clusters, we decide to conduct a cluster analysis. By doing this, we

identified three CEM clusters, a “suburban” cluster, which is small concerning its number of

CEMs included, number of inhabitants, and GVA, and two, concerning these characteristics,

larger “semi-rural” and “rural” clusters. The CEMs together account for about 25% of Austria’s

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population and GVA. We ascertain that the suburban cluster provides the lowest potentials to

become energy self-sufficient, as it is characterized by only a small primary sector with a small

production of agriculture and forestry products that can be used for RES production. In

contrast, the semi-rural and rural clusters are more similar. The rural cluster can be

characterized as structurally weak, which complies with the definition of CEMs by the KLIEN.

While the rural and semi-rural clusters differ in the economic structure regarding shares in

primary and secondary sector and their heat potentials, they are both feasible to achieve at

least electricity self-sufficiency until 2020.

The simultaneous achievements of RES potentials in CEMs, which are distributed throughout

Austria, require an appropriate economic policy analysis technique to achieve the

consequently arising cross-sectoral economic spillover and macro-economic feedback effects.

In section 4, we identify the CGE approach as most suitable, based on a literature review of

the CGE history, its theoretical background, strengths, and weaknesses. CGE models can be

grouped in global, national, and sub-national CGE models, which differ especially regarding

modeling of trade and factors. We argue that a sub-national CGE approach is the best choice

to answer our third research question and to identify socio-economic effects from deploying

RES technologies in the Austrian CEMs. In addition to the basic CGE approach, we analyze

the sub-national CGE approach and the methodological and data related challenges.

Based on the CGE literature review and to depict the characteristics of the CEM approach as

accurately as possible, we decided to deploy a bottom-up sub-national multi-sectoral CGE

model of Austria and make use of the small open economy assumption with Armingtion trade.

Our CGE model is sub-national, as regional production and regional household consumption

is regionalized, while we model only one government, mobile factors across Austria, and no

inter-regional trade flows. The CGE model comprises four model regions, which are the three

CEM clusters and a fourth, Rest of Austria model region, which covers the remaining

municipalities of Austria. We decide to use the bottom-up sub-national CGE approach, full

factor mobility and no inter-regional trade, to cover the cross-national distribution of CEMs and

other municipalities across model regions.

In chapter 5, we describe the structure and the challenges of our bottom-up sub-national multi-

sectoral CGE model in detail, including data processing to obtain sub-national SAMs and the

selection of two policy scenarios. The two policy scenarios differ in their ambition of

exogenously specified RES electricity targets in the three CEM cluster model regions, resulting

in an ambitious RES scenario (Scenario 1) and a less ambitious RES scenario (Scenario 2).

102

These exogenously specified renewable electricity production targets are chosen, because

CGE models are limited in their capability to represent real world regional RES supply

constraints and hence to identify an economically optimal as well as technologically feasible

future RES mix.

Finally, we present the results of the CGE model assessment of our two policy scenarios,

compared to a BAU 2020 scenario. We find that fostering RES electricity technologies in the

Austrian CEMs leads to positive GDP effects in both scenarios triggered by cross-sectoral

spillover and macro-economic feedback effects. While the positive GDP effects are partly

driven by relative price effects, we obtain negative effects on aggregate production quantities

in Austria in both scenarios. Concerning employment effects, we found negative effects in

Scenario 1 and positive employment effects in Scenario 2. Scenario 2 provides relatively more

positive effects for GDP and less negative effects for aggregate output than Scenario 1

compared to BAU 2020.

Next to these national effects, we identify regional and sectoral winners and losers compared

to BAU 2020. While aggregate output is reduced in both scenarios, the output of agriculture,

forestry, and fishing (AGRICU) is increased in all regions and scenarios, while the production

of other manufacturing (MANU_O) is increased in Scenario 2 only. All other sectors lose in all

other scenarios and regions. The sectoral losers are especially manufacture and distribution

of gas (MD_GAS), mining and quarrying (MINING), and electricity (ELECTR) in relative terms.

In both scenarios, the rural CEM cluster is performing the best and Rest of Austria the worst

regarding changed aggregate production relative to BAU 2020. While the rural CEM cluster is

least affected by losses in Scenario 1, they can even increase their aggregate production in

Scenario 2. It is in this context no surprise that the rural CEMs are the regions with the largest

share of aggregate production from the agriculture, forestry, and fishing (AGRICU) sector. In

contrast, Rest of Austria as the regional loser is the model region with the largest shares of

manufacture and distribution of gas (MD_GAS), mining and quarrying (MINING), and electricity

(ELECTR). Regarding regional household welfare, we find less negative effects in the

suburban CEM cluster model region, which is endowed by a large proportion of capital, the

factor, which becomes more employed in both scenarios. The households of the semi-rural

and rural CEMs are negatively affected, due to smaller proportions of capital endowment.

Our results show that the fostering of RES technologies leads to an increase in GDP (both

scenarios) and employment (only Scenario 2), and environmental positive effects, through

decreased CO2 emissions in electricity production, while we are faced by sectoral and regional

103

trade-offs including sectoral and regional winners and losers. We find that rural regions, where

the agriculture, forestry, and fishing (AGRICU) sector is more important than in other regions,

can benefit the most from fostering biogas and biomass electricity with agriculture and forestry

fuel inputs. As the CEM approach particularly mentions rural and structurally weak regions as

target regions for new CEMs, our findings are in line with their outlined strategy. Considering

the identification of some of the active CEMs as rather semi-rural or even suburban, we

propose for the future to choose regions as new CEMs, which are characterized by a more

rural economic structure, where especially agriculture and forestry play important roles. In

addition, we would recommend avoiding the present biomass technology in the electricity

production, since it is the least competitive RES technology of our analysis, despite it receives

large subsidies. While this avoidance will reduce the positive output effects of the agriculture,

forestry, and fishing (AGRICU) sector, these positive effects will not disappear as long as

biogas will be used in a large proportion in electricity production.

In this context, it is important to mention that our results do not provide an optimization of the

electricity mix concerning RES improvement, which means that the CEM approach, and

fostering RES technologies, have additional potentials to increase socio-economic positive

effects compared to Scenario 2. Nevertheless, to limit the negative overall economic effects of

fostering RES deployment in the CEMs, we can recommend the less ambitious RES targets

(Scenario 2) compared to an ambitious RES target (Scenario 1). However, this

recommendation does not mean that less electricity from RES is always preferable, since we

found an increase in GDP in both scenarios compared to the scenario without RES deployment

(BAU 2020). Although, the CEM approach is not identified as no-regret strategy yet, we have

shown that the CEM approach is well suited as rural development policy to foster

environmental sustainability and economic growth in rural and structurally weak regions.

Finally, the question arises how the economic effects of this energy transition approach can

be improved. In this context, especially future technological changes and the selection of more

economically competitive technologies, can lead to overall positive economic effects, while

deploying an environmental preferable ambitious RES target. Here we suggest for future

research the calculation of effects of individual technologies with the CGE model solely, and

an identification which technology contributes the most to positive socio-economic results.

Additionally, it should be tested if a changed feed-in-tariff regime promoting more economically

competitive technologies or carbon pricing in BAU 2020 can improve the macroeconomic

consequences of strong RES development, in terms of GDP, employment, and sectoral

104

production. Future research should also include other energy sectors, such as heat and

mobility.

105

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