Computational General Equilibrium Modelling...

Computational General Equilibrium Modelling Assessment Nuclear Fuel Cycle Royal Commission

Attorney-General’s Department

Government of South Australia

May 2016

Nuclear Fuel Cycle Royal Commission Computational General Equilibrium Modelling Assessment EY i

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Nuclear Fuel Cycle Royal Commission Computational General Equilibrium Modelling Assessment EY ii

Mr. Ashok Kaniyal Technical Research Officer Nuclear Fuel Cycle Royal Commission Attorney-General’s Department Government of South Australia

Level 5, 50 Grenfell Street Adelaide SA 5000

Via email: [email protected]

Dear Ashok

5 May 2016

Computational General Equilibrium Modelling Assessment

Thank you for the opportunity to prepare the Economic Assessment of Nuclear Fuel Cycle (“Study” or “Report”) for the Nuclear Fuel Cycle Royal Commission. In accordance with our engagement agreement dated 24 August 2015, we are pleased to present you with the findings from this Study.

The report has been constructed based on information current as of January 2016 (being the date of completion of the economic modelling), and which has been provided by the business cases commissioned for this study and by the Commission. Since this date, material events may have occurred since completion, which are not reflected in the report.

This report may be relied upon by the Commission for the purpose of understanding the potential economic impact of nuclear fuel cycle activities on the South Australian economy. It should not be relied upon for any other purpose. Other persons accessing this report should do so for their general information only as EY has only acted for, and advised the Commission, and has not acted for or advised anyone else in respect of the contents of this report. EY disclaims all liability to any party for all costs, loss, damage and liability that the third party may suffer or incur arising from or relating to or in any way connected with the provision of the deliverables to the third party without our prior written consent. Any commercial decisions taken by the Commission and third parties are not within the scope of our duty of care and in making such decisions you should take into account the limitations of the scope of our work and other factors, commercial and otherwise, of which you should be aware of from sources other than our work.

EY has prepared this economic assessment in conjunction with, and relying on, information provided by the Commission and business cases commissioned for this study. We do not imply, and it should not be construed that we have performed audit or due diligence procedures on any of the information provided to us. We have not independently verified, or accept any responsibility or liability for independently verifying, any such information nor do we make any representation as to the accuracy or completeness of the information. We accept no liability for any loss or damage, which may result from your reliance on any research, analyses or information so supplied.

It should also be noted that this assessment does not constitute a Cost Benefit Analysis. Further, it is important to note that the identification of economic impact is not a precise science.

Yours sincerely

Craig Mickle Jyothi Gali Partner Director

mailto:[email protected]

Nuclear Fuel Cycle Royal Commission Computational General Equilibrium Modelling Assessment EY iii

Table of contents

1. Executive summary ....................................................................................................... 1

1.1 Key findings ............................................................................................................ 1

1.2 Background ............................................................................................................. 2

1.3 Key questions .......................................................................................................... 2

1.4 Scenarios ................................................................................................................ 2

1.5 Economic impact of nuclear fuel cycle investments ..................................................... 4

2. Context for CGE modelling assessment .......................................................................... 14

2.1 The project ............................................................................................................ 14

2.2 Purpose ................................................................................................................ 14

2.3 Our approach and framework .................................................................................. 14

2.4 Understanding the CGE modelling assessments ........................................................ 17

2.5 Structure of this report .......................................................................................... 19

3. Carbon abatement scenarios: baselines for NFC business cases ....................................... 20

3.1 Description of scenarios ......................................................................................... 20

3.2 Carbon abatement assumptions .............................................................................. 23

3.3 Abatement task in carbon policy scenarios ............................................................... 24

3.4 Carbon prices ........................................................................................................ 24

3.5 Emission Reduction Fund ........................................................................................ 25

3.6 Economic impact of carbon mitigation ..................................................................... 27

4. Nuclear fuel cycle business case inputs .......................................................................... 30

4.1 Expansion of the radioactive minerals extraction industry ......................................... 30

4.2 Establishing a set of further processing facilities ...................................................... 31

4.3 Establishing integrated radioactive waste storage and disposal facilities ..................... 36

4.4 Establishing a nuclear power plant and systems in South Australia ............................. 44

5. Electricity modelling inputs and results .......................................................................... 50

5.1 Electricity demand ................................................................................................. 50

5.2 Fuel prices ............................................................................................................ 55

5.3 Carbon price.......................................................................................................... 56

5.4 Technological assumptions ..................................................................................... 56

5.5 Renewable Energy Target ....................................................................................... 59

5.6 Capacity in South Australia ..................................................................................... 60

5.7 Long term capacity mix planning ............................................................................. 61

5.8 Generation ............................................................................................................ 64

5.9 Wholesale electricity prices ..................................................................................... 66

5.10 Emissions .............................................................................................................. 67

5.11 Summary .............................................................................................................. 68

6. Economic impacts of establishing nuclear fuel cycle activities in SA ................................. 70

6.1 Uranium mining expansion ...................................................................................... 70

6.2 Further processing facilities .................................................................................... 74

6.3 Radioactive waste storage and disposal facilities ...................................................... 79

6.4 Nuclear fuel leasing arrangements .......................................................................... 86

6.5 Nuclear power generation ...................................................................................... 92

Appendix A EY Computational General Equilibrium Modelling Approach ................................. 97

A1 Main features of VURM .......................................................................................... 97

Nuclear Fuel Cycle Royal Commission Computational General Equilibrium Modelling Assessment EY iv

A2 Operationalising the nuclear Fuel cycle in VURM ...................................................... 99

A3 Key behavioral relationships in the VURM ............................................................. 100

A4 Electricity generation industry in the VURM .......................................................... 100

A5 Electricity demand elasticity ................................................................................ 101

Appendix B Detailed macroeconomic assumptions.............................................................. 102

B1 Demography ....................................................................................................... 102

B2 Labour market assumptions ................................................................................. 103

B3 Productivity and technical change assumptions ..................................................... 106

B4 Macroeconomic assumptions ............................................................................... 107

B5 Terms of trade and commodity price assumptions ................................................. 108

Appendix C: Electricity generation technology assumptions .................................................... 111

C1 Generation technologies ...................................................................................... 111

C2 Capital cost estimates ......................................................................................... 111

C3 Variable Operating and Maintenance cost estimates ............................................... 112

C4 Fixed Operating and Maintenance cost estimates ................................................... 112

C5 Thermal efficiency .............................................................................................. 113

C6 Levelised cost estimate ....................................................................................... 113

Appendix D: EY electricity modelling ..................................................................................... 115

D1 Introduction ....................................................................................................... 115

D2 Long Term Integrated Resource Planner ............................................................... 116

D3 2-4-C® ............................................................................................................... 116

Appendix E: GST impacts ..................................................................................................... 118

E1 Background ........................................................................................................ 118

E2 GST effects in NFC investment scenarios ............................................................... 120

E3 GST effects of developing radioactive waste storage and disposal facilities .............. 124

E4 GST effects of developing a set of further processing facilities ................................ 125

E5 GST effects of developing nuclear power plant facilities.......................................... 126

Glossary ............................................................................................................................. 128

Nuclear Fuel Cycle Royal Commission Computational General Equilibrium Modelling Assessment EY 1

1. Executive summary

There are opportunities across the nuclear fuel cycle which should remain under consideration by South Australia, so that the option of participating at a later date remains open.

1.1 Key findings

The computational general equilibrium (CGE) modelling assessment suggests that:

► There is likely to be substantial growth in the global demand for Uranium ore. This growth will be driven by global carbon abatement policies which are expected to lead to more than a doubling of nuclear electricity generation by 2040. This will occur regardless of:

► The carbon abatement policies Australia chooses to adopt over the longer term; ► Whether South Australia (SA) chooses to participate in the downstream stages of the

nuclear fuel cycle (NFC).

► SA is likely to be a major beneficiary of the global growth in Uranium ore demand (e.g. Australian exports could potentially increase by more than 3 times by 2040). This can occur under existing policies, assuming new mining activity and exports are permitted.

► The growth in the use of nuclear fuel globally, and the need to find long term solutions for the storage and disposal of radioactive waste, presents significant opportunities for SA in the downstream stages of the NFC. These opportunities will arise regardless of:

► The carbon abatement policies Australia chooses to adopt over the longer term; ► Whether nuclear electricity generation occurs in Australia.

► There are economic benefits associated with SA becoming involved in the further processing of

Uranium ore, although these are likely to be of a much smaller magnitude than those associated with radioactive waste storage and disposal.

► The economic benefits of SA becoming involved in the storage and disposal of radioactive waste are likely to be particularly significant. Over the longer term, that industry could be as large as SA’s utility (i.e. electricity, gas and water) sector.

► The economic benefits of involvement in the processing, storage and disposal stages of the NFC are likely to counteract some of the expected negative economic impacts of carbon abatement policies. The economic benefits are also likely to be of particular importance to regional SA, as this is where most of the activity and employment would occur.

► Nuclear fuel leasing should be considered in further detail as it may increase the economic benefits of being involved in the processing, storage and disposal stages of the NFC, as opposed to conducting these activities in isolation. The economic analysis undertaken here has not been specific enough to capture any additional benefits of fuel leasing. However, it would be reasonable to expect additional economic benefits to arise because of the value that the end-to-end service would provide to fuel users (i.e. removal of a key uncertainty). It may also increase the size of the market for nuclear fuel.

► The case for SA entering into nuclear electricity generation does not appear to be strong. This reflects the relative economics of nuclear electricity generation, even under high carbon prices, and the changes that are occurring in the electricity market, which are undermining the economics of less flexible sources of electricity generation.

► The economic analysis of the electricity generation stage of the NFC, however, involves particular uncertainties. The electricity market is undergoing substantial change as a result of higher network costs, technological developments in distributed generation and end use, and government support to renewable electricity generation. The outcomes of this process are highly uncertain and there are plausible scenarios in which nuclear electricity generation has a role in the Australia’s electricity market. For instance, if deeper reductions in carbon emissions


are required sooner whilst maintaining electricity supply reliability, or if the expected increase in global nuclear electricity generation leads to material capital cost reductions. In these circumstances, an options based approach to assessing the potential future role of nuclear electricity generation is likely to be appropriate. The economic analysis undertaken here does not suggest that the option should be closed off.

1.2 Background

The Nuclear Fuel Cycle Royal Commission (the Commission) has been established by the South Australian Government (the Government) to examine whether the State should expand its participation in the nuclear fuel cycle (NFC), and to consider the associated risks and opportunities. This expansion could either involve increasing existing activity in the exploration and extraction of radioactive minerals (mining), or entering the other parts of the NFC. In particular, the latter could involve SA becoming involved in:

► The conversion and enrichment (further processing) of Uranium ore; ► Nuclear electricity generation (nuclear generation); and/or ► The management, storage and disposal (storage and disposal) of radioactive waste.

The Commission has engaged three consultants to prepare business cases in order to assess the commercial merits of investment in these parts of the NFC. The work involves examining, at a high level, the associated private costs, risks, required returns and financing issues of each part of the NFC.

The Commission has engaged EY to undertake Computational General Equilibrium (CGE) modelling to assess the potential economic impacts on the SA economy that would result from additional investment in any part of the NFC. This modelling uses Victoria University Regional Model (VURM) and information from the NFC business case analyses (e.g. cost and revenue data) to estimate the wider economic costs and benefits associated with the investments.

1.3 Key questions

Our approach is focused on answering the key questions that the Commission put forward for the CGE modelling to answer. They are:

1. What is the impact of expanding the mining of Uranium, in continuation of the prohibition of all other activities in the NFC (other than mining) in SA?

2. What is the impact of developing a set of further processing (e.g. conversion, enrichment) facilities on the SA economy and the Australian economy?

3. What is the impact of developing radioactive waste storage and disposal facilities on the SA economy and the Australian economy – incorporating an assumed level of royalty?

4. What is the impact of integrating the front end of the NFC with the waste storage facility component as part of a fuel leasing arrangement?

5. Assuming nuclear power plant — Small Modular Reactor (SMR) and large Light Water Reactor (LWR) — is proactively introduced through government policy into the SA region of the National Electricity Market (NEM). What would be the impact on the NEM, SA economy and greenhouse gas emissions?

1.4 Scenarios

The economic impacts of additional investment in different stages of the NFC are assessed under one of three different Investment Scenarios, which differ in terms of both assumed carbon dioxide emissions abatement (carbon abatement) policies and domestic nuclear policy settings (i.e. the NFC activities permitted/supported).

Different climate action baselines are used for different potential NFC investments, as the potential opportunities associated with these investments are strongly associated with the carbon abatement


policies chosen to mitigate climate change, both internationally and domestically. These baselines are also developed to assess whether nuclear power would form part of the least-cost electricity technology mix in SA.

A No Action scenario is developed to assess the economic growth and emission profiles of the Australian and SA economies if there is no explicit carbon abatement policy in Australia. Global climate action assumptions in the No Action scenario were based on the International Energy Agency (IEA) New Policies Scenario, which represent the intended nationally determined contributions made prior to the CoP21 summit.1 The No Action scenario is not used as a baseline for assessing the impact of any of the NFC investments. However, it is necessary in order to specify the abatement task required in each of the three Investment Scenarios, thereby determining carbon prices, energy prices and other variables important for the feasibility and economic impacts of the NFC investments.

Figure 1 below outlines the climate action scenarios under which each of the potential NFC investment is assessed.

Figure 1: Scenarios

In Investment Scenario One (IS1), there is no new activity in the downstream stages of the NFC in Australia as it is assumed that current prohibitions continue. The economic impacts of an expansion of Uranium mining and exports (relative to current production) are assessed under this scenario.

Australia’s carbon abatement goal in IS1 is a:

► 5% reduction in emissions below 2000 levels by 2020, achieved through the emissions reduction fund (ERF);

► 27% reduction in emissions relative to 2005 levels by 2030, achieved through an expansion of the ERF between 2020 and 2030; and

► 80% reduction in emissions relative to 2000 levels by 2050, achieved through a globally linked carbon price post 2030.

1 21st Conference of the Parties, a United Nations Climate Change Conference held in Paris from 30 November to 12

December 2015


Under IS1 (and all other Investment Scenarios) it is assumed the world seeks to meet the global target for stabilisation of atmospheric CO2 concentrations at 450ppm by 2100.

Investment Scenario Two (IS2) reflects the same abatement objective for Australia as IS1, but it differs in terms of the mechanism through which it is achieved. In particular, emission abatement beyond 2020 (as opposed to 2030 in the IS1) is achieved using a globally linked carbon price.

The economic benefits of SA entering either the further processing or storage and disposal parts of the NFC are both assessed under IS2.

The economic benefits of nuclear fuel leasing (which is a combination of further processing, storage and disposal) are also assessed in this context. Fuel leasing would involve retaining effective ‘ownership’ of the nuclear fuel throughout its life cycle from the provision of the enriched fuel to repossession of the waste for storage and disposal. In essence, fuel leasing provides nuclear fuel users with an end-to-end solution.

The abatement targets under IS1 and IS2 are consistent with the current and past Government targets in Australia and with the 2oC target modelled by the Australian Government.

Investment Scenario Three (IS3) reflects a more stringent abatement target than in IS1 or IS2. In particular IS3 involves a:

► 65% reduction in emissions relative to 2005 levels by 2030, achieved through a globally linked carbon price between 2020 and 2050; and

► 100% reduction in emissions relative to 2000 levels by 2050, achieved through a globally linked carbon price (Australian decarbonisation by 2050).

Under IS3 the assumed emission abatement target of Australia is consistent with the 1.5oC aspirational target agreed at the CoP21 Climate Summit in Paris in December, 2015.

The potential economic benefits to SA from investment in nuclear power generation are assessed under this carbon abatement scenario. 2

1.5 Economic impact of nuclear fuel cycle investments

1.5.1 Key inputs

The models used in this study make a number of assumptions and use a number of inputs. Some inputs are common and consistent across work streams, including the commissioned NFC business cases, the CGE model and EY’s electricity model. For example, commodity prices — coal, oil and gas — are assumed to be similar across all streams of work.

The three commissioned NFC business cases are:

► Quantitative analyses and business case for radioactive waste storage and disposal facilities in SA;

► Quantitative analyses and business case for Uranium conversion, enrichment and fuel fabrication facilities in SA; and

► Quantitative analyses and business case for developing nuclear power plant in SA.

2 It is noted that the underlying scenario in respect of carbon abatement does not necessarily alter the merits of the case

for entering the Further Processing and Radioactive Waste storage and disposal parts of the NFC, as these will be driven by international developments in the use of nuclear fuel, which will be similar under both IS1 and IS2.


Similarly, carbon and electricity price assumptions under the various scenarios are common across the CGE modelling and business cases.3

The CGE model considers the carbon abatement objectives, and associated policies, and estimates the economic impact of each NFC investment activity separately, both on the SA and Australian economies.

Table 1 provides a high level summary of the key inputs to the NFC business cases.

Table 1: Main nuclear fuel cycle inputs

Construction start Year

Operation start Year

Total capex to 2050 $m*

Annual revenue

Sm*

Nuclear conversion and enrichment facilities (Further Processing)

2024-25 2029-30 7,142 657

Radioactive waste storage and disposal facilities

2019-20 2049-50 13,814 5,814

Nuclear fuel leasing facilities 2019-20 2049-50 20,956 6,471

Nuclear electricity generation

Large: GW-scale PWR (1125 MWe) 2020-21 2030-31 13,081 **

Small: Generic SMR (285 MWe) 2022-23 2030-31 4,575 **

*Reported in 2014-15 prices and undiscounted

**Revenue impacts in respect of nuclear generation are more complex and discussed further below.

The capital expenditure, revenue, electricity technology market share and electricity prices are key inputs to the CGE model which determine the macroeconomic impact of entry into each part of the NFC.

For the first three NFC business cases listed above, the total capex and annual revenues are drawn from the business cases that the Commission had requested be evaluated.

For the nuclear electricity generation business case, the capital costs are drawn from the business cases developed, with revenue impacts determined through the interaction with EY’s electricity market model. Electricity technology market share and electricity prices are determined through EY’s electricity market model, by taking the electricity demand inputs from the CGE model with technology cost assumptions drawn from reputable sources.

Although capital expenditures and revenues associated with NFC activities will continue beyond 2050, the modelling considered the impacts only up to 2049-50. This is because of a lack of sensible data and assumptions upon which to project economic activity beyond 2049-50.

1.5.2 Key metrics

The CGE modelling assessment provides several measures of net economy-wide benefit.

The CGE model focuses on gross national or state income (GNI/GSI) as the highest level measure of economic welfare followed by gross domestic or state product (GDP/GSP) and labour market variables.

3 Due to early completion of business case studies, a post CoP21 wholesale electricity price trajectory is not considered by

the business cases.


► Real GDP is defined as the sum of value added by all producers who are Australian residents, plus any product taxes (minus subsidies) not included in output. Real GSP is the equivalent measure at the State level. A positive deviation of GDP/GSP from baseline scenario implies that the proposed NFC investment grows the economy of Australia/SA.

► Real GNI/GSI reflects changes in GDP/GSP as well as changes to the terms of trade and net international income transfers in order to provide a measure of what a country or state can afford to buy as a result of its production. Introducing NFC activities in SA may involve transfers of income between economies, and changes to Australia’s terms of trade. In that context, GNI is a better measure of welfare than GDP, as it excludes income accruing to overseas residents, thereby representing the current and future consumption possibilities available to Australian residents.

► The CGE model also estimates a labour market impact of additional investment in the NFC. Additional investment generally increases demand for labour as a result of more output and capital growth, leading to short run increases in employment in SA at the expense of other jurisdictions, with long run improvements in real wages. The model produces estimates of employment by industry and real wages by occupation when labour is allowed to move between industries and between States.

The modelling quantifies the total (direct and indirect) potential impacts of NFC activities on the Australian and SA economies to 2049-50, evaluating these against a baseline which reflects the pathway of the economy absent of the NFC investment but with various climate abatement policies in place.

A forecast horizon of more than a decade was chosen to allow time for the changes to fully work through the economy to reach a steady state, and for the impacts of the capital and operational phases of each part of the NFC to be captured. As such, deviations from the baseline can then be interpreted as the potential economic effect of the NFC investments.

1.5.3 Key caveats

The modelling does not predict what will happen in the future. Rather it is an assessment of what could happen, given the structure of the economy-wide model, input assumptions and business case inputs.

The modelling scenarios can help in understanding NFC investment impacts, relativities of different options, and the extent to which parts of the economy (technology, preferences and so on) need to shift from current trends to achieve particular outcomes, given the assumptions.

Scenarios are an analytical lens through which to view the effects of NFC activity; they do not factor in all elements of the ‘real world’. For instance, the analysis does not necessarily capture all the potential costs (health or environmental risks) or benefits of investment in the NFC.

1.5.4 Expansion of existing Uranium mining

The first scenario (IS1) considered growth in the Uranium mining sector. The IS1 scenario depicts what happens to the SA Uranium mining sector under the existing global and domestic carbon abatement and nuclear policies, assuming a continuation of the prohibition of all other activities in the NFC, other than exploration and mining in SA.4

The International Energy Agency (IEA) is forecasting that to achieve the 450ppm target, global nuclear capacity would need to more than double to 862 GW in 2040.5 If this happens, the world demand for Uranium ore would increase to over 200kt from current levels of 76kt. Australia would

4 At the start of the project this scenario was called the Baseline Investment Scenario (BIS). Wholesale electricity prices

under this scenario are as reported by other business case studies. 5 World Energy Outlook (WEO) (2014), International Energy Agency. OECD/IEA, Paris,www.iea.org


supply nearly 12kt of Uranium ore to the world market by 2029-30 and 20kt by 2049-50 from its current level of 6kt, assuming the current market share remains unchanged.6 IS1 estimates the impact of the potential growth in Uranium demand based on the IEA’s forecast.

Table 2 provides the economic impact of Uranium mining expansion.

Table 2: Uranium mining expansion impacts on SA (deviation from IS1 scenario)

In 2029-30 In 2049-50

% $m % $m

Real GSI 0.11 147 0.08 160

Real GSP 0.23 320 0.19 386

Uranium mining (IGVA)** 32 80 20 116

Real wages 0.01 0.01

Unemployment rate (percentage points) -0.014 -0.009

Employment 0.09 800* 0.06 636*

Uranium mining 266* 197*

*FTE is Full Time Equivalent. Employment includes direct employment in the Uranium mining industry and indirect employment in other industries in SA. ** Industry Gross Value-Add

Expected growth in world demand for Uranium has a significant impact on the SA Uranium mining sector but a relatively moderate benefit for the SA economy.

► The difference in gross value produced by SA (i.e. GSP) from growth in Uranium mining is expected to be approximately $320m in 2029-30 and $386m by 2049-50. This translates to a difference in overall revenue flowing as income to SA (i.e. GSI) of around $150m per annum.

► To put these figures into a quantitative context, real GSP of the SA economy in 2014-15 is $98.6 billion. This is expected to increase to $140.6 billion by 2029-30 and $202.9 billion by 2049-50. If Uranium mining expands to take advantage of opportunities in the global Uranium market under a carbon constrained world, SA’s GSP would increase to $140.9 billion by 2029-30 and $203.3 billion by 2040-50. That is an increase of 0.23% in 2029-30, and 0.19% in 2049-50.7

► Under the baseline scenario, Uranium production in SA increases by 30% between 2016-17 and 2029-30. Under the mining expansion scenario, this increase is nearly 50%.

► However, relative to the current contribution of the Uranium mining sector to the SA economy, growth in production does not significantly change the relative contribution of the sector to GSP.

► The difference between production and income gains can be explained by relative prices and foreign income flows - terms of trade are lower, relative to the IS1 baseline scenario, and since mining capital stock is partly owned by foreigners, the increase in exports causes some income outflow.

► Higher net exports from the state would contribute to the majority of the gains to real GSP. ► State-wide impacts — real GSP and real GSI — mask impacts at the regional level. Uranium

mining real industry gross value added in SA increases by 32% by 2029-30 relative to the baseline. Since this activity is located in regional areas, this would represent a significant increase in activity in those areas with consequential regional impacts on economic growth and jobs.

► The revenue generated from the value of goods produced by SA and the higher income will generate some jobs but has a negligible impact on real wages, given the growth in real wages is largely offset by greater employment in the period to 2029-30 and 2049-50. One-third of jobs created are estimated to be in the Uranium mining sector in this scenario.

6 EY estimates based on the Australian Department of Industry, IEA and World Nuclear Association data and assumptions. 7 The economic effects decline over time, as the level of production is shifted due to a demand shock rather than to a supply

shock determined endogenously through innovations in production. That is, Uranium mining expansion has a level effect rather than the growth rate effect on the economy. This also applies to other NFC activities examined in this study.


1.5.5 Further processing facilities

The second scenario (IS2) considers investments in conversation and enrichment facilities in SA, assuming that all restrictions and prohibitions on investment in the downstream stages of the NFC are lifted.

We note that the underlying scenario in respect of domestic carbon abatement does not necessarily alter the merits of entering the further processing and storage and disposal parts of the NFC, as these will be driven by international developments in the use of nuclear fuel, which will be similar under both IS1 and IS2.

Table 3 shows the estimated economic impact of investment in further processing facilities.

Table 3: Further processing facilities impacts on SA (cumulative deviation from IS2 scenario)

2029-30 2049-50

% $m % $m

Real GSI 0.65 898 0.39 794

Real GSP 0.47 671 0.45 914

Further Processing (IGVA)** 66 131



Employment 0.33 1,013* 0.27 1,001*

Further Processing 210* 324*

*FTE is Full Time Equivalent. Employment includes direct employment in the Further Processing industry and indirect employment in other industries in SA. ** Industry Gross Value-add

Entering into the further processing of nuclear fuel produces modest economic benefits. In particular:

► Relative to the outcomes under a business as-usual scenario that represents a carbon constrained world, the development of a further processing industry in SA would add around $800m (or 0.39%) to state income by 2049-50 and over $900m (or 0.45%) to GSP.

► The development of conversion and enrichment facilities in SA is estimated to lead to the employment of approximately 1,000 additional persons on a full time basis by 2049-50. Again these will largely be located in regional areas.

The modest economic benefits of the further processing of nuclear fuel reflect the modest value add that this activity can generate relative to the significant capital (and operating) costs involved in generating that value.

1.5.6 Radioactive waste storage and disposal facilities

Investment in the radioactive storage and disposal facility components of the NFC was also assessed under IS2.

The Commission suggested that radioactive waste storage revenue would flow between the waste holders (International Parties) and hypothetical public corporations — the SA Waste Storage Public Corporation (SA WSPC), State Wealth Fund (SWF) and the SA State Government. The Commission suggested SWF is assumed to be the primary beneficiary of all profits generated by the acceptance of radioactive waste into SA. The SA WSPC is assumed to be the primary developer and operator of all radioactive storage and disposal facilities in SA. Figure 2 shows the relationship between those three bodies and a stylized model of the assumed revenue flows from radioactive waste imports.


Figure 2: Assumed revenue flows between hypothetical public corporations

The development of waste storage facilities by the SA WSPC is funded by a small amount of debt, which is amortised with less than one year of payments made by international parties. Over 90% of all capital outlays are funded through payments made in advance of the development of the highly, capital-intensive and geological disposal facility and associated transport infrastructure.

A scenario based on the acceptance of 3,000 tonnes of High Level Waste (HLW) per annum, at a payment rate of A$1.75 million per tonne, was used to generate the following outcomes for the SA economy. An Intermediate Level Waste (ILW) of 10,000 m3 per annum, at a payment rate of A$40,000 per m3 in the period to 2050, was also used (see Table 4).

Table 4: Impacts of radioactive storage and disposal facilities on SA

(cumulative deviation from IS2 scenario) 2029-30 2049-50

% $m % $m

Real GSI 5.0 6,837 3.6 7,290

Real GSP 4.7 6,699 3.6 7,367

Waste facilities (IGVA)** 2,829 2,816

Real wages 0.0% 0.1%


Employment*** 1.9% 9,603* 1.4% 7,544*

Waste facilities 890 656

*FTE is Full Time Equivalent. Employment includes direct employment in the radioactive storage and disposal industry and indirect employment in other industries in SA. ** Industry Gross Value-add ***Total employment effects are not only an outcome of establishing investment facilities (direct effect) but also of how this revenue is distributed and spent as well as ongoing fiscal stimulus to the SA economy from the real government expenditure. It is an upper limit on highly mobile and flexible labour market assumed at low or moderate wage growth rate. High value adding activities generally create high indirect employment through income and consumption effects.

Under the assumed model of revenue flows, the SA Government’s consolidated revenue was estimated to receive nearly $3billion if it is not used by 2049-50, or 19% of total revenue received in 2014-15. This will be gradually spent on the infrastructure projects and government expenditure activities in SA. This represents an ongoing fiscal stimulus from the State Government.

Investing in radioactive storage and disposal facilities is estimated to produce significant economic benefits to SA relative to a case where there is no such investment.

Nuclear waste inventory imports $$$

SA State Wealth Fund

SA Waste Storage Public Corporation

85 Gross revenue

Incorporates fixed, operating expenses and perpetual cost

fund for facility lifecycle

15 Gross revenue

SA Government Consolidated Revenue

50 Return on investment @4% compounded



► Relative to a scenario without investment in a radioactive waste storage facility in SA, state GSP in 2029-30 is expected to grow by an additional 5% to$148billion and $210billion in 2049-50, representing an additional 3.6% relative to a scenario without this investment. The real GSP of the SA economy in 2014-15 was $99billion.

Radioactive storage and disposal facilities:

► Have a significant positive impact on SA’s income of around 5% ($6.8billion) by 2029-30 and 3.6% (approximately $7.3billion) by 2049-50;

► Gross state income (GSI) per person in today’s dollars will be around $3,500 higher by 2029-30 and around $3,300 higher by 2049-50;

► The radioactive storage and disposal industry is estimated to become as big as the utilities (electricity, gas and water services) industry in SA by 2029-30 and contribute to 2% of GSP; and

► This new industry adds more jobs indirectly to the economy, leading to a significant increase in SA employment of around 9,600 jobs by 2029-30 and over 7,500 by 2049-50. This significant increase in employment means that extra workers could come from the underemployed or unemployed segments of the workforce, from SA or from other states.

The significant economic benefits of developing radioactive waste storage and disposal facilities reflect the impact of substantial capital flows that arise from the acceptance of used fuel for permanent disposal.

State income gains mainly come from the gross revenues generated from waste inventory imports to the SA WSPC (85%) and to the SA SWF (15%). The modelling also shows that there are significant gross income gains from the establishment of waste storage facilities, which is translated to significant growth in employment in the state.

Capital inflows and income gains could potentially increase the SA government’s revenue and affect its revenue raising potential in this scenario. As a result the SA’s share of the national Goods and Services tax (GST) revenue could be re-assessed on a policy neutral basis by the Commonwealth Grants Commission (CGC). Assuming that the greater revenue flows could be considered by the CGC, if that is the case, the modelling suggests that the SA GST relativity could decrease to 1.26 from the current projected level in 2018-19 of 1.43. If this happens, SA could receive less GST transfers otherwise would have been. The GST transfer effect would depend on the judgment and assessment by the CGC on policy neutral basis. Given long-time horizon, it is difficult provide precise magnitude of GST transfer effects relate to SA government revenue raising potential.

1.5.7 Nuclear fuel leasing arrangements

Fuel leasing is a concept based on the sale of uranium concentrate or a value-added form of Uranium fuel (i.e. as converted Uranium, enriched Uranium or as fabricated fuel assemblies) from Australia to international nuclear power utilities before its eventual return to SA for storage and eventual disposal.

While there are potential synergistic benefits from a fuel leasing arrangement that might enable higher revenues for a waste storage and disposal facility established in SA, these have not been considered under this investment scenario. Instead, the potential economic benefits from a nuclear fuel leasing investment scenario is assessed, relative to the IS2 baseline, as a combined investment in both the further processing (represented by the development of conversion and enrichment facilities in SA) and waste storage components of the NFC. This scenario thus assumes the same cost and revenue assumptions as that assumed under the further processing and waste storage facility investment scenarios.

In practice, it would be reasonable to expect nuclear fuel leasing to have additional economic benefits because of the value of end to end service it provides to nuclear fuel users (i.e. it might


expand demand particularly from Australia), because it is removing a major uncertainty for those users. It may therefore increase the size of the market for nuclear fuel and / or SA’s market share.

Investment under the assumed nuclear fuel leasing arrangement was estimated to enable significant economic benefits, that are largely associated with the development of radioactive waste storage and disposal facilities rather than the development of the further processing component of the NFC (represented in this case by conversion and enrichment). Investment in the NFC under this scenario:

► Leads to a 5.6% increase in gross income received by SA (7.7billion) by 2029-30 and 4% increase (over $8.1billion) by 2049-50, relative to no investment in this component of the NFC;

► On per capita basis, this increase in income is estimated to be $4,000 higher by 2029-30 and around $3,600 higher by 2049-50 in today’s dollars; and

► Leads to a significant increase in SA full time employment around 11,000 by 2029-30 and over 9,300 by 2049-50.

The estimated increase in employment reflects the reduction in the number of persons in SA and Australia who are either underemployed or unemployed.

A key determinant of real GSP gains under all scenarios is capital deepening. Increased availability of capital to build and operate NFC facilities significantly contributes to real GSP relative to their respective baselines. An increase in capital to labour ratio in response to capital works increases the relative price of labour during the construction phase. As a result, the amount of capital per unit of production in the economy increases (capital deepening). As a consequence, the economy is more capital intensive than the baselines; the greater the capital works, the more pronounced the effect.

1.5.8 Investment in nuclear generation

Investment in a nuclear power plant (NPP) was evaluated under the strong climate action scenario that is IS3. This business case presents an evaluation of the effects on SA, the National Electricity Market (NEM) and emissions with respect to a case with no new nuclear power plant, but a state of the economy that reflects the Australian ambition to achieve deeper levels of decarbonisation by 2049-50.

To assess the economy-wide effects of a proactive policy to develop nuclear power in SA, a nuclear power plant was forced into the mix of electricity generating assets in SA in 2030-31.

Based on EY electricity modelling and an assumed overnight capital cost for a nuclear power plant of $7,828/kW, a GW-scale nuclear power plant does not emerge as part of the least cost mix of electricity generation assets in SA, assuming a commercial discount rate of 10% and a real carbon price of $123/tonne-CO2-e in 2030-31 rising to $254/tonne CO2-e in 2049-50.

However, it is important to note that this scenario is contingent upon assumptions made in relation to private investment in residential rooftop PV and battery storage systems to which the wholesale price of electricity is sensitive. No assessment of the profitability of private investment in these systems was made as part of the modelling undertaken.

The estimated wholesale price trajectory is also sensitive to the assumed level of carbon permit imports. Although the Australian economy is assumed to be meeting the targets by 2050, a quarter of this objective is met through imported carbon permits. This means that under the forecast IS3 scenario (representing the strong climate action baseline), the electricity generation sector is not completely decarbonised by 2050 and the NEM still has average emissions intensity of generation equal to 0.12 tCO2-e /MWh (a reduction of 87% from the current, average intensity of 0.85 tCO2-e/MWh). This is largely driven by the CO2 emissions contribution of Combined Cycle Gas Turbine (CCGT) systems that provide system support to intermittent renewable generation at the grid-level.


If Australia's 2050 emissions abatement objectives have to be met without access to carbon permit imports, a higher carbon price by 2050 may be required than has been assumed in this assessment. This would lead to yet higher wholesale price trajectories than have been predicted using the EY electricity market model. The development of a single large, GW-scale nuclear power plant in SA has the effect of reducing wholesale electricity prices at the SA regional reference node by 24% in 2030-31 relative to a case with no nuclear power plant. In comparison, the development of a small nuclear power plant also operating as a base load plant reduces wholesale electricity prices by only 6% relative to a case with no nuclear power plant operating in SA. A smaller reduction in wholesale electricity prices is also observed in Victoria from the integration of a new GW-scale nuclear power plant in SA and from the expansion of transmission interconnector capacity to 2000 MW in 2030-31.

Given the development of a nuclear power plant is unprofitable under commercial conditions; this scenario required an assumption to include a subsidy of $5.4billion to be provided over a period of 20 years to enable a consistent comparison between the renewable and nuclear options of the impact on the SA economy. The subsidy is funded by reduction in SA government expenditure on services. The combined economy-wide effects of substantial wholesale electricity price reductions at the SA and Victorian regional reference nodes on SA are presented in Table 5.

Table 5: Impacts of nuclear power generation in SA (cumulative deviation from IS3 scenario)

2029-30 2049-50

Small nuclear % $m % $m

Real GSI 0.27 370 -0.03 -68

Real GSP 0.24 344 0.05 107

Small NPP (IGVA)** 47 46

Real wages -0.02 0.14

Unemployment rate (ppt) -0.02 0.00

Employment 0.26 540* 0.08 473*

Small NPP 167 120

Large nuclear % $m % $m

Real GSI 0.36 486 -0.30 -594

Real GSP 0.37 524 0.10 201

Large NPP (IGVA)**


Unemployment rate (ppt) -0.09

Employment 575* 620*

Large NPP 330 258

Large nuclear with subsidy funded by reduction in SA government services

% $m % $m

Real GSI 0.36 486 -3.64 -7,178

Real GSP 0.37 524 -3.00 -6,000

*FTE is Full Time Equivalent. Employment includes direct employment in the nuclear power generation industry and indirect employment in other industries in SA. **Industry Gross Value-add

Investment in NPP in SA produces negligible or marginally negative economic impacts. In particular, it has:

► A small negative impact on SA’s income with a small nuclear reactor (-0.03% by 2049-50) and also with a large nuclear reactor (-0.30% by 2049-50); and

► A moderate impact on SA’s production between 0.05% (with a small nuclear reactor) and 0.1% (with a large nuclear reactor) by 2049-50.


The difference between the economic impacts of small and large nuclear plant is explained in part by the relative capital expenditure and its timing. The large NPP requires a larger capex ($9.1bn) than the small NPP ($4.6bn) and it is spread out over more years, and the operational costs are a bit larger.

The capital works phase of the NPP provides positive income gains due to higher wages and more employment in the state. But when production begins, most of the benefits of lower electricity prices flow through to other states in the NEM, particularly Victoria, although those benefits are modest. The expansion in nuclear generation is largely offset by a contraction in electricity generated from gas powered plants.

The negligible or negative economic impact of nuclear generation reflects that it does not provide a competitive source of electricity generation relative to the alternatives that are available under the high carbon prices assumed. This largely reflects the high capital costs involved and changes in the structure of the electricity market, which reduces the viability of less flexible generation. There is no significant state production gains associated with the small nuclear power plant. Production gains associated with the large nuclear power plant are also relatively modest. The wholesale electricity price decline due to the operation of large nuclear power plant in the SA region of the NEM is significant but does not translate into economy-wide gains.

A lower wholesale electricity prices in SA arising from the introduction of a large NPP as part of the generation mix reduces the profitability of other electricity generators and consequently the income of SA through flow-on effects to other prices observed in the economy.

Commissioning of NPP in SA does not contribute significantly to further emission reductions in the NEM, as NPP is assumed to be introduced when substantial reductions in the emission intensity of generation across the NEM states has been achieved.


2. Context for CGE modelling assessment

2.1 The project

The Nuclear Fuel Cycle Royal Commission (the Commission) has been established by the South Australian Government (the Government) to examine whether the State should expand its participation in the nuclear fuel cycle (NFC), and to consider the associated risks and opportunities. This expansion could either involve increasing existing activity in the exploration and extraction of radioactive minerals (mining), or entering the other parts of the NFC. In particular, the latter could involve South Australia (SA) becoming involved in:

► The conversion and enrichment (further processing) of Uranium ore; ► Nuclear electricity generation (nuclear generation); and/or ► The management, storage and waste disposal (storage and disposal) of radioactive waste.

The Commission has engaged three consultants to prepare business cases in order to assess the commercial merits of investment in these parts of the NFC. The work involves examining, at a high level, the associated private costs, risks, required returns and financing issues of each part of the NFC. In essence, it makes the private economic case for investment in these stages of the NFC.

The Commission has engaged EY, along with the Centre of Policy Studies (CoPS) at Victoria University, to undertake Computational General Equilibrium (CGE) modelling to assess the potential economic impacts on the SA economy that would result from additional investment in any part of the NFC.

The CGE analysis informs the public case for greater participation in the industry (i.e. what it could mean for SA as a whole) and, to do so, explores a number of scenarios.

The CGE modelling brings together the business case analyses for each part of the NFC (e.g. by relying on the cost and related data generated through business case financial assessments) with the wider economic costs and benefits.

2.2 Purpose

The key objective of this study is to assess the potential economic merits for the SA economy of greater involvement in any part of the NFC and what it means for GDP, employment etc. It does so by using justifiable assumptions and a mathematically rigorous description of objective scenarios.

It also determines the economy-wide effects on inter and intra-state flows of labour, capital and other inputs on the SA economy, industries and regions.

To undertake CGE modelling, EY has developed a comprehensive set of production and consumption functions for new NFC activities. They simulate the cost and distribution of factor inputs in the economy arising from an investment in the components of the NFC.

This report provides the results of EY computational general equilibrium modelling assessment.

2.3 Our approach and framework

NFC activities operate over very long timeframes, with significant periods of time between generation and the resulting nuclear waste. Consequently, quantitative analysis of the NFC activities in SA must take a long-term view. This report provides projections to 2050, although some activities will operate beyond 2049-50. This difficult exercise requires assumptions for a wide range of economic, demographic, technological and environmental variables which can change in unpredictable ways.


This report uses models to estimate long-term economic impacts of the NFC activities on the SA and Australian economies. CGE model mathematically represent how the economy operates and how various participants – businesses, government, exporters, importers and households - respond to the opportunities and risks provided by the NFC activities.

Our approach integrates with the electricity market and with the business cases to provide comprehensive economy-wide impacts of the NFC activities on the SA economy.

2.3.1 Integration of models and business cases

No single existing model adequately captures all the required dimensions of the NFC activities. Each business cases focuses on one of the three stages of the NFC.

EY uses a suite of models to create an economy-wide holistic framework for assessing the impact of the NFC activities.

Figure 3 brings all the models and business cases together.

The CGE modelling centres on Victoria University Regional Model (VURM) developed at the Centre of Policy Studies (CoPS), Victoria University (left panel in Figure 3). See Appendix A for more details of this CGE model. This whole-of-economy model captures interactions between different sectors of the economy and among producers and consumers. It is rich in industry detail. The EY bottom-up electricity sector, CSIRO transport sector assumptions and three business cases commissioned provide inputs to the VURM.

Given the importance and inherent uncertainty about the evolution of the electricity generation sector, detailed bottom-up models of the sector were used. This highly detailed model provides an analysis of the Australian electricity generation sector, with projections for levels of generation, total capacity (installed), emissions (of carbon dioxide equivalent), energy use (fuel use), wholesale and retail electricity prices and the profit streams of generators (important for asset values and financing). Results are generally provided at the generator level or by the unit within each generator, giving insights into the transformation of the electricity generation sector.

The electricity market is important to all the NFC business cases (as a cost input), but particularly for the nuclear generation business case.

Inputs CGE modelling provide to the business cases

Based on the CGE and the EY electricity market model runs, EY provided the following key data points that are required for the business cases. They are:

► Exchange rate by year which in turn is determined from relative prices and the terms of trade ► Fuel prices in $AUD by year ► Wholesale and retail electricity prices by year ► Electricity technology shares by year ► Emissions by year ► Electricity demand by user by year (taking into consideration structural changes in the

Australian economy)

The CGE model also produces a number of macro and industry variables that may be useful to cross check with the other available projections about the economy, relative prices and industry composition in SA.


Figure 3: Integrated economy-energy and nuclear fuel cycle business cases

Source: EY


Inputs CGE modelling taken from the business cases

The three business cases commissioned by the Commission are:

► Quantitative analyses and business case for uranium conversion, enrichment and fuel fabrication facilities in SA;

► Quantitative analyses and business case for developing nuclear power plant in SA; and ► Quantitative analyses and business case for radioactive waste storage and disposal facilities in

SA

The main focus of these business cases is to quantify the private costs – capital and operational - associated with the engineering, construction, operation and decommissioning phases of the NFC. The CGE model took these inputs from the business cases to develop the new industry production and investment functions but also expansion of the investment and operation phases of these new activities.

2.4 Understanding the CGE modelling assessments

As with all CGE modelling assessments, the modelling results require careful explanation.

The analysis in this report estimates the potential net economic impact of commissioned NFC business cases. This modelling does not predict what will happen in the future. Rather it is an assessment of what could happen, given the structure of the models, input assumptions and business case inputs.

Scenarios are an analytical lens through which to view an assessment of NFC activity; they do not factor in all elements of the ‘real world’. The investment scenarios guide understanding of NFC investment impacts, relativities of different options and the extent that parts of the economy (technology, preferences and so on) need to shift from current trends to achieve particular outcomes, given the assumptions.

The input and policy assumptions are particularly important. Many variables affect the estimated potential net benefit of investing the NFC activities in SA. The future path of these variables is not known. However, these values are required for assessing the NFC activities, so assumptions must be made.

EY developed these assumptions through research and consultation with the Commission, stakeholders, Socio-economic Modelling and Assessments Committee (SEEMAC) and domestic and international experts. While they are intended to be plausible central estimates within a range of uncertainty, other analysts could well form different judgments.

The inherent difficulty in developing assumptions and undertaking simulations is compounded by the long timeframes required for the CGE assessment. Generally, more caution is needed in interpreting results that are well into the future. As the timeframe expands, assumptions are more speculative.

2.4.1 Economic measure of potential net benefit

The CGE modelling assessment provides several measures of the net economic benefit.

The CGE model focuses on gross national or state income (GNI/GSI) as the high level measure of economic welfare impact followed by the gross domestic or state product (GDP/GSP).

► GDP is defined as the sum of value added by all producers who are Australian residents, plus any product taxes (minus subsidies) not included in output. GSP is equivalent measure at State level and GRP is equivalent measure at regional level. Similarly, positive deviation of


GDP/GSP/GRP from the baseline investment scenario implies that the proposed NFC investment is welfare enhancing for Australia.

► GNI reflects changes in GDP, the terms of trade and international income transfers. Introducing the NFC activities in SA may involve transfers of income between economies, and influence nations’ terms of trade. In that context, GNI is a better measure of welfare, as it excludes income accruing to overseas residents, thereby depicting the current and future consumption possibilities available to Australian residents. It measures what a nation can afford to buy.

► The CGE model also produces the labour market impact of additional investment in the NFC. The additional investment increases the demand for labour as a result of more output and capital growth. In the short run there will be an increase employment in SA at the expense of other jurisdictions but in the long run real wages will be improved in SA. Thus, the model produces employment by industries, real wages by occupations and allows the labour to move between industries and between the States

Expected improvements in economic output due to large scale investment in the NFC, would induces a possibility of some additional tax revenue for both Commonwealth and State Governments in addition to the royalties on some economic activities.

2.4.2 Presentation of results

The CGE modelling results need careful interpretation. The results from the CGE model can mean different things when reported in different contexts and against different baselines.

One aspect of understanding CGE modelling results is the baseline against which the assessments are made. In this study, three baselines are developed at the request of the Commission to assess different parts of the NFC. The three baselines developed for this study considered various degrees of carbon abatement under which various NFC activities can be assessed. Comparing results with a hypothetical future such as a baseline is a common and reasonable way to explain how a policy will influence the economy in isolation from other events.

However, such results must not be interpreted as suggesting policy will have an absolute impact relative to the current world. For example, if an investment in the NFC would raise economic growth in SA by 0.5 per cent relative to what otherwise would have been, this should not be interpreted as saying the SA economy would necessarily rise 0.5 per cent from its current level, if investments were made. The statement is only relative to how the economy would have evolved in the absence of an investment. To help and enrich understanding of the potential net economic implications of investments in the NFC, this report presents a range of measures when reporting results, including levels (real economic impacts) and percentage deviations from the baseline investment scenario against which we assessed the different part of the NFC.

The CGE model provides outputs in ‘real’ dollars and so is abstract from the devaluing influence on purchasing power from inflation. The VURM model used in this study does not capture any market failures caused by asymmetric information, strategic interaction between agents, public goods (goods for which the consumption by one individual does not preclude the consumption by others) and externalities.

The EY electricity model allows for learning, to make some technologies cheaper over time. For example, renewable technologies for electricity generation are subject to learning-by-doing, thus their capital costs fall with their output, which leads to a greater uptake of renewable technologies in the electricity generation sector. In these circumstances, nuclear generation will likely be subject to some learning by doing thus the nuclear power capital costs will fall with the increased generation mix of nuclear. However, due to the uncertainty of nuclear power, no learning rates are assumed for nuclear power technologies in this study. Technological improvements are generally exogenous to the CGE model. The sensitivity analysis, particularly in the electricity generation sector, explores alternative technology assumptions to check the robustness of key results associated with different nuclear investment business cases in this study.


2.5 Structure of this report

The remainder of this report is structured as follows:

► Section 3 sets out the description of scenarios and their key features ► Section 4 details the sources of key inputs and assumptions used in various scenarios. ► Section 5 provides detailed electricity modelling assumptions and results. ► Section 6 provides the results of the CGE modelling assessment of the NFC components

individually on the SA economy and Australian economy.

Other detailed technical information is provided in Appendixes for technical users of this Report.

► Appendix A provides the technical details of the Victoria University CGE model (VURM), its parameters and elasticities. This section also reports changes to the model theory and database to suit the objectives of the study. EY has sub-contracted Victoria University to provide the VURM for this study.

► Appendix B provides the macroeconomic assumptions used to develop the No action scenario, a scenario that needs to be developed before developing three baseline scenarios for the NFC assessments.

► Appendix C provides electricity generation technology assumptions. ► Appendix D provides details on EY electricity models. ► Appendix E provides potential goods and services tax impacts.


3. Carbon abatement scenarios: baselines for NFC business cases

This chapter outlines the scenarios used in the modelling and their underlying key assumptions and features. They are:

► Assumptions related to the carbon abatement policies; and ► Assumptions related to the NFC activities.

The macroeconomic and technological assumptions are reported in Appendixes B and C.

3.1 Description of scenarios

The Commission identified scenarios to be tested by the CGE modelling, to assess the economic impacts of additional investment in any part of the NFC. Figure 4 and Table 6 below outlines those scenarios.

Since carbon dioxide emission (CO2) abatement (carbon abatement) policies associated with the efforts to mitigate climate change (both internationally and domestically) are strongly associated with the potential opportunities for additional investment in the NFC, different baselines are developed for the study to reflect different levels of abatement.

A ‘No action’ scenario was developed to assess the economic growth and emission profiles of the Australian economy and the SA economy in the event that there is no explicit carbon abatement policy in Australia. Global climate action assumptions in the No action scenario were based on the International Energy Agency (IEA) New Policies Scenario, which represents the intended nationally determined contributions made prior to the CoP21 summit.8 This scenario provides the abatement task required in carbon abatement scenarios modelled in this study.

Investment Scenario one (IS1) reflects the current global carbon abatement policy settings and Australia’s existing carbon abatement policies. In this scenario, there is no new activity in the downstream stages of the NFC in the Australian economy.

Existing Uranium mining and exports continue in response to global market conditions, with the world seeking to meet the global target for stabilisation of atmospheric CO2 concentrations at 450ppm by 2100. Australia’s carbon abatement goal in this scenario is a:

► 5% reduction in emissions below 2000 levels by 2020, achieved through the emissions reduction fund (ERF);

► 27% reduction in emissions relative to 2005 levels by 2030, achieved through an expansion of ERF between 2020 and 2030; and

► 80% reduction in emissions relative to 2000 levels by 2050, achieved through a globally linked carbon price.

Investment scenario two (IS2) reflects the same abatement objective for Australia as IS1, but it differs in term of the mechanism through which it is achieved. In particular, emission abatement beyond 2020 (as opposed to 2030 in the IS1) is achieved using a globally linked carbon price.

IS1 and IS2 represent the moderate climate action scenarios.

In IS2, the economic merits of SA entering the further processing, storage and disposal parts of the NFC are assessed. The merits of fuel leasing are also assessed in this context. Fuel leasing, as the

8 21st Conference of the Parties, a United Nations Climate Change Conference held in Paris from 30 November to 12

December 2015.


name suggests, would involve retaining effective ‘ownership’ of the nuclear fuel throughout its life cycle from the provision of the enriched fuel to repossession of the waste for storage and disposal. In essence, fuel leasing provides nuclear fuel users with an end-to-end solution.

Figure 4: Modelled nuclear fuel cycle scenarios

Investment scenario three (IS3) reflects a more stringent abatement objective than in IS1 or IS2. In particular a:

► 65% reduction in emissions relative to 2005 levels by 2030, achieved through the globally linked carbon price between 2020 and 2050; and

► 100% reduction in emissions relative to 2000 levels by 2050, achieved through the globally linked carbon price.

Against IS3 scenario, the economic merits of SA entering into nuclear electricity generation are assessed. IS3 baseline scenario reflects the high carbon price with no nuclear power generation in Australia.

The CGE modelling brings together the business case analyses for each part of the NFC (e.g. by relying on the cost and related data generated through business case financial assessments) with the wider economic costs and benefits.

A summary of description of scenarios is presented in Table 6.

Scenarios assessed under IS2

► Investment in a set of radioactive storage and disposal facilities of the NFC alone; ► Investment in a set of further processing facilities of the NFC alone (e.g. conversion,

enrichment, and no fuel fabrication); and ► Development of a fuel leasing investment model whereby the front end (e.g. conversion and

enrichment) and the back end of the NFC are integrated.


Table 6: Description of NFC scenarios

NFC Component IS2 – Moderate Climate Action IS3 – Strong Climate Action

A set of radioactive storage and disposal facilities

► What will be the impact of developing a set of radioactive waste storage facilities on the SA economy?

► What will be the impact of integrating the front end of the NFC with the waste storage facility component as part of a fuel leasing arrangement?

A set of further processing facilities

► What will be the impact of developing a set of further processing (e.g. conversion, enrichment) facilities on the SA economy?

Electricity generation through nuclear power

► What will be the impact on the electricity market, SA economy and GHG emissions of introducing SMR into the SA region of the NEM?

► What will be the impact on the electricity market, SA economy and GHG emissions of introducing a large PWR into the SA region of NEM?

Scenarios assessed under IS3

► No investment in nuclear power plant in SA ► Investment in a SMR nuclear power plant in SA, while maintaining the current level of

interconnector capacity, or with a minor augmentation to 1 GWe (from current 600 MWe) ► Investment in a large PWR nuclear power plant in SA, and expanding the current level of

interconnector capacity to 2 GWe (from 600 MWe).

3.2 Carbon abatement assumptions

All three baseline scenarios assume a global target for the stabilisation of atmospheric CO2 concentrations at 450 ppm by 2100.

The Commission has provided the following carbon abatement assumptions based on the recent literature in Australia. 9, 10 11

Table 7 outlines the assumptions used, the specific emissions reduction targets assumed for Australia and the burden that Australia has taken on towards meeting the target.

Table 7: Carbon abatement policy assumptions

2015-2020 2021-2030 2031-2050

Investment Scenario One (IS1)

5% reduction in emissions below 2000 levels.

Mechanism: expansion of ERF from 2016-17 to 2019-20

27% reduction in emissions relative to 2005 levels*

Mechanism: expansion of ERF from 2019-20 to 2029-30.

80% reduction in emissions relative to 2000 levels by 2050 Mechanism: carbon price policy implemented in 2030-31 and linked to the international market

► Assumes 2019-20 and 2029-30 emissions reduction targets will be met through an expansion of the emissions reduction fund (ERF) and that up to 25% of the reduction target can be achieved through imported permits.

► Assumes 2049-50 emissions reduction targets will be achieved through the implementation of a globally linked carbon price mechanism and reflects Australia taking a less than an adequate share of the worldwide burden to achieving global stabilisation of CO2 concentrations at 450 ppm.

► Assumes NEM wide electricity consumption derived endogenously in the CGE model, with exogenous assumption with regards to electrification of transport sector based on the Future Grid Forum Renewables Thrive Scenario

► Assumes existing regulations and policies in relation to restrictions on nuclear activities.

IS2: Investment scenario 2

5% reduction in emissions below 2000 levels

Mechanism: expansion of ERF from 2016-17 to 2020.

27% reduction in emissions relative to 2005 levels

Mechanism: carbon price policy implemented in 2020-21 and linked to the international market

80% reduction in emissions relative to 2000 levels by 2050

Mechanism: carbon price policy implemented and linked to the international market

► Assumes all post 2019-20 emissions reduction targets will be met through the implementation of a carbon price mechanism with the 2029-30 target based on the policy announced by the Current Commonwealth Government and that up to 25% of the reduction target can be achieved through imported permits.

► Assumes 2049-50 emissions reduction targets will be achieved through the implementation of a carbon price mechanism and reflects Australia taking a less than an adequate share of the worldwide burden to achieving global stabilisation of CO2 concentrations at 450 ppm.

► Assumes NEM wide electricity consumption derived endogenously in the CGE model, with exogenous assumption with regards to electrification of transport sector based on the Future Grid Forum Renewables Thrive Scenario

► Assumes removal of existing regulations and policy restrictions on nuclear activities.

► Assumes investment in the further processing, radioactive waste management and storage component of the NFC and the development of nuclear power only if it emerges endogenously in the market modelling analyses.

9 DPMC 2015. Australia’s 2030 Emission Reduction Target. Australian Government 2015. http://bit.ly/1Me4pZm 10 CCA 2013. Targets and Progress Review Issues Paper. Australian Government. Available at http://bit.ly/1Zc95EA 11 Jotzo F. Australia’s emissions reduction ambition. Submission to the Climate Change Authority’s Caps and Targets

Review. July 2013.

http://bit.ly/1Me4pZm

http://bit.ly/1Zc95EA


Table 7: Carbon abatement policy assumptions

IS3: Investment scenario 3

5% reduction in emissions below 2000 levels

Mechanism: expansion of ERF from 2016-17 to 2019-20

65% reduction in emissions relative to 2005 levels

Mechanism: Carbon price policy implemented in 2020-21 and linked to the international market

100% reduction in emissions relative to 2000 levels by 2050

Mechanism: carbon price policy implemented and linked to the international market

► Assumes removal of existing regulations and policies restricting nuclear activities.

► Assumes investment in the development of nuclear power exogenously if it doesn’t emerges endogenously in the market modelling analyses

*Current Australian Government targets are based on the 2005 base year and the previous Government and Kyoto targets are based on the 2000 base year.

Source: Nuclear Fuel Cycle Royal Commission

3.3 Abatement task in carbon policy scenarios

In the No action scenario, the size of the Australian economy in real GDP terms is projected to grow by around one-third to 2020 and a further two-thirds over the decades to 2049-50. As the population grows and the economy expands, it presents a substantial abatement task at the economy-wide level. Without either the carbon pricing mechanism or Emission Reduction Fund or other policy mechanisms, Australia’s emissions are projected to rise to 626 Mt CO2-e in 2019-20, 716 Mt CO2-e in 2029-30 and 948 Mt CO2-e in 2049-50:

► To achieve a 5 per cent reduction in emissions in 2019-20 compared to 2000 levels implies an abatement task of 95 Mt CO2-e in all three climate action scenarios.

► To achieve a 27 per cent reduction in emissions in 2029-30 compared to 2005 levels implies an abatement task of 271 Mt CO2-e in IS1 and IS2 scenarios.

► To achieve an 80 per cent reduction in emissions in 2049-50 compared to 2000 levels implies an abatement task of 836Mt CO2-e in IS1 and IS2 scenarios.

► To achieve a 65 per cent reduction in emissions in 2029-30 compared to 2005 levels implies an abatement task of 320 Mt CO2-e in IS3 scenario.

► To achieve a 100 per cent reduction in emissions in 2049-50 compared to 2000 levels implies an abatement task of 948 Mt CO2-e in IS3 scenario.

This abatement task is lower than previous economy-wide assessments.12 These EY projections take account of more recent historical emissions levels and a lower rate of underlying growth in key emissions-intensive industries, including the closure of some facilities.

In addition, there have been a range of changes to the scope and measurement of emissions compared to the Australia’s Emissions Projections 2012 report,13 as well as changes to reflect emissions accounting in Australia’s second commitment period under the Kyoto protocol.14

3.4 Carbon prices

A key assumption in all scenarios to achieve the abatement task is the inclusion of abatement policies either through an Emission Reduction Fund (an implicit carbon auction price) or carbon pricing mechanism. Based on the model iterations to achieve the target for allowed imports, we have estimated a level of carbon price that meet the targets mentioned in Table 7.

12 Australian Government 2008, Australia’s Low Pollution Future, the economics of climate change mitigation,

http://lowpollutionfuture.treasury.gov.au/ and Australian Government 2011, Strong Growth Low Pollution, modelling a carbon price, http://carbonpricemodelling.treasury.gov.au/content/default.asp 13 Department of the Environment (2015), Australia’s Emissions projections 2014-15,

https://www.environment.gov.au/climate-change/publications/emissions-projections-2014-15 14 Climate Change Authority (CCA), 2015. Final Report on Australia’s Future Emissions Reduction Targets. Australian

Government July 2015. Available at http://bit.ly/1ewsHQg

http://lowpollutionfuture.treasury.gov.au/

http://bit.ly/1ewsHQg


The CGE modelling estimates the economic effects of domestic climate change mitigation measures (ERF and emission pricing) to 2050 under various climate action scenarios. It does not estimate the benefits of preventing harmful climate change. The modelling assumes domestic carbon prices associated with the Emissions Reduction Fund are initially applied to the domestic emitting industries. After initial period, the Australian domestic price follows the international price through linking globally. Global carbon price equals the Australian carbon price, adjusted for exchange rate changes. The carbon prices assumed in this study are comparable in all three scenarios with the previous modelling of ambitious action scenarios by the Australian Government.15 The Climate Change Authority (2013) has assumed global carbon prices starting US$75/tCO2-e (2011-12 prices) in 2020-21 growing at rate of 4% and reaching US$217/ tCO2-e by 2049-50. The Australian Government in its Strong Growth Low Pollution Report (2011) has assumed Australian domestic carbon price starting A$27.5/tCO2-e (2009-10 prices) in 2012-13 growing at an annual rate of 4% and reaching A$275/ tCO2-e by 2049-50. Australian domestic carbon prices assumed in this study are provided in Figure 5. Since there are no binding restrictions on international emissions trade when Australian emission prices are linked to global prices in three mitigation scenarios, changes to Australia’s actual emission level will change the number of permits Australians buy or sell, rather than the price of permits in Australia.

Figure 5: Australian real carbon price $/tCO2-e (2014-15 prices)

Source: EY estimates based on the VURM

3.5 Emission Reduction Fund

The current Government has adopted a Direct Action Plan (DAP) to mitigate emissions in the Australian economy through an Emission Reduction Fund (ERF). The ERF is designed to reward emission reduction through a producer grant type system. It has a number of parts. This study considers only in that part which relates to the crediting of emission-reductions and the sale of those credits to the Government through a reverse auction.

The crediting and auction scheme effectively puts in place a mechanism for the government to subsidise industries to cover the cost of investment in abatement technologies. In principle, the new investment would otherwise not occur without the subsidy. The Government expects that the auctioning mechanism will ensure the investment is efficient. It meant that the investment generated by the subsidy delivers abatement at least cost for the economy.

15 Australian Government 2011, Strong Growth Low Pollution, modelling a carbon price,

http://carbonpricemodelling.treasury.gov.au/content/default.asp

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3.5.1 Difference between ERF and carbon price

In general terms, the ERF is a fiscal outlay that encourages investment in abatement activity without necessarily increasing the cost of production. On the other hand, a carbon price, or the price in an auction-based Emissions Trading Scheme (ETS), delivers fiscal revenue and abatement, but with increased costs of production through higher fossil-fuel costs.

Some key differences between the ERF and emission price are summarised in Table 8.

Table 8: Comparison of ERF with the emission price

Characteristic Emission Reduction Fund Emission price

Initial fiscal consequence Outlay to industries necessary to achieve required abatement

If a price, income from the receipt of tax revenue

Purchase price of CO2-e intensive energy

No initial effect Initial increase reflecting the incidence of the CO2-e tax on primary energy (coal, oil and gas) and petroleum products.

Production costs of industries producing non-combustion emissions

No initial effect Initial increase reflecting the imposition of the tax on production.

Sources of abatement Investments that allow for fuel switching

away from CO2-e intensive fuel;

investments that allow for reductions in

energy used per unit of activity in buildings,

metal manufacturing, etc.; and

Closure of energy-inefficient operations.

The impact is similar but the scope of industry coverage is different

3.5.2 Modelling the Emission Reduction Fund

Modelling a carbon price, or a price within an auction-based ETS, is reasonably straight forward.16

Modelling the ERF is less so.

Recent research by Clarke et al (2014) tackles the task of modelling the ERF topic in an indirect way in general equilibrium model.17 In that study, the authors use a CGE model to generate a Marginal Abatement Cost (MAC) curve for the whole Australian economy. The MAC curve shows the least cost amount of economy-wide abatement available across range of prices for CO2-e. The area under the curve is interpreted as the cumulative subsidy necessary to achieve a given amount of abatement if the ERF, rather than carbon price, were the policy instrument. In modelling an ERF, EY has adopted a similar approach, but with additional modelling to handle the fiscal implications. The ERF modelling has three components.

► Initially the scheme is modelled as if it imposes directly a price on CO2 emissions, with revenue returned to the government. This induces abatement via fuel switching and new production technologies.

► The price is interpreted as the value of the subsidy necessary to achieve the abatement levels generated. The revenue generated is removed from the fiscal accounts and is replaced by a subsidy of the same value. Ultimately, the subsidy is paid by the household sector via a lump-sum, non-distorting, payment to the government.

16 See for example, Philip D. Adams and Brian R. Parmenter, “Computable General Equilibrium Modelling of Environmental

issues in Australia: Economic Impacts of an Emissions Trading Scheme” in P.B. Dixon and D. Jorgenson (eds) Handbook of CGE Modelling, Vol. 1A, 2013, Elsevier B.V5. 17Clarke, H., I. Fraser and R. Waschik (2014), How Much Abatement Will Australia's Emissions Reduction Fund Buy?

Economic Papers, 33(4), 315—26.


► The initial increases in costs of production incurred by emitting firms paying the price are offset by general production subsidies. The subsidies will reduce the initial levels of abatement because they will eliminate the abatement brought about by increase cost-induced reductions in production. The production subsidies, like the CO2 emissions tax, are phantom fiscal instruments and do not appear in the official fiscal accounts reported in the general modelling.

In mathematical terms, the ERF subsidy is calculated as $/tCO2-e of abatement is equivalent to the Marginal Abatement Cost (MAC).

3.6 Economic impact of carbon mitigation

All scenarios show Australia, at the-whole-of-economy level, could achieve emission reductions as outlined in Section 3.2 with relatively small reductions in economic growth.

3.6.1 National level impacts

From 2014-15 to 2049-50, Australia’s real GNP grows at an average annual rate of 2.6 per cent in all three investment scenarios (i.e. IS1, IS2 and IS3), compared to 2.8 per cent in the No action scenario. By 2029-30, real GNP is around 52 per cent above current levels, compared to around 55 per cent in the No action scenario.

Australian real GNI impacts in IS1, IS2 and IS3 are shown in Figure 6.

Figure 6: Real GNI impacts

% cumulative deviation from No action scenario

$billion

Source: EY estimates based on VURM.

Emission pricing has a slightly smaller impact on Australia’s GDP, as GDP does not include income transfers associated with the international emissions trading.

From 2014-15 to 2049-50, real GDP grows at an average annual rate of 2.6-2.7 per cent in three investment scenarios, compared to 2.8 per cent in the No action scenario.

Australian real GDP impacts in IS1, IS2 and IS3 are shown in Figure 7.

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3.6.2 SA level impacts

In percentage terms state level impacts are of a lower negative magnitude than the national level impacts because of the difference in SA industry composition compared with the national level industry composition.

From 2014-15 to 2049-50, SA’s real GSI grows at an average annual rate of 2.2 per cent in all three investment scenarios (i.e. IS1, IS2 and IS3), compared to 2.3 per cent in the No action scenario.

By 2029-30, real GSI is around 44 per cent above current levels in all three scenarios, compared to around 47 per cent in the No action scenario.

SA real GSI impacts in IS1, IS2 and IS3 are shown in Figure 8.

Figure 8: Real GSI impacts



Emission pricing has a slightly smaller impact on SA’s GSP, as GSP does not include income transfers associated with the international emissions trading.

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From 2014-15 to 2049-50, real GSP grows at an average annual rate of 2.0-2.1 per cent in three investment scenarios, compared to 2.2 per cent in the No action scenario.

SA real GSP in IS1, IS2 and IS3 are shown in Figure 9.

Figure 9: Real GSP impacts



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4. Nuclear fuel cycle business case inputs

Assumptions regarding annual capital and operational expenditure by item, price and quantity for new NFC activities were taken from the three business case analyses undertaken on behalf of the Commission. These were:

► Quantitative analyses and business case for radioactive waste storage and disposal facilities in SA;

► Quantitative analyses and business case for Uranium conversion, enrichment and fuel fabrication facilities in SA; and

► Quantitative analyses and business case for developing nuclear power plant in SA.

These business cases quantified the costs – capital and operational - of engineering, construction, operation and decommissioning phases of the NFC activities. The cost and revenue estimates produced as an output of these three business cases were a key input to the CGE model – in developing a comprehensive set of production and consumption functions of the new industries or activities associated with the NFC activities and in assessing the impacts of these investments on the SA economy.

The CGE model also seamlessly integrated the EY bottom-up electricity market model (formerly known as the ROAM model), which integrated electricity demand, exchange rate and fuel prices outputs from the CGE model as inputs to establish the lowest cost mix of electricity generation under the scenarios outlined earlier. Wholesale electricity prices forecast by the electricity market model were a major input into the nuclear power business case as they provided an indication of the returns that a commercial investor may be able to recoup from that investment.

4.1 Expansion of the radioactive minerals extraction industry

Uranium has been mined in Australia since 1954. Three mines are currently operating: Ranger (NT), Olympic Dam (SA) and Beverley (SA). In 2014-15, Australia produced 6,101 tonnes of Uranium Ore Concentrate (UOC). Australia is the world's third-largest producer, behind Kazakhstan and Canada. All Australian production is currently exported.18

Consistent with the global goal of meeting a two degree warming target, significant growth in nuclear power may be expected by 2050. The resulting growth in installed nuclear power generation capacity may lead to a significant expansion in the uranium mining sector. Based on assumptions consistent with the IEA’s World Energy Outlook19 worldwide growth in uranium demand forecast and this is presented in Figure 10.

Based on Australia’s current share of global uranium supply, Australian exports to the world were also predicted. Growth in Australian production under this scenario is shown in Figure 11. These are consistent with Department of Industry projections to 2030.20

Under this scenario on the basis that SA contributes to 50% of Australia’s total uranium production:

► Uranium production in SA was estimated to almost double in the period 2016-17 and 2029-30 to 6100 t UOC and 7700 t UOC in 2039-40 (see Figure 11),

► The value of uranium exports from SA in 2029-30 would be about $770m and in 2039-40 it would be $980m assuming a price of A$128 per kgU, which represents a price increase of about 50% from its present level,

18 http://dfat.gov.au/about-us/publications/international-relations/asno-annual-report-2013-14/html/section-2/australias-

uranium-production-and-exports.html 19 World Energy Outlook 2014. 20 http://www.industry.gov.au/resource/Documents/Mining/uranium/Uranium-Industry-factsheet.pdf


► This was estimated to correspond to total royalties of $39m to 2029-30 and $50m to 2039-40.

Figure 10: World demand for Uranium in baseline investment scenario

Source: IEA, World Energy Outlook 2014.

Figure 11: Australian exports of Uranium in IS1

Source: EY estimates based on IEA, World Energy Outlook 2014 and BREE data.

4.2 Establishing a set of further processing facilities

Conversion, enrichment and fuel fabrication – steps in the further processing or uranium oxide – have the potential to add value to Australia’s exports of uranium ore concentrate. Unlike coal, natural uranium requires substantial processing before it can be used to generate electricity in a nuclear power plant.

For the majority of light water reactors, the production steps involved are conversion, enrichment and fuel fabrication. The uranium oxide (U3O8) is first purified and then converted into uranium hexafluoride (UF6), which in gaseous form is required for the enrichment stage. Enrichment increases the proportion of U-235 from 0.7 per cent to between 3 and 5 per cent. The enriched UF6 is subsequently converted to uranium dioxide (UO2) and transferred to a fabrication plant for assembly into fuel (commonly pellets and fuel rods).

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Figure 12 shows the relative volumes of uranium as it moves through the enrichment cycle.21

Figure 12: Front-end nuclear fuel cycle

Source: Australian Government (2006), p.33.

The World Nuclear Association (WNA) (2015) has estimated that the price for 1 kg of uranium as enriched reactor fuel was US$1880. It takes approximately 9 kg of U3O8 to produce 1 kg of reactor fuel. Conversion, enrichment and fabrication of uranium are included in the cost of the fuel. WNA assumed that 8.9 kg of U3O8 was required at a price of US$97/kg. The U3O8 is then converted into 7.5 kg of UF6at US$16/kg and then enriched using 7.3 separative work units (SWU) (enrichment is measured SWU) at US$82 per SWU. Finally, the uranium is fabricated into 1 kg of fuel at US$300/kg. In July 2015, the approx. US$ cost to get 1 kg of uranium as UO2 reactor fuel (at current long-term uranium price) is $1,882.

Table 9: Component costs of 1kg of Uranium as enriched reactor fuel

Units Qty Unit price (US$) Total cost (US$) Cost share

Uranium Kg 𝑈3𝑂8 8.9 97 863 46%

Conversion Kg 𝑈𝐹6 7.5 16 120 6%

Enrichment SWU 7.3 82 599 32%

Fuel fabrication Per Kg 300 16%

Total 1,882

Source: WNA (2015), The Economics of Nuclear Power, http://www.world-nuclear.org/info/Economic-Aspects/Economics-of-Nuclear-Power/

4.2.1 Construction costs to build further processing facilities

EY were provided with the engineering cost estimations and financial model data from the business case developed for the Commission by Hatch, for the uranium conversion and enrichment steps.22 While several alternative investment scenarios were considered in the business case study, the CGE model was used to investigate the development of conversion and enrichment facilities alone based on the proven, wet conversion process and using the gas centrifuge enrichment technology. Investment in fuel fabrication facilities was not considered in the present assessment because of the significant technical hurdles that would need to be overcome to establish sufficient market share in a competitive market for these services. The CGE model also did not consider the impact of

21 Australian Government (2006), Uranium mining, processing and nuclear energy: opportunities for Australia? Department

of the Prime minster and Cabinet. Uranium mining, processing and nuclear energy Review. http://www.ansto.gov.au/__data/assets/pdf_file/0005/38975/Umpner_report_2006.pdf 22 Hatch (2016), Quantitative analysis and Business Case for the development of Uranium conversion, Enrichment and fuel

fabrication facilities in South Australia. http://nuclearrc.sa.gov.au/app/uploads/2016/02/Hatch.pdf

•200 tonnes of requires

150,000 tonnes of rock and ore

Mining and milling

• 170 tonnes of uranium as

Conversion

• 24 tonnes of uranium as enriched

Enrichment

•24 tonnes of

Fuel fabrication

•146 tonnes of uranium as tail

Depleted Uranium tails

Roughly equivalent to the amount of fresh fuel required annually by a 1000MW reactor

http://www.world-nuclear.org/info/Economic-Aspects/Economics-of-Nuclear-Power/

http://www.world-nuclear.org/info/Economic-Aspects/Economics-of-Nuclear-Power/

http://www.ansto.gov.au/__data/assets/pdf_file/0005/38975/Umpner_report_2006.pdf


investments in as yet unproven or niche technologies such as the laser enrichment and dry conversion process respectively.

For each facility analyses undertaken by Hatch estimated costs in the following categories building and site development; process equipment, bulk materials, engineering and site selection, regulatory approval, labour costs, other construction and commissioning and annual fixed and variable operating costs. Decommissioning costs were calculated in the business case model but were not considered in the present analyses because they fall beyond the timeframe of the CGE model.

It was assumed that construction would begin in 2024-25 and end in 2028-29. The majority of the capital costs were related to establishing the enrichment facilities and the capital works were estimated to peak in 2027-28. The total estimated capital cost to build the conversion and the enrichment facilities in SA was estimated to be $7.1bn in 2014-15 prices.

Annual and cumulative capital works are shown in Figures 13 and 14.

Figure 13: Real annual conversion and enrichment facilities construction costs

Source: Hatch Pty Ltd. 2015. Figure 14: Real cumulative conversion and enrichment facilities construction costs

Source: Hatch Pty Ltd. 2015.

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Conversion facility 3 27 178 238 86

Enrichment Facility 32 340 2,210 2,960 1,068

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Table 10: Capital works associated with further processing facilities

Further processing facilities capital works $million %

Fuel Conversion Facility 532 6.7%

Enrichment Facility 6,610 83.6%

Total Project Capital 7,142 90.4%

Changes in working Capital 114 1.4%

Closure Costs - Fuel Conversion Facility 87 1.1%

Closure Costs - Enrichment Facility 560 7.1%

Closure Costs 647 8.2%

Total Capital Costs 7,902 100%


Table 11: Detailed costs associated with the further processing facilities

Total capital expenditure by item Fuel Conversion

Facility Enrichment

Facility Total %

Roadworks 3.4 3.4 6.9 0.10%

Earthworks 9.4 48.1 57.6 0.81%

Concrete 58.4 162.9 221.3 3.10%

Structural Steel 110.9 376.3 487.2 6.82%

Architectural 49.4 41.4 90.8 1.27%

Mechanical 116.5 4,497.9 4,614.4 64.61%

Piping 15.7 143.4 159.1 2.23%

Electrical 12.5 111.5 124.0 1.74%

Instrumentation 37.9 117.1 155.0 2.17%

Total Directs 414.2 5,502.1 5,916.2 82.84%

Basic Engineering Phase (G1) 5.0 45.6 50.6 0.71%

Detailed Engineering (G2) 10.9 100.4 111.3 1.56%

Procurement (G3) 1.8 16.4 18.2 0.25%

Project Controls (G3) 1.4 12.8 14.2 0.20%

Project and Construction Management (G4) 4.0 36.5 40.5 0.57%

Commissioning (G5) 35.2 23.0 58.2 0.81%

Construction Indirects (G4) 24.9 0.0 24.9 0.35%

Freight (G3) 18.0 330.1 348.1 4.87%

Vendor Representatives, Third Party Eng. (G2) 4.1 352.8 357.0 5.00%

First Fills (G5) 5.5 55.0 60.5 0.85%

Camp and Catering - Excluded 0.0 3.3 3.3 0.05%

Spares (G3) 4.8 0.0 4.8 0.07%

Regulatory Licence (G1, G2) 2.4 110.0 112.4 1.57%

Subtotal Indirects 117.8 21.9 139.7 1.96%

Total Installed Cost (Excluding Owners Cost) 531.9 6,610.0 7,141.9 100.00%



4.2.2 Operating costs of further processing facilities

Real annual operating costs of further processing facilities are provided in Figure 15. Operation of the facility was assumed to start in 2029-30. Labour costs constitute a larger share of the fixed total operating costs. Total annual operating costs for conversion and enrichment facilities are $300m. Electricity is major intermediate input to these facilities, nearly $50million each year will be spent on the electricity at the facilities.

Figure 15: Annual operating costs by major item

Conversion facility annual variable costs ($50m)


Enrichment facility annual variable costs ($51m)


Fixed annual operating costs ($199m)


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4.2.3 Revenue projections

Annual revenue estimates for the conversion and enrichment facilities are provided in Figure 16. Annual revenue projection for exporting converted and enriched uranium in real terms is $657m.

Figure 16: Annual revenue by major item


4.2.4 Price projections

Prices used to forecast the revenue for uranium further processing products are provided in Table 12. These prices are comparable with the prices estimated based on the World Nuclear Association (WNA) reported in Table 12.

Table 12: Uranium product prices

Products Units $

Yellowcake AUD$/kg U3O8 148

Conversion Cost AUD$/kg U as UF6 8

UF6 AUD$/kg U 183

UO2 AUD$/kg U in UO2 (powder) 183

SWU price AUD$/SWU 129


4.3 Establishing integrated radioactive waste storage and disposal facilities

There is a growing volume of used fuel and intermediate level of nuclear waste worldwide without a permanent disposal solution. The business case studies undertaken for the Commission by Jacobs MCM evaluated the feasibility of developing a set of integrated facilities for the interim storage and permanent disposal of used fuel, intermediate level waste and low level waste that may be produced from the activities associated with the operation of these facilities.23

The business case also provided the capital and lifecycle operating costs over the engineering, construction, operation, decommissioning and post-closure monitoring phases of a possible set of radioactive waste storage and disposal facilities in SA under a number of scenarios.

23 Jacobs MCM (2016), Radioactive waste storage and disposal facilities in South Australia: Quantitative cost analysis and

business case. http://nuclearrc.sa.gov.au/app/uploads/2016/03/Jacobs-MCM.pdf

$163m

$18m

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UF6

UO2

SWU price


In addition to the cost of establishing the facility, the business case assessment also incorporated estimations of costs for developing enabling infrastructure such as road, rail and port infrastructure.

For the purpose of the CGE modelling the Commission advised EY to undertake modelling to assess the economic impacts of the investment under the baseline scenario described by Jacobs MCM. This scenario is based on the collocation of the repository for storing intermediate level waste and used fuel – combining the intermediate depth repository and geological disposal facility, it also assumes that an interim storage facility is developed near a port at a coastal location and that a low level waste repository is located at a second inland location separate from all other facilities. This is summarised in Table 13 below.

Table 13: A set of radioactive storage and disposal facilities

Coastal location 1 Inland location 1 (semi-rural)

Inland location 2 Inland location 3

W3-C W1-A W2,W4-E

W1 – LLW (Low Level radioactive Waste); W2 – ILW (Intermediate Level Waste); W3 – ISFS (Intermediate Storage Facilities); and W4 – GDF (Geological Depository Facilities).

Source: Jacobs Group (Australia) Pty Limited, 2015.

While activities associated with establishing capital infrastructure and operating those facilities extend over a period of 120 years, modelling is undertaken to 2049-50. The value of capital works undertaken over this period was estimated from the engineering cost estimations developed by Jacobs and is equal to $13.8bn as shown in Figure 17.

Figure 17: Total capital works


4.3.1 Construction costs to build storage and disposal facilities

The total estimated capital cost of building nuclear waste storage and disposal facilities over the modelling period was estimated to be $13.8bn in 2014-15 prices. The components of capital expenditure for a set of modelled facilities are provided in Table 14.

The majority of the capital works costs comprise:

► Contingency fee accounts nearly 32% of the capital expenditure followed by initial underground construction (22%), design fees (11%) and enabling infrastructure (9%).

► The investment items mapped to the investment commodities in the VURM to develop investment functions in the general equilibrium model for the embryonic radioactive waste

LLW ISFS ILW, ISFS

A C E

W1 W3 W2, W4

Total capital works $0.6 $1.6 $28.4

Modelled capital works $0.4 $1.1 $12.3

$-

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$bn


storage and facilities industry in SA. This investment function become active when the construction starts, that is in 2019-20.

Table 14: Total capital costs by expenditure item

W4 - GDF (Port & ISFS then Construction of GDF) $A million %

Scoping Work 5 0.04

Site identification and preliminary investigations 550 4.69

Site(s) purchase

Location Enabling Infrastructure

Port 196 1.67

Rail 720 6.15

Road 156 1.33

Airport 7 0.06

Site Enabling Infrastructure

Haul Road from Port ~ 5km 4 0.03

External Works 72 0.61

External Services 35 0.30

Site Buildings / Construction Works 0 0.00

Buildings / Construction 753 6.43

Underground construction – Initial 2650 22.62

Underground construction – Future 482 4.11

Site works 142 1.21

Site services 6 0.05

Special Equipment 0 0.00

Specialist equipment 0 0.00

Other 0 0.00

Other Allowances 0 0.00

EPCM fee 794.9 6.78

Design Fees 1314.6 11.22

Commissioning 79 0.67

Project Contingency 3692.1 31.51

Authorities Fees and Charges 57.5 0.49

Total 11716.1 100.00


The nuclear waste facilities capital work projects source their inputs from SA, rest of Australia and the rest of the world. As shown in Figure 18, nearly 46% of capital works (mainly non-residential construction) is provided by SA. Over 37% (mainly specialized machinery) is imported.


Figure 18: Sourcing capital inputs for radioactive waste storage and disposal facilities

Source: EY estimates based on Jacobs Group (Australia) Pty Limited, 2015

Annual and cumulative capital expenditure related to a set of radioactive waste storage and disposal facilities are provided in Figures 19 and 20.

Figure 19: Real annual capital works ($million)


Figure 20: Real cumulative capital works ($million)


5% 5% 3%

46%

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37%

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Construction of these facilities is expected to start in 2019-20 and the first shipment of used fuel and intermediate level waste is expected to arrive in 2026-27.

4.3.2 Annual operation costs

The total estimated operational expenditure, including wages, salaries and supplements of employed persons in each year is $722 million per annum in 2014-15 prices. The details of operational expenditure are provided in Table 15. Operation costs related encapsulation plant constitutes 36% followed by GDF repository (19%) and labour (15%).

Table 15: Total annual real operation expenditure by item

Cost item Labour Other Opex** Total Employed

$m $m $m Persons

Head office 21 9 30 118

Port 3 3 6 13

Transport Port to ISFS (W3) 0 2

ISFS and handling 8 114 122 50

Rail from W3 to GDF W4 2 14 16 11

LILWS Repository (W2)* 20 65 85 95

LLW (W1) repository (near surface) 3 7 10 12

Encapsulation plant (W4) 27 262 288 158

GDF Repository (W4) 23 141 164 113

Total 107 615 722 572

* note some duplication with W4 if both developed; ** note excludes facility and equipment renewals


These operational expenditures are mapped to the intermediate inputs and primary factors in the VURM to develop production functions in the general equilibrium model for a new sector in the SA economy – i.e. the radioactive waste storage and disposal facilities industry.

The integrated waste storage facilities source their intermediate inputs from SA, rest of Australia and the rest of the world (nearly 25% of intermediate inputs imported from overseas). The operation of these facilities starts in 2026-27. According to the business case assessment, the integrated waste storage and disposal facilities create 572 permanent jobs over the operational

period.24

4.3.3 Nuclear waste revenue projections

Jacobs MCM estimate the potential revenue from providing radioactive waste storage and disposal facilities for Australia based on the following assumptions.

Potential customer base

24 This business case estimate differs from the general equilibrium estimate of the direct employment in the industry. As

reported in Table 4, the general equilibrium estimate shows over 650 permanent full-time equivalent jobs will be created in the industry. The difference is mainly related to the treatment of effective units of labour required to maintain the facilities in general equilibrium model differ from the business case estimates.


► The customers for an Australian radioactive waste storage facility are primarily those countries with a historic, operational or well advanced nuclear power program which lack their own national or regional solution for the nuclear waste.

► There is an established ‘backlog inventory’ of spent fuel from heavy water (HW) and light water (LW) reactors in existence throughout the world for which there isn’t a known long term storage or disposal solution.

► There is also a known global backlog of intermediate-level waste (ILW) which also lacks a credible storage or disposal solution.

► Given rates of high level waste / spent fuel arising from reactor activity (and ILW arising from operations and decommissioning) an estimate of the future waste can be made, using a straight line simplifying assumption. Current operational nuclear programs and hypothecated, future programs are both considered. Table 16 summarises them.

Table 16 Total Inventories – Spent Fuel / high Level Waste and Intermediate Level Waste

Waste Form

Current (2015) backlog from

current programs (a)

New post 2015 waste created

by 2080 - current

programs (b)

New post 2015 waste created by 2080 - new programs (c)

Total post 2015 waste available by

2080

(b+c)

Total available by 2080 (current and planned) (a+b+c)

LWR Spent Fuel (tHM)^

74,021 103,617 41,800 145,417 219,438

HWR Spent Fuel (tHM)

12,262 27,712 0 27,712 39,974

Total SF (LWR+HWR) (tHM)

86,283 131,329 41,800 173,129 259,412

ILW (m3) 258,866 336,671 132,000 468,671 727,537

Source: Jacobs MCM estimates ^tHM refers to metric tonnes of heavy metal.

While the total volume of material requiring storage (from current and future likely programs) can be estimated, the portion of this which may be attracted to a disposal solution in SA is unknown without primary market research. We make the simplifying assumption that 50% of the foreseeable spent fuel and ILW arising will accrue to Australia as a ‘base case’ with +25% and -25% as upper and lower bounds to the analysis.

Prices

► Payment rate per tonne high level waste (HLW) at point and time of entry was assumed to be A$1.75million per tonne heavy metal

► Payment rate per cubic metre (m3) of ILW at point and time of entry A$0.04 million per cubic metre.

Volumes

► 3,000 tonnes HLW in the intermediate storage facility (ISF) per annum starting from 2026-27 ► 1,500 tonnes HLW disposed of in geological deposit facilities (GDF) per annum starting from

2041-42

The time frame over which used fuel and intermediate level waste (ILW) were estimated to be imported to SA are presented in Figures 21 and 22. These figures also show when used fuel and ILW is transferred from interim storage to the geological disposal facility (for used fuel) or intermediate depth repository (for ILW). While forecasts of imports of used fuel and ILW have been estimated, based on the accessible market over a period of 83 years and an operational time-horizon for these facilities of 120 years, the CGE model only considers the impact of these investments and revenue flows to SA in the time frame to 2049-50.


Figure 21: High level nuclear waste volume


Figure 22: Low level nuclear waste volume


Revenue projections

Based on the volume and prices, the total expected revenue over the lifecycle of the storage facilities is over A$257bn in 2014-15 prices. Operation of the facility starts in 2026-27 and for the CGE modelling we considered the revenue received up to 2049-50.

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Figure 23: Revenue projections from nuclear waste


4.3.4 Operation of radioactive storage and disposal facilities

The Commission developed a hypothetical revenue and dividend transfer arrangements between several entities to outline a realistic basis for the operation of the integrated waste storage facilities in South Australia. The entities that comprise the integrated waste storage facilities business case are the hypothetical SA Waste Public Corporation and SA State Wealth Fund. When used fuel and intermediate level waste is imported to South Australia 85% of gross revenues are assumed to be transferred to the SA Waste Storage Public Corporation and 15% of gross revenues are assumed to be transferred to the State Wealth Fund that is assumed as shown in Figure 24. The particular assumptions governing the operation of these two hypothetical facilities are given below.

SA Waste Storage Public Corp (SA WSPC)

► Funding for investments that need to be made by the ‘SA Waste Storage Public Corp’ before revenues are secured from international sources is assumed to be undertaken through the normal course of State Treasury operations, using Foreign and/or Australian capital which are assumed to form part of the State Government’s debt portfolio,

► All funding for ‘SA Waste Storage Public Corp’ for the years beyond 2027, are assumed to be based on revenues from waste inventory imports, which exceed the capital requirements to establish the necessary infrastructure such that no further debt funding is required to establish the waste storage facilities (WSF) infrastructure,

► Annual profits (Pann) net of Commonwealth taxes, working/sustaining capital requirements, operating costs and reserves for maintenance and monitoring of the facility over its lifecycle is transferred to the SWF and State Government Consolidated Revenue.

SA State Wealth Fund (SA SWF)

► Interest income in the SWF is generated at a real rate of 4% per annum. 50% of the interest income generated within the SWF is transferred to the consolidated revenue of the SA State Government for unspecified expenditure.

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Figure 24: Revenue disposal

Source: Nuclear Fuel Cycle Royal Commission

Revenue that are transferred from the State Wealth Fund to the South Australian government’s consolidated revenue is assumed to be expended on other SA infrastructure and Government

activities.

4.4 Establishing a nuclear power plant and systems in South Australia

An engineering cost modelling and business case analysis was undertaken by WSP | Parsons Brinckerhoff (PB) on behalof of the Commission, to investigate the use of nuclear fuels for electricity generation in SA. WSP|PB provided whole-of-life costs including that of development, construction, operation and decommissioning for different types of nuclear power plant and their associated systems and infrastructure.

The time frame for the nuclear deployment in SA is by July 1, 2030. The available technologies were reviewed to identify the most likely technology classes which could be completed and operated by 2030 while avoiding prototype risks of adopting technologies that have not been commercially developed in other regions.

The PB has estimated economic viability of nuclear power plants under a range of scenarios reflecting different assumptions relating to plant construction costs, discount rates, costs of uranium and carbon prices. Their estimates are based on realised costs for establishing nuclear power plants in the US and the cost of establishing supporting infrastructure in South Australia using commercial databases.

These costs are detailed below and were included in the general equilibrium modelling to assess the potential macroeconomic effects of operation of nuclear power plant in SA region of the NEM. The cost estimates produced by WSP|PB were included in the EY electricity market model to assess whether a nuclear power plant would emerge under any of the two market based carbon abatement scenarios as part of the lowest cost mix of electricity supply. The outputs from the electricity market model are included in the CGE model to provide macroeconomic effects of nuclear power plant in SA.

Nuclear waste inventory imports $$$

SA State Wealth Fund

SA Waste Storage Public Corporation

85 Gross revenue

Incorporates fixed, operating expenses and perpetual cost

fund for facility lifecycle

15 Gross revenue

SA Government Consolidated Revenue




4.4.1 Nuclear technologies

Nuclear power has been used for power generation for over 60 years during which many nuclear reactor technologies have been developed. Although developments continue with new concepts and possibilities, nuclear technologies which have demonstrated safe, reliable and economic operation are limited.

The leading technologies internationally are:

► Pressurised Water Reactor (PWR) ► Boiling Water Reactor (BWR) ► Pressurised Heavy Water Reactor (PHWR)

Both PWR and BWR can be categorised as Light Water Reactors (LWR). Parsons Brinckerhoff has reviewed the available products in each of the above categories which fit the potential application in SA. They have suggested two nuclear technologies that could be potentially built in SA. They are:

► PWR AP1000 with net capacity of 1,125 MW; and ► NuScale Small Modular Reactor (SMR) with capacity 285 MW.

Potential economic effects are estimated for these two possibilities in the sections to follow.

4.4.2 Components of nuclear power costs and levelised costs

One of the fundamental issues underlying the debate on the potential role of nuclear power in meeting the future global energy needs relates to the continuing lack of consensus on the likely cost of new nuclear generating plants.25

The cost of nuclear power was modelled on the basis of comprising four major components:

► Capital or construction costs (CAPEX): incurred during the planning, preparation and construction of a new nuclear power station;

► Operations and maintenance (O&M): These relate to administration, management and support of power station (labour, material and supplies, capital upgrades and additions, insurance, security, planned maintenance and other services);

► Fuel costs: reflect the cost of fuel for the power station; and ► Back-end costs: related to the decommissioning and dismantling of nuclear facilities at the end

of their operating life and long-term management and disposal of radioactive waste.

Parsons Brinckerhoff has estimated the above costs for selected nuclear technologies suitable for SA.

4.4.2.1 Capital and construction costs

Capital costs represent 60% to 70% of the whole-of-life cost of nuclear power while total O&M account for 30-40% whereas cost of fuel is only 4-6% in the designs studied by Parsons Brinckerhoff. Most of the O&M components include expenses related to health and environmental protection and accumulation of funds for spent fuel management and final waste disposal and for eventual plant decommissioning. It also includes the cost for insurance coverage against accidents. Several potential externalities are internalized in the O&M costs.

Capital costs for a new nuclear power station have increased in recent years mainly due to tougher safety requirements and unexpected construction difficulties. Figure 25 shows estimates of the

25 Kessides I N (2010) Nuclear Power: Understanding the economic risks and uncertainties, Energy Policy 38 (2010), 3849—

3864.


overnight capital cost of the potential new nuclear reactor commissioned in Australia.26 An additional factor adding to the increase in cost in Australia was the declining value of the Australian dollar, as large portion of the expenditure is denominated in other currencies.

Figure 25: Light Water Reactor capital cost estimates

Source: BREE (2012), BREE (2013), CO2CRC (2015), Parsons Brinckerhoff (2015).

Pre-construction capital costs assumed for SMR and PWR nuclear facilities are the same. They include site assessment, design, project management and regulatory, licensing and public enquiry costs. They are reported in Tables 17 and 18.

Table 17: Capital costs PWR (Central)

Cost category Unit Cost

Project development – overseas USD m 63

Project development – local AUD m 308

Regulatory and licensing – local AUD m 65

Overnight construction cost – overseas USD m 3,476

Overnight construction cost – local AUD m 3,814

Total CAPEX including development cost AUD m 8,807

Total CAPEX including development cost AUD/kW 7,828

Total CAPEX excluding development cost AUD/kW 7,424

Source: Parsons Brinckerhoff (2015).

Table 18: Capital costs SMR (Central)


Project development – overseas USD m 63

Project development – local AUD m 308

26 Both BREE (2012) and BREE (2013) assume nth-of-a-kind LWR built in Australia expressed in 2012-13 Australian dollars.

CO2CRC (2015) cost estimate represents a generic plant built in Australia in 2015-16 Australian dollars. Parsons Brinckerhoff (2015) assumes cost of a PWR (AP1000) built in SA expressed in 2014-15 Australian dollars. All sources except

CO2CRC include development costs in their cost estimates.

3,470

5,268

9,000

7,828

0

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7,000

8,000

9,000

10,000

AETA (2012) AETA (2013) CO2CRC (2015) PB (2015)

A$/

kW


Table 18: Capital costs SMR (Central)


Regulatory and licensing – local AUD m 65

Overnight construction cost – overseas USD m 1,114

Overnight construction cost – local AUD m 998

Total CAPEX including development cost AUD m 2,907

Total CAPEX including development cost AUD/kW 10,202

Total CAPEX excluding development cost AUD/kW 8,604


The central case overnight capital cost estimates for the PWR is AU$7,828/kWe and the smaller SMR is $AU10,202/kWe

4.4.2.2 Non-fuel operations and maintenance

In case of a nuclear power station, the non-fuel O&M cost, which includes staffing and equipment maintenance, are largely fixed, which a relatively small variable component. The following costs estimated by PB have been used in the study.

Table 19: O&M cost PWR (Central)


O&M – overseas USD/MW per annum 56,000

O&M – local AUD/MW per annum 96,100

Insurance AUD/MW per annum 17,100

Total non-fuel O&M AUD/MW per annum 191,528


Table 20: O&M cost SMR (Central)


O&M – overseas USD/MW per annum 48,900

O&M – local AUD/MW per annum 105,400

Insurance AUD/MW per annum 19,800

Total non-fuel O&M AUD/MW per annum 195,084


4.4.2.3 Fuel costs

Power plants generally fall into two separate categories based on the contribution of fuel costs to overall production costs. Gas powered plants fall into the low capital cost/high fuel cost category. These plants are relatively low cost to build but have high fuel costs. The cost of fuel can contribute well over half of the total cost of electricity production. Coal fired power station are more capital intense with lower fuel cost compared to gas fired power stations. Renewables have no costs associated with the fuel but relatively high capital costs associated with the initial construction of the plant. For nuclear the fuel costs are very small compared with the recovery of the capital cost investment. As a general rule, LWR (enriched) fuel would typically contribute less than10% of the total production cost of electricity in a new nuclear power plant.

Parsons Brinckerhoff has estimated the fuel cost based on data from the US Nuclear Energy Institute, which documents average fuel costs for all operating US nuclear power plants annually. As nuclear fuel is an international commodity, such estimated should be a reasonable indicator for international nuclear fuel cost. In this study fuel cost was assume at US$7.60/MWh


(AU$9.92/MWh) for PWR or US$9.1/MWh (AU$11.88/MWh) for SMR. Higher cost for SMR is related to lower thermal efficiency and higher fuel enrichment requirement.

An indispensable cost related to nuclear power generation includes spent fuel cost. Once the fuel is burnt and removed from the reactor core it is stored on site in the cooling pools. After several years of cooling down the spent fuel is transfer to a storage facility. Parsons Brinckerhoff (2015) estimates the cost of managing and storing the spent fuel as US$3.75/MWh (AU$4.90) in case of PWR and US$4.50/MWh (AU$ 5.87/MWh) in case of SMR.

4.4.2.4 Decommissioning costs

This category includes cost of decommissioning of the plant and site rehabilitation, which will be a significant end of life cost commitment in case of a nuclear power station. Following the assumed 60 years of operations and period of cooling down, the plant will be dismantled and its parts safely disposed or stored. Due to a fact to the decommissioning costs of a nuclear power station are expected to occur in a very distant perspective, usually an annual allowance is deducted to build up the fund over the operating life of the plant. The decommissioning cost is estimated by PB at US$500 m (AU$653 m) in case of PWR and US$250 m (AU$326 m) in case of SMR.

4.4.2.5 Levelised cost of electricity

The cost assumptions can be summarized by estimating a Levelised Cost of Electricity (LCOE). The LCOE captures the average cost of producing electricity from a technology over its assumed lifetime, subject to assumptions about how the generator will operate (e.g. load factor, efficiency). It allows the comparison of technologies with very different cost and operating profiles. The LCOE captures following costs: fuel, carbon, variable O&M (VOM), fixed O&M (FOM) and capital cost, all expressed in $/MWh.

EY calculated the LCOE for PWR and SMR technology, along with other power generation technologies considered in the electricity market modelling as shown in Figure 26. The LCOE calculation assumes the following:

► Discount rate of 10%; ► Commissioning year 2030-31; ► Costs include: CAPEX (excluding pre-construction costs), non-fuel O&M, fuel (including spent

nuclear fuel cost). Decommissioning cost and infrastructure cost are excluded; ► No interest during construction included in the calculation;27 ► Economic lifetime of 20 years for all technologies;28 ► Carbon transport and storage cost of 30 $/t in case of CCS; ► Capacity factors: coal 90%, CCGT 50%, CCGT CCS 50%, OCGT 10%, onshore wind 35%, offshore

wind 40%, solar 25%, wave 35%, nuclear 90%. ► Two carbon trajectories: no carbon price and IS3 carbon price.

27 The inclusion of the interest incurred during the construction would increase the LCOE, particularly in case of

technologies with extended construction period like nuclear. 28 For the purpose of the LCOE calculation, all technologies are assumed to have the same economic life of 20 years. The

actual physical life of the assets is different. The 20 year horizon can be interpreted as a period over which the capital costs are to be recovered by the investors. Similar approach is used in the LCOE calculation included in the referenced reports by BREE and CO2CRC. If a life of 60 years was used in case of PWR, the LCOE would reduce by approximately $15/MWh.


Figure 26: LCOE of new entrant in 2030-31

Source: EY

76

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5. Electricity modelling inputs and results

The CGE modelling applied for the NFC assessment incorporates detailed bottom-up electricity market modelling. The electricity market models require macroeconomic inputs from the CGE model and simulate operation of the electricity market under these assumptions. The key inputs to the electricity market models include: electricity demand, fuel and carbon prices. Outputs from the electricity modelling include generation, CO2 emissions, fuel consumption and wholesale electricity prices, which are used in the CGE model and further analysis. A description of the electricity modelling inputs and results used in the CGE model are provided in this section. A description of the modelling approach can be found in Appendix D.

Table 21 summarises the key characteristics of the NFC scenarios from the electricity market modelling perspective.

Table 21: Summary of modelled scenarios

IS1 IS2 IS3 IS3 Large IS3 Small

Climate action Moderate

(ERF + carbon price)

Moderate (carbon price)

Strong (carbon price)



Nuclear power plant

Not allowed Allowed29

Allowed

Committed

PWR commissioned in SA in 2030-31

Committed

SMR commissioned in SA in 2030-31

Interconnector upgrade

No No No Yes No

EV uptake Moderate (FGF) Moderate (FGF) Strong

(ClimateWorks) Strong

(ClimateWorks) Strong

(ClimateWorks)

Source: EY

5.1 Electricity demand

Electricity demand is an input into the electricity market model and is derived in the macroeconomic model. In this study a key focus is placed on modelling the electricity market in SA and the NEM therefore the national electricity demand figures from the CGE model are converted into electricity demand in the five NEM regions. In the electricity market model, demand on the ‘sent out’ basis is used, i.e. final electricity demand less behind the meters rooftop PV generation, as shown on Figure 27. In other words, it is electricity supplied to the grid by large scale generation.

29 Nuclear power plant is allowed in the long term planning model (LTIRP), however is not determined endogenously in the

modelling and not included in the 2-4-C® model in scenario IS2 and IS3. In order to assess impact of the nuclear power plant on the market, the NPP is then imposed exogenously in 2-4-C® model in scenarios IS3 Large and IS3 Small. It is assumed that NPP is commissioned in South Australia on 1 July 2030 and sells electricity to the NEM.


Figure 27: Final electricity demand definition

Source: EY

An iterative process between the CGE model and the electricity market model captures demand response to changes in electricity prices (price elasticity of electricity demand) i.e. increase in electricity price leads to lower demand and vice versa. The evolution of demand between initial and final model iterations has been shown on Figure 28.

Figure 28: Evolution of the demand (sent out) in the NEM

Source: EY

Figure 29 shows the forecast of electricity demand in the NEM and SA in more detail. The large grey area on the chart represents final demand (residential, business and industrial) excluding of production from rooftop PVs (which is consumed on site) and consumption associated with charging electric vehicles (EV). The yellow area represents the behind-the-meter generation of residential and commercial rooftop PVs. The behind the meter generation reduces electricity consumption from the grid, which is depicted with a blue dotted line. The grey area represents electricity demand from electric vehicles.

Demand ‘sent out’

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generation

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electricity demand

Network losses

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demand

Business

electricity demand

Residential

electricity demand

Final costumer

electricity demand= =

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Figure 29: Electricity demand in the NEM and SA

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Source: EY

Final customer demand (grey dotted line on Figure 29) is expected to grow in the NEM and SA in all modelled scenarios. Demand in the NEM is expected to grow on average 0.7%, 0.9% and 0.8% per annum in IS1, IS2 and IS3 respectively. Higher demand growth under IS2 can be explained by additional economic activities associated with the NFC. A high carbon price under the strong

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climate action scenario (IS3) leads to higher electricity price and eventually to reduced electricity demand. However, the growth is sustained by a significant increase in EV consumption (bright grey area). Electric vehicles (EV) are assumed to play an important role in decarbonising transport sector under the strong climate action scenario. In this scenario, a forecast by ClimateWorks was used to model EV consumption. In scenario IS1 and IS2 a lower forecast of EV electricity consumption was used, based on Future Grid Forum study30 as shown in Figure 30.

Figure 30: EV electricity consumption in the NEM

Source: EY based on public sources of information, Future Grid Forum CSIRO and ClimateWorks

In this study, EY uses the rooftop PV growth as presented in the National Electricity Forecast Report.31 AEMO predicts rooftop PV generation to continue to grow at a rapid but declining rate. Between 2011 and 2015 PV generation grew by approximately 1 TWh a year (average annual growth rate of 65%). A similar incremental increase per year is forecast until 2020 leading to PV contribution of 10.6 TWh in the NEM. Beyond 2020 PV uptake slows down with single digit year to year growth rates.

Due to significant increase in rooftop PV generation, the network-delivered energy is expected to decline in SA in all three scenarios. Rooftop PVs have also a profound effect on a within the day demand shape leading to a midday reduction in the demand from the grid. Figure 31 illustrates this effect in SA in year 2030-31, where PV output would reduce the demand from the grid on average by 1,200 MW.

30 Change and choice: The Future Grid Forum’s Analysis of Australia’s Potential Electricity Pathways to 2050 (2013), CSIRO 31 National Electricity Forecasting Report (2015), AEMO.

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Figure 31: Average daily demand profile in SA in 2030-31

Source: EY

The high differential between maximum and minimum demand within a day and hence price differential is expected to lead to the deployment of storage technology. Battery storage is expected to continue making significant cost improvements in the future. A study by CSIRO32 suggests that the cost of battery can halve by 2030, leading to new market opportunities, particularly in the behind-the-meter residential and commercial applications. In this study battery storage uptake is modelled exogenously and is linked to rooftop PV uptake. An assumption is made that in the long term 60% of all rooftop PV systems will be coupled with battery, as shown in Figure 32.33 The effect of storage on the demand sent out in SA is shown on Figure 31, where the difference between maximum and minimum demand is reduced.

Figure 32: Storage penetration and energy in SA

Source: EY

32 www.aemc.gov.au/Major-Pages/Integration-of-storage/Documents/CSIRIO-Future-Trends-Report-2015.aspx (accessed

January 2016). 33 As part of this study EY did not model the uptake of storage technology or assess economics of investments in batteries.

The uptake of storage was derived based on the PV uptake and assumed target penetration. The penetration level is calculated as a ratio between estimated numbers of rooftop PV systems equipped with battery and estimated total number of rooftop PV systems. For the purpose of this calculation it was assumed that the average size of a rooftop PV system is 5 kW (installed capacity) and a battery 7 kWh (energy).

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It is assumed that the batteries will operate in a daily cycle, charging during solar generation and discharging mainly during the evening peak, as illustrated below.

Figure 33: Battery operating profile

Source: EY

5.2 Fuel prices

Fuel prices are a key variable cost driver in electricity generation and hence have a significant impact on power market economics. In this study, EY uses a fuel price forecast from the National Transmission Network Development Plan. AEMO’s planning assumptions are based on the Fuel and Technology Cost Review prepared by ACIL Allen Consulting.34

Figure 34 below depicts fuel prices used for generic new plants. Existing generators may have different fuel price trajectories as a result of site specific conditions and long-term contracts.

Figure 34: Projected fuel price assumptions

Source: ACIL Allen Consulting.

34National Transmission Network Development Plan 2014, AEMO

http://www.aemo.com.au/Electricity/Planning/RelatedInformation/~/media/Files/Other/planning/2014%20Assumptions/Fuel_and_Technology_Cost_Review_Data_ACIL_Allen.ashx

0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00

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Gas prices are expected to increase across all regions as a result of increasing exposure to international markets. No significant changes in coal prices are expected during the modelling horizon, with brown coal remaining as the least cost fossil fuel used in the electricity generation.

5.3 Carbon price

A carbon price is the key climate policy instrument which will have a significant impact on the wholesale electricity market. In the electricity market modelling, EY assumes carbon prices derived in the CGE model, which drive assumed level of reduction in the greenhouse gases emissions in the Australian economy.

Figure 35: Carbon price trajectories

Source: EY

Scenario IS1 assumes an introduction of carbon price from year 2030-31. Prior that year, no carbon price mechanism is assumed in Australia and the reduction in emissions is expected to be driven by the ERF. It was assumed that the fund will be increased and extended to 2029-30. It is expected that the fund will not have significant impact on the electricity market, as it will attract emission abatement primarily outside the electricity sector, hence no carbon price assumed until 2030-31.

It is assumed that carbon price will be introduced from 2016-17 in scenarios IS2 and IS3. IS2 carbon trajectory closely follow price in scenario IS1, as both scenarios are assumed to deliver similar level of carbon emission abatement. The carbon price under scenario IS3 is higher compared to scenarios IS1 and IS2, as this scenario reflects strong climate action. It is assumed that the full cost of carbon is reflected in the generators bids and hence in the wholesale electricity prices. Such effect was observed during 2012-13 and 2013-14 in the presence of Carbon Tax in Australia, when the wholesale electricity prices increased in the NEM by more than $20/MWh compared to year 2011-12.

5.4 Technological assumptions

Assumptions on technology availability and costs play an important role in determining the future capacity mix. The key assumptions used in the long term system planning (LTIRP model) include CAPEX, OPEX and efficiency. More detailed information on the technology assumptions can be found in Appendix C.

The assumptions on capital expenditures (CAPEX) have significant implications on the average cost of electricity particularly from capital intense technologies like renewables and nuclear, and hence

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affect the future electricity market. CAPEX values for 2015 are sourced from the Australian Power Generation Technology Report35 published by CO2CRC in November 2015. This study provides most up to date estimates of the investment costs of power generation technologies in Australia. The evolution of the capital costs (driven by technology learning curves) and regional differences are sourced from the Australian Energy Technology Assessments36 published by BREE in December 2013.

New entrant CAPEX is summarized on Figure 36. The capital costs used in the electricity market modelling include equipment, materials, labour, engineering and construction management, and contingencies (process and project). The costs exclude development costs, taxes and CO2 transportation / storage facilities in case of CCS plant. In case of NPP pre-construction capital costs i.e. project development, regulatory and licensing costs are excluded from the LCOE calculation and the long term system modelling. Similarly, any decommissioning and infrastructure costs are excluded. All capital costs are modelled in a consistent way and represent overnight cost required to commission a power station.

Figure 36: Capital cost trajectory

Source: EY based on CO2CRC, BREE and Parsons Brinckerhoff.

Cost assumptions for the nuclear technology was sourced from the assessment prepared by Parsons Brinckerhoff for the Commission and are summarized in the table below. The cost estimates are for the first power station built in SA; hence the CAPEX does not change over time, as shown in Figure 36.

Table 22: Nuclear power plant cost assumptions

Unit Large NPP Small NPP

Technology - Pressurised Water Reactor (PWR)

– AP1000 Small Modular Reactor (SMR) -

NuScale

Net capacity MW 1,125 285 (6 x 47.5)

CAPEX A$/kW 7,424 8,604

35 http://www.co2crc.com.au/dls/Reports/LCOE_Report_final_web.pdf, accessed 26 November 2015 36 http://www.industry.gov.au/Office-of-the-Chief-Economist/Publications/Documents/aeta/AETA-Update-Dec-13.pdf,

accessed 26 August 2015

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http://www.co2crc.com.au/dls/Reports/LCOE_Report_final_web.pdf

http://www.industry.gov.au/Office-of-the-Chief-Economist/Publications/Documents/aeta/AETA-Update-Dec-13.pdf


Table 22: Nuclear power plant cost assumptions

Unit Large NPP Small NPP

CAPEX including development cost

A$/kW 7,828 10,202

Variable operations and maintenance (VOM) (fuel cost + spent fuel fee)

A$/MWh 14.82 17.75

Fixed operations and maintenance (FOM)

A$/MW 191,528 195,084

Source: Parsons Brinckerhoff (2015)

The technology assumptions can be summarized with the help of Levelised Cost of Electricity (LCOE). The LCOE captures the average cost of producing electricity from a technology over its assumed life, subject to assumptions about how the generator will operate (e.g. load factor, efficiency). It allows the comparison of technologies with very different cost and operating profiles. The LCOE captures following costs: fuel, carbon, VOM, FOM and capital cost, expressed in $/MWh. Figure 37 and Figure 38 illustrate the LCOE of selected low carbon technologies calculated under the following assumptions:

► Discount rate of 10% ► Costs include CAPEX (excluding pre-construction cost), non-fuel O&M, fuel (including spent

nuclear fuel cost). Decommissioning cost and infrastructure cost are excluded ► No interest during construction included in the calculation37 ► Economic lifetime of 20 years38 ► Carbon transport and storage cost of 30 $/t in case of CCS ► Capacity factors: coal CCS 90%, nuclear 90%, wind 35%, solar 25%, CCGT 50% ► No carbon price and IS3 carbon price trajectories

As a result of rapidly declining capital costs, solar PV and wind are expected to become the least cost technologies from the next decade or earlier in case of re-introduction of carbon price. The LCOE of large scale solar PV plant is expected to drop below $50 /MWh from 2037-38. A large nuclear reactor appears to be more expensive compared to solar and wind, but less expensive compared to CCS technology and CCGT in the presence of carbon price from 2029-30. It should be noted that the LCOE of the nuclear plant was calculated at 90% capacity, and would increase significantly if the capacity factor is reduced.

37 The inclusion of the interest incurred during the construction would increase the LCOE, particularly in case of

technologies with extended construction period like nuclear. 38 For the purpose of the LCOE calculation, all technologies are assumed to have the same economic life of 20 years. The

actual physical life of the assets is different. The 20 year horizon can be interpreted as a period over which the capital costs are to be recovered by the investors. Similar approach is used in the LCOE calculation included in the referenced reports by BREE and CO2CRC. If a life of 60 years was used in case of PWR, the LCOE would reduce by approximately $15/MWh.


Figure 37: LCOE (no carbon)

Source: EY based on CO2CRC, BREE and Parsons Brinckerhoff (2015)

Figure 38: LCOE (IS3 carbon price)

Source: EY based on CO2CRC, BREE and Parsons Brinckerhoff (2015

5.5 Renewable Energy Target

A Renewable Energy Target (RET) has been legislated in Australia since 2000. The current expanded RET (eRET) contains two facets. The Small-scale Renewable Energy Scheme (SRES) provides support to domestic solar thermal and PV installations. The Large-scale Renewable Energy Target (LRET) is a certificate based scheme to support grid-scale projects. The LRET was devised to meet 20% of Australia’s energy by 2020 and the target was fixed at 41,000 GWh in 2007. Since then, a falling demand meant that 41,000 GWh is likely to be significantly more than 20% of energy demand. This has led to calls for the RET to be revised down and a revised target of 33,000 GWh has been legislated in June 2015. In the study EY assumes the RET to be 33,000 GWh in 2020 and remain at this level until 2030, as shown on Figure 39.

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Figure 39: Renewable Energy Target

Source: EY based on the current legislation

The RET will be the key driver behind investments in renewable energy source until 2020. In all modelled scenarios EY assumes renewable energy to be at least at the RET level. In scenarios IS2 and IS3 which assumes introduction of carbon price from 2016-17, the renewable energy exceeds the RET, as carbon price is driving the investments above the RET.

5.6 Capacity in South Australia

Table 23 shows installed capacity in SA in 2015-16, which serves as a starting point in the modelling. Based on the announcement by Alinta Energy39, the owner of Northern Power Station, EY assumes that this coal fired power station will close by end of March 2016, removing last coal power station from the SA electricity market. According to the announcement by AGL40 regarding Torrens Island Power Station A, EY assumes closure of this power station in 2017-18, reducing the Gas Steam capacity by 480 MW.

In addition there are a number of wind farm projects that are either under construction or in the advanced stage of development with a high probability of completion including Waterloo 2, Hornsdale and Lincoln Gap. In the modelling EY assumes that these projects will add approximately 400 MW of wind capacity to SA system by 2019-20.

Table 23: Installed capacity (gird connected) in South Australia in 2015-16

Technology Installed capacity [MW]

Coal 0

CCGT 658

Peaking plant (oil) 97

Peaking plant (gas) 852

Gas Steam 1,280

Wind 1,475

Solar PV 0

Source: EY

39 https://alintaenergy.com.au/about-us/news/flinders-operations-update, accessed 18 January 2016 40 https://www.agl.com.au/about-agl/media-centre/article-list/2014/december/agl-to-mothball-south-australian-

generating-units, accessed 18 January 2016

https://alintaenergy.com.au/about-us/news/flinders-operations-update

https://www.agl.com.au/about-agl/media-centre/article-list/2014/december/agl-to-mothball-south-australian-generating-units

https://www.agl.com.au/about-agl/media-centre/article-list/2014/december/agl-to-mothball-south-australian-generating-units


5.7 Long term capacity mix planning

The key output from the LTIRP model is a future capacity mix which is defined by set of investment and retirement decisions. The derived capacity mix is a combination of the generation technologies with the lowest cost of supplying electricity including capital cost, VOM, FOM, fuel, carbon and retirement cost.

Figure 40 illustrates annual generation under IS1 scenario modelled in the LTIRP model. An introduction of carbon price is the key driver behind changes in the capacity occurring from 2030-31, including:

► Increase in wind and large scale solar capacity; ► Closure of brown coal generators; ► Development of gas capacity; ► Closure and reduction in black coal output.

Declining cost of wind and solar paired with high carbon price is fuelling a transition of the electricity sector in Australia. Wind becomes the main source of energy in the NEM with more than 50% contribution towards the end of the study.

Figure 40: IS1 generation mix (LTIRP) in the NEM

Source: EY

Similar transition into a low carbon future energy mix is expected under scenario IS2, however occurring earlier, as the carbon price is introduced from 2016-17 (Figure 41).

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Source: EY

The higher carbon price in scenario IS3 leads to accelerated retirements of brown and black coal capacity in Australia, as shown in Figure 42. Under scenarios IS2 and IS3, an investment in a nuclear power plant is allowed in the LTIRP model. However, NPP does not emerge through the modelling as part of the optimal capacity mix. Assumed technology cost trajectories, specifically steep learning rates for renewables and none for nuclear, means that the NPP is not cost competitive compared with the other technologies - a mix of wind, solar and gas emerges as the least cost option for the NEM under assumed conditions.

Declining cost of wind and solar and their high penetration in the market contribute to a volatile net demand (demand less wind and solar generation) and prices. A technology that best fits into such system is a gas fired power station (CCGT) that can respond to changes in the net demand more quickly and economically. A system with high penetration of variable renewable generation has lower requirement for a base load type capacity. An NPP can provide low cost of electricity only if operated at high capacity factor. In a system with high penetration of intermittent generation, an NPP would not be competitive, hence its absence in the LTIRP results.


Source: EY

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An NPP would become competitive and emerge in the optimal long term capacity mix modelled in the LTIRP model, if CAPEX, which is the key cost element in case of the NPP, was reduced by at least 20% to allow the plant to compete at a lower capacity factor level with the gas technology, as illustrated in Figure 43. The plant would emerge in New South Wales and Victoria, as these two states provide strong demand base, which allows the NPP to operate at a high capacity factor. With further capital cost reductions, more nuclear capacity would be deployed in the NEM as part of least cost capacity mix, primarily in New South Wales, but also in SA.41 The greater the cost reduction of CAPEX, the more nuclear capacity would be expected in the NEM (Figure 44).

Figure 43: IS3 generation mix (LTIRP) in the NEM, sensitivity CAPEX -20%

Source: EY

Figure 44: IS3 generation mix (LTIRP) in the NEM, sensitivity CAPEX -30%

Source: EY

In a situation when the development of renewables was limited or the technology did not follow the aggressive cost reduction trajectory (as assumed), we would see lower penetration of renewables.

41 With a 30% nuclear capital cost reduction, a nuclear reactor of capacity of 300 MW would be commissioned in South

Australia by 2049-50 according to least cost optimization. This modelling assumes no interconnector upgrade.

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With less volatile net demand, wholesale electricity prices and in the presence of high carbon price, there would be more room in the electricity system for a base load type generator.

5.8 Generation

The long term investment and retirement decisions from the LTIRP model are then applied in the 2-4-C® model of the NEM. The model simulates half hourly operation of the market taking into account constraints of the power stations (e.g. ramp rates, fuel availability) and the network (e.g. thermal limits of transmission lines). The primary purpose of running the 2-4-C® model is to determine the dispatch of power stations and the wholesale electricity prices in a manner as close as possible to actual operation of the NEM. The model is also used to test and adjust LTIRP results, as the LTIRP is a high level model used to determine long term solution, and not a half hourly dispatch engine.

Initial runs of the 2-4-C® model with LTIRP derived investment and retirement decisions of the modelled scenarios indicate excessive deployment of wind and solar capacity, which leads to curtailment and financial losses of these generators in the 2-4-C®. It is an expected result as LTIRP does not capture all limitations of the NEM related to the intermittent generation. In order to improve the profitability to the renewable energy sector to the required rate, wind and solar capacity is reduced, and gas capacity is introduced to help balance the system in the 2-4-C® model. The final capacity mix and resulting generation levels are presented on Figure 45.

Figure 45: Generation mix (2-4-C ®) in the NEM and SA

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In scenarios IS3 Large and IS3 Small EY assumed that the NPP would operate as a base load generator, dispatching most of its capacity ahead of all other generators in the market. For the modelling purposes it was assumed that minimum 70% of the large reactor capacity will be dispatched when available, resulting in an average daily generation profile shown in Figure 46. EY did not investigate other possible bidding strategies and operational modes, as base load operation seems to be most reasonable for the purpose of this study. Nuclear reactors usually operate at the rated capacity as it is the most economical and technically simple mode of operations. However

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nuclear power stations (especially in case of modern reactors) are usually technically capable to operate in a load following mode. Deviations from the rated capacity can be driven by technical constraints (e.g. safety shutdown, network constraints) or commercial aspects (e.g. low wholesale electricity price during periods of high renewables generation). Operation of an NPP in a relatively small electricity system with high penetration of renewables like SA is unprecedented and hence uncertain. Currently most of the existing reactors globally operate primarily as base load generators, even in markets with relatively high penetration of renewables like Germany. The analysis of the nuclear power stations’ utilisation in Germany shows that the utilisation does not fall below 70% of the available capacity (even during negative price events).42

Figure 46: Average generation profile of PWR in 2030-31

Source: EY

The assumed operation mode and plant availability (provided by Parsons Brinckerhoff) result in an achieved average annual capacity factor of 89% for PWR between 2030-31 and 2049-50. It should be noted that such operation would not be possible in the absence of the assumed interconnector upgrade.

5.9 Wholesale electricity prices

One of the key outputs from the 2-4-C® model is the forecast of the wholesale electricity price. Figure 47 illustrates evolution of the prices in SA. The prices are closely linked to the carbon price trajectories, as presented in Figure 36.

42 https://www.ise.fraunhofer.de/en/downloads-englisch/pdf-files-englisch/data-nivc-/electricity-production-from-solar-

and-wind-in-germany-2014.pdf, accessed January 2016

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Figure 47: Annual average wholesale electricity price in South Australia (IS1, IS2, IS3)

Source: EY

Figure 48 shows prices under scenario IS3, IS3 Large and IS3 Small and captures the impact that an NPP would have on the wholesale electricity prices in SA. Commissioning of a PWR with capacity of 1,125 MW would have a significant implication on the SA electricity market reducing the wholesale electricity price by approximately $30 /MWh from 2030-31. A smaller reactor would have a less profound impact on the wholesale electricity market, reducing the price by $9 /MWh on average.

Figure 48: Annual average wholesale electricity price in South Australia (IS3 scenarios)

Source: EY

5.10 Emissions

Figure 49 shows a forecast of CO2 emissions from power stations in the NEM. All scenarios deliver similar long term carbon abatement of approximately 80% compared to the current levels. As a result of carbon price assumptions abatement occurs later in the study in scenario IS1. Commissioning of the NPP in SA does not contribute significantly to further emission reduction in the NEM due to the fact that the NPP would displace already low carbon intense generation from renewables and gas.

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Figure 49: CO2 emission forecast

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5.11 Summary

The approach employed in this study allows EY to determine the potential long term evolution of the electricity market subject to assumed economic conditions and based on the best available information. Once such an optimal capacity mix is established, EY simulates the operation of such a system to determine generation, emissions and wholesale electricity prices.

Under modelled scenarios and assumed technology cost trajectories (specifically steep learning rates for renewables and none for nuclear), the NPP is not cost competitive compared to other technologies. A mix of wind, solar and gas emerges as the least cost option for the NEM. In the system with high penetration of intermittent energy sources like wind and solar, there is a limited requirement for a ‘base load’ type generator like nuclear power plant. A CCGT technology would fit into such system better than a nuclear plant, as it remains competitive under lower capacity factor. The levelised cost of electricity (LCOE) from the NPP under lower capacity factor (< 90%) increases significantly.

A nuclear power plant would become a competitive source of electricity in the NEM under following the exemplary scenarios:

► Nuclear CAPEX reduction by at least 20%; ► Limited development of renewables e.g. due to technical constraints; or ► Absence of renewables cost reduction.

As the NPP does not emerge in the LTIRP model, EY has also investigated scenarios where such power station is commissioned in SA, in order to assess the impact it would have on the market. Such investment would have a significant implication on the wholesale electricity market in SA, leading to a reduction in the wholesale price by $30 /MWh on average from 2030-31 (24% in 2030-31) in a case of large nuclear reactor.

The presented results and prices are derived based on a number of assumptions. It should be noted that none of the future events are certain and any change in the assumptions discussed in this section of the report would render different electricity market forecast. Key assumptions related to the electricity market affecting the results and NPP financial feasibility include:

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Driver Comment

Demand Electricity demand drives future investments in the generation fleet. Higher demand projections would likely create better conditions for the development of the nuclear power station.

Carbon price

Carbon price and climate action are supporting the development of low carbon technologies including nuclear. High carbon price might not automatically create favourable conditions for nuclear, as it might not be competitive against other low carbon technologies, particularly renewables, as discussed in this report.

Technology cost and availability

Availability of certain power generation technologies and their costs have significant implications on the future capacity mix. Assumed steep learning curve for renewables and none for nuclear is resulting in the absence of the nuclear technology in the least cost capacity mix of the future. If renewables were more expensive and / or nuclear less expensive compared to the assumed levels, nuclear could be competitive and be economically feasible.

Rooftop PV

Recent years have seen rapid development of residential rooftop PV systems in Australia. If this trend is to continue, as assumed, it will contribute to the reduction in the demand from the grid, and hence the need for additional capacity in the NEM. In addition, solar generation affects the average daily demand profile, shifting and deepening daily minimum demand. High penetration of solar generation (without storage) results in a variable demand profile, which is challenging for the feasibility of the nuclear power station.

Battery storage

Battery storage is expected to continue making significant cost improvements in the future, and hence play an important role in the electricity market. Strong development of the solar technology, both in-front and behind-the-meter will likely drive deployment of batteries. If the battery penetration was lower compared to the assumed level (with no changes in the capacity mix), the average wholesale price would be higher (e.g. with the half of the storage capacity, the average wholesale price in scenario IS3 in SA would be $1-8 /MWh higher from 2030-31 onwards). Higher wholesale electricity price does not necessarily mean improved economics for the NPP, as the spot price would be more volatile as well reducing the utilisation of the plant.

Interconnectors Interconnectors play an important role in the NEM, allowing export and import between the interconnected regions. Expansion of the interconnector between SA and Victoria is required to allow the operation of the PWR type plant in SA.

Fuel price and availability

All modelled scenarios project a significant increase in gas generation in Australia. For the purpose of this study it was assumed that gas will be available in such volumes and at the assumed price. Should the gas be unavailable or highly priced, the role of gas generation in the NEM would be limited, creating more favourable conditions for development of other types of dispatchable capacity including nuclear.

Regulations and market rules

The modelling was performed assuming the continuation of the established regulation and market rules, and introduction of expected changes in the regulations discussed in the report like carbon price. Electricity markets are changing rapidly and hence changes in the regulation and market rule are likely to occur, affecting all the market players.


6. Economic impacts of establishing nuclear fuel cycle activities in SA

EY estimated the potential high level economic effects associated with investments in different parts of NFC in SA using the VURM CGE model.

In addition to calibrating the production and investment functions of the new NFC industries in SA, the timing and magnitude of direct effects estimated by the business cases are included in the CGE model to estimate the economy-wide effects of new NFC activities on real gross national or state product (GNI/GSI), gross domestic or state product (GDP/GSP).

The CGE model enables the determination of movements in capital and labour – the primary resources - to these new industries in SA based on their relative competitiveness in attracting them to SA. The other industries in the economy are affected to the extent that these new activities move resources from high productive activities to low productive activities.

Appendix A provides key features of VURM model — a CGE model — used for this study to assess the NFC business cases.

Three baseline scenarios — IS1, IS2 and IS3 — were used to assess the potential economic impact of six NFC business cases assessed in this study. These were:

1. Uranium mining expansion in response to global action and Australian action to reduce CO2 emissions;

2. Investments in a set of conversion and enrichment facilities (further processing); 3. Investments in a set of radioactive storage and disposal facilities; 4. Investments nuclear fuel leasing opportunities; 5. Investments in small nuclear reactor; and 6. Investments in large nuclear reactor.

Economic impacts on SA and Australian economies are reported in this section.

6.1 Uranium mining expansion

Growth in the Uranium mining sector was assessed in the CGE model under the IS1 scenario. The IS1 scenario assumes continuation of the prohibition of all activities in the NFC, other than mining in SA.

The International Energy Agency (IEA) is forecasting that to achieve the 450ppm target, global

nuclear capacity would more than doubles to 862 GW in 2040.43

If this happens, the world demand for uranium ore would increase to over 200kt from current levels of 76kt. Australia would supply nearly 12kt of uranium ore to the world market by 2029-30 and close to 20kt by 2049-50 from its

current level of 6kt.44

This Uranium mining expansion scenario estimates the impact of potential growth in Uranium demand for SA based on the IEA’s forecast.

6.1.1 SA macroeconomic impacts

Table 24 and Figure 45 provide the impact of Uranium mining expansion on the SA economy.

43 World Energy Outlook (WEO) (2014), International Energy Agency. OECD/IEA, Paris,www.iea.org 44 EY estimates based on the Australian Department of Industry, IEA and World Nuclear Association.


Table 24: Uranium mining expansion impacts on SA (cumulative deviation from IS1 scenario)

2029-30 2049-50

% $m % $m

Real GSI 0.11 147 0.08 160

Real GSP 0.23 320 0.19 386

Uranium mining (IGVA) 32 80 20 116


Unemployment rate (ppt) -0.014 -0.009

Employment 0.09 800* 0.06 636*

Uranium mining 29 266* 21 197*

*FTE is Full Time Equivalent. Employment includes direct employment in the uranium mining industry and indirect employment in other industries in SA. Source: EY estimates based on VURM.

Expected growth in world demand for Uranium has a moderate impact on the SA Uranium mining and consequently on the SA economy.

► Growth in the mining industry is expected to improve gross state income by $160m and gross state product by $386m relative to a scenario where this growth did not occur. The higher incomes and production was estimated to generate 636 jobs but only a 0.01% improvement to real wages. In percentage terms, improvements to gross state product are approximately 0.23% or by 2029-30 and 0.19% by 2049-50.45

► Real GSP of SA economy in 2014-15 is $98.6billion and this is expected to increase to $141billion by 2029-30 and $203.2billion by 2049-50 with the expanded mining industry. These figures also account for the cost of carbon abatement in SA towards meeting the national target over these time frames.

► Similarly, incomes also rise but not as much as production gains. The difference between production and income gains are explained by the relative prices and foreign income flows. Terms of trade is lower relative to the IS1 baseline scenario and also mining capital stock is partly owned by foreigners, this caused some income outflows from the increased uranium mining exports.

► Economy-wide impacts — real GSP and real GSI — mask the impacts at the industry level. Uranium mining in SA will increase by 32% by 2029-30 and 20% by 2049-50. Since this activity is located in regional areas, there is a possibility some regional flow-on jobs will be created.

► There is small positive impact on the labour market, long run increase in employment level relative to the baseline is around 600 persons and one-third of this new employment will be in the Uranium mining sector.

► Real wages in the SA economy because of Uranium mining expansion will be negligible at around 0.01%.

► We allowed unemployment rate (UNR) to respond in the short to medium term to take opportunities of Uranium mining expansion. Flexible SA labour market responded by decreasing the UNR from 7.1% current level to 7.08% by 2029-30.

Figure 50 shows the year-on year impacts of the expansion of uranium mining on the SA relative to its baseline of no such demand increases.

45 The economic effects decline over time as they shift the level of production due to the demand shock rather than the supply shock determined endogenously through innovations in production. The uranium mining expansion has a level effect rather than the growth rate effect on the economy. This is also applies for other nuclear fuel cycle activities in this study.

Figure 50: Uranium mining expansion macro impacts at SA level (cumulative deviation from IS1)

GSI % cumulative deviation from IS1 baseline GSI $m cumulative deviation from IS1 baseline

GSP % cumulative deviation from IS1 baseline GSP $m cumulative deviation from IS1 baseline


A key determinant of GSP gains for SA as shown in Figure 51 are greater net exports. The net exports mainly contribute to GSP gains in this scenario if it is followed by investment in greater capacity. Real consumption, which is an indicator of welfare, also increases as a result of construction activities but has no significant impact on the real GSP gains for SA in later years.

Figure 51: Real SA GSP expenditure components (percentage point contributions to cumulative deviation from IS1)


6.1.2 SA employment impacts

Total (cumulative) employment level changes by industry in SA are provided in Figure 52. Nearly one-third of employment created is direct employment in the Uranium mining industry.

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Figure 52: Employment impacts (full time equivalent persons)


6.1.3 Industry impacts at state level

Since Uranium mining has a very small share of the SA economy, the industry compositional changes at the state level are not pronounced or significant, though a fraction of mining share in the economy is increased in this scenario due to an expansion of Uranium mining. Services and construction sectors increase their production to support the Uranium mining expansion in SA.

Figure 53: Industry impacts (Industry gross value-add)


6.1.4 National macroeconomic impacts

Macroeconomic impact of Uranium mining expansion on Australia reflects both the expansion in SA and also in Northern Territory, where potentials for Uranium mining currently exist.

Real GNI and real GDP impacts of mining expansion are shown in Figure 54. National impacts closely follow the State level macro impacts and are larger than the SA economic impacts.

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Figure 54: Real GNI and real GDP impacts of uranium mining expansion

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6.2 Further processing facilities

Current statutory prohibitions prevent further stages of the NFC beyond mining being established in Australia. A robust national legal and regulatory framework would need to be established, before any commercial development in the nuclear fuel processing sector.

Investment Scenario Two assumes that all restrictions/prohibitions on investment in the downstream stages of the NFC are lifted.

Under IS2 carbon abatement scenario development of the further processing component of the NFC is assessed as an investment in conversion and enrichment (but not fabrication) facilities in SA.

Over $7.1bn will be invested between 2024-25 and 2029-30 on conversion and enrichment facilities in SA. They generate annual export revenue of $657million. Economic impact of the capital works and on-going export revenue of further processing facilities are assed in this sub-section.


Australia’s exports of uranium oxide could potentially be transformed into a further in value-add

after conversion, enrichment and fuel fabrication.46

46 Fuel fabrication is not assessed in this study because the fuel fabrication market differs from the conversion and

enrichment markets because each fuel assembly is customised to a specific reactor. There are more than 100 different fuel rod specifications for nuclear reactors. In addition, required enrichment levels can differ within reactor cores, based on the fuel management strategy of each utility.

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Table 25 provides the economic impact of further processing facilities on SA economy.

Table 25: Further processing facilities impacts on SA (cumulative deviation from IS2)

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Real GSI 0.65 898 0.39 794

Real GSP 0.47 671 0.45 914

Further processing (IGVA) 66 131


Unemployment rate -0.03 -0.03


Further processing 210* 324*

*FTE is Full Time Equivalent. Employment includes direct employment in the further processing facilities industry and indirect employment in other industries in SA. Source: EY estimates based on VURM.

Entering into the further processing of nuclear fuel produces modest economic benefits. In particular, it has:

► To put them into a quantitative context, real GSP of SA economy in 2014-15 is $98.6billion. This would increase to $141.4billion by 2029-30 and $202.4billion by 2049-50 in IS2 scenario.

► If further processing facilities in SA operates to take the opportunities in global uranium processed products market under carbon constraint world.

► In this scenario, SA GSP would increase to $142.1billion by 2029-30 and $203.3billion 2049-50. That is an increase of 0.47% or under $700million by 2029-30 and 0.45% or over $900million by 2049-50 relative to the IS2 baseline.

► A moderate impact on SA employment of around 1,000 employees by 2049-50. The direct employment in further processing sector will be around 300 employees. Entry into the value-added sector will create professional, skilled and unskilled employment, both directly and indirectly. However, it must be noted that companies in the NFC worldwide are grappling with a shortage of skilled personnel, partly due to the lack of growth in the nuclear industry over the

last few decades.47

The modest economic benefits of the further processing of nuclear fuel reflect the modest value add that this activity can generate relative to the significant capital (and operating) costs of involved in generating that value. This activity is already undertaken in a number of other countries.

Figure 55 shows the year-on year impacts of further processing facilities on SA relative to its baseline of no such facilities.

47 Commonwealth of Australia 2006, Uranium Mining, Processing and Nuclear Energy — Opportunities for Australia?, Report to the Prime Minister by the Uranium Mining, Processing and Nuclear Energy Review Taskforce, December 2006

Figure 55: Further processing facilities macro impacts at SA level (cumulative deviation from IS2)

GSI % cumulative deviation from IS2 GSI $m cumulative deviation from IS2

GSP % cumulative deviation from IS2 GSP $m cumulative deviation from IS2


Two main determinants of these moderate GSP gains obtained for further processing in the long are investment and net exports as shown in Figure 56.




Total (cumulative) employment levels by industry in SA are provided in Figures 57 and 58. Around quarter of employment created is direct employment in the further processing industry. Employment peaks during the construction period. Services and other mining industry provide inputs to further processing as a result employment in those industries also increase.

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Figure 58: Employment impacts by industry (full time equivalent persons)



Since further processing would still be a very small share of the SA economy (0.1% of GSP), the industry compositional changes at the state level are not pronounced or significant, though a fraction of manufacturing share in the economy has increased. Services and mining sectors increase their production to support the further processing activity in SA.

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Figure 59: Industry impacts (industry gross value-add)



The possibility of Australia becoming involved in one or more of the stages of conversion and enrichment presents both significant challenges and some opportunities. The integrated nature of the industry worldwide makes entry difficult. While Australia may have the capability to build an enrichment plant, any such decision would likely involve a number of considerations beyond the economic case. The business case commissioned for this study assumed that potentially attractive returns from conversion and enrichment services would need to be balanced against high barriers to entry into this part of the cycle and securing high contract price.

That said macroeconomic impacts analysed in this study shows a moderate impact on the Australian economy. Real GNI and Real GDP impacts from establishing further processing facilities are shown in Figure 60.

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Figure 60: Real GNI and real GDP impacts of establishing further processing facilities




To put them into a quantitative context, real GDP of Australian economy in 2014-15 is $1,610billion. This would increase to $2,469billion by 2029-30 and $4,060billion by 2049-50 under IS2 scenario. By establishing further processing facilities adds on average around $500million to the national economy.

6.3 Radioactive waste storage and disposal facilities

Investment in the radioactive storage and disposal facility components of the NFC was also assessed under IS2.

Over $13.8bn will be invested between 2020-21 and 2049-50 on radioactive storage and disposal facilities in SA. They generate annual export revenue of $5.8billion. Economic impact of the capital works and on-going export revenue of radioactive waste are assessed in this sub-section.

The waste storage facility business case assumed the following revenue flows between the waste holders (International Parties), the SA Waste Storage Public Corporation (SA WSPC), State Wealth Fund (SWF) and the SA State Government. The SWF is assumed to be the primary beneficiary of all profits generated by the acceptance of radioactive waste into SA. The SA WPC is assumed to be the primary developer and operator of all radioactive storage and disposal facilities in SA.

A scenario based on the acceptance of 3,000 tonnes of High Level Waste (HLW) per annum at a payment rate of S1.75million per tonne and 10,000 m3 Intermediate Level Waste (ILW) per annum at a payment rate of $40,000 per m3 to SA in the period to 2049-50 was estimated to generate following outcomes for SA.

The SA WSPC borrows money during the construction phase and pays-off debt as storage and disposal facilities continue to operate and further capital works occur. If it is unspent, real revenue that is accruing to SA consolidated revenue will be around $2.7bn by 2049-50.

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The annual revenue that is accruing to the SA Government consolidated revenue is provided in Figure 61.

This will be gradually spent on the infrastructure projects and government expenditure activities in SA. This is an ongoing-fiscal stimulus to the economy of SA. On average around $100m additional expenditure will be spent on education, health and other government activities in this scenario.

Figure 61: Annual real revenue flow from SWF to consolidated revenue

Source: EY estimates based on the Commission’s assumptions.


Entering into the radioactive storage and disposal facilities produces significant economic benefits to SA relative to a case where there is no such investment.

Table 26 summarises the economic impact of radioactive waste storage and disposal facilities on the SA economy.

Table 26: Impacts of radioactive storage and disposal facilities (cumulative deviation from IS2)

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Real GSI 5.0 6,837 3.6 7,290

Real GSP 4.7 6,699 3.6 7,367

Waste storage facilities (IGVA) 2,829 2,816

Real wages 0.4 0.1


Employment** 9,603* 7,544*

Waste storage facilities 890 656

*FTE is Full Time Equivalent. Employment includes direct employment in the radioactive storage and disposal industry and indirect employment in other industries in SA. **Total employment effects are not only outcome of establishing investment facilities (direct effect) but also the how this revenue is distributed and spent as well as ongoing fiscal stimulus to the SA economy from the real government expenditure. It is an upper limit on highly mobile and flexible labour market assumed at low or moderate wage growth rate. High value adding activities generally create high indirect employment through income and consumption effects. Source: EY estimates based on VURM.

► To put them into a quantitative context, real GSP of SA economy in 2014-15 is $98.6billion. This would increase to $141.4billion by 2029-30 and $202.4billion by 2049-50 in IS2 scenario.

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► Has a significant positive impact on SA’s income and production of around 5% or $7billion higher by 2029-30 and 3.6% or $7.3billion higher by 2049-50;

► Gross state income (GSI) per each SA in today’s dollars will be around $3,500 higher by 2029-30 and around $3,300 higher by 2049-50;

► Similarly, incomes also rise consistent with the production gains. There is no significant difference between production and income gains. This is due to facilities are owned by the SA Government and a national term of trade is higher relative to the IS2 baseline;

► Economy-wide impacts — real GSP and real GSI — mask the impacts at the industry level. Radioactive waste storage facilities are a new industry in SA and generate a significant value-add to the economy adding nearly $3bn value-add by 2029-30, which is 2% of state GSP in 2029-30;48

► Construction and operation of facilities also leads to a significant increase in SA employment around 10,000 by 2029-30 and under 8,000 by 2049-50. This significant increase in employment means that extra workers could come underemployed or unemployed from SA or from other states;

► There is significant impact on the labour market. A long run increase in employment level in the State relative to the baseline is around 8,000 persons and on average 8% of this new employment will be in the radioactive storage and disposal facilities industry;

► Real wages in the SA economy will improve by around 0.1%. Labour market impacts are more on the employment levels rather than the real wages; and

► We allowed unemployment rate (UNR) to respond in the short to medium term to take opportunities of in this new industry. Flexible SA labour market responded by decreasing the UNR from 7.1% current level to 7.05% by 2029-30.

The significant economic benefits of developing radioactive waste storage and disposal facilities reflect the substantial revenues and value add this activity can generate.

State gains mainly come from the gross revenues generated from waste inventory imports flow to the SA Waste Storage Public Corporation (85%) and to the SA State Wealth Fund (15%). The terms of trade gains associated with this income flow and reinvestment of the net revenue in SA infrastructure further increases incomes to the residents of SA.

Figure 62 shows the year-on year impacts of radioactive storage and disposal facilities on SA relative to its baseline of no such facilities.

48 This indicates economy should produce goods and services that are of high value and generates higher incomes for

domestic residents after accounting for any negative externalities. In technical terms, this is similar to Kaldor-Hicks studies of efficiency, by shifting resources in the economy from low value add to high value add to generate higher incomes for the economy.


Figure 62: Radioactive waste facilities macro impacts at SA level (cumulative deviation from IS1)




Key determinant of GSP gains for SA as shown in Figure 63 are related to the net exports, that is providing export services for global radioactive waste material. This service exports mainly contributing to the GSP gains, followed by investment. Real consumption, which is an indicator of welfare has a positive impact in earlier years due to construction activities but has no significant impact on the real GSP gains for SA in later years.




Total (cumulative) employment level changes by year when radioactive storage and disposal facilities operate in SA are provided in Figure 64. Direct employment at the facilities is a small

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fraction of the total employment increases; this is around 8% of employment changes at SA labour market.



Total (cumulative) employment level changes by industry when radioactive storage and disposal facilities operate in SA are provided in Figure 65.

Services industries and construction industry benefit most from the existence of radioactive storage and disposal facilities in SA. Employment in services industry increase by more than 6,600 full time by 2029-30 followed by construction industry (1,250 persons)

Figure 65: Industry employment impacts (full time equivalent persons


Labour is one of the important factor of production and labour mobility between Australian states is relatively easy. The VURM allows labour to move between states in response to the employment opportunities in SA created by this new industry. This significant increase in employment means that extra workers could come underemployed or unemployed from SA or from other states. Nearly 10% of extra workers in the State are from SA and the rest are from other states.

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Since radioactive storage and disposal facilities are 2% by GSP of the SA economy, the industry compositional changes at the state level are significant.

Services and construction sectors increase their production to support the radioactive storage and disposal facilities in SA.

Since this new industry’s value adding is seen as way of increasing employment, increasing export performance and improving the incomes.

Figure 66: Industry impacts (Industry gross value-add)*

*They may not add to the GSP gains reported in Table XX due to the rounding errors and indirect tax gains not reported in this figure. Source: EY estimates based on VURM.

SA industry composition is shown in Figure 67. The radioactive storage and disposal industry will become a new and high value-adding industry as big as existing utilities — electricity, gas and water — industry in SA.

Figure 67: SA industry composition (industry gross value-add shares)


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Macroeconomic impact of establishing and managing radioactive storage and disposal facilities has a significant positive impact also at national level because of its massive investment and revenue generation.

Real GNI and real GDP impacts of establishing radioactive waste storage and disposal facilities are shown in Figure 68.

Figure 68: Real GNI and real GDP impacts of establishing radioactive storage facilities




Figure 69: Real GNI decomposition


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Important determinant of real GSI/GSP gain is capital deepening.

Increased availability of capital to build and operate NFC facilities particularly radioactive waste disposal facilities significantly contributes to real GSI/GSP relative to the baseline of no such activity.

An increase in capital to labour ratio in response to capital works increases the price of labour relative to the cost of capital. As a result, the amount of capital per unit of production in the economy increases (capital deepening). As a consequence the economy is more capital intensive than the baseline, the greater the capital works, the more pronounced these effects. These effects will eventually comedown as these are temporary effects induced by capital works. However, the continued capital works and re-investments from the SA Government consolidated revenue has provided on-going benefit to the Australia in this scenario.

6.3.5 GST impacts

Capital inflows and income gains could potentially increase the SA government revenue and affect its revenue raising potential in this scenario. As a result, the SA’s Goods and Services tax (GST) relativity could be re-assessed on a policy neutral basis by the CGC for the purpose of GST distribution. If the CGC consider this additional revenue (a form of royalty) to be included in SA GST relativity calculations, if all else unchanged this would affect the SA GST relativity. The modelling suggests that the GST relativity could decrease to 1.26 from the current projected level in 2018-19 of 1.428. If this was to occur, SA could receive less GST transfers otherwise would have been. The GST transfers to SA would depend on the judgment and assessment made by the CGC on policy neutral basis in this scenario.

More details on GST impacts are provided in Appendix E.

6.4 Nuclear fuel leasing arrangements

Fuel leasing would take Australia’s uranium through the front end of the NFC to the production of fuel elements which would be leased to overseas nuclear power programs. The spent fuel would be returned to Australia, stored, reprocessed. The high level waste would then be converted and placed in a deep repository in the most suitable part of SA.

Fuel leasing is an important option for the current fuel cycle by its ability to extend the positive benefits of the current non-proliferation arrangements to countries where the small scale nuclear reactors and the complexity of geology make final disposition challenging.

Fuel leasing is a concept based on the sale of uranium concentrate or a value-added form of Uranium fuel (i.e. as converted Uranium, enriched Uranium or as fabricated fuel assemblies) from Australia to international nuclear power utilities before its eventual return to SA for storage and eventual disposal.

While there are potential synergistic benefits from a fuel leasing arrangement that might enable higher revenues for a waste storage and disposal facility established in SA, these have not been considered under this investment scenario. Instead, the potential economic benefits from a nuclear fuel leasing investment scenario is assessed, relative to the IS2 baseline, as a combined investment in both the further processing (represented by the development of conversion and enrichment facilities in SA) and waste storage components of the NFC. This scenario thus assumes the same cost and revenue assumptions as that assumed under the further processing and waste storage facility investment scenarios.

In practice, it would be reasonable to expect nuclear fuel leasing to have additional economic benefits because of the value of end to end service it provides to nuclear fuel users (i.e. it might expand demand particularly from Australia), because it is removing a major uncertainty for those users. It may therefore increase the size of the market for nuclear fuel and / or SA’s market share.


Investment under the assumed nuclear fuel leasing arrangement was estimated to enable significant economic benefits, that are largely associated with the development of radioactive waste storage and disposal facilities rather than the development of the further processing component of the NFC (represented in this case by conversion and enrichment).


Entering into the fuel leasing arrangements as a bundled service produces significant economic benefits to SA relative to a case where there is no such arrangement.

Table 27 provides the summary of economic impacts of nuclear fuel leasing facilities (conversion, enrichment, radioactive waste storage and disposal).

Table 27: Impacts of nuclear fuel leasing arrangements on SA

(cumulative deviation from IS2 scenario) 2029-30 2049-50

% $m % $m

Real GSI 5.6 7,745 4.0 8,106

Real GSP 5.2 7,370 4.1 8,274

Fuel leasing facilities (IGVA) 2,902 2,964

Real wages 0.4 0.1


Employment** 11,400* 9,364*

Fuel leasing facilities 1,100 980

*FTE is Full Time Equivalent. Employment includes direct employment in the further processing industry, radioactive storage and disposal industry and indirect employment in other industries in SA. **Total employment effects are not only outcome of establishing investment facilities (direct effect) but also the how this revenue is distributed and spent as well as ongoing fiscal stimulus to the SA economy from the real government expenditure. It is an upper limit on highly mobile and flexible labour market assumed at low or moderate wage growth rate. High value adding activities generally create high indirect employment through income and consumption effects. Source: EY estimates based on VURM.

Entering into the nuclear fuel leasing produces significant economic benefits mainly from radioactive waste storage and disposal facilities rather than front-end further processing facilities. In particular, it:

► Has a significant positive impact on SA’s income of around 5.6% or just under $8billion by 2029-30 and 4% or over $8billion by 2049-50;

► Gross state income (GSI) per person in today’s dollars will be around $4,000 higher by 2029-30 and around $3,600 higher by 2049-50; and

► Leads to a significant increase in SA employment around 11,000 by 2029-30 and over 9,000 by 2049-50.

This significant increase in employment means that extra workers could come underemployed or unemployed from SA or from other states.

A key determinant of real GSP gains under all scenarios is capital deepening. Increased availability of capital to build and operate NFC facilities significantly contributes to real GSP relative to their respective baselines.

An increase in capital to labour ratio in response to capital works increases the relative price of labour during the construction phase. As a result, the amount of capital per unit of production in the economy increases (capital deepening). As a consequence, the economy is more capital intensive than the baselines; the greater the capital works, the more pronounced the effect. These effects will eventually unwind as these are temporary effects induced by capital works.


Figure 70 shows the year-on year impacts of fuel leasing facilities on SA economy relative to its baseline of no such facilities.

Figure 70: Fuel leasing facilities macro impacts at SA level (cumulative deviation from IS1)




Key determinant of GSP gains for SA as shown in Figure 71 are related to the net exports, that is providing export services for radioactive waste material. This service exports mainly contributing to the GSP gains, followed by investment. Real consumption, which is an indicator of welfare has a positive impact in earlier years due to construction activities but has no significant impact on the real GSP gains for SA in later years.




Total (cumulative) employment levels by year when fuel leasing arrangements starts and operate in SA are provided in Figure 72 Direct employment at the facilities is a small fraction of the total

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employment increases; this is on average around 9% of total employment changes at SA labour market.



Total (cumulative) employment level changes by industry when fuel leasing facilities operate in SA are provided in Figure 73.

Services industries and construction industry benefit most from the existence of radioactive storage and disposal facilities in SA. Employment in services industry increase by more than 7,600 full time by 2029-30 followed by construction industry (1,380 persons)

Figure 73: Industry employment impacts (full time equivalent persons)


Labour is one of the important factor of production and labour mobility between Australian states is relatively easy. The VURM allows labour to move between states in response to the employment opportunities in SA created by this new industry. This significant increase in employment means that extra workers could come underemployed or unemployed from SA or from other states. Nearly 10% of extra workers in the State are from SA and the rest are from other states.

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Since radioactive storage and disposal facilities are 2% of the SA economy, the fuel leasing industry compositional changes at the state level is significant.

Services and construction sectors increase their production to support the radioactive storage and disposal facilities in SA.

Since this new industry’s value adding is seen as way of increasing employment, increasing export performance and improving the incomes.

Figure 74: Industry impacts (Industry gross value-add)*

*They may not add to the GSP gains reported in Table XX due to the rounding errors and indirect tax gains not reported in this figure.


SA industry composition is shown in Figure 75. The nuclear fuel leasing industry will become a new and high value-adding industry as big as existing utilities — electricity, gas and water — industry in SA.

Figure 75: SA industry composition (industry gross value-add shares)


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Macroeconomic impact of establishing and managing fuel leasing facilities has a significant positive impact also at national level because of its massive investment and revenue generation.

Gross national income (GNI) per person in today’s dollars will be around $200 higher by 2049-50.


Figure 76: Real GNI and real GDP impacts of establishing fuel leasing facilities






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6.5 Nuclear power generation

Investment in a nuclear power plant (NPP) was evaluated under the strong climate action scenario that is IS3. This business case presents an evaluation of the effects on SA, the National Electricity Market (NEM) and emissions with respect to a case with no new nuclear power plant, but a state of the economy that reflects Australian ambition to achieve deeper levels of decarbonisation by 2049-50.

Based on the EY electricity modelling and an assumed overnight capital cost for a nuclear power plant of $7,828/kW, a GW-scale nuclear power plant does not emerge as part of the least cost mix of electricity generation assets in SA, assuming a commercial discount rate of 10% and a real carbon price of $123/tonne-CO2-e in 2030-31 rising to $254/tonne CO2-e in 2049-50. However, it is important to note that this scenario is contingent upon assumptions made in relation to private investment in residential rooftop PV and battery storage systems to which the wholesale price of electricity is sensitive. No assessment of the profitability of private investment in these systems was made as part of the modelling undertaken.

The estimated wholesale price trajectory is also sensitive to the assumed level of carbon permit imports. Although the Australian economy is assumed to be meeting the targets by 2050, a quarter of this objective is met through imported carbon permits. This means that under the forecast IS3 scenario (representing the strong climate action baseline), the electricity generation sector is not completely decarbonised by 2050 and the NEM still has average emissions intensity of generation equal to 0.12 tCO2-e /MWh (a reduction of 87% from the current, average intensity of 0.85 tCO2-e /MWh). This is largely driven by the CO2 emissions contribution of CCGT systems that provide system support to intermittent renewable generation at the grid-level.

If Australia's 2050 emissions abatement objectives, will have to be met without access to carbon permit imports a higher carbon price by 2050 may be required than has been assumed in the present assessment. This would lead to yet higher wholesale price trajectories than have been predicted using the EY electricity market model.

To assess the economy-wide effects of a proactive policy to develop nuclear power in SA, a nuclear power plant was forced into the mix of electricity generating assets in SA in 2030-31.

The development of a single large, GW-scale nuclear power plant in SA has the effect of reducing wholesale electricity prices at the SA regional reference node by 24% in 2030-31 relative to a case with no nuclear power plant. In comparison, the development of a small nuclear power plant also operating as a base load plant reduces wholesale electricity prices by only 6% relative to a case with no nuclear power plant operating in SA. A smaller reduction in wholesale electricity prices is also observed in Victoria from the integration of a new GW-scale nuclear power plant in SA and from the expansion of transmission interconnector capacity to 2000 MW in 2030-31

Key reasons are:

► By 2029-30 much of the carbon abatement in Australia is already happened due to assumed carbon abatement policies.

► With high carbon prices and relatively modest increase in other fuel prices, subdued electricity demand, other electricity generation technologies has become relatively more competitive in the NEM.

In comparison the development a small nuclear power plant operating also as a base load plant reduces wholesale electricity prices by only 6% relative to a case with no nuclear power plant in SA. A smaller reduction in wholesale electricity prices is also observed in Victoria from the integration of a new GW-scale nuclear power plant in SA and from the expansion of transmission interconnector capacity to 2000 MW in 2029-30. Its share in NEM electricity generation is less than 1%.


Given the development of a nuclear power plant is unprofitable under commercial conditions; the scenario thus required an assumption to include a subsidy of $5.4billion to be provided over a period of 20 years to enable a consistent comparison between the renewable and nuclear options of the impact on the SA economy. The subsidy is funded by reduction in SA government expenditure on services as shown in this subsection.


The net effect of nuclear power plants construction and operation in SA is provided in Table 28. Large nuclear with subsidy funded by reduction in Government expenditure is also provided in Table 28.

Table 28: Impacts of nuclear power generation in SA (cumulative deviation from IS3 scenario)

2029-30 2049-50

Small nuclear % $m % $m

Real GSI 0.27 370 -0.03 -68

Real GSP 0.24 344 0.05 107

Small NPP (IGVA) 47 46

Real wages -0.02 0.14

Unemployment rate (ppt) -0.02 0.00

Employment 0.26 540* 0.08 473*

Small NPP 167 120

Large nuclear % $m % $m

Real GSI 0.36 486 -0.30 -594

Real GSP 0.37 524 0.10 201

Large NPP (IGVA)

Real wages 0.11 0.5

Unemployment rate (ppt) -0.09


Large NPP 330 258

Large nuclear with subsidy funded by reduction in SA government services

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Real GSI 0.36 486 -3.64 -7,178

Real GSP 0.37 524 -3.00 -6,000

*FTE is Full Time Equivalent. Employment includes direct employment in the nuclear power generation industry and indirect employment in other industries in SA. Source: EY estimates based on VURM.

Entering into nuclear generation produces negligible or negative economic impacts. In particular, it has:

► A small negative impact on income (-0.03%) and a small positive impact on production (0.05%) with a small nuclear reactor by 2049-50.

► The scenario setting for large nuclear is different from the small nuclear reactor as the large nuclear reactor receives a subsidy at the expense of reduction in SA government expenditure. This reduction in government expenditure has attributed significant negative macroeconomic effects at the state level rather than the construction and operation of large nuclear power plant per se.

The difference between the economic impacts of small and large nuclear is explained partly by the relative capital expenditure and its timing. The large NPP has higher capex ($9.3bn) than the small NPP ($4.3bn) and it is spread out over more years, and the operational cost are a bit larger. In


addition the large nuclear receives a subsidy from the SA government funded through a reduction in government services elsewhere in the economy.

The capital works phase of the NPP provides positive income gains due to higher wages and more employment in the state. But when production begins, most of the benefits of lower electricity prices flow through to other states in the NEM, particularly Victoria, although those benefits are modest. The expansion in nuclear generation is more or less offset by a contraction in electricity generated through gas powered plants.

The negligible or negative economic impact of nuclear generation reflects that it does not provide an economic source of electricity generation relative to the alternatives that are available even under high carbon prices. This largely reflects the high capital costs involved and changes in the structure of the electricity market, which reduces the viability of less flexible generation.

There is no significant state production gains associated with the small nuclear power plant. Production gains associated with the large nuclear power plant are also relatively modest. The wholesale electricity price decline due to the operation of large nuclear power plant in the SA Australian region of NEM is significant but they do not translate into economy-wide gains.

A lower electricity price caused by nuclear power in the electricity generation mix is affecting the profitability of other electricity generators and consequently affecting the incomes of SAs through other prices in the economy.

Commissioning of the NPP in SA does not contribute significantly to further emission reduction in the NEM due to the fact that the NPP would displace already low carbon intense generation from renewables and gas.

Furthermore, the provision of a subsidy (funded by SA Government) to large NPP reduces the fiscal expenditure in other government services in SA and this is not balanced by reduced electricity prices in SA leading to an overall negative impact on GSP relative to a scenario without investment in a NPP.

Figure 78 shows the year-on year impacts of nuclear power plants construction and operation on SA economy relative to its baseline of no such facilities.


Figure 78: Nuclear power plants macro impacts at SA level (cumulative deviation from IS3)




Key determinant of GSP gains for SA as shown in Figure 79 are related to the investment and consumption. Low electricity prices increase real consumption.


Small NPP Large NPP



Macroeconomic impact of NPP facilities in SA has negligible or no impacts at national level because of its effect on the other low cost electricity generation producers.

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Figure 80: Real GNI and real GDP impacts of establishing fuel leasing facilities





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20

44

20

45

20

46

20

47

20

48

20

49

20

50


$m

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

0.025

0.030

2025 2031 2040 2050

Terms of trade

Other foreign transfers

Productivity

Allocative effciency

Capital

Labour

% deviation from the BIS-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

2025 2031 2040 2050

Terms of trade

Other foreign transfers

Productivity

Allocative effciency

Capital

Labour

% deviation from the BIS


Appendix A EY Computational General Equilibrium Modelling Approach

EY has employed the Victoria University Regional equilibrium Model (VURM) to estimate the impacts of NFC investment scenarios against the baseline of no nuclear power scenario. The VURM is the only CGE model that addresses key requirements of the study. It is a dynamic model of Australia six states and two territories. It models each region as an economy in its own right, with region specific prices, region specific consumers, regions specific industries and son on.

Our modelling methodology consists of two parts.

► As a complete NFC industry doesn’t not current exits in Australia, we first modified the VURM database and theory to account for its production and demand functions to ensure its interaction with the rest of the economy is adequately captured based on the business case inputs, cost structures and sales pattern of each nuclear cycle activity.

► Model simulation set-up for No action scenario, three baselines and nuclear investment scenario analysis.

This section outline key modelling features of VURM, operationalising the NFC in the VURM and some behavioural responses implicit in the model.

A1 Main features of VURM

Key features of the model are reported in Table A1.

Table A1: Main features Victoria University Regional Model

The VURM’s ability to meet CGE modelling criteria

For credibility purposes, it should be well known and documented, and have been used in the past to provide sound assessments of nuclear and energy issues.

VURM is well documented

VURM and its predecessor MMRF have been used extensively to analyse energy issues in Australia and, with suitable modifications, overseas. Most of these studies have employed VURM/MMRF in stand-alone mode to analyse purely domestic policies and developments. Other studies have used VURM/MMRF in conjunction with other models. For example, VURM/MMRF has been linked to global models, such as the GTEM model developed at the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), to analyse the implications for Australia of global greenhouse policies. VURM/MMRF has also been linked to detailed technological models of the Australian energy system, such as electricity and gas models, to analyse a range of federal and state measures to curb fossil-fuel generation and/or to reduce greenhouse gas emissions from the energy sector. In these linked exercises, the detailed energy system model is used to infer the impacts on state and territory generation mixes and wholesale electricity prices of specific government measures. VURM/MMRF is then used to infer the broader economic consequences of the energy-specific changes. Between 2004 and 2008 CoPS researchers contributed to the AGO’s Tracking to the Kyoto Target project. CoPS completed three projects for the National Emissions Trading Taskforce (NETT) examining the economic implications of an Emissions Trading Scheme for Australia and its regions. CoPS’ October-2007 report for the Climate Institute on the economic implications of forcing large cuts in Greenhouse Emissions in Australia was reported widely in the Australian Press. CoPS’ modelling expertise was used extensively by the Garnaut Climate Change Review and the Federal Treasury, assisting them in model development and application to issues associated with climate change and adaptation, and greenhouse gas abatement through emissions trading. The work for Treasury has led to two major Treasury reports, one published in late 2008, dealing with the Federal plan for a Carbon Pollution Reduction Scheme), the other published in 2011, dealing with the Clean Energy Future.




To ensure relevance, it should have a database reflecting the most recently available economic data available from the Australian Bureau of Statistics.

VURM has an up-to-date database

VURM is calibrated to the most recent input/output data released by the Australian Bureau of Statistics (2009-10). Calibration of large scale CGE models is and expensive and time consuming task. Work for the current calibration was funded by the Productivity Commission and the Federal Treasury, and has just been completed. The current year-of-record for the model’s database, though up-to-date from a statistical point of view, is nonetheless dated from a historical point of view. To deal with this problem, we update the 2009-10 to the present year (2014-15) via model simulation. The update simulation uses as input all observed data for Australia’s states and territories (national accounts information, balance of payments statistics, labour force data, etc.), and utilises the model to fill in the missing parts. In this way we develop 2014-15 input/output database for each state jurisdiction including one for SA, which is fully consistent with known economic information for that year. This includes information relevant to energy systems covering the supply of electricity, and fossil fuels.

To handle the specific requirements of the Tender, it should be flexible enough to accommodate new embryonic industries that will be created directly and indirectly from investment in NFC activities.

VURM is flexible to handle embryonic industries

Most of the industries directly associated with the NFC do not exist in the Australian economy. The only industries currently present are those associated with radioactive minerals extraction. In the current database of the model, this activity is aggregated with other mining activities into a composite of non-ferrous metals mining. For this project we plan to remove the radioactive minerals extraction energy from its composite parent and treat it as an industry in its own right. To that industry we will create a number of embryonic industries covering the activities of power extraction from nuclear fuels, radioactive materials processing, and nuclear waste management. The expanded database added 4 additional industries producing 4 new products in SA. These industries and products will have strong input/output linkages, reflecting the vertical integration of the nuclear cycle. The cost and sales patterns of these new industries extracted from information provided to EY/CoPS by the other consultants. The embedding of these data into the model’s Input/Output database was undertaken by CoPS. In the simulations of the investment scenarios (IS1, IS2 and IS3). Investment flow into these new industries, causing them to increase in size in line with inputs supplied. When operations begin, these industries generate new activity (jobs, value added income, taxation revenue) which are be picked up by the model as deviations away from the initial no-investment scenario.

To handle the regional dimension of the work, it should be a top-down regional model with the State of SA modelled as an economy in its own right.

VURM is regional

VURM models, each of the states and territories of Australia as economies in their own right. Thus the modelling will show the effects of increased activity in SA on the rest of the country and, hence, the impacts on key national macroeconomic variables. It has top-down regional disaggregation of Statistical Divisions in SA. They are:

Adelaide

Outer Adelaide

Yorke and Lower North

Murray Lands

South East

Eyre

Northern SA.

Our stylized CGE framework

This will allow the impacts will be reported at regional levels using this model. However, regional impacts are not reported in this study

To deal with the long-time frames associated with possible nuclear investments, it should be a

VURM is dynamic

VURM contains many dynamic mechanisms. Perhaps the most important is capital accumulation - investment undertaken in one year is assumed to become operational at the start of next year. Under this assumption, capital in an industry and region accumulates according to:

The quantity of capital available at the start of a year

The quantity of new capital created during the year

Depreciation during the year.




dynamic model able to trace the evolution of the Australian and state economies through real time with and without nuclear investments.

Given a starting value for capital and with a mechanism for explaining investment, the VURM model traces out the time paths of each regional industry’s productive capital stocks. Following the approach taken in the model, investment in any one year is determined as increasing function of the ratio of the expected rate of return on investment to required rate of return. In standard closures of the model, the required rate of return is treated as an exogenous variable which can be moved to achieve a given growth rate in capital. In VURM, it is assumed that investors take account only of current rentals and asset prices when forming expectations about rates of return (static expectations).

A2 Operationalising the nuclear Fuel cycle in VURM

A complete NFC industries doesn’t not exist currently in Australia, we therefore first modified the VURM database and theory to account for this new industries ensure its interaction with the rest of the economy is adequately captured. This is developed in consultation with the Commission. This involves a review of reputable data sources, public submissions, relevant literature and commissioned business cases.

In the current version of the VURM, there are 73 commodities and 73 industries, which are reported along with the new commodities and industries in Table A2. To undertake this project, we added three new industries related to the NFC and separate out one — uranium mining from the existing model database. They are:

► Uranium mining ► Uranium conversion and enrichment (further processing) ► Electricity generation from nuclear power ► Storage and disposal of nuclear waste

A stylized database of VURM model and changes made to incorporate the NFC and its inter linkages with other sectors in the economy and its capital, labour, taxes and operating costs are provided in Figure A1. Apart from uranium mining, for other new industries there is no data in the model database. Based on data from other business cases, we developed these industries in the model.

Figure A1: Operationalising the NFC in our CGE model

Source: EY

Agriculture Coal Oil Gas Iron ore

Other

mining

Other

exploration

and support

Uranium

extraction

& milling

Uranium

exploration

Other

manufacturing

Further

processing of

radio active

minerals Coal Gas Solar Wind Hydro Nuclear

Storage and

disposal of

nuclear

waste

Electricity

distribution Construction Trade Transport ICT Finance

Business

services

Public

services

Other

services

Total

intermediate

use

Household

consumption

Govt

consumption Investment Exports

Total final

use

Agriculture

Mining √ √ √ √ √Coal

Oil

Gas

Iron ore

Other mining

Other exploration and support

Uranium extraction & milling √ √ √ √ √

Uranium exploraton √ √ √ √ √Other manufacturing

Further processing of radio active

Electricity generation √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √Coal

Gas √ √ √ √ √Solar

Wind

Hydro

Nuclear

Storage and disposal of nuclear waste

Electricity distribution √

Construction √ √ √ √ √Trade

Transport √ √ √ √ √ √

ICT √ √ √ √ √

Finance √ √ √ √ √

Business services √ √ √ √ √

Public services √ √ √ √ √Other services

Total intermediate inputs

Compensation of employees (COE)

Gross operating surplus (GOS)

Taxes

Value added GDP

Australian production

Imports

Total supply

Mining Electricity generation

cost structures incorporated as seprate industry in the economy

wide model

Included as separate products

Total intermediate use

Labour costs

Profitability

Energy mix

Production


A3 Key behavioral relationships in the VURM

The implementation of the VURM model relies on calibration to data drawn from input/output statistics from the ABS and relating to the year 2009-10, and to data on various substitution elasticities relating to inputs for production and consumption and to exports. The values for these elasticities vary across commodities, but not regions. They have been drawn from a range of source, including econometric estimation and literature surveys. The key elasticities govern the ease of substitutability between capital and labour (capital/labour substitution elasticities), the slope of foreign demand schedules for Australian exports (export demand elasticities), and the ease with which imports substitute for domestically produced products with the same name (the so-called Armington elasticities).

In the version of VURM used for this study, the capital/labour substitution elasticities for all industries are set to 0.5. This implies a short-run elasticity of demand for labour equal to 0.6, which is a value consistent with a range of labour-market econometric studies. Export demand schedules are set to five. There is very little contemporary evidence on values for these elasticities. A value of five delivers sensible terms of trade changes in response to standard shocks to the model. The Armington elasticities are set to values used in the COPS model. These values, in turn, come from a range of sources including a survey undertaken at the Impact Centre in the early 1980s.

A4 Electricity generation industry in the VURM

Electricity-generating industries in VURM are differentiated according to the type of fuel used. We added new nuclear power industry to the model. There is also an end-use supplier (Electricity supply) in each region and a single dummy industry (NEM) covering the six regions that form Australia’s National Electricity Market (New South Wales, Victoria, Queensland, SA, the Australian Capital Territory and Tasmania). Electricity flows to the local end-use supplier either directly in the case of Western Australia and the Northern Territory or via the NEM in the remaining regions. Purchasers of electricity from the generation industries (the NEM in the case of those regions in the NEM or the Electricity supply industry in each non-NEM region) can substitute between the different generation technologies in response to changes in generation prices, with the elasticity of substitution between the technologies typically set at around 5.

The NEM is a wholesale market covering nearly all of the supply of electricity to retailers and large end-users in NEM regions. VURM represents the NEM as follows.

► Final demand for electricity in each NEM region is determined within the CGE-core of the model in the same manner as demand for all other goods and services. All end users of electricity in NEM regions purchase their supplies from their own-region Electricity supply industry.

► Each of the Electricity supply industries in the NEM regions sources its electricity from a dummy industry called NEM, which does not have a regional dimension. In effect, the NEM is a single industry that sells a single product (electricity) to the Electricity supply industry in each NEM region.

► NEM sources its electricity from generation industries in each NEM region. Its demand for electricity is price-sensitive. For example, if the price of hydro generation from Tasmania rises relative to the price of gas generation from New South Wales, then NEM demand will shift towards New South Wales gas generation and away from Tasmanian hydro generation.

The explicit modelling of the NEM enables substitution between generation types in different NEM regions and is well suited for the current study in assessing the impact of nuclear power on NEM. It also allows for interregional trade in electricity, without having to trace explicitly the bilateral flows. Note that Western Australia and the Northern Territory are not part of the NEM and electricity supply and generation in these regions is determined on a region-of-location basis. This modelling of the NEM is adequate for many VURM simulations proposed in this study.


A5 Electricity demand elasticity

As noted above the VURM allows for substitution between production and consumption inputs at both the firm and household levels. The aggregate response of electricity demand to electricity prices depends on several factors, including: the assumed constant partial elasticities; and the induced changes in the industrial and consumption structure of the economy.

The constant partial equilibrium elasticities (expenditure and implied own-price elasticities) reflect only part of the response to electricity prices. They indicate the expected change in demand for electricity, given a change in price or expenditure, assuming nothing else in the economy changes.

As VURM is a general equilibrium model, important second round effects to electricity demand should be included in any estimate of the total price elasticity of demand. Higher costs reduce profitability and return on capital in electricity generation industries. This reduces resources flowing to these sectors and reduces the overall demand for electricity in the economy at industry and household levels. The modelled outcomes also change depending on the size of the price shift and the impact of electricity and other prices in the economy.

VURM analysis suggests a total price elasticity of demand of around -0.3. That is, a 10 per cent increase in wholesale electricity prices leads to a 3 per cent decrease in electricity demand across the economy in the medium term. This is the same analyses used by the commonwealth Treasury in developing their Strong Growth and Low Pollution Future Report (SGLP).49 A review of domestic and international literature concluded a 10 per cent increase in prices leads to a fall in demand of between 2 and 4 per cent in the short term and 5 and 7 per cent in the long term.50

49 Australian Government (2011), Strong Growth Low Pollution Future, Modelling a Carbon Price,

http://carbonpricemodelling.treasury.gov.au/content/default.asp 50 Fan and Hyndman, 2010, The price elasticity of electricity demand in South Australia http://www.robjhyndman.com/papers/Elasticity2010.pdf; and Productivity Commission, 2011. Carbon emission policies in key economies, Productivity Commission, Canberra


Appendix B Detailed macroeconomic assumptions

B1 Demography

The majority of the baseline demography data is sourced from the Australian Bureau of Statistics (ABS) and provided in Table B1. The assumptions and sources of information for the following categories are used to project the economy to 2050.

Table B1: Demography data sources

Demography data Description Source

Population

Population for SA and Australia available.

Assumptions;

Based on demographic factors such as fertility, mortality, net overseas migration and net interstate migration. Projections include population level, growth, distribution and composition.

Population is at 30 June 2012 to 2101 for Australia for Australia

Population is projected to 30 June 2061 for the states, territories, capital cities and regions

Population Projections, Australia, 2012 (base) to 2101 (cat. no. 3222.0)

(http://www.abs.gov.au/ausstats/[email protected]/Lookup/3222.0main+features42012%20(base)%20to%202101)

Working age population

Working age population (population 15 years and over) for SA and Australia available.

Assumptions

Population is at 30 June 2012 to 2101 for Australia

Population is projected to 30 June 2061 for the states, territories, capital cities and regions

Population Projections, Australia, 2012 (base) to 2101 (cat. no. 3222.0)

Number of Households

Number of households available for Australia and SA.

Assumptions

Projections based on changes in proportion of people to be in particular living arrangements. These propensities are taken from the Census of population and housing (Census). Rates of change based over the past four census (1996,2001,2006,2011)

Household and Family Projections, Australia, 2011 to 2036 (cat. No. 3236.0)

http://www.abs.gov.au/ausstats/[email protected]/mf/3236.0

Based on patterns of migration, fertility and life expectancy (mortality), Australia’s population is projected to grow at 1.3 per cent per year, which is slightly below the average growth rate of the past 40 years. If this were to occur, the Australian population would reach 30.1 million in 2029-30, up from 23.9 million today and would reach to 37.6 million in 2049-50.

SA’s population is projected to grow at 0.74 per cent per year over next three decades. If this were to occur, the SA population would reach 1.96 million in 2029-30, up from 1.7 million today and would reach to 2.2 million in 2049-50. Currently, the share of SA population in total Australian population is 7.2% and share will decline to 5.8% by 2049-50. Both Australian and SA population levels and growth rates are provided in Figure B1.

Ratio of working age population (15+) to population in Australia and SA are provided in Figure B2. On average, SA is below the national average ratio of working age population to population, implying relatively older people living in SA.

http://www.abs.gov.au/ausstats/[email protected]/Lookup/3222.0main+features42012%20(base)%20to%202101)







Figure B1: Population growth and level

Source: ABS and Treasury Intergenerational Report 2015.

Figure B2: Ratio of working age population (15+) to population

Source: ABS and Treasury Intergenerational Report 2015.

B2 Labour market assumptions

The primary source for labour market data is ABS population projections and labour supply. This is supplemented with the assumptions to project long run participation rates. Data sources and assumptions related to the labour market are provided in Table B2.

Table B2: Labour market data sources

Labour market data

Description Source

Labour supply

SA labour supply calculated as; working age population (Ages 15 and over)/total population of SA

Australian labour supply calculated as; working age population (Ages 15 and over)/total population

Population Projections, Australia, 2012 (base) to 2101 (cat. no. 3222.0), ABS


Table B2: Labour market data sources

Labour market data

Description Source

Participation rate

Participation rates calculated as labour force/working age population).

Assumptions

SA long term participation rates assumed at 61.9%

Australia long term participation rate assumed at 64.8% consistent with the Treasury Intergenerational Report 4 (IGR4, 2015).

Labour Force, Australia, Detailed, Jul 2015 (cat.no. 6291.0.55.00), ABS

Unemployment rate and NAIRU

SA long term unemployment assumed at 7.5%

Based on EY analysis

Australian long term unemployment assumed as 5% consistent with the Treasury Intergenerational Report 4 (IGR4, 2015).

2015 Intergenerational Report, Australia in 2055, Treasury

Weekly hours worked per employed person

Weekly hours worked available for Australia only


Labour force participation rates are affected by changes in the age distribution of the population and changes in participation rates within each age group. Factors affecting each age group’s participation in the labour force, such as educational attainment, also play an important role in changes to overall participation rates. Labour force participation refers to the proportion of the population of people aged 15 years and over who are actively engaged in the workforce. Over the next few decades, the proportion of the population participating in the workforce is expected to decline as a result of population ageing. A lower proportion of Australians working will mean lower economic growth over the projection period.

By 2049-50, the participation rate for Australians aged 15 years and over is projected to fall to 63 per cent, compared with 64.6 per cent in 2014-15. That said, female employment is projected to continue to increase, following on from strong growth over the past 40 years. In 1975, only 46 per cent of women aged 15 to 64 had a job. Projected participation rate to 2049-50 for Australia and SA are provided in Figure B3.

Figure B3: Participation rates

Source: ABS, Treasury Intergenerational Report 2015 and EY analysis.

Projections in this report use an assumption of a constant rate of unemployment of around 5 per cent over the projection period assumed in IGR4 Report for Australia. While employment growth depends on the dynamics of the labour force and the wider economy, the assumption of 5 per cent unemployment is based on estimates of the Non-Accelerating Inflation Rate of Unemployment


(NAIRU). The NAIRU is the lowest sustained unemployment rate that does not cause inflation to increase. The NAIRU varies over time, driven by a complex range of economic, demographic and institutional factors, including the way inflation expectations are formed, the wage-setting environment, labour mobility, and the education and skills of people in the labour force. The NAIRU cannot be measured directly and is typically estimated using economic models. There is a wide range of uncertainty around estimates for the NAIRU, of the order of ½ to 1 percentage point. Under the Treasury projections methodology, the unemployment rate returns to 5 per cent as shown in Figure B4 for Australia. Based on EY Macro model for SA the estimated NAIRU is 6.3 per cent.

Figure B4: Unemployment rates


In addition to the participation rate, the average number of hours worked also has a significant impact on economic outcomes. Over the past three decades, the average number of hours worked per week has decreased, due partly to an increase in the number of people working part-time, reflecting the increase in female and older workers, who particularly benefit from a flexible workplace environment. The average number of hours worked is projected to fall slightly over the next 40 years. Population ageing is expected to be the main driver of the decline in average hours worked. Historically, those in older age groups have worked for fewer hours per week on average, than those in younger age groups. This is expected to continue. Changes in the Age Pension eligibility age are projected to have a minimal impact on the average hours worked as the effects of a higher proportion of older workers on part-time hours are taken into account.

Figure B5: Average hours worked for week

Source: ABS, Treasury Intergenerational Report 2015.


B3 Productivity and technical change assumptions

Productivity is a measure of output from a production process, per unit of input. Growth in output per unit of labour input — labour productivity — is estimated to have accounted for over half of Australia’s GDP growth over the last few decades, with the remainder contributed by growth in employment. The baseline CGE modelling uses Treasury forecasts and IGR4 projections for aggregate labour productivity growth to 2049-50 as shown in Figure B6.

Figure B6: Aggregate labour productivity growth

Source: ABS, Treasury Intergenerational Report 2015.

Labour productivity has two components: capital deepening, and multifactor productivity. Capital deepening reflects the increases in the ratio of capital to labour, and allows more to be produced in each hour worked. Multifactor productivity measures the efficiency with which the key inputs of labour and capital are used to produce goods and services. Many factors can influence changes in measured multifactor productivity, particularly over shorter periods of time, including educational attainment, the extent and type of regulation, levels of competition and other incentives for businesses to operate efficiently, business and economic cycles, economies of scale, and weather patterns.

In addition to aggregate national economy productivity growth, model also assumes sector specific labour productivity growth rate. Sector-specific labour productivity growth then gradually transitions to the assumed aggregate rate of 1.5 per cent per year in the long run. VURM uses an aggregate labour productivity assumption by adjusting its labour-augmenting technical change variable at an industry level, thereby dispersing technical change across industries, based on historical estimates. The growth rates of labour-augmenting technical change across industries are not the same. The growth rates are estimated from the ABS National Accounts (see Table 11 for sources). They remove the effect of capital deepening on output by adjusting multifactor productivity estimates by industry-level labour income shares.

Table B3: Industry productivity data sources

Labour productivity by state and industry

We calculate historical labour productivity levels and growth rates by industry based on the ABS data and we project them to 2050 based on historical annual average growth rates.

ABS (2014). Estimates of Industry Multifactor Productivity http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/5260.0.55.0022013-14?OpenDocument

ABS (2014) Australian System of National Accounts, http://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/5

http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/5260.0.55.0022013-14?OpenDocument


http://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/5204.0?OpenDocument


Table B3: Industry productivity data sources

204.0?OpenDocument

ABS (2014) State Accounts

http://www.abs.gov.au/AusStats/[email protected]/MF/5220.0

Multifactor productivity by state and industry

We calculate historical multifactor productivity levels and growth rates by industry based on the ABS data and we project 2050 based on these projections.

ABS (2014). Estimates of Industry Multifactor Productivity http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/5260.0.55.0022013-14?OpenDocument

ABS (2014) Australian System of National Accounts, http://www.abs.gov.au/ausstats/[email protected]/PrimaryMainFeatures/5204.0?OpenDocument

ABS (2014) State Accounts


Figure B7: Labour productivity growth at industry level

Source: ABS.

Energy efficiency

Energy efficiency increases when the same amount of output is produced using less energy. This can occur when: energy prices rise relative to other inputs; existing technology is used more efficiently; upgrading existing technology; or when new technology is developed through research and development and learning by doing. In the baseline scenario, VURM assumes a rate of improvement in energy efficiency of 0.5 per cent per year, except for specific sectors such as transport, iron and steel, non-metallic minerals, non-ferrous metals, chemicals, rubber and plastics.

B4 Macroeconomic assumptions

The majority of the baseline macroeconomic assumptions are based on the IGR4 and EY analysis for States which will be consistent with the IGR4. Real GDP and real GSP projections are based on the population, participation and productivity assumptions. By 2049-50 Australia’s real GDP levels will be $4.3 trillion and SA’s real GDP will be $0.23trillion. SA’s economic growth is slightly slower than the national average growth. The annual growth in real GDP is projected to average 2.8 per cent over the next 40 years, compared with 3.1 per cent over the past 40 years.









Figure B8: Real GDP and Real SA GSP levels


Figure B9: Real GDP growth and Real SA GSP growth


B5 Terms of trade and commodity price assumptions

The substantial increase in demand for industrial material inputs by global demand has caused commodities prices to increase substantially until 2012 in Australia (Figure B10). Higher commodity prices have substantially lifted our terms of trade with substantial benefits/costs to Australia. It is an important driver of resource reallocation in the economy. When one sector of the economy experiences higher returns (such as the resource sector), freely mobile factors of production (capital and labour) tend to move in response to those higher factor returns (wages and the return on capital) away from lower return sectors (the manufacturing sector for example).

Based on global demand and supply models for Australia's key commodity exports, Treasury analysis suggests that even over the long–run, the real prices received for Australia's key commodity exports are likely to remain elevated compared with prices received in the early 2000s. The export price projections from this modelling underpin the 2015-16 Budget, and the IGR 4 report, and indicate that Australia's long–run terms of trade will settle at the level observed in 2005–06 by 2019–20. This assumption has an important influence on the level of exchange rate and carbon prices in Australian dollars for this study. The exchange rate also required by the three

0

0.05

0.1

0.15

0.2

0.25

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

2048

2050

Real GDP (LHS) Real SA GSP (RHS)

$ trillion2014-15 prices

$ trillion2014-15 prices


Business cases commissioned for their capital cost estimates. A changed assumed exchange rate would require new estimates for capital costs to be derived based on the proportion of the imported content of each technology.

Figure B10: Terms of trade


Assumptions about the global coal, oil, gas and uranium prices have an important impact on the fuel cost and have implications for the energy mix and the uptake of nuclear power. There are a number of data sources available for commodity price forecasts with different assumptions and different timeframes as noted in Table B4. The three main data sources we have reviewed for this study include: International Energy Agency (IEA), US Energy Information Administration (EIA); and the World Bank.

Table B4: Data sources for terms of trade and commodity prices

Commodity data Description Source

Australia’s terms of trade

The terms of trade projection are expected to stabilize at 78.5 from 2019-20 onwards.

Assumptions

Based on Treasury global demand and supply models for Australia’s key commodity exports; commodity prices will settled at 2005-06 levels


‘Treasury’s medium-term economic projection methodology’, Treasury working paper 2014-02, Commonwealth Treasury

Coal IEA projection to 2050

EIA projection to 2040

World Bank projections to 2025

They have different models and based on different demand supply assumptions

OECD International Energy Association (IEA) World Energy Outlook (WEO) and Energy Technology Perspectives (ETP)

US Energy Information Administration (EIA) Annual Energy Outlook (AEO)

http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=2&pid=27&aid=12&cid=&syid=2004&eyid=2008&unit=BKWH

World Bank Commodity Prices (Pink sheet)

Oil IEA projection to 2050




OECD International Energy Association (IEA) World Energy Outlook (WEO) and Energy Technology Perspectives (ETP)


http://www.eia.gov/cfapps/ipdbproject/iedindex3.cfm?tid=2&pid=27&aid=12&cid=&syid=2004&eyid=2008&uni







Table B4: Data sources for terms of trade and commodity prices

Commodity data Description Source

t=BKWH


Natural gas IEA projection to 2050




1.OECD International Energy Association (IEA) World Energy Outlook (WEO) and Energy Technology Perspectives (ETP)




Uranium Global nuclear electricity generation available from the EIA

IAEA’s forecasts have global nuclear electricity generating capacity rising between 17 per cent and 94 per cent from 2012 to 2030 and by as much as almost 200 per cent by 2050.

The IEA projects nuclear power capacity increases by almost 60 per cent in the New Policies Scenario (Central case), from 392 GW in 2013 to 624 GW in 2040.

OECD NEA sees uranium demand rising between 22 and 106 per cent from 2013 to 2035.

BREE forecasts global uranium consumption and prices to rise through to 2019.

UxC has uranium price forecasts to 2027

1.OECD International Energy Association (IEA) World Energy Outlook (WEO) and Energy Technology Perspectives (ETP)




OECD Nuclear Energy Economics

Bureau of Resource and Energy Economics

International Atomic Energy Agency

UxC http://www.uxc.com/data/uxc_AboutUraniumPrices.aspx

EY in consultation with the Commission, other consultants and the industry experts we have used commodity price forecasts to 2050. EY has used the relevant world commodity prices as inputs into the CGE model and provide Australian dollar terms, coal, gas and oil prices both in the baseline and investment scenarios. Through CGE model iteration with the EY electricity model, regional east and west coast gas prices generated and they are provided to other business cases.








http://www.uxc.com/data/uxc_AboutUraniumPrices.aspx


Appendix C: Electricity generation technology assumptions

C1 Generation technologies

List of modelled generation technologies are provided in Table C1.

Table C1: Generation technologies

Cost representative region

Supercritical PC - Brown coal without CCS VIC

Supercritical PC - Brown coal with CCS VIC

Supercritical PC - Black coal without CCS NSW

Supercritical PC - Black coal with CCS NSW

CCGT - without CCS SA

CCGT - with CCS SA

OCGT - without CCS SA

PV SA

Onshore Wind SA

Offshore Wind SA

Wave SA

Landfill gas SA

Nuclear LWR SA

Nuclear SMR SA

Solar / coal hybrid NSW

C2 Capital cost estimates

EY capital cost estimates are provided in Table C2.

Table C2: Capital cost estimates ($/kW, real 2014-15)

2015 2020 2030 2040 2050

Supercritical PC - Brown coal without CCS 3,990 3,964 3,943 3,918 3,931

Supercritical PC - Brown coal with CCS 8,549 8,526 6,722 6,612 6,546

Supercritical PC - Black coal without CCS 3,230 2,929 2,918 2,907 2,900

Supercritical PC - Black coal with CCS 7,262 7,243 5,710 5,617 5,561

CCGT - without CCS 1,553 1,556 1,576 1,599 1,639

CCGT - with CCS 3,216 3,261 2,495 2,490 2,493

OCGT - without CCS 1,057 1,058 1,069 1,090 1,112

PV 2,303 1,287 1,078 787 730

Onshore Wind 2,495 1,701 1,725 1,752 1,770

Offshore Wind 4,533 4,617 4,393 4,342 4,295

Wave 6,160 6,927 4,074 3,997 3,768


Table C2: Capital cost estimates ($/kW, real 2014-15)

2015 2020 2030 2040 2050

Landfill gas 3,162 3,819 3,733 3,638 3,581

Nuclear LWR 7,118 7,481 7,424 7,415 7,407

Nuclear SMR 8,218 8,677 8,604 8,593 8,582

Solar / coal hybrid 3,654 3,308 3,135 3,039 2,981

C3 Variable Operating and Maintenance cost estimates

EY Variable Operating and maintenance cost estimates are provided in Table C3.

Table C3: Variable Operating & Maintenance cost (VOM) estimates ($/MWh)

2015 2020 2030 2040 2050

Supercritical PC - Brown coal without CCS 3.00 3.16 3.51 3.90 4.32

Supercritical PC - Brown coal with CCS 54.12 54.76 56.16 57.70 59.42

Supercritical PC - Black coal without CCS 2.50 2.63 2.92 3.25 3.60

Supercritical PC - Black coal with CCS 37.81 38.35 39.51 40.79 42.22

CCGT - without CCS 1.50 1.58 1.75 1.95 2.16

CCGT - with CCS 22.20 22.84 24.24 25.78 27.50

OCGT - without CCS 12.00 12.64 14.04 15.58 17.30

PV 0.00 0.00 0.00 0.00 0.00

Onshore Wind 0.00 0.00 0.00 0.00 0.00

Offshore Wind 30.00 28.17 24.84 21.91 19.32

Wave 0.00 0.00 0.00 0.00 0.00

Landfill gas 10.00 10.54 11.70 12.98 14.41

Nuclear LWR 13.69 15.03 14.82 14.78 14.75

Nuclear SMR 16.41 18.01 17.75 17.72 17.68

Solar / coal hybrid 8.00 8.43 9.36 10.39 11.53

C4 Fixed Operating and Maintenance cost estimates

EY FOM assumptions are provided in Table C4.

Table C4: Fixed Operating & Maintenance cost estimates ($/MW, real 2014-15)

2015 2020 2030 2040 2050

Supercritical PC - Brown coal without CCS 55,000 57,949 64,329 71,412 79,275

Supercritical PC - Brown coal with CCS 65,000 68,485 76,025 84,396 93,688

Supercritical PC - Black coal without CCS 45,000 47,413 52,633 58,428 64,861

Supercritical PC - Black coal with CCS 55,000 57,949 64,329 71,412 79,275

CCGT - without CCS 20,000 21,072 23,392 25,968 28,827

CCGT - with CCS 35,000 36,876 40,937 45,444 50,447


Table C4: Fixed Operating & Maintenance cost estimates ($/MW, real 2014-15)

2015 2020 2030 2040 2050

OCGT - without CCS 8,000 8,429 9,357 10,387 11,531

PV 25,000 23,476 20,701 18,254 16,097

Onshore Wind 55,000 57,949 64,329 71,412 79,275

Offshore Wind 77,600 72,870 64,257 56,662 49,964

Wave 190,000 200,187 222,228 246,696 273,858

Landfill gas 150,000 158,042 175,443 194,760 216,203

Nuclear LWR 184,302 192,884 191,528 191,321 191,119

Nuclear SMR 188,293 196,359 195,084 194,890 194,699

Solar / coal hybrid 72,000 75,860 84,213 93,485 103,778

C5 Thermal efficiency

EY thermal efficiency assumptions are provided in Table C5.

Table C5: Thermal efficiency (%)

2015 2020 2030 2040 2050

Supercritical PC - Brown coal without CCS 32% 34% 37% 40% 43%

Supercritical PC - Brown coal with CCS 21% 22% 26% 29% 32%

Supercritical PC - Black coal without CCS 42% 43% 46% 49% 52%

Supercritical PC - Black coal with CCS 31% 33% 36% 39% 43%

CCGT - without CCS 51% 53% 55% 56% 57%

CCGT - with CCS 44% 46% 49% 50% 51%

OCGT - without CCS 35% 37% 40% 43% 46%

PV 100% 100% 100% 100% 100%

Onshore Wind 100% 100% 100% 100% 100%

Offshore Wind 100% 100% 100% 100% 100%

Wave 100% 100% 100% 100% 100%

Landfill gas 33% 33% 33% 33% 33%

Nuclear LWR 34% 34% 34% 34% 34%

Nuclear SMR 34% 34% 34% 34% 34%

Solar / coal hybrid 43% 44% 47% 50% 53%

C6 Levelised cost estimate

EY LCOE assumptions are provided in Tables C6 and C7.


Table C6: LCOE no carbon ($/MWh, real 2014-15)

2015 2020 2030 2040 2050





CCGT - without CCS 80.24 94.13 107.02 113.62 114.96

CCGT - with CCS 157.97 175.64 170.04 177.81 179.70

OCGT - without CCS 221.60 241.69 256.88 265.14 264.09

PV 123.71 73.49 62.00 46.73 42.93

Onshore Wind 104.83 78.14 81.06 84.29 87.50

Offshore Wind 190.28 189.67 177.04 170.39 164.47

Wave 276.51 306.53 214.37 219.68 220.54

Landfill gas 99.56 111.83 114.23 116.89 120.62

Nuclear LWR 133.48 140.82 139.66 139.48 139.31

Nuclear SMR 151.59 160.43 159.04 158.82 158.61


Table C7: LCOE IS3 carbon price ($/MWh, real 2014-15)

2015 2020 2030 2040 2050





CCGT - without CCS 99.18 116.35 146.59 169.62 196.80

CCGT - with CCS 161.06 179.25 176.41 186.73 192.65

OCGT - without CCS 248.71 273.41 311.42 338.34 364.88

PV 123.71 73.49 62.00 46.73 42.93

Onshore Wind 104.83 78.14 81.06 84.29 87.50

Offshore Wind 190.28 189.67 177.04 170.39 164.47

Wave 276.51 306.53 214.37 219.68 220.54

Landfill gas 99.56 111.83 114.23 116.89 120.62

Nuclear LWR 133.48 140.82 139.66 139.48 139.31

Nuclear SMR 151.59 160.43 159.04 158.82 158.61



Appendix D: EY electricity modelling

D1 Introduction

The CGE modelling applied for the Commission incorporates detailed bottom-up electricity market modelling. The electricity market models require macro-economic inputs from the VURM model and simulate operation of the electricity market under these assumptions. The key inputs to the electricity market models include: electricity demand, fuel and carbon prices. Outputs from the electricity modelling include generation, CO2 emission, fuel consumption and wholesale electricity prices, which are used in the VURM and further analysis. Figure D1 illustrates exogenous and endogenous inputs / outputs between the CGE and the electricity market models.

Figure D1: CGE and electricity market models

Source: EY

In this study we focus on the electricity market in SA as assumed location of the NPP. The SA electricity market is part of the National Electricity Market (NEM) – an interconnected electricity system combining five regions in East Australia including Queensland, New South Wales (including ACT), Victoria, SA and Tasmania. Because SA is connected with the NEM (via Haywood and Murraylink interconnectors), in this study we model all regions of the NEM. The NEM is key electricity market in Australia making up almost 90% of overall electricity consumption in Australia.

Due to the fact that the VURM is an Australian-wide macro-economic model, in addition to the NEM we estimate key parameters of the electricity markets in Western Australia and Northern Territory. These parameters include generation, emissions and prices. However as these regions are not connected with the NEM where the investment in the NPP is considered and are of a less interest in the context of the purpose of this study, we do not report inputs and results for these regions.

In this study when modelling electricity market we employ a two-step approach. In the first step we set up and run the Long Term Integrated Resource Planner (LTIRP) model in order to develop a least cost plan for generation over the study horizon, including the retirement of existing generators when they are no longer the least cost means of providing energy supply, and new investments. We analyse the outcomes of the LTIRP model which inform the retirement and investment decisions applied in the second step, which involves detailed electricity market simulation using EY proprietary 2-4-C® model. The model is used to determine generation, emission, fuel consumption and wholesale electricity prices in the NEM. This two-step approach is depicted in Figure D2.


Figure D2: Electricity market models

Source: EY

D2 Long Term Integrated Resource Planner

In the first step we employ the Long-Term Integrated Resource Planner (LTIRP) software package. LTIRP is an optimisation software that delivers the least cost capacity and generation mix under defined market conditions including inputs like demand, fuel, carbon prices and technology costs. Calculations are performed for each scenario to determine the optimal combination of retirements, new build and output in the study horizon.

The LTIRP model optimises the development and operation of generation and transmission for the purpose of long-term planning. The model utilises central planner approach, which means that costs are optimised from the whole system perspective and not from the perspective of individual power stations. It is likely that in a well-functioning market a least cost solution would be equivalent to a profit maximising solution where all participant are earning economic profit.

In order to manage the problem size, the model uses a ‘load block’ approach, which converts the half hourly load profile of consumers and wind / solar generation into a collection of load points. Each load block represents aggregated demand over the load block duration. The model is not time sequential, but the complexity of the load representation allows for a detailed simulation.

The model incorporates variable wind and solar generation. All wind and solar farms are aggregated on the regional basis and follow variable profile based on a historical reference year. Half hourly wind and solar generation forecast is combined with half hourly demand forecast in the process of load blocks creation.

D3 2-4-C®

In the second step we model the least cost planning outcome from LTIRP in a half hourly time sequential manner from 2016 to 2050 in electricity modelling software. 2-4-C® is a complete proprietary electricity market forecasting package. It was built to match as closely as possible the dispatch engines used by market operators to manage real dispatch in wholesale electricity markets. 2-4-C® implements the highest level of detail, and bases dispatch decisions on generator

1. Long-Term Integrated Resource Planner 2. 2-4-C ® (dispatch engine)

Long term optimization of electricity

market to derive least cost capacity mix

Detailed simulation of the NEM to

determine power station dispatch and

wholesale electricity prices

Key inputs: Key outputs:

Fuel prices

Carbon price

Emission reduction

Technology cost

Demand

Rooftop PV

Electric Vehicle

Battery storage

Wind, solar trace

New entrant

Retirements

Indicative capacity

and generation mix

ste

p

ste

p

Key inputs: Key outputs:

New entrants

Retirements

Fuel prices

Carbon price

Demand

Rooftop PV

Electric Vehicle

Battery storage

Wind, solar trace

Bidding strategy

Dispatch

Generation

Emissions

Wholesale

electricity price

Revenues

LT

IRP

re

su

lts u

se

d in

2-4

-c ®


bidding patterns and availabilities in the same way that the real markets operate. The model includes full and partial forced outages and planned outages for each generator, renewable energy generation and inter-regional transmission capabilities and constraints.

The model includes detailed representation of supply side on the electricity market i.e. all power stations in the NEM. The generators are defined with parameters like capacity, heat rates, ramp rates, location and bids. Demand side is represented by forecasted regional half hourly load. In each half hourly period the model calculates the least cost dispatch decision subject to set of constraints to meet the demand. The price is set by a clearing generator and its clearing bid in each half hourly period. Generator bidding strategies are derived from real bid profiles and operational behaviours taken from generators in the relevant system, adjusted over time for any changing market conditions. Such conditions might include fuel and carbon prices, water availability, changes in regulatory measures or fuel availability.

All large existing wind farms connected to the grid in the NEM are represented in the model. For each modelled wind farm a half hourly generation profile is derived using historical wind speeds recorded at the closest weather station. This method captures the daily and seasonal variation of wind at different sites, and also the likely correlation in the operation of nearby wind farms, which is highly material for assessing likely transmission congestion.

Solar profile is derived using solar radiation data for Australia and historical methodological data (cloud cover) from the closest weather station for selected reference year. From this method, EY produces half hourly generation data for existing and new solar farms.


Appendix E: GST impacts

Potential involvement of SA in any part of the new NFC activities could attract large capital inflows to SA. Capital inflow that would build up capital, incomes and wages in SA. These will affect the income taxes and commodity taxes, including the goods and services tax (GST) through increased nominal consumption associated with higher capital and labour income and also higher investment in the State.

The Nuclear Fuel Cycle Royal Commission (the Commission) has requested that EY estimate the GST transfer effects associated with potential large capital inflows to SA. Particularly the Commission emphasised the potential impact upon GST transfers to SA under the current rules under each of the three NFC investment scenarios. The GST effects associated with the three scenarios are:

► What is the impact of developing a set of further processing (e.g. conversion, enrichment) facilities on the SA economy and the Australian economy?

► What is the impact of developing radioactive waste storage and disposal facilities on the SA economy and the Australian economy – incorporating an assumed level of royalty?

► Assuming nuclear power plant — Small Modular Reactor (SMR) and large Light Water Reactor (LWR) — is proactively introduced through government policy into the SA region of National Electricity Market (NEM). What would be the impact on the NEM, SA economy and greenhouse gas emissions?

This Appendix provides an indicative estimate of the potential GST transfers to SA relative to their respective baseline scenarios.

E1 Background

Under the Intergovernmental Agreement on Federal Financial Relations, the states are entitled to receive payments from the Commonwealth equivalent to the revenue received from the GST. GST entitlement refers to the amount of GST which is entitled to be distributed to the states.

GST revenue is collected by the Commonwealth, and then distributed to the State and Territory governments (‘the states’). The current GST distribution framework is not based on where transactions physically take place, or in which state GST revenue is raised. In fact, it could prove complicated to try and allocate particular transactions to one location — sales that are liable to GST made across a state border would need to be allocated to one state or another. The starting point for Commonwealth Grants Commission (CGC) (the body which provides advice on the distribution of GST) is the population distribution. Specifically, the CGC estimates the share of GST revenue that the states would receive if GST was distributed according to each state’s population (an equal, per-capita distribution). The CGC also uses a complex assessment formula to calculate ‘relativity’ for each state as described in the Commonwealth Budget Papers51 and CGC documentation.52

The relativities determine how much GST each State receives compared with an equal per capita share and are determined such that, if each State made the same effort to raise revenue from its own sources and operated at the same level of efficiency, each State would have the capacity to provide services and the associated infrastructure at the same standard.

The CGC has written extensively on how it calculates relativities.53 A 2012 review of GST distribution also examined and provided more details on the calculation of relativities.54 A state’s

51 Budget 2015, Budget Paper 3, Federal Financial Relations 2015-16, http://www.budget.gov.au/2015-

16/content/bp3/html/index.htm 52 https://cgc.gov.au/attachments/article/43/The_GST_Distribution_Model_-_A_Mathematical_Representation.pdf 53 https://cgc.gov.au/

https://www.cgc.gov.au/index.php?option=com_content&view=article&id=61&Itemid=107

https://www.cgc.gov.au/index.php?option=com_content&view=article&id=61&Itemid=107


relativity shows the proportion of the average GST per person it should receive if it is to have the average fiscal capacity. It does not measure how much of the GST raised in the state should be returned to it.

Multiplying a state’s equal per-capita share of GST revenue by its relativity gives the actual share of GST it receives (there are some minor adjustments when the GST revenue is more or less than expected). A relativity greater than one means the state gets more than it would on an equal per-capita basis; a relativity less than one means it gets less revenue than on an equal per-capita basis.

In the Table E1 below, a state’s actual share (C) is calculated by multiplying its equal per-capita share (A) and its relativity (B) in 2014-15 and 2015-16.

Table E29: Calculation of GST entitlements

Estimated 31

December population (A)

GST relativities (B)

Adjusted population

A x B = C

Share of adjusted population

Share of GST pool $million

2014-15

NSW 7,578,490 0.97500 7,389,028 31.3% 16,897

Vic 5,895,618 0.88282 5,204,769 22.0% 11,902

Qld 4,761,589 1.07876 5,136,612 21.8% 11,746

WA 2,606,666 0.37627 980,810 4.2% 2,243

SA 1,692,871 1.28803 2,180,469 9.2% 4,986

Tas 515,707 1.63485 843,104 3.6% 1,928

ACT 388,579 1.23600 480,284 2.0% 1,098

NT 247,108 5.66061 1,398,782 5.9% 3,199

Australia 23,686,628 23,613,857 100% 54,000

2015-16

NSW 7,682,441 0.94737 7,278,114 30.3% 17,295

Vic 6,001,823 0.89254 5,356,867 22.3% 12,730

Qld 4,847,938 1.12753 5,466,196 22.8% 12,990

WA 2,686,269 0.29999 805,854 3.4% 1,915

SA 1,708,718 1.35883 2,321,857 9.7% 5,517

Tas 517,161 1.81906 940,747 3.9% 2,235

ACT 394,899 1.10012 434,436 1.8% 1,032

NT 251,940 5.57053 1,403,439 5.8% 3,335

Australia 24,091,189 23,613,857 100% 57,050

Source: Budget 2015, Budget Paper 3, Federal Financial Relations 2015-16, Table 3.6, Page 79. http://www.budget.gov.au/2015-16/content/bp3/html/index.htm.

In 2015-16, SA received $5.5 billion GST entitlements from the Commonwealth, which is more than $500 million over the previous financial year.

54 Commonwealth Grants Commission (2012), GST Distribution Review Final Report,

http://www.gstdistributionreview.gov.au/content/Content.aspx?doc=reports/finaloctober2012/default.htm

http://www.budget.gov.au/2015-16/content/bp3/html/index.htm


Figure E1 shows historical relativities and the projected relativities in the 2015-16 Budget.

Figure E1: Historical and projected relativities to 2018-19

Source: Budget 2015, Budget Paper 3, Federal Financial Relations 2015-16, Table 3.6, Page 79. http://www.budget.gov.au/2015-16/content/bp3/html/index.htm and previous Budget Paper 3. Commonwealth Grants Commission, Report on 2014 Revenue Sharing Relativities, Table E-1, p. 135. Treasury projections for 2016–17 onwards are from the 2015–16 Budget, Budget Paper No. 3, Table 3.7.

For most states, relativities have generally been unchanged since the introduction of the GST. However, some states, particularly Western Australia and the Northern Territory have experienced significant changes in the relativities. In its most recent update, the CGC stated that Western Australia’s above average revenue raising capacity drives its fiscal strength, the relatively low fiscal capacities of SA, Tasmania and the ACT stem mostly from below average capacities to raise revenue while Queensland and the Northern Territory face very high costs of providing services.55

SA’s GST relativity has increased from 1.18 in 2000-01 to 1.36 in 2015-16 and expected to increase its relativity to 1.43 by 2018-19.

E2 GST effects in NFC investment scenarios

If all the NFC activities were potentially feasible in SA, then the capital inflows would reach by $60 billion between 2020 and 2050.

The economic impact of capital inflows is well understood.56 Capital inflows not only provide macroeconomic benefits but also provide a fiscal contribution indirectly through additional capital spending, household consumption increases due to the increase in real wages and employment and capital incomes. Apart from radioactive storage and disposal scenario, all scenarios assume no change in the existing GST distribution rules. This is because of relatively high revenue received in radioactive waste scenario may affect the SA GST relativities relative to the baseline.

55 https://cgc.gov.au/index.php?option=com_content&view=article&id=243&Itemid=489 56 Gali J and T Bruce (2012), The macroeconomic effects of limited capital inflow, Economic Roundup Issue 3, 2012.

http://www.treasury.gov.au/PublicationsAndMedia/Publications/2012/Economic-Roundup-Issue-3/Report/The-macroeconomic-effects-of-lower-capital-inflow


https://cgc.gov.au/index.php?option=com_attachments&task=download&id=1905

http://www.budget.gov.au/2013-14/content/bp3/download/Consolidated_BP3.pdf


https://cgc.gov.au/index.php?option=com_content&view=article&id=243&Itemid=489


Key drivers for GST receipts are nominal household consumption, nominal investment and exports. How these variables change at the national level determine the size and the magnitude of the GST receipts by the Australian Government.

Key drivers for GST distributions to states are residential population at 31 December and the GST relativities. Population projections for GST are based on the ABS medium population projection series57 and Australian Demographic Statistics.58

Following assumptions are made in projecting the GST relativities beyond 2020 in radioactive storage and disposal scenario.

► For developing a set of further processing facilities and nuclear power plants, the State GST relativities are kept at 2018-19 Commonwealth Treasury assumed relativities in 2015-16 Budget. This is because of the assumption that the potential capital inflows may not significantly change the revenue raising patterns of the states in these scenarios.

► For developing radioactive storage and disposal facilities, where SA may receive windfall gains through a hypothetical SA Waste Storage Public Corporation and SA State Wealth Fund may affect the revenue raising potential of SA Government. This could potentially affect its relativity to receive the GST revenue from the Commonwealth if the CGC considers this revenue in its GST relativity calculations.

To capture this, EY has developed an approximate method to project the GST relativity for SA assuming all other states GST relativities remain unchanged in radioactive storage and disposal facilities scenario, which means that there will be no significant shifts in the revenue raising potentials of other states. The approximate method is based on the potential revenue received by the SA State Government in radioactive storage and disposal scenario.

EY approach to project SA GST relativities is illustrated in Box E1. It is based on the simple econometric analysis. There are number factors that affect the GST relativity of a state but a key factor is revenue raising potential, which is determined by the macro and microeconomic factors.

Variation in SA GST relativities is mainly explained by variation in SA taxation revenue and SA other revenue (excluding GST transfers, other grants and subsidies). Future SA revenues are determined in the CGE model by macroeconomic effects of developing radioactive waste storage and disposal facilities in SA.

57 ABS (2012) Population projections by State, Cat no 3222.0. 58 ABS Cat. No. 3101.0, Australian Demographic Statistics, Table 4 Estimated Resident Population States and Territories.


𝑌𝑡 = 𝑎 + 𝛽1𝑅𝑡 + 𝛽2𝑅𝑡−1 + 𝛽3𝑅𝑡−2 + 𝛽4𝑅𝑡−3 + ⋯ 𝜀𝑡

Box E1: SA GST relativity’s projection model Historical relationship between SA GST relativities and percentage change SA tax revenue; and growth in total revenue (excluding GST transfers, other grants and subsidies) is shown in Figures below.

As expected there is a negative relationship between the revenue growth rates and SA GST relativities. To test the strength of statistical and rolling average relationship between the revenue growth rate and GST relativity and to project the SA GST relativities to 2050, we have developed a simple econometric model as specified below. SA state Government revenue growth rate was taken from the CGE model output in radioactive storage and disposal scenario.

𝑌𝑡 is the level of SA GST relativity; 𝑎 is the constant; 𝛽1, … . , 𝛽𝑛 are parameters; and 𝑅, … 𝑅𝑡−𝑛 are current and past years of state revenue growth rates.

The explanatory power of the model with total revenue excluding GST transfers is relatively higher than the tax revenue only as explanatory variable in the model. The central projection was used to estimate the $ value of GST transfers to SA in the radioactive waste storage and disposal scenario.

EY approach to forecast GST relativity is different from the CGC approach which is detailed in Box E2. A version of the CGE model that used for this project does not forecast the GST relativities endogenously in response to changing economic and demographic conditions. It estimates GST distributions based on the current level of relativities and population levels. Therefore EY has used econometric model to forecast the SA GST relativities assuming everything else is unchanged. These forecasts were used to distribute the GST revenue in the radioactive storage and disposal scenario.

A key reason for deviating from CGC approach is the uncertainty about forecasting a number of variables at State level that may influence GST relativities in the CGC model to 2050. Therefore, SA GST relativity forecasts provided in this report are only an indicative.

y = -0.002x + 1.2374R² = 0.0435

1.16

1.18

1.2

1.22

1.24

1.26

1.28

1.3

0 5 10 15

SA

GS

T R

ela

tiv

ity

Growth in SA Taxation Revenue

y = -0.0017x + 1.2359R² = 0.1566

1.16

1.18

1.2

1.22

1.24

1.26

1.28

1.3

-15 -10 -5 0 5 10 15 20 25 30

SA

GS

T r

ela

tiv

ity

SA Govt Revenue growth (excluding GST, current grants and subsidies)


Projected SA GST relativities are provided in Figure E2.

Box E2: CGC’s approach

CGC approach to GST relativity’s forecasts is short to medium run based on the terms of reference from the Treasurer. They use historical state budget and other data to calculate differences in state fiscal capacities which arise from differences in: the costs of providing services; the need for new investment in infrastructure and financial assets; own-source revenue raising capacity; and other Commonwealth payment receipts. Their assessment of the GST required by each state in each historical year (also called the assessment years) is intended to recognise those differences and to equalise state fiscal capacities. The assessed GST requirements for each of the three most recently completed assessment years are used to derive the recommended relativities. For example, the relativities recommended in the 2016 Update, which are to be used in 2016-17, are based on data for 2012-13 to 2014-15. This three year averaging reduces year to year volatility in the GST revenue distribution. CGC calculates relativities in a sequential way. The process includes:

1. compiling the adjusted budget to obtain average expenses on state services, average revenues for

state own-source revenues and from Commonwealth payments, average state investment and

average state net lending

2. calculating, for each state expense, how much more or less than the average each state would need

to spend to deliver the average service (assessed expenses)

3. calculating, for each state revenue, how much more or less than the average each state would raise if

it adopted the average tax policy of the states (assessed revenue)

4. calculating how much each state would need to invest to give it the average stock of infrastructure,

recognising differences between states in the quantity they require and its costs (assessed

investment)

5. calculating how much each state needs to save to give it the average stock of financial assets

(assessed net lending).

Once these assessments are made, CGC derive the GST each state needs to leave it with the average stock of savings (net financial worth) after having provided the average level of services, acquired the associated assets and made the average effort to raise revenue. Relativities are then calculated by expressing each state’s assessed GST revenue requirement per capita as a proportion of the average GST per capita for the year. Relativities measure how a state’s calculated GST per capita differs from the average per capita GST. The recommended relativities are derived by averaging the relativities calculated for the most recent three years for which data are available.

Source: https://cgc.gov.au/attachments/article/43/The_GST_Distribution_Model_-_A_Mathematical_Representation.pdf

https://cgc.gov.au/attachments/article/43/The_GST_Distribution_Model_-_A_Mathematical_Representation.pdf


Figure E2: Projected SA GST relativities to 2049-50

Source: Budget 2015, Budget Paper 3, Federal Financial Relations 2015-16, Table 3.6, Page 79. http://www.budget.gov.au/2015-16/content/bp3/html/index.htm and previous Budget Paper 3. Commonwealth Grants Commission, Report on 2014 Revenue Sharing Relativities, Table E-1, p. 135. Treasury projections for 2016–17 onwards are from the 2015–16 Budget, Budget Paper No. 3, Table 3.7. and EY projections beyond 2020.

E3 GST effects of developing radioactive waste storage and disposal facilities

Over $13.8 billion will be invested between 2020-21 and 2049-50 on radioactive storage and disposal facilities in SA. They generate annual export revenue of $5.8 billion. This revenue will be mainly received by the SA Waste Storage Public Corporation and SA State Wealth Fund. Since these two are SA government entities and would affect their revenue raising potential in providing government services on equal per capita basis. To capture this revenue raising affect the SA GST relativities are projected as shown in Figure E.2. With potential expected changes in GST relativities due to the expected windfall gains associated with the radioactive waste storage and disposal facilities in SA, could lower GST transfers to SA from the Commonwealth. An indicative profile of the GST transfers to SA in nuclear waste storage and disposal scenario is provided in Figure E.3.

If the existing GST relativities remain unchanged to 2050, that is SA GST relativity remain at 2018-19 level, SA would have received additional GST revenue of nearly $500m in 2050. The additional GST is higher than in the further processing case because waste storage and disposal facilities generate greater investment, jobs and economic activity.

If the CGC assess this as additional revenue to be included in the GST relativity calculations, SA would receive less GST revenue otherwise would have been. Again this depends on the judgment and assessment by the CGC on policy neutral basis.

In effect, with the changes in relativities some of the economic benefits of nuclear waste storage and disposal facilities are transferred to other states due to higher GST transfers and thus less reliance on other taxation to fund their operations.

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http://www.budget.gov.au/2013-14/content/bp3/download/Consolidated_BP3.pdf


Figure E82: Potential GST transfers to SA

Current prices Constant prices

Source: EY estimates

E4 GST effects of developing a set of further processing facilities

Over $7.1 billion will be invested over 2024-25 and 2029-30, on a set of further processing facilities in SA. They generate annual export revenue of $657million. There is no GST on exports of further processing products from Australia. Since these products are not directly consumed by households in Australia, the likely direct GST effects are negligible. Any potential GST revenue effects under this scenario are indirect. Indirect effects are mainly through additional consumption of goods and services related to the new employment and higher wages both during the construction and the operation of further processing facilities.

Additional GST transfers in further processing scenario are provided in Figure E4.

Population share of SA in 2050 is 9.3% and the GST share in this scenario is nearly 12%. This is based on keeping the Treasury relativities unchanged from 2018-19 onwards.

By 2050, the commonwealth would receive over $152 billion of GST receipts in the IS2 baseline scenario. If the existing GST relativities remain unchanged SA would receive nearly 20 billion by 2050. The additional GST revenue will be over $100 million in 2028 in the further processing scenario and SA would receive additional $12 million GST transfers from the Commonwealth over and above its baseline.

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Figure E4: Additional GST transfers in further processing scenario relative to the IS2 baseline


GST revenues increase in the scenario primarily because further processing generates over $900m GSP gains mainly contributed by investment and exports and creates 1000 FTE jobs in SA.

E5 GST effects of developing nuclear power plant facilities

Over $13.1billion will be invested between 2020-21 and 2030-3 on large GW-scale PWR and $4.6billion on small SMR nuclear power plants in SA. This investment will have direct effect on the electricity prices and there by GST effects. These effects are provided in Figures E5-E8. A key assumption underlying these transfers is that the Treasury projected 2018-19 GST relatives for all states unchanged to 2050.

Figure E5: Additional GST transfers in SMR scenario relative IS3 baseline


In SMR scenario, nearly $90 million additional GST would be raised and transferred to all states according to their existing relativities. SA would receive over 13% of this additional GST transfers from the Commonwealth Government. GST effects of developing large Light Water Reactor (LWR) — is proactively introduced through government policy into the SA region of National Electricity Market (NEM).


Figure E6: Additional GST transfers in LWR scenario relative IS3 baseline: subsidy unfunded


The subsidy amount of $5.4 billion is unfunded in this scenario and the GST effects of this scenario are nearly $600 million higher relative to the IS3 baseline and SA receives its share based on the existing GST relativities and the SA Government continually accumulates debt in funding this subsidy.

GST effects of developing large Light Water Reactor (LWR) — is proactively introduced through government policy into the SA region of National Electricity Market (NEM) with the subsidy funded through the reduction in other SA government services. GST effects of this scenario are provided in Figure E7.

Figure E7 Additional GST transfers in LWR scenario relative IS3 baseline: subsidy funded



Glossary

Acronym Description

CCGT Combined Cycle Gas Turbine

CCS Carbon Capture and Storage

CGE Computational General Equilibrium

CoP21 21st Conference of the Parties, a United Nations Climate Change Conference held in Paris from 30 November to 12 December 2015

CoPS Centre of Policy Studies

ERF Emission Reduction Fund

EV Electric Vehicles

EY Ernst & Young

FTE Full Time Equivalent

GDF Geological Depository Facilities

GDP Gross Domestic Product

GNI Gross National Income

GSI Gross State Income

GSP Gross State Product

GW Gigawatt

HLW High Level Waste

IEA International Energy Agency

ILW Intermediate Level Waste

IS1 Investment scenario one

IS2 Investment scenario two

IS3 Investment scenario three

ISF Intermediate Storage Facilities

LCOE Levelised cost of electricity

LLW Low Level radioactive Waste

LTIRP Long –term Integrated Resource Planner

LWR Light Water Reactor

MW Megawatt

MWh Megawatt Hour

NEM National Electricity Market

NFC Nuclear Fuel Cycle

NPP Nuclear Power Plant

OCGT Open Cycle Gas Turbine

PB WSP | Parsons Brinckerhoff

ppm Parts per million


Acronym Description

PV Photovoltaics

PWR Pressurised Nuclear Reactor

RET Renewable Energy Target

SA South Australia

SEEMAC Socio-economic modelling and assessments committee

SMR Small Modular Reactor

SWF State Wealth Fund

UOC Uranium Oxide

VURM Victoria University Regional Model

WEO World Energy Outlook

WSPC Waste Storage Public Corporation

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© 2016 Ernst & Young, Australia All Rights Reserved.

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In line with EY’s commitment to minimize its impact on the environment, this document has been printed on paper with a high recycled content.

Ernst & Young is a registered trademark. Our report may be relied upon by the Royal Commission for the purpose of assessing nuclear fuel cycle on SA only pursuant to the terms of our engagement letter dated 24 August 2015. We disclaim all responsibility to any other party for any loss or liability that the other party may suffer or incur arising from or relating to or in any way connected with the contents of our report, the provision of our report to the other party or the reliance upon our report by the other party.

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