Thesis Is Presented for the Degree of Doctor Philosophy the University of Western Australia Business School Economic Discipline

GLOBAL CARBON ABATEMENT EQUILIBRIA: A REASSESSMENT

Sigit Pria Perdana GradDip App.Ec, M.Ec (ANU)

This thesis is presented for the degree of Doctor Philosophy The University of Western Australia Business School Economic Discipline

2018

ABSTRACT

Preventing global warming has been the focus of international agreements that include the Kyoto Protocol and the more recent climate convention on COP Paris 21. Opposition in some regions and slow implementation progress in others suggests the importance of evaluating strategic interaction between countries over global mitigation policy. This thesis offers three related studies that are associated with countries’ strategic decisions to deal with carbon abatement policy.

In the first, the focus is on the strategic interactions amongst region over carbon abatement policy implementation. These determine whether the equilibria exist that will allow the implementation by a critical mass and the ultimate sharing of the costs and the benefits of carbon abatement at the global level. In this first study, these issues are examined with the focus on the traditional fossil fuels. An integrated assessment model is developed to enumerate the costs of abatement and their international distribution. These are then integrated with the estimation of the benefits from mitigation, due to moderate global surface temperature changes, based on a meta-analysis that links carbon concentration with region-specific measures of economic welfare.

Multiplayer normal form games are then constructed, equilibria from which reveal that the US and China would be net gainers from unilateral implementation in 2015 net present value terms, while the dominant strategy for all other regions is to free ride. Moreover, it is shown that net economic gains to the three large economies of China, the US and Europe would be further bolstered by universal adoption, which could be induced by affordable side payments. Yet the revealed downside is that net gains to all regions are negative for at least two decades, rendering commitment to abatement politically difficult.

The second study integrates the clean energy technology into the global analysis. While these energy sources have been minor contributors up to the base period of this thesis, the very recent growth in their use has been extra ordinary and it is clear that they will affect both the climate and the strategic issue determining the carbon abatement policy implementation. This study examines strategic interaction between regions and the resulting global equilibria when the productivity of renewable energy technologies is growing. The integrated assessment model is therefore extended to

incorporate renewable energy, leading to significant deviations from the global equilibria calculated earlier. Global emissions are significantly reduced, discouraging further commitment to carbon abatement policy. Strong productivity growth in renewable energy causes region such as Europe, which previously been advantaged by carbon abatement policy in other regions, to lose economic competitiveness so it makes European non-cooperation more likely.

Finally, in the third study, the domestic economic effects of carbon abatement policy are examined for a key member of the smaller group of countries that prior studies had suggested would free ride, leaving the larger regions to bear the cost of the global agreement. While this outcome is shown in the thesis to apply to all regions other than the US, China and Europe, some are comparatively significant carbon emitters. The case in point considered here is that of Indonesia. The focus is on implications of Indonesian and global abatement policy on the Indonesian economy. Indonesia’s importance stems from its level of development that engenders a strong drive to complete the electrification of the country, combined with its abundant and cheaply accessible coal resources. Emissions from clearing are also a critical matter in Indonesia, though these are not considered in this thesis. Continued rapid economic expansion will see very substantial growth in Indonesian emissions by global standards.

Indonesian policy alternatives considered are the unilateral implementation of carbon pricing and the acceleration of renewables development via either subsidy or regulation and underwriting. Carbon pricing yields strong early emission reductions, but significantly retards Indonesia’s overall economic performance. The impact on its economic structure is mixed, but the effect on the electricity sector is detrimental and substantial. On the other hand, renewables development alternatives deliver reduced early emission reductions but build to very strong results by 2050. Indeed, strong productivity growth in the renewables sector brings positive spill-overs to the domestic economy. The results suggest that Indonesia’s best strategy to reduce its regional emissions and support global mitigation actions is the encouragement of frontier energy technologies and the fostering of new growth based on clean energy.

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THESIS DECLARATION

I, Sigit Perdana, certify that: This thesis has been substantially accomplished during enrolment in the degree. This thesis does not contain material which has been accepted for the award of any other degree or diploma in my name, in any university or other tertiary institution. No part of this work will, in the future, be used in a submission in my name, for any other degree or diploma in any university or other tertiary institution without the prior approval of The University of Western Australia and where applicable, any partner institution responsible for the joint-award of this degree. This thesis does not contain any material previously published or written by another person, except where due reference has been made in the text. The work(s) are not in any way a violation or infringement of any copyright, trademark, patent, or other rights whatsoever of any person. This thesis contains published work and/or work prepared for publication, some of which has been co-authored.

Signature: Date: 15 November 2018

ACKNOWLEGEMENT

I would like to thank Winthrop Professor Rodney Tyers for his excellent supervision, encouragement and continuous support to my study. It is such a blessing to have him as my supervisor, and I cannot thank enough for his tremendous help and patience in directing my research and supervising my work. His knowledge and professional experience are outstanding and inspirational. And his lead to the high standard has extraordinarily improved not only my research skills but also all general aspects of my life. He is my guru, and it has been a great honour to be one of his students.

Some parts of the thesis were presented in seminars at UWA, the 2017 Annual CGE Workshop of Centre of Policy Study, and the 2018 Australian Conference of Economist. They were also presented in the international conference i.e. the 5th IAEE Asian Conference in Perth, the 40th IAEE International Conference in Singapore, and the 21st GTAP conference on Global Economic analysis in Cartagena, Colombia. And for this, I would like to thank the participants for the comments and suggestions. In revising chapter 2 of the thesis, in which has been submitted to the Energy Journal, I have been benefited from the feedback from Prof. Richard S.J Tol of the University of Sussex.

I gratefully acknowledge the Indonesian Endowment Fund for Education for my being the sponsorship of my study. Being an Indonesian, I am so proud of my country's educational support system for the youths. As Nelson Mandela said, education is the most powerful weapon which we can use to change the world. I believe that giving this chance, Indonesian scholars could define the future of the world and make it better.

I also want to thank Prof. Alla Golub from Purdue University for her valuable advice in my early stages of my Ph.D. and Dr. Michael Jerie from the Victoria University for technical advice and support since I was trained until I made my modification of the model. My special thanks to Dr. Ishita Chatterjee, Dr. Bei Li and Dr. Luciana Fiorini of UWA Business School and all my friends, in particular, Ezmiralda Mellisa, Mayang Sunyoto, Harsha Paravithana, Troy Barry, Thomas Favory, Jill Trinh, Xing Shi, James Ma, Qing Li, , Kelly Neill, Akbar Riznaldi and Noor Mohammad for their encouragement and help over my years.

Finally, I want to thank my parents and my family, especially my mama who never stop praying for me. And also to my beloved bapak in heaven, my dedication is to respect and to honour you. And to the all that I cannot mention by name, I hope that my hard work, my love, and my friendship will mean a lot rather than a name- checked. Terima Kasih.

TABLE OF CONTENTS

ABSTRACT ...... i THESIS DECLARATION...... iii ACKNOWLEGEMENT ...... iv LIST OF TABLES ...... ix LIST OF FIGURES ...... xi AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS ...... xiii

CHAPTER 1 INTRODUCTION: CLIMATE TARGETS AND STRATEGIC INTERACTION ...... 1 1.1 Development of Climate Negotiation ...... 1 1.1.1 Initiation, Establishment, and Global Movement ...... 1 1.1.2 Kyoto Failure, The Paris Agreement and the US Withdrawal ...... 3 1.2 Climate Mitigation and Strategic Interaction ...... 5 1.2.1 Mitigation Policy Coordination: A “Moderately Structured” Problem...... 6 1.2.2 Climate Negotiation: Inequality and Collective Leadership ...... 7 1.3 Contribution and Organization of the Thesis ...... 8 1.3.1 Contributions of Thesis ...... 8 1.3.2 Organization of the Thesis ...... 10 References ...... 12

CHAPTER 2 GLOBAL CLIMATE CHANGE MITIGATION: STRATEGIC INCENTIVES ...... 15 2.1 Introduction ...... 15 2.2 Climate Policy Interaction and Strategic Behaviour ...... 17 2.3 Estimating the Future Economic Costs of Carbon Abatement ...... 19 2.4 Estimating Benefits from Climate Change Mitigation ...... 22 2.4.1 Emissions, Climate and Global GDP Effects ...... 22 2.4.2 Causal Factors Affecting Regional Divergences in GDP Effects ...... 25 2.4.3 Region Specific Climate Benefit Factors ...... 26 2.5 Analysis of Strategic Interactions ...... 27 2.5.1 Strategic Analysis: The Big Three (China, USA, EU) ...... 29

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2.5.2 Strategic Analysis: Five Countries ...... 31 2.5.3 Free Riding and the Potential Affordability of Side Payments ...... 33 2.5.4 Sensitivity Analysis: Discount Rate ...... 37 2.6 Conclusions ...... 38 References ...... 40 Annex to Chapter 2 ...... 46

CHAPTER 3 RENEWABLE PRODUCTIVITY AND GLOBAL ABATEMENT EQUILIBRIA ...... 55 3.1 Introduction ...... 55 3.2 Global Renewable Energy Trends ...... 57 3.2.1 Declining Costs ...... 57 3.2.2 Renewable and GHG Emission ...... 59 3.2.3 Intermittence and Competition with Fossil Fuels ...... 61 3.3 Modelling Approach ...... 63 3.3.1 Adding Renewables Source to the Model...... 63 3.3.2 Database and Data Aggregation ...... 66 3.3.3 The Baseline Simulation ...... 66 3.4 Renewable Productivity Scenarios ...... 70 3.4.1 Renewable Productivity Shock: Implication Analysis ...... 70 3.4.2 Re-Examining Strategic Interactions with High Renewable Productivity ...... 74 3.4.3 Re-Evaluating Transfer Payment and Unilateral Incentives to Abate ...... 77 3.5 Conclusions ...... 79 References ...... 80 Annex to Chapter 3 ...... 86

CHAPTER 4 GLOBAL MITIGATION POLICY: DOMESTIC IMPLICATIONS FOR INDONESIA ...... 89 4.1. Introduction ...... 89 4.2. Recent Developments in Indonesian Energy and Emissions ...... 92 4.3. Abatement Strategy: Carbon Tax or Renewable Intensity ...... 95 4.3.1 An Indonesian Carbon Tax Study ...... 95 4.3.2 Carbon Tax or Renewables Subsidy ...... 97 4.4. The Baseline Projection to 2050 ...... 99

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4.4.1 Database and Baseline ...... 99 4.4.2 Indonesian Growth: Baseline Projection 2015-2050 ...... 99 4.5. Abatement Scenarios: Implications on Emission and Domestic Economy ...... 103 4.5.1 Potential Emission Reduction and Renewable Shares ...... 104 4.5.2 Abatement Scenarios: Economic Performance ...... 106 4.5.3 Domestic Implications: Sectoral Analysis ...... 108 4.5.4 China and US Unilateral Carbon Policy: Implication on Indonesia ...... 113 4.6. Conclusion ...... 115 References ...... 117 Annex to Chapter 4 ...... 121

CHAPTER 5 CONCLUSIONS AND POLICY IMPLICATIONS ...... 123 5.1 Summary of Major Findings ...... 123 5.2 Policy Implications ...... 125

CHAPTER: APPENDIX THE MODEL ...... 127 A.1 Structure of Gdyn-E and the Modification ...... 127 A.1.1 Firm...... 127 A.1.2 Representative Household Expenditure ...... 130 A.1.3 Government Expenditure ...... 133 A.1.4 Taxation and Armington Substitution ...... 134 A.1.5 Capital, Global Trust, Saving and Investment Theory ...... 134 A.1.6 International Emission ...... 143 A.1.7 Regional Household and Balance of Payment...... 144 A.1.8 Equilibrium Conditions ...... 144 A.2 Welfare Estimation of Net Climate Benefit ...... 145 A.2.1 Present Value of Estimated Welfare ...... 145 A.2.2 Welfare Improvement for Side Payment Analysis ...... 146 Reference ...... 146

LIST OF TABLES

Table 2.1 IPCC Temperature Scenarios and Estimated Temperature Changes ...... 21

Table 2.2 Regional to Global Benefit Ratios ...... 27

Table 2.3 Estimated Average Global Welfare Effect from Emission Abatement by Regions and Regional Groupings, Percentage of GDP Deviation ...... 30

Table 2.4 Present Value of Improvement Relative to No Abatement Case ...... 35

Table 2.5 Cumulative Discounted Dollar Value of Net Welfare Benefit or Loss due to Uniform Tax (2015 US$ Trillions) ...... 35

Table A.2.1 Regional Aggregation, Mapping and Investment Target ...... 47

Table A.2.2 Sectoral Aggregation ...... 48

Table A.2.3 Sectoral Productivity Growth ...... 48

Table A.2.4 Technological Improvement in Energy Usage in Production ...... 49

Table A.2.5 Regional Carbon Emission in 2015 and Projection to 2050 ...... 50

Table A.2.6 Studies on Temperature and Welfare Impacts ...... 50

Table A.2.7 The Net Aggregate Effects per Temperature Rising (o C) ...... 51

Table 3.1 Average Growth Rate of Energy Commodities in Baseline Scenario ...... 69

Table 3.2 Sectoral CO2 Emission Various Renewable Productivity Scenarios ...... 73

Table 3.3 Net Gains from Abatement to the Three Large Emitters ...... 75

Table 3.4 Net Gains from Abatement for Five Regions ...... 76

Table 3.5 Extra Incentives to Defect due to Renewable Productivity Acceleration ...... 77

Table A.3.1 Regional Aggregation in New Database ...... 86

Table A.3.2 Sectoral Aggregation in New Database ...... 87

Table A.3.3 Endowment Factors Aggregation in New Database ...... 87

Table A.3.4 Summary World Electricity Production from All Energy Sources (GWh) .... 88

Table A.3.5 GDP Growth Rate and Nominal GDP Share in Baseline Projection ...... 88

Table 4.1 Fuel Mixed of Power Generation (Per cent) ...... 94

Table 4 2 Projected Annual Growths in Baseline Indicator ...... 100

Table 4.3 Emission Deviation Relative to Baseline and Renewable Share ...... 106

Table 4.4 Changes in Output Production and Market Price (Percentage Change) ...... 108

Table 4.5 Deviation (Percentage Change) on Demand of Factor Endowment ...... 111

Table 4.6 Deviation (Percentage Change) on Aggregate Export ...... 112

LIST OF FIGURES

Figure 1.1 Annual CO2 Emissions: Historical Trend ...... 4

Figure 2.1 Real GDP Annual Growth Rate Deviations, Abatement Policy Adoption ...... 21

Figure 2.2 Emission and Global Temperature Change (IPCC Scenarios) ...... 23

Figure 2.3 Temperature Changes and Welfare Consequences ...... 24

Figure 2.4 Global Emissions and Global Economic Welfare (GDP) ...... 24

Figure 2.5 Normal Form Static Games on Present Values for Three Countries (US, China, EU) ...... 29

Figure 2.6 Normal Form Static Games on 2015 Present Values for Five Regions ...... 32

Figure 2.7 Side Payments from China, the USA and the EU Sufficient to Induce Universal Participation ...... 37

Figure 2.8 Estimated Net Welfare Benefit due to Unilateral Implementation of Abatement Policy by China, USA, and EU Welfare ...... 37

Figure A.2.1 Regional GDP Growth Baseline Projection...... 46

Figure 3.1 Global LCOE from Utility-Scale Renewable Power Generation Technologies 2010-2017 ...... 58

Figure 3.2 Renewable Share of Global Primary Energy Consumption (MToE) ...... 59

Figure 3.3 CES nested Electricity Production Structure ...... 64

Figure 3.4 Regional Emission Projections in GT of CO2 ...... 68

Figure 3.5 Renewable Energy Share in Energy Input of Electricity Production ...... 69

Figure 3.6 Emission Forecast from Renewable Productivity Shocks ...... 71

Figure 3.7 Renewable Input Shares in Electricity Production ...... 72

Figure 3.8 Average Growth of Global Fossil Fuel Demand in Secondary Industries Relative to Baseline ...... 73

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Figure 3.9 Side Payments to Induce Universal Participation in IPCC High- Temperature Scenario (Renewable Productivity Acceleration) ...... 78

Figure 3.10 Side Payments to Induce Universal Participation in IPCC High- Temperature Scenario (No Renewable Productivity Acceleration) ...... 79

Figure 4.1 Indonesia’s Energy Consumption: Historical Trends ...... 93

Figure 4.2 Indonesia’s Energy Consumption and Emission Growth ...... 95

Figure 4.3 Projected Energy Output Share ...... 101

Figure 4.4 Emission Historical Trends and Projections...... 102

Figure 4.5 Projected CO2 Emissions from Energy Used and Industrial Activity: Various Scenarios ...... 105

Figure 4.6 Abatement Implications on GDP and Other Macro Indicators ...... 107

Figure 4.7 Energy in Electricity Production: Various Scenarios ...... 110

Figure 4.8 Deviations on Capital Allocation and Income from Foreign Equity ...... 113

Figure 4.9 Implication on Unilateral Carbon Price in China and USA: Deviation from Baseline in Aggregate Export ...... 114

Figure A.4.1 Capital Outflows: Indonesia Unilateral Carbon Tax Implementation ...... 121

Figure A.4.2 Implications of Unilateral Carbon Price in China and USA: Deviation from Baseline of Indonesia Macro Indicators ...... 121

Figure A.4.3 Implications on Unilateral Carbon Price in China and USA: Deviation from Baseline of Indonesia Sectoral Output ...... 122

Figure A.4.4 Implications on Unilateral Carbon Price in China and USA: Deviation from Baseline of Capital Allocation and Indonesian Income from Foreign Equity ...... 122

Figure A.1 General Production Structure ...... 128

Figure A.2 Expansion in Energy Composite in Electricity Production ...... 129

Figure A.3 Households Expenditure Structure ...... 131

Figure A.4 Government Expenditure Structure ...... 133

Figure A.5 Global Trust Scheme ...... 136

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AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS

This thesis contains work that has been published and prepared for publication. The bibliographical details of the work and where it appears in the thesis are outlined as follows: Details of the work: 1. Chapter 2 is co-authored with Winthrop Professor Rodney Tyers Perdana, S & Tyers, R, 2018, ‘Global Climate Change Mitigation: Strategic Incentives’, CAMA Working Paper No. 10/2018. Available from: https://cama.crawford.anu.edu.au/publication/cama-working-paper- series/12187/global-climate-change-mitigation-strategic-incentives. The paper has also been submitted to Energy Journal by 17 September 2018, and being under reviewed. 2. Chapter 3 is prepared for co-authored paper with Prof Rodney Tyers. 3. Chapter 4 is co-authored with Winthrop Professor Rodney Tyers. This chapter is the revision of the UWA Economic Discussion Paper in 2016. Perdana, S, & Tyers, R 2016, Unilateral Carbon Taxation in Indonesia: Economic Implications, Economic Discussion Paper, University of Western Australia. Student contribution to work: I worked on the data management, model construction and development, simulation and policy analysis, and writing the papers.

Co-author signatures and dates:

15 November 2018

Student signature: Date: 15 November 2018

I, Winthrop Professor Rodney Tyers, certify that the student statements regarding their contribution to each of the works listed above are correct

Coordinating supervisor signature: Date: 15 November 2018

CHAPTER 1 INTRODUCTION: CLIMATE TARGETS AND STRATEGIC INTERACTION

The global policy movement to control emissions began three decades ago, long pre- dated by developments in climate science. International climate negotiations have achieved some significant milestones; yet they are still hampered by difficulties in reaching agreement among countries. Diversity of development levels and energy dependence also complicates the negotiations. This chapter evaluates the development of global climate policy, focusing on the sources of disagreement arising. The first section reviews recent developments in global climate negotiations. It is then followed by a discussion of key hurdles facing the negotiations and the nature of strategic interaction among countries. The last section describes the original contribution of the thesis and provides an overview of its contents.

1.1 Development of Climate Negotiation

For more than thirty years research institutions have endeavoured to understand the determinants of climate change, with the aim to minimizing the potentially catastrophic impact of deteriorating climate pattern. Because the climate is a global system, human induced change in it is inherently a public goods problem. Collective action to control Green House Gas (GHG) emissions is therefore an international policy priority. Since the first world climate conference in 1979, to the latest US’ controversial decision to abstain from the Paris agreement, climate negotiations have shown progress without reaching an overarching pact that would embody the full participation of all countries.

1.1.1 Initiation, Establishment, and Global Movement

The first global climate awareness was initiated by World Meteorological Organization (WMO) in Geneva in 1979. Large variations in climate observed in the lead-up to that conference highlighted the need to present and to improve on the stock of scientific knowledge related to the climate behaviour and then to foresee the associated level of human vulnerability. The conference identified the build-up of

carbon dioxide as the cause of global warming. It broadened the scope of the assessment of climate vulnerability to incorporate the quantification of interdependencies between nations and the pace of economic development.1 This led to the creation of the Intergovernmental Panel on Climate Change (IPCC) in 1988 by WMO and the United Nations Environment Program (UNEP). IPCC's initial task was to prepare a comprehensive review of knowledge of climatology, scientific conclusion as to change through time, the potential risk to human civilization and the options for the adaptation and mitigation.

The second climate conference was held also in Geneva, in 1990. It was the first formal negotiation on climate change, but it failed to offer a binding commitment to abatement.2 The key issue in this failure was the setting of specific carbon dioxide (CO2) emission targets. Many critics considered the European Community (EC) target of the 1990 emission level by the year 2000 should be a minimum acceptable policy for developed countries. But no individual country or region agreed. The final declaration did not specify any agreed target.

The format of climate negotiations was altered by the invention of the convention framework, under the United Nation (UN) with the Intergovernmental panel as the authority. In 1992, the UN Framework Convention on Climate Change (UNFCCC) was held during the Earth Summit in Rio de Janeiro and was signed by 154 states, in addition to the European Community. A comprehensive and complex set of negotiations resulted in the 27 principles in the declaration,3 but it is regarded nonetheless as having failed to produce enough binding new principles of international environmental regulation (Palmer 1992). The Convention entered into force on 21 March 1994, and the intergovernmental panel was replaced by a Conference of Parties (COP) to become the ultimate convention authority.

The first COP session was held in Berlin in 1995, where the delegates from 117 parties and 53 observers agreed that the commitments for developed countries were inadequate. The convention launched the “Berlin Mandate” petitioning for additional commitments. The COP 2, held in Geneva in 1996, was followed the COP 3 of 1997 in Kyoto, Japan. The Kyoto convention left a significant milestone in the evolution

1 https://survivingprogress.files.wordpress.com/2013/03/wcc1-extended_summaries.pdf. 2 https://unfccc.int/resource/ccsites/senegal/fact/fs221.htm. 3 http://www.unesco.org/education/pdf/RIO_E.PDF.

of the climate negotiation when the protocol was formally adopted and became the widespread basis of today’s climate development.4

The Protocol legally binds developed countries to emission reduction targets but leaves the contribution of developing economies as voluntary. It acknowledges differences capability in the capability of countries in combatting climate change but nonetheless obliges developed countries to reduce their emissions. This treatment is perceived as unfair among the targeted countries, particularly the US, which has withdrawn its commitment.

1.1.2 Kyoto Failure, The Paris Agreement and the US Withdrawal

The Kyoto agreement entered into force on 16 February 2005, specifying particular targets for the first five years, starting in 2008 and ending in 2012.5 A second set of agreements emerged in 2012, formally known as the Doha amendment to Kyoto Protocol. These amendments include further commitments for the Annex I parties6 over the period January 2013 to December 2020. Yet only 37 countries have ratified these second-round targets. Australia and the EU have adopted their full commitment, while Japan, New Zealand and Russia have not adopted proposed new targets in the second period. Canada withdrew, followed by the US.

The targets of the Kyoto Protocol were therefore not met, causing continued global growth in GHG emissions. This failure became the main cause of some developed countries rolling back commitment under the agreement.7 Until the 2010’s, only the European Union (EU) had successfully cut emissions and reduced their contribution to global GHGs. Indeed, changes in GHG are critically high for China and India, expanding worldwide emissions by 38 per cent from the 1992 emission level.

4 The Kyoto Protocol prescribes the aims of the UNFCC, which are to reduce greenhouse gas concentration in the atmosphere to “a level that would prevent dangerous anthropogenic interference with climate system”. Six greenhouse gases are targeted: Carbon Dioxide (CO2), Methane (CH4), Nitrous Oxide (N2O), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs) and Sulphur Hexafluoride (SF6). See Article 2 of Kyoto Protocol: https://unfccc.int/resource/docs/convkp/kpeng.pdf. 5 The detailed rules for the implementation of the Protocol were adopted at COP 7 in Marrakesh, Morocco, in 2001, and are referred to as the "Marrakesh Accords." The accord was detailing the rules for implementation of the Kyoto Protocol, setting up new funding and planning instruments for adaptation, and establishing a technology transfer framework. 6 Annex 1 includes all industrialized countries member of OECD (except the US) and countries with economic transition. 7 https://www.forskningsradet.no/en/Newsarticle/Why_the_Kyoto_agreement_failed/1253963392536.

The negotiations since Kyoto have not been proceeding quickly and effective enough to stem the growth of global emissions. The absence of a binding target for all countries and the US political stance brought skepticism about the future achievements under Kyoto. Figure 1.1 traces the history of annual GHG emission for selected regions. While the US and the EU emission levels have been declining, China's emissions have grown significantly.

Figure 1.1 Annual CO2 Emissions: Historical Trend

Source: Author re-estimation from UNFCC Global Carbon Project and Carbon Dioxide Information Analysis Centre (CDIAC).8

The most recent negotiation was held during COP 21 in Paris9, and this determined the measures to be taken after the second commitment period ends in 2020. It resulted in the 2015 adoption of the “Paris Agreement”, under which the long-term goal is to constrain collective emissions so that the temperature rise is below 2°C above pre-industrial levels; and to limit the absolute increase to 1.5 °C, as this would, as the predicted by the IPCC, substantially reduce the risks and effects of climate change. These Paris commitments are universal and legally binding climate commitments for the signatories. And this time the signatories are numerous. As on July 2018, 195 UNFCCC members have signed the agreement, and 180 have become a party to it.

8 Data is retrieved from http://www.globalcarbonproject.org/carbonbudget/17/data.htm 9 COP21 was held from November 30th to December 12th, 2015 in Paris.

The Paris accord is seen as a separate instrument under the UNFCCC rather than an amendment of the Kyoto Protocol.10 There is no enforcement to set country-specific targets by specific dates, but each signatory is required to determine, plan and report their contribution to global emission reduction. Targets are required, to go beyond the previous commitments, with the accord coming into force by November 2017. In June 2017, however, the US President Trump announced that the US would withdraw from the agreement on November 2020. As the earliest effective day of the withdrawal is shortly before the end of his presidential term, this new US position is unlikely to be changed in the interim. The rationale given for the US withdrawal is that it is economically costly to the US while advantaging other larger emitters, including China and India. It is seen as undermining the US competitive edge, harming the energy industries, leading to permanent disadvantages for the US economy.11

As a significant emitter, the lack of a committed target for the US will aggravate the coordination problem that stands in the way of global climate mitigation. With the EU facing many social issues associated with Brexit, the absence of the US will leave the success of the Paris accord to depend on the greater role in mitigation from China (Zhang et al. 2017). This standard “tragedy of the commons” problem cannot be solved if governments focus only on their domestic abatement costs. It requires consideration of the economic gains from action to constrain temperature increases and their distribution. The subsequent subsection discusses some key issues, obstacles and opportunities associated with strategic interaction in climate negotiation.

1.2 Climate Mitigation and Strategic Interaction

This subsection focuses on the problems, hurdles and the opportunities in the current negotiation process as represented in key elements of the economics literature. Insights are drawn from three analytical points of view: the structure of the climate

10 Paris was distinguished from Kyoto by its universal participation for both developed and developing countries (Rosenzweig 2016). 11 https://www.whitehouse.gov/briefings-statements/statement-president-trump-paris-climate-accord/. There are numerous factors made the US withdrawal decision. Most on political partisanship and social polarization. The development of this chapter focuses on economic perspectives, as Trump stated in his withdrawal speech.

issue, inequality in negotiation capability and outcomes, and leadership in mitigation (Gupta 2012). The substantial issues behind changes in coalition formation and tactics are highlighted, motivating the subsequent core chapters of the thesis.

1.2.1 Mitigation Policy Coordination: A “Moderately Structured” Problem

Hisschemoller & Gupta (1999) classify the problem of climate change as moderately structured, indicating failure to reach the full agreement either the science or the normative principles.12 Current climate negotiations have achieved little consensus that have yielded emission targets that are politically unrealistic.

The scale of disagreement over the science of climate change is comparatively trivial. Climate negotiations were based on the IPCC scientific report, in which its summary policy section has been thoroughly scrutinized and approved by the policymakers. There are only a few studies such as some of those detailed by Lomborg (2001) which have not strongly contested the scientific climate basis for abatement policy but rather its priority by comparison with other global issues such as the health and agricultural productivity.

The “moderate structured problem” emerged from the principals of common but differentiated responsibilities (CBDR). Under the UNFCCC, the current agreement binds the emissions on developed countries but is remarkably forbearing for developing economies. This differential treatment became a fundamental challenge that has caused major disagreement in climate negotiations (Gupta 2012).

Differential treatment is central to a number of critical factors that make the CBDR principle problematic. The first is disagreement over the criteria motivating the differentiation. The UNFCCC target proposals have taken account of country size and population, disregarding transboundary harm and the sovereign equality of the state (Weisslitz 2002). The second factor is the perceived unfairness of targets based on current conditions that do not account for prospective growth and change. Most of the reduction targets are based on the late 1990’s or early 2000’s emission levels, yet

12 Hisschemoller & Gupta (1999) use a taxonomy that defines a structured problem as engendering agreement on both the underlying science and the ethical principles. An unstructured problem arises where there is no agreement. A moderately structured problem has disagreement either over the science or the normative principles.

advanced economies are growing slowly, with comparatively static emission, while many of the developing ones have much larger emissions in prospect.

Of course some of this differentiation is deliberate. Weaker target zones and voluntarily policy choice yield more space for the large yet developing economies of China, India, and Brazil to grow. Nonetheless, the direction of the UNFCCC negotiation is in favor of the weak states and tends to inhibit effective policy action that might be taken (Harris 1999; Honkanen 2009).

Thus, targets under negotiation do not reflect the actual economy and ecological scale of individual countries’ contribution both to emissions and to abatement.13 This suggests the adoption of a unidimensional, dynamically efficient and relatively fair mitigation instrument that can be widely adopted and easily verified. In three chapters to follow, the instrument considered is uniform carbon taxation.

1.2.2 Climate Negotiation: Inequality and Collective Leadership

Negotiations have been slow to progress toward win-win solutions since internal debates continue in all countries, while disagreement remains over the underlying climate science and the principles that must guide the negotiations (Berger et al. 2003). Developing countries choose simultaneous defensive and offensive strategies by refusing climate targets, and consistently pointing out that responsibility for climate action rest with advanced countries. The advanced economies accepted this early on, but their role became more divisive when growth in emissions from China and other emerging economies accelerated.

Positions are said to be too oriented to short term collective action, which encourage the unwillingness to act (Stavin 2011). Longer perspectives can account for trade- offs between environmental and development priorities, economic growth and equity, the evaluation of mitigation cost (Saul & Seidel 2011) and the stability of collaboration (Walker et al. 2009). The methodological development of the thesis takes such a longer-term perspective on abatement strategy.

Also, countries are likely to take no action because of likely free riding by others. While this is inevitable, the view has been expressed that global coordination might

13 Najam et al. (2003) and Dinda (2004) see this as a failure to perceive the inverted U shape of the environmental Kuznets curve - the relationship between emissions and per capita income.

be based on the logic of appropriateness rather than the logic of consequences (March & Olsen 1998). This suggests cooperation, instead of punishment – side payments rather than penalising tariffs. Yet collaboration requires leadership capable of moral persuasion so that the marginal countries could join the new climate consensus and abide by its joint rules. This would eventually establish a coordination critical mass and eventually ends the climate deadlock (Skodvin & Andersen 2006). The EU has commonly been thought to be the best candidate for climate leader (Killian & Elgstrom 2010; Orberthur 2011), while the US is a straggler in global mitigation (Andersen & Agrawala 2012). This is a key point of departure for this thesis. In subsequent chapters, the roles of large emitters and the action they can take will feature strongly.

1.3 Contribution and Organization of the Thesis

1.3.1 Contributions of Thesis

The central theme of the thesis is the employment of a long-term collective approach to addressing the economic interests of large and smaller countries in carbon abatement policy. For this purpose, it is necessary to develop a new integrated global model for better assessment of each country's position in abatement policy. It includes the analysis of uniform carbon taxation as a potential strategic instrument that is transparent and comparable across countries and therefore likely to induce more participation and to achieve a stable coalition.

The practical effect of this is to simplify, not only the options available to countries, but also the analysis to allow a focus on strategic behaviour by them. Carbon pricing is also central to the climate change problem. A key focus in the literature has been on the implementation of a constant carbon tax rate at the global level (Aldy et al. 2003). This form of abatement policy is strongly supported by many as the most dynamically efficient option compared to qualitative regulation approach or permit trading systems. These systems, for example, create financial instruments the quality of which is opaque and heterogeneous. This leads to excess volatility in markets and potential market failure (Shiller1981; LeRoy 2005).

Uniform carbon pricing embodies collective reciprocity and the potential for broad participation (Barrett 2002; Cramton et al. 2015). It is easier to regulate and to

coordinate, centering on the tax rate instead of country-specific quantities and prices. Thus it perfectly fits the unidimensional virtue of global agreement (Avi-Yonah & Uhlmann 2009). On the other hand, it is criticised as shallow (Schmalensee 1998), since such a global agreement can create different effects for different countries. If it is applied at the point of fuel production, the cost will fall on comparatively small countries while the eventual benefits from mitigation accrue mostly to the larger ones.

The impacts of uniform carbon taxation, however, have not been widely analyzed. Amongst the few available studies are those by Weitzman (2013) and Rezai & van der Ploeg (2014), who address the effectiveness of a uniform tax in two different perspectives. Weitzman establishes that the extra cost to agents from a rising carbon price would be counter-balanced by an additional benefit for all parties in the form of emission reductions, thus ensuring an efficient and equally shared median marginal benefit per capita. On the other hand, Rezai and Van der Ploeg note that the mitigation gains are larger than the costs at the optimal tax rate and that the impacts are heterogeneous across countries. They suggest that the effects of the global carbon tax will fall through time as intergenerational inequality rises.

The consolidation of abatement policy into a single carbon tax option may be thought of as a limitation so far as the implications of the thesis for policy reform. Yet all effective carbon abatement policies have their equivalent indirect tax regimes and the calculation of the tax equivalents of many abatement policy variants, or the equivalent of the uniform carbon tax rate adopted herein in other policy variants, is here left to others. The longer time frame considered allows the thesis to address, instead, intergenerational inequality and the economic implications of mitigation policy for overall economic performance and for the economic interdependencies between regions.

The use of a highly disaggregated model, in combination with a meta-analysis of the economic benefit from lower global temperatures, to evaluate the strategic benefits and costs associated with participation in carbon taxation regimes by countries offers better than previous evaluation of game theoretic abatement policy equilibria. Each country's best strategy is readily evaluated, based on the net mitigation effect on the global temperature and the change in the terms of trade and investment pattern that occur as a consequence of mitigation policy. Regions with high emissions or

otherwise influential effects on global emission abatement are readily identified and compensatory side payments needed to induce more universal participation are readily evaluated.

1.3.2 Organization of the Thesis

The thesis is divided into six chapters, with three core chapters representing three different but related essays. The first two, the core chapters 2 and 3, address the best possible outcomes coalition formation by abatement policy, describing the global abatement of equilibria with and without strong productivity growth in renewable energy supply. The last essay, offered in chapter 4, focuses on the benefits and costs of participation in abatement policy from the view point of a smaller region facing the incentives to defect, namely Indonesia. It examines in more detail mitigation costs of unilateral abatement policy implementation and follows logic leading to the strategy to free ride. Chapter 5 highlights the general conclusion and policy implications. Chapters 2 and 3 both have substantial annexures, offering supporting details as to the modelling and the database used. A final appendix to the thesis then presents the mathematics of the model used. The background, aim and contributions of the three essays are summarized in turn, below.

Global Climate Change Mitigation and Strategic Incentives

Carbon abatement negotiations rest on the presumption that no country or cohesive economic region is large enough to gain from mitigation policy. Moreover, even if very large economies did perceive unilateral gains, those would not be large enough to justify side payments that might induce free riders to participate. These are empirical questions, answers to which this essay contributes.

This study addresses the critiques of Böhringer et al. (2017) on the Coalition-Dice Model of Nordhaus (2015) for its finding on the potential climate club with uniform carbon pricing. A new integrated assessment model is constructed that is more disaggregated than Nordhaus', capturing all the relevant dynamics and regional interactions with fully endogenous capital and GDP growth paths. It is used to solve for the economic cost of the global abatement equilibria and for assessing the affordability of compensatory side payments.

The integrated model embodies the complete global economy, with abatement policy affecting the fossil fuels industries of petroleum, gas and coal. It measures the

immediate cost of carbon pricing implementation, including those transmitted across the domestic industries and the links between industries via trade and investment. It is complemented by a meta-analysis that quantifies the potential economic benefit of mitigation for each region. The analysis then proceeds to the construction of multiplayer, normal form games with net payoffs derived from both the meta-analysis and the modelling. It establishes that, under a range of assumptions, the US and China would be net gainers from unilateral implementation and that, if the US, China and the EU act together, they would each be net gainers even if all other countries defect.

Renewable Productivity and Global Abatement Equilibria

The global perception of balancing economic demand with environmental concern has led, in some countries, to high preparedness to pay for "clean energy" (mainly from wind, hydropower and solar conversion) and hence government subsidies that favour the production and use of these types of energy. At the same time, there is a rapid technical change in renewable energy industries, causing a substantial reduction in their cost. Despite the storage issues that plaque wind and solar energy, this cost reduction has major implications for the rate of global temperature rise and carbon abatement policy equilibria. In every large literature on climate change issues, the assessment of this role for renewables is surprisingly rare.

The analysis offered in Chapter 3 is based on a modification of the integrated model used in Chapter 2, to represent renewable energy as an input to electricity sectors of all regions. This further separates the model used from the starting point of Chapter 2, which is based on the dynamic global model of Golub (2013). This addresses the critiques of Furlan & Montarino (2018) and Blazques et al. (2018) of existing climate negotiation models. The modification also involves a newly designed electricity sector, following the same technique as the MIT- Joint Program Model (Paltsev et al. 2005) and the OECD_ENV Linkage Model (Chateau et al. 2014). The results indicate a substantial narrowing of the conditions under which carbon abatement policies would be unilaterally beneficial in the US, the EU and China.

Global Mitigation Policy: Domestic Implications for Indonesia

In Chapter 4 the thesis examines the domestic implications of carbon abatement policy for a mid-sized country with no-commitment strategic position, focusing on Indonesia as the study case. Indonesia is actively involved in global mitigation

discussions, yet its strategic analysis fails to establish any economic rationale the implementation of carbon abatement policies. This might be surprising given Indonesia is a large emitter for its economic size. But its emissions are simply not large enough to have a significant effect on the global climate and therefore to yield net welfare improvement domestically.

The long run performance of the Indonesian economy is evaluated with and without carbon tax regimes, focussing on the domestic implications of unilateral carbon abatement policy. This is followed by a comparative analysis of policy alternates, including the intensification of renewables via production subsidies or regulation and underwriting of private renewable projects that would raise renewables productivity growth. Emphasis is placed not only on contributions to global emissions but also on the domestic implications. The impact on the Indonesian economy once China and the US implement abatement policies is also examined. This approach differs from earlier works on Indonesian mitigation policy as it widens the scope of primary analysis by incorporating the renewables sector and global interactions via international trade and investment.

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CHAPTER 2 GLOBAL CLIMATE CHANGE MITIGATION: STRATEGIC INCENTIVES

2.1 Introduction

Even in countries that are signatories to the Kyoto Protocol and the more recent climate convention on COP 21 Paris, policies restricting carbon emissions remain controversial (Cooper et al. 2016; Dimitrov 2016). Indeed, current evidence suggests that these agreements have not been effective in mobilizing all signatories to reduce emissions. This ineffectiveness can be explained by disagreement over the scale of mitigation costs (Mahapatra & Ratha 2017), the weakness of voluntary agreements in the presence of a “tragedy of the commons” (Fehr & Gächter 2000; Clarke & Waschik 2012), tardiness in some large economies in the implementation of emission controls (Falkner et al. 2010), strong political preferences to free ride (Hovi et al. 2010) and the issue of carbon leakage (Burniaux & Martins 2012).

Yet all of these perspectives rest on the presumption that, for no country or cohesive economic region is there a unilateral gain from mitigation policy. Moreover, even if the very large economies did perceive unilateral gains, they would not be large enough to justify side payments that might induce free riders to participate. These are empirical questions, answers to which this chapter contributes.

The particular literature complemented is that addressing these strategic interactions, led by Nordhaus (2015) and the follow up studies by Sælen (2016) and Hovi et al. (2017). Nordhaus uses the Coalition-DICE Model and an “evolutionary algorithm approach” to examine the potential for international “clubs” that implement uniform carbon taxes with target prices that range between 12.5 USD and 100 USD per ton of CO2. A uniform carbon pricing regime without trade sanctions is shown to lead only to a non-cooperative equilibrium with minimal abatement. It is concluded that non- participation is the best strategy even for the larger key players: China, the US and

the EU. More generally, these studies suggest the need for penalties in the form of trade tariffs in order that stable coalitions should be formed.1

This chapter commences with a detailed meta-analysis of benefits from mitigation that link carbon emissions to average surface temperature and then to region-specific changes in economic welfare. These results are then combined with region-specific mitigation costs that are calculated using a dynamic model of the global economy that is more highly disaggregated across products and regions than has been used in the previous studies. The level of disaggregation is particularly useful in capturing the interactions across regions that operate through changes in the terms of trade and the global distribution of investment that, in turn, stem from the implementation of mitigation policies alone. Net costs thus calculated are then combined with the results from the meta-analysis to construct matrices of regional pay-offs in 2015 present values that are amenable to analysis as multi-player normal form games.

For reasonable ranges of parameters such as the discount rate, critical mass turns out to be smaller than the individual contributions of the two largest economies, the US and China, so that they would be unilateral gainers from the adoption of carbon taxation. They contribute large enough shares of global carbon emissions that the gains from their abatement alone exceed their mitigation costs. It follows, then, that a “climate club” comprising the US, China, and Europe, would also be a unilateral gainer. Moreover, their collective net gains in present value terms prove sufficient to finance side payments that would induce universal adoption. Nonetheless, the net gains do not turn positive in any region for two decades, rendering these policy outcomes politically difficult to achieve.

Section 2 briefly reviews the substantial prior literature on strategic interactions influencing potential agreement on climate policy and draws contrasts with the work to be presented. Section 3 introduces the global modelling of mitigation costs and their distribution and section 4 reviews studies that quantify the climate impacts of different levels of mitigation and their consequences for global economic welfare,

1 Numerous alternative approaches have been offered that are not within our present scope. Aldy et al. (2003) provide a review. In particular, McKibbin & Wilcoxen (2002) offer a hybrid approach directed at tackling the inefficiency of the tradeable permit system and the political impracticability of an uncoordinated carbon price. Model regimes emphasising linkages and dominant factors are suggested by Stewart et al. (2013). Climate policy “clubs” that can be large enough to influence the global carbon price were initially suggested by Cooper (1998; 2001; 2007) and further examined by Weitzman (2015).

combining these in a meta-analysis. Section 5 uses the results from the previous two sections to construct multiplayer normal form games and to derive policy-relevant equilibria. The section 6 then concludes and summarizes the research findings.

2.2 Climate Policy Interaction and Strategic Behaviour

The prior literature on the topic of this chapter is both rich and vast. Here we mention only those studies most relevant to the research to be subsequently presented, with the purpose of clarifying our mission in the context of this literature and highlighting key points of difference. A major concern in studies like this is the prospect of regionally heterogeneous climate policies. Accounting for these is a herculean task for analysts and one that we will avoid, choosing instead to imagine that regional policies have equivalents in carbon taxation and that the policy choice is simply whether or not to implement a rate of US$ 20 per tonne. 2

The most prominent work on strategic carbon abatement policy interactions at the global level is represented in the suite of articles by Nordhaus & Yang (1996), Nordhaus & Boyer (1999), Nordhaus & Yang (2006), Nordhaus (2010; 2011; 2014). It employs two models of the global economy, namely DICE, which is dynamic and global in scope but highly aggregated, and RICE, in which the world is divided into several regions. These models integrate the climate sector with the global economy, and each country is assumed to produce a single commodity for either consumption or investment based on Cobb Douglas technology. Nordhaus (2015) assesses climate policy coalitions and examines the role of trade sanctions, imposed for stability, using the Coalition-DICE (C-DICE) model. In this work macroeconomic, bilateral trade and environmental data are used to determine each country’s strategic incentive to join a coalition of countries adopting abatement policies. Payoffs are impacts on net national income and countries interact on carbon prices and punitive tariffs.

2 Support for this level of carbon taxation is offered by Shiller1981; Barrett (2002); Aldy et al. (2003); LeRoy 2005; Avi-Yonah & Uhlmann (2009); Cramton et al. (2015); and McKibbin, Morris & Wilcoxen (2008). Yet carbon taxes as abatement policy are not without their detractors. See, for example, Schmalensee (1998), who emphasises uncertainty and regional heterogeneity of outcomes, Bolton & Ockenfels (2000) and Kraft-Todd et al. (2015) who posit a coordination game structure in which first implementers lose.

Sælen (2016) and Hovi et al. (2017) extend the work of Nordhaus by considering the potential of transfer payments to induce full participation. They use a simple and stylized binary decision model with empirical foundations, which includes population and GDP levels, associated emissions and vulnerability to climate change. Sælen’s results indicate that there is substantial potential for side payments to facilitate an effective club. Hovi et al. extend Sælen’s work and offer a more regionally disaggregated approach emphasising the importance of initiation by the large emitters, particularly the US and the EU. They classify each country into two types, reluctant or enthusiastic, based on assumptions about intrinsic motivation to start a climate agreement.

A number of points of difference arise between these prior works and ours to follow. First, we follow the critique of Böhringer et al. (2017) and build on the innovative work of the Nordhaus team by using a model with fully endogenous capital growth paths and a level of disaggregation that enables the capture of both the leakage of emissions through trade in energy products and the effects of terms of trade changes due to carbon taxation in one region on the net gains achieved by others. One result of this approach is that the costs of participation differ significantly between regions and over time, as do the benefits at the regional level that are derived from our meta- analysis. We capture all the relevant dynamics and regional interactions but make the assumption that regional governments are able to pre-commit to mitigation policies at the outset, depending on their assessments of discounted net present values of economic gains over the coming 50 years.

As to coalition stability, we recognise that self-enforcing structures (Barret 1994; 2003) are required. The more members the greater are the incentives to free ride.3 If there are regions that derive unilateral benefits from implementing abatement policy but benefit further if other regions join, then coalition stability is readily retained by the conditionality of side payments.4 We give emphasis to positive side payments, rather than punitive tariffs, following Sælen (2016) and Hovi et al. (2017), but evaluate the affordability of side payments by comparing them with the measured present value of the net benefits from global abatement that accrue to the initiating

3 This is a condition called the “small paradox” that makes free riding become inevitable (Carraro & Siniscalco 1993). 4 See Carraro & Siniscalco (1998), Martimort & Sand-Zantman (2015) and Caparrós & Péreau (2017).

region.5 In the interests of analytical economy, however, we imagine that there is only one policy choice, as between the status quo and a US$20 carbon tax, and that governments commit as of 2015 to a once and for all policy choice based on the net present value of net regional benefits. We begin with the modelling of costs in the section to follow.

2.3 Estimating the Future Economic Costs of Carbon Abatement

This work proceeds in two phases. First, a dynamic model of the global economy is adapted to the assessment of carbon taxation at the regional level, the structure chosen is described and the database used is then summarised. The model is then applied to the construction of a baseline projection of global economic performance through 2050. On this projection is then superimposed a number of alternative mitigation scenarios.

First, the model used is an adaptation of the Gdyn-E model of Golub (2013). The embodied dynamics accommodate current account imbalances, international capital mobility and capital accumulation via an adaptive adjustment theory of investment at the country or regional level. The database for the model draws on the Global Trade Analysis Project (GTAP-7). It includes five primary factors: land, natural resource, skilled and unskilled labour, and capital. The full set of 57 commodities is condensed into 12, amongst which the energy sector consists of coal, crude oil, gas and petroleum products. Eight regions are included: Indonesia, Australia, China, Japan, the US, the European Union (EU), ASEAN and rest of the world (ROW).6

Our applications have required considerable recoding of the model and modifications to the original GDyn-E database, as well as the construction of a distinct baseline projection of the global economy that incorporates feasible demographic and technological scenarios. Critical to the scenarios constructed is the productivity performance in China, which is the single largest carbon emitter, and in the base period at least, the most rapidly expanding economy. The baseline, about which

5 We acknowledge numerous applications of Nash’s restricted stability condition in climate conflict studies, such as those by Howard (1971), Selbirak (1994), Pittel & Rubbelke (2008), Decanio & Fremstad (2013) and Madani (2013). 6 A complete description of the adapted version of Gdyn-E is explained in the Appendix Chapter. The aggregation and its database are provided in the annex section of this chapter.

more is said in the next subsection, assumes slower productivity growth than Golub (2013) and a graduated slowing beyond the base year.

Among the 21 aggregated regions, the USA emitted the most CO2 in 2015, followed by China and the European Union. The USA emitted around 7,348 million tonnes of carbon or 20 per cent of global emissions. China emitted 6,958 million tonnes or 18.6 per cent of the global total. These large shares ensure that the USA and China are the most significant regions in affecting carbon emission control. Emissions released by countries such as Japan, India, Australia and Indonesia all contributed less than five per cent.

The model is solved over the period 2015-2050 with a focus on the effects of carbon taxation on measures of economic welfare and the associated interactions between economies through trade and investment flows. A baseline projection is first constructed. It is designed to represent the path of the global economy with no additional carbon taxation, nor any other changes to government intervention.7 In it the overall Asian economy grows at around four to five per cent per year while advanced economies retain lower yet stable growth. China’s growth rate declines from more than seven per cent per year in the early years to less than five per cent per year by 2050. Since this growth rate remains comparatively high, China is prominent throughout as carbon emitter. According to this projection, by 2020, it would surpass the US as the biggest emitter in the world. By 2050, China would contribute 16.7 per cent of global emissions, followed by emerging India with 12.1 per cent.

Several carbon abatement scenarios are then constructed to examine the global and regional effects of abatement policy. Since GDP is a measure of the total income generated within an economy, the immediate cost of mitigation is calculated based on the deviation of regional real GDP from baseline levels.8 To make the task manageable, the carbon tax rate considered in all regions is restricted to 20 USD per

7 Recent emission mitigation action has taken the form of technological improvements in fossil fuel energy usage in the production process for several countries. Technical improvement rates are, accordingly, represented in our baseline simulation for the cases of Japan, USA and EU, as well as China and India. The energy intensity rates used are based on the IEA ETP 2010 Report on each Region’s energy intensity improvement from 1990-2007. The rates are in the annex section of this chapter. 8 This treatment is an advance over the identical abatement cost parameter of the DICE_C model used by Nordhaus (2015).

tonne. This rate is central among those discussed and it has proven to be sufficient to achieve static targets in numerous countries, including China’s 65 per cent declared reduction by 2030.9

When all regions in the world commit to the 20 USD carbon tax, there is a slight but widespread slowing of growth. Figure 2.1 exhibits these negative effects on the paths of real GDP. Economic restructuring occurs both regionally and globally (Ekins & Speck 1999) and there are changes to each region’s terms of trade and real exhange rate. Gains are enjoyed by some regions that more than offset the cost of implementing the tax.

Figure 2.1 Real GDP Annual Growth Rate Deviations, Due to Abatement Policy Adoption (% Change from the Baseline)

Source: Simulations of the model described in the text.

Japan and the European Union, in particular, enjoy expansions in their GDP levels, relative to the baseline.10 This departs from what would be observed had either region implemented the tax unilaterally, in which case their real GDP growth would be curtailed relative to the baseline. The difference is due in part to their comparatively strong baseline emission controls, which reduce the burden of the eventual tax and to their assumed relative productivity performance paths in energy-

9 A USD 20 per tonne CO2 tax rate could meet Indonesia's ambitious target of 26-41 per cent, based on Unilateral Carbon Taxation in Indonesia: Economic Implications (Perdana & Tyers 2016). 10 This proves to be consistent with Nordhaus’ (1996) finding as regards energy efficiency in Japan and Europe.

intensive industries. These cause the rate of return on future investment in these regions to grow relative to other regions subject to the tax and so they enjoy faster capital growth.

2.4 Estimating Benefits from Climate Change Mitigation

It is impossible to evaluate the strategic interaction between regions over mitigation policies without consideration of the benefits that might be expected from the associated levels of climate stabilization. Notwithstanding a large literature devoted to such benefits, the results have considerably greater variance than the more readily modeled economic costs of mitigation. One of the reasons for this is that the benefits are public – non-rival and non-excludable. Another is that they rely on at least three research links, each of which carries uncertainty, namely the link between fossil fuel burning and atmospheric carbon, that between atmospheric carbon and temperature change and that between temperatures change and economic welfare. Here we rely on a survey of the literature that covers these links and a meta-analysis to quantify them. In the subsection to follow, we use what is known about these links to establish the link between carbon emissions and the climate-related impacts on global GDP. In the next subsection, we investigate divergences in these impacts across regions.

2.4.1 Emissions, Climate and Global GDP Effects

Here we link projected total carbon emissions (in Gigatonnes, or GT) to average global temperature (in o Celsius) via the atmospheric concentration of greenhouse gas (parts per million, or ppm). In particular, we link the projected level of total carbon emissions to average surface temperature using the 2000-2100 global temperature scenarios of the Intergovernmental Panel on Climate Change (IPCC). First, each carbon taxation scenario from our model yields a separate simulated trajectory for carbon emissions. The IPCC temperature estimates are based on GHG Emission

(CO2 combined with other greenhouse gases, including Methane, CFC and Nitrous

Oxide). Since the relative contribution of CO2 is about 80 per cent (Nordhaus 1991), here we neglect the contributions of other greenhouse gases. Second, the associated rises in average global surface temperature are taken from IPCC scenario ranges (Table 2.1), which are projected through 2090 from the year 2000 with a very wide

error band. For this reason, we adopt IPCC terminology and construct three different temperature scenarios: “low”, “best” and “high”. Total emissions and temperature estimates under these three IPCC scenarios are illustrated in Figure 2.2.

Table 2.1 IPCC Temperature Scenarios and Estimated Temperature Changes

Emission IPCC Developed Temperature IPCC IPCC Best (GT) in IPCC Likely Estimate Atmospheric Temperature 2050/ (GHG Uncertainty Low Best High Concentration Estimate Lower scenario) Range Estimate Estimate Estimate (PPM) (o C) Border (o C) (o C) (o C) (o C) 90 A1F1 660-790 4.0 2.4-6.4 2.4 4.0 6.4 80 A2 570-660 3.0 2.0-5.4 2.0 3.0 5.4 70 A1B 485-570 2.8 1.7-4.4 1.7 2.0 4.4 60 A1T 440-485 2.4 1.4-3.8 1.4 1.5 3.8 37.2 - - - 0.0 0.0 0.0

Source: IPCC (2007).

Figure 2.2 Emissions and Global Temperature Changes (IPCC Scenarios)

Source: IPCC cases and fitted relationships (IPCC 2007).

Next, we investigate the global welfare impacts of changes in average surface temperature. Our meta-analysis of the economic welfare impacts of warmer temperatures draws on the survey by Tol (2009), which covers 15 sources offering measured impacts.11 These include enumerative studies based on natural experiments as well as statistical analyses. In the enumerative approach, the welfare estimates are extrapolated from selected individual locations to the global scale and from the immediate past to the distant future. The statistical studies rely on

11 The surveyed estimates offer economic evaluations of temperature rises from one to three degrees Celsius. Toll’s cited impacts range from -4.8 per cent of GDP for 3 degrees Celsius, to positive 3.0 per cent of GDP for one degree Celsius.

uncontrolled experiments and measured differences across regions in climate and income.

Despite the difference in analytical approach, these studies tend to agree that a rise in the average surface temperature by a single degree Celsius would actually benefit the global economy on average. Rises above two degrees are injurious, however. The resulting fitted relationship between the aggregate welfare impact (average global GDP loss) and the corresponding global average surface temperature rise is shown in Figure 2.3.

Figure 2.3 Temperature Changes and Welfare Consequences

Source: Based on estimated economic welfare measures from 15 sources, as compiled by Tol (2009).

Figure 2.4 Global Emissions and Global Economic Welfare (GDP)

Source: Authors’ estimates as described in the text.

The final step in estimating the global mitigation benefit involves calculating the welfare impacts of changes in global emissions, for each of the three IPCC scenarios. This is done by combining the fitted polynomial functions illustrated in Figures 2.2 and 2.3. The global welfare reduction per gigatonne of emissions is derived as shown in Figure 2.4. The high variance of IPCC temperature scenarios results in considerable divergence in welfare reduction estimates. The low-temperature scenario embodies the least economic risk, though our focus will mostly be directed at the IPCC “best” scenario.

2.4.2 Causal Factors Affecting Regional Divergences in GDP Effects

There is considerable evidence that warming will not affect all regions equally. Climate change impacts depend on variations in temperature that depend on geographical location, altitude, geology, and micro-climate. Here we account for these variations. To quantify different climate impacts for each region identified in our study, we draw on the integrated assessment study of Roson & Sartori (2015) of six climate impacts based on their own meta-analysis sourced in the scientific and economic literature. Our focus in accounting for this regional variability is on four climate indicators: sea level rise (SLR), agricultural output, impacts of heat on labour productivity and the human health effects of climate.12

Roson and Sartori first calculate the SLR impacts as the percentage losses due to changes in endowments of productive land. The results suggest considerable regional variation in endowment loss that is particularly large for small islands states, Central America and Asia but may be negative for arctic states. The impact on agriculture is based on effects on the output of the staples, maize, wheat, and rice. Their conclusion accords with the findings by Schlenker & Roberts (2009). The most modest impacts turn out to be at the highest latitudes, particularly in Europe and North America.

The effect on labour productivity is estimated by the change in the wet bulb globe temperature (WBGT) per degree Celsius, since this appears to diminish effective working hours. They generalize based on a minimum threshold from Kjellstrom (2009), with rising temperatures impairing labour productivity in agriculture,

12 Here we omit climate impacts on tourism and household energy demand which require secondary feedbacks associated with mitigation benefits that are not captured in our modelling.

manufacturing, and services. The agricultural labour productivity is reduced most by temperature rise and the effect is largest in the humid tropics. Additionally, the vector-borne, heat-related diseases are also more destructive the higher the temperature climbs. The estimation of this effect relies on the earlier work of Bosello et al. (2006) and the 2014 IPCC Assessment Report.

2.4.3 Region Specific Climate Benefit Factors

We estimate ratios of the regional to the average global climate benefits in two steps. In the first, the per-region net effects on GDP are estimated for all the causal factors outlined in the previous section. Second, for each region the four GDP effects are multiplied by their respective GDP shares and added. More specifically, region- wide effects of agricultural output changes are approximated by multiplying by agriculture’s value-added share; labour productivity effects due to heat stress and disease incidence are estimated via the product of the sectoral productivity effect and the ratio of sectoral labour income to total GDP. Finally, for SLR, estimated changes of land productivity are multiplied by the share of land rent in total GDP. The addition of these causal effects yields the region-specific GDP incidence per degree increase in the average surface temperature.13

The second step is to construct the regional benefit ratio, with numerator and denominator measuring the percentage change in GDP. If the global benefit (per cent change in global GDP) per degree reduction in average surface temperature is

BW and the corresponding regional benefit is Bi, the ratio of regional to global benefit is BBiW, which must be normalised to satisfy:

    nnYYBi i i BBWi  or    1 (2.1) ii11YYB    jjj   j   W

To be consistent with the three different IPCC temperature scenarios introduced in section 4.1, the regional benefit ratio is calculated for temperature rises of between one and five degrees Celsius. An average is then taken across five sets of estimates, as indicated in Table 2.2.

13 These results are summarised in Table A.2.7 of the annex section.

Table 2.2 Regional to Global Benefit Ratios

Temperature Rising No COUNTRY/ REGION AVERAGE +1oC +2oC +3oC +4oC +5oC 1 Indonesia 0.36 0.82 1.14 1.10 0.98 0.88 2 Singapore 0.08 0.14 0.18 0.17 0.15 0.14 3 Malaysia 0.19 0.32 0.41 0.38 0.34 0.33 4 Other ASEAN 0.33 0.57 0.73 0.70 0.63 0.59 5 Other Asia 0.88 1.16 1.34 1.29 1.21 1.17 6 Australia 0.20 0.23 0.25 0.25 0.25 0.24 7 New Zealand & Oceania 0.09 0.07 0.07 0.07 0.07 0.07 8 China (PRC) 3.97 3.82 3.79 3.79 3.80 3.83 9 Japan 0.76 0.77 0.85 0.94 1.02 0.87 10 Korea 0.18 0.18 0.18 0.20 0.21 0.19 11 India 1.74 2.32 2.68 2.52 2.31 2.32 12 Brazil 0.37 0.58 0.75 0.81 0.81 0.66 13 USA 2.76 2.50 2.34 2.29 2.35 2.45 14 Canada 0.41 0.17 0.01 0.04 0.09 0.14 15 Other America 0.85 1.64 1.87 1.88 1.75 1.60 16 EU_28 4.70 2.75 1.50 1.70 2.16 2.56 17 Russia 0.62 0.28 0.05 0.08 0.16 0.24 18 FTA Europe 0.15 0.08 0.04 0.04 0.06 0.07 19 Ex. Soviet Union & Other EU 0.43 0.29 0.20 0.21 0.24 0.27 20 Middle East 1.24 1.15 1.10 1.07 1.06 1.13 21 Africa 0.69 1.18 1.53 1.47 1.35 1.24

Source: Estimation as described in the text.

2.5 Analysis of Strategic Interactions

The payoffs for each region are based on comparisons of the benefits from mitigation at the regional level, derived from the previous section, with the associated mitigation costs, calculated using the model as per Section 2.3. Net global and regional gains are calculated for each year from 2015 to 2050. The global scale of these gains depends, however, on the mix of regions participating. Moreover, the results arise in triplicate given the three IPCC temperature scenarios discussed previously. The costs (positive or negative) facing regions that do not participate stem only from changes in the paths of their terms of trade, real exchange rate and investment that are due to the implementation of the tax by other regions. These costs also vary depending on the mix of regions participating. For regions that do participate, these effects are added to those stemming directly from their

implementation of the tax. These mitigation costs are then deducted from gains due to moderated temperature changes at both the global and regional levels.

Here the payoffs are discounted net present values in 2015 US$, constructed over the period 2016 to 2050, of net welfare effects in 2015. The net welfare effect for each year is derived by multiplying the net percentage change in projected GDP for each region by its corresponding level and discounting the result to 2015 at the 2017 US 10 year Treasury bond yield of 2.35 per cent.14

Our starting hypothesis was that these payoffs would structure to resemble a coordination game, with multiple Nash equilibria. In this case the mix of current policies would leave the world in an inferior equilibrium from which no region would gain by moving unilaterally. To change to a better equilibrium, all (or at least a “critical mass” of) countries and regions would then need to act together. Under these conditions, Schelling (1980) identified “focal points” as a means to help resolve the coordination problem, an idea which favours the constant-rate carbon tax as a device for simplifying the interaction choice (Avi-Yonah & Uhlman 2009). Our subsequent analysis tests this hypothesis and finds it wanting.

Significant emission contributions from China, the US, and the EU distinguish these large regions from others. For this reason, the first scenario focusses on these three regions. Other regions are assumed to free ride (not to participate in carbon taxation and hence abatement). Since there are more than two players, the normal form game requires more than four combinations of decision options. These follow Pascal’s triangle, where the number, k, of combinations of n elements is the sum of the n-th row of the binomial coefficients.15

n n 2n 00k  nck  k  n  k (2.2)

14 The discount rate of 2.35 per cent is considered to be high compared to the less than one per cent chosen for the Stern Review (Stern 2006). A tendency toward a declining social discount rate is proposed by Weitzman (2001), Gollier & Weitzman (2010), Gollier (2012) and Arrow et al. (2014). We follow Heal (2017), who see difficulties in arriving at a Pareto efficient path given the nature and extent of climate change externalities. 15 n The number, k, of combinations given set , S , with n elements is denoted as Ck . n Alternatively,  is read as n chose k . k

And the number of subsets in each combination is obtained by calculating the whole number between 1 and n , where n, k R , or:

n n!  (2.3) 0kn k k!! n k 

It follows that the game between the three largest players (the US, China and Europe), with each facing just the two options (participate in the carbon tax program or defect), has eight strategic combinations, which comprise one zero-participation subset, three subsets where one country participates, three subsets where two countries participate and one subset in which three countries participate. The static, normal form game analysis for this case is illustrated in Figure 2.5 and discussed in the following subsection.

2.5.1 Strategic Analysis: The Big Three (China, USA, EU)

This analysis is undertaken for each of the three IPCC temperature scenarios. We take them in turn.

The IPCC “low temperature” case:

Figure 2.5-A Normal Form Static Games on 2015 Present Values for Three Regions (US, China, EU)

LOW Scenario (in Trillion USD) EU EU Participate Defect China China Participate Defect Participate Defect 10.11 3.66 9.04 8.58 9.90 3.25 8.29 7.79 Participate Participate 14.02 11.92 17.85 15.13 USA USA 13.86 2.99 11.73 7.20 13.38 2.41 10.73 6.25 Defect Defect 12.81 9.58 16.34 12.49 *All Defect

Source: Simulation results and meta-analysis benefits calculated as described in the text.

The payoffs, in this case, prove inconsistent with the hypothesized coordination game. A single Nash Equilibrium appears and it combines defection by all three regions. In this case, the global welfare losses due to average surface temperature changes are very modest and so there is a disincentive to participate for all regions. Further investigation (Table 2.3) reveals that, even if all three regions were to participate, estimated global emissions would be reduced to 87.35 GT in the year 2050, suggesting a welfare gain of only 0.16 per cent of global GDP. By

comparison, China’s participation cost would be 2.76 per cent of its projected GDP. The calculus is similar for the US and the EU.

In this case, the dominant strategy to defect yields the best economic outcome, at least for China, and the US. The option of all participating offers no superior outcome. With defection, China avoids a present value loss amounting to three trillion USD while the US enjoys a slightly higher benefit. By contrast, despite the fact that the EU’s best individual strategy is to defect, it would be better off if all three were to choose mitigation.

Table 2.3 Estimated Average Global Welfare Effect from Emission Abatement by Regions and Regional Groupings, Percentage of GDP Deviation

PV of Global Welfare PV Participation Cost Selected Scenario Global Impact (% Global GDP (% Country GDP No Carbon Tax Emission Baseline)a Baseline)b Implementation (GT) Low Best High CHN USA EU 1 Global (All Countries) 64.90 2.02 1.77 -11.40 -0.84 -0.57 0.19 2 US, China, EU (Big Three) 87.35 0.16 -8.96 -37.80 -2.76 -0.64 -0.17 3 US China 89.18 -0.12 -10.33 -40.64 -3.04 -0.70 0.42 4 China EU 90.08 -0.28 -11.03 -42.07 -3.13 0.04 -0.45 5 US EU 95.01 -1.18 -15.17 -50.36 0.65 -0.62 -0.31 6 China 92.65 -0.72 -13.12 -46.29 -3.41 -0.02 0.14 7 USA 96.80 -1.55 -16.79 -53.55 0.37 -0.68 0.28 8 EU 98.47 -1.92 -18.38 -56.62 0.28 0.06 -0.59 9 NONE 100.28 -2.32 -20.18 -60.05 0.00 0.00 0.00 a These effects stem from reduction in global surface temperature change, due to mitigation. b The cost the implemented mitigation policy is a USD 20 Uniform Carbon Tax. Positive rate reflects benefit rate as free rider. Source: Estimation as described in the text. The “best” IPCC temperature scenario:

Figure 2.5-B Normal Form Static Games on 2015 Present Values for Three Regions (US, China, EU)

BEST Scenario (in Trillion USD) EU EU Participate Defect China China Participate Defect Participate Defect -2.57 -6.40 -16.18 -11.50 -5.17 -8.81 -20.66 -15.21 Participate Participate 0.30 -15.52 1.54 -16.42 USA USA -3.65 -11.07 -20.92 -18.67 -7.15 -14.09 -26.27 -22.96 Defect Defect -6.14 -26.04 -5.93 -27.94 **EU Defect

Source: Simulation results and meta-analysis benefits calculated as described in the text.

In this case, while a coordination game is not evident, it is distinctive that the US and China now have unilateral incentives to participate. The Nash equilibrium has the US and China implementing mitigation policy while Europe is better off free riding. Decisions to defect or participate are influenced by China. Once China participates, the EU’s best strategy is to defect and hence to free ride on mitigation by the US and China.

The IPCC “high” temperature scenario:

Figure 2.5-C Normal Form Static Games on 2015 Present Values for Three Regions (US, China, EU)

HIGH Scenario (in Trillion USD) EU EU Participate Defect China China Participate Defect Participate Defect -114.52 -88.47 -164.20 -118.48 -125.82 -96.83 -178.07 -128.61 Participate Participate -124.34 -180.74 -132.90 -192.21 USA USA -132.55 -104.72 -187.24 -138.14 -145.05 -113.88 -202.26 -149.04 Defect Defect -149.88 -211.88 -159.80 -224.67 ***All Participate

Source: Simulation results and meta-analysis benefits calculated as described in the text.

The mitigation gains are more significant in this case. All three regions choose to participate. The benefits far exceed the economic costs of mitigation and so all three participate and hence impose the carbon tax.

2.5.2 Strategic Analysis: Five Countries

In the three region analysis and the “best” and “high” temperature cases, each region’s contribution to the abatement of global emissions plays a critical role in determining its incentive to participate. It is therefore likely that strategic incentives differ for regions with smaller emissions. Here we add to the analysis two smaller regions that, nonetheless, generate high emissions per capita, namely Indonesia and Australia. With five regions the game has 32 combinations (25), including the subsets in which either no regions or all regions participate in implementing the tax. There are also five subsets pairing one region with four and ten pairing two and three. The payoff matrices for this multi-player game are summarized and illustrated in Figure 2.6.

Figure 2.6 Normal Form Static Games on 2015 Present Values for Five Regions

Source: Estimation as described in the text

The IPCC “low temperature” case:

Given that the large regions all defect in the three player game it is not surprising that no regions participate in this variant. All face dominant non-participation strategies. For the two smaller regions the cost of mitigation is comparatively high compared with their share of the global benefits that would accrue from it. Implementing unilaterally would, for Australia and Indonesia, cause 2015 present value GDP losses of 0.28 and 0.26 Trillion USD respectively, yielding no abatement action. This dominant strategy, for all to defect, is also not consistent with a prisoner’s dilemma, in that participation by all does not confer net benefits for all regions.

The IPCC “best” temperature scenario:

As in the three players case there is no coordination game. Except for the US and China, all regions choose to free ride. The costs that the other regions face in implementing the tax are set against comparatively small increments to shared global welfare from the resulting mitigation. By contrast with the “low” scenario, and consistent with the earlier unilateral analysis, the US and China enjoy unilateral gains from abatement even when, because other regions defect, no other region contributes to that abatement. This finding emphasizes the central role of these big two emitters in global climate change abatement.

The IPCC “high” temperature scenario:

This yields a more striking result. The two smaller economies that consider investment in abatement, Australia, and Indonesia, still choose to free ride. Even with larger benefits from abatement, the implementation cost is too high to justify participation by these small emitters. The accumulated present value of their net impact is negative if both commit to the tax.

While in this case universal participation does yield net benefits at the global level, the free riding incentives are shown to prevent participation by all regions, even in this “high” scenario. If the free rider losses are smaller than the collective welfare or revenue gains by the larger regions that would participate, then this excess gain could feasibly finance side payments to induce universal participation. The following subsection assesses the potential affordability of such side payments, as a means to overcome this free rider problem.

2.5.3 Free Riding and the Potential Affordability of Side Payments

We have seen that the US and China consistently derive net gains from bearing the cost of abatement in both the “best” and the “high” IPCC temperature scenarios, while the EU is a net gainer from unilateral implementation only in the “high” scenario. In these cases the three largest economies taken together (the “big three”) would implement carbon taxation irrespective of the behavior of other regions. Yet this raises difficult politics. It is easier to advocate the implementation of a tax that is costly in the short term if other regions are committed to it. Free riding therefore remains a political stumbling block. Moreover, if the rest of the world were also to implement the tax the global gains from mitigation would be substantially increased and the implementation cost would be smaller for the big three.

It is therefore important to consider whether side payments to all the smaller economies are feasible. The net benefit from mitigation that is enjoyed by the big three is enhanced if mitigation policy is implemented by all other regions. If the quantum of this enhancement exceeds the total additional cost borne by the other

regions, it follows that there exist affordable transfer payments that would improve welfare in the big three and globally.16

The most practical way to assess this would be based on receipts from carbon taxation, since these would be readily available for redistribution. In our model, however, revenue from carbon taxation raises domestic government expenditure and its diversion abroad would create unaccounted-for, indirect national losses in implementing regions. We therefore consider affordability from the accumulated present value of net future benefits. If net benefits from abatement are sufficient to compensate for the losses in other regions then side payments are affordable, even though this would require considerable intertemporal redistribution in the big three.

Our first step is to measure the present value of the stream of global welfare improvements, relative to a no-abatement case, for each carbon tax implementation (Table 2.4). Then this number is adjusted by each region’s benefit share and its mitigation cost in terms of the present value of their projected GDP. This yields the net welfare effect of carbon mitigation, both in regions that implement the tax and those that do not. We calculate the present value of the stream of extra benefits gained by the big three carbon taxing emitters (China, the US, and the EU) when all the otherwise free-riding regions also implement the tax. This extra benefit arises both from reduced global surface temperature and from terms of trade changes due to taxation in the other regions. We then estimate the present value of actual net losses that would be incurred by the previously free-riding regions, due to their switch to abatement policy.

Should the present value of the extra gains by the top three exceed the present value of aggregate incremental losses by these regions, there is room for side payments. For parsimony, we limit the side payment analysis to only the “best” temperature scenario, where the free riding and the prisoner’s dilemma structure are clearest. Table 2.5 summarizes the total extra benefit accruing to the big three emitters from full global implementation, and the aggregated losses borne by other regions due to their switching from free riding to implementation. The present value of the

16 This approach is consistent with the Carraro & Siniscalco (1993) definition of stable transfer payments. To achieve a stable coalition, where there are no incentives to free ride, the total transfers should be lower than the gain the committed member would obtain from the expanding coalition, but must be larger on net than the potential member’s net loss associated with joining other regions in adopting abatement

additional net welfare benefits to China, the US, and the EU, which arises from the implementation of the tax by all other regions, is estimated roughly at 27 trillion USD. This is sufficient to compensate for the present-value incremental net loss of 15 trillion USD in the other regions.

Table 2.4 Present Value of Improvement Relative to No Abatement Case

Percentage Improvement Global PV Global Welfare Impact from “No Abatement” Selected Scenario Carbon Emissions (Trillion USD) Case Relative to Baseline Tax Implementation (GT) GDP Value Low Best High Low Best High None (No Abatement) 100.28 -1.41 -12.24 -36.43 0.00 0.00 0.00 Global (All Countries) 64.90 1.23 1.08 -6.92 4.34 21.95 48.65 US, China, EU (Big Three) 87.35 0.10 -5.44 -22.93 2.48 11.21 22.25 Big Three + Indonesia 86.42 0.18 -5.03 -22.08 2.62 11.87 23.65 Big Three + Other ASEAN 86.66 0.16 -5.14 -22.30 2.59 11.70 23.29 Big Three + Australia 86.84 0.15 -5.21 -22.46 2.56 11.58 23.02 Big Three + Japan 86.91 0.14 -5.24 -22.53 2.55 11.53 22.92 Big Three + India 80.42 0.65 -2.71 -16.98 3.39 15.71 32.06 Big Three + Russia 84.32 0.36 -4.17 -20.22 2.91 13.31 26.72 Big Three + Middle East 85.22 0.29 -4.53 -21.01 2.79 12.70 25.42 Big Three + New Zealand 87.31 0.10 -5.42 -22.90 2.49 11.24 22.31 Big Three + Brazil 87.21 0.11 -5.38 -22.80 2.50 11.31 22.46 Big Three + Korea 86.80 0.15 -5.20 -22.43 2.56 11.61 23.08 Big Three + Canada 86.93 0.14 -5.25 -22.55 2.54 11.51 22.89 Big Three + Latin Americas 86.25 0.20 -4.96 -21.93 2.65 11.99 23.91 Big Three + Other Asia 85.90 0.23 -4.81 -21.61 2.70 12.24 24.43 Big Three + FTA Europe 87.32 0.10 -5.42 -22.90 2.49 11.23 22.30 Big Three +Ex. Soviet Union 86.14 0.21 -4.91 -21.83 2.66 12.07 24.07 Big Three + Africa 85.04 0.30 -4.46 -20.85 2.82 12.83 25.68

Source: Estimation as described in the text.

Table 2.5 Cumulative Discounted Dollar Value of Net Welfare Benefit or Loss due to Uniform Tax (2015 US$ Trillions) Extra Benefit By Unilateral “BIG Three” Universal Regions Universal Implementation Implementation Implementation (A) Implementation (B) (C) (D) (D-C) China 21.37 16.56 26.68 10.12 USA 13.63 23.70 31.36 7.60 EU 5.23 28.23 37.58 9.35 Total “Big Three” 68.48 95.62 27.14

“BIG Three” Joining “Big Effect of Altering Regions Implementation/ Benefit As Three” Mitigation Strategy (A) Free Rider (C) (C-B) (B) Indonesia 0.59 0.20 -0.39 Other ASEAN Countries 0.64 0.17 -0.47 Australia 0.35 0.00 -0.35 Japan 4.12 3.37 -0.75 India 4.74 3.54 -1.20 Russia 0.01 -3.26 -3.27 Middle East & North Africa 2.37 -0.51 -2.88 New Zealand & Oceania 0.06 0.00 -0.06 Brazil 0.83 0.53 -0.30 Korea 0.50 -0.06 -0.56 Canada 0.35 -0.14 -0.49 Latin America 4.49 2.47 -2.02 Other Asia 1.58 1.05 -0.53 FTA Europe 0.15 0.05 -0.10 Ex. Soviet Union 0.32 -0.75 -1.07 Africa 1.58 0.89 -0.69 Total 22.66 7.53 -15.12 Source: Estimation as described in the text.

If this analysis is implemented on an annual basis, rather than in present value terms, there are years in which the capacity for compensation is sufficient and years in which it is too small.17 Sadly it is during the early years that the compensation would be inadequate. Indeed, the extra benefit gained by the big three would not be sufficient to compensate the total loss carried by other regions, at least until the year 2035. As illustrated in Figure 2.7, the total loss due to switching from free riding to the implementation of the tax in the smaller regions is stable throughout the simulation period at something under 0.5 trillion USD per year. For the big three, however, the net gains are negative throughout the first two decades.

Indeed, as Figure 2.8 shows, within the first two decades, the big three would experience net negative benefits from carbon abatement, substantial for China and the US yet comparatively modest for Europe. The turning point would be two decades after the tax is first implemented. While this is consistent with our finding that both the US and China are net gainers from unilateral tax implementation, the task is made politically difficult due to the need to wait almost an entire generation for the net gains to begin to flow.

17 There is clearly a time consistency issue with this analysis in that it is our assumption that regional governments can pre-commit to 35 years of future climate policy as of 2015. Dealing with the possible failure of this assumption is necessarily the subject for further research.

Figure 2.7 Side Payments from China, the USA and the EU Sufficient to Induce Universal Participation

Source: Estimation as described in the text. Figure 2.8 Estimated Net Welfare Benefit due to Unilateral Implementation of Abatement Policy by China, USA, and EU

Source: Estimation as described in the text.

2.5.4 Sensitivity Analysis: Discount Rate

There are many facilitating assumptions required for this analysis.18 We regard the rate at which future benefits and costs are discounted to be the strongest of these and so it is important that the associated sensitivity of results should be explored. Here

18 Beyond the structural assumptions embodied in our modelling of costs, there is our omission of risk preferences from the analysis and our assumption that the regional distribution of economic effects does not depend on which regions implement abatement policy but only on the collective effect on global emissions. These are clearly matters for further work.

we consider the 10 year US Treasury bond rate to be at the low end of a possible range, given that abatement policies are to be implemented in economic environments where the opportunity cost of capital depends on commercial financing rates, and particularly those in the energy and electricity sectors. We therefore consider two additional discount rates: five per cent captures the cost of equity in commercial activity in open economies generally. We also consider a rate of 7.2 per cent to represent cost of capital in energy and energy intensive industries.19

With these different discount rates, the game theoretic results from all scenarios confirm the previous findings, for almost all regions. Variations are, nonetheless, of particular interest. In the “best” temperature scenario, we find that the choices of the USA and China are sensitive to the discount rate. At the five per cent rate, the US participation strategy becomes less dominant, yet the equilibrium still has unilateral participation alongside China.

At 7.2 per cent, China and the US are highly interactive. Two equilibria emerge in which either China or the US participates, but they do not do so together. The best reaction of the US is now to defect if China commits and to participate if China defects. China’s optimal reactions are the converse, highlighting the strong incentives for members of the big three to free ride once one other large emitter decides to commit.

As to the affordability of side payments, under the “best” temperature scenario, at the higher discount rates, the big three would not be able to afford compensation sufficient to induce universal implementation. In the “high” temperature scenario, however, the total discounted value of the larger benefits to the big three from temperature stability would still be sufficient to finance the necessary transfers, albeit with considerable intertemporal wealth transfers domestically.

2.6 Conclusions

The ineffectiveness of the Kyoto Protocol and the constraints facing the success of the Paris Accord arise from hesitation among nations in the face of costly mitigation

19 For consideration of the discount rate as the cost of capital see Nordhaus (2007) for an evaluation of the cost of capital facing US industries. See: http://www.stern.nyu.edu/~adamodar/New_Home_Page/data.html.

actions, combined with incentives for smaller regions to free ride on the commitments of larger ones. Because the uniform carbon taxation scheme offers a simple and internationally transparent negotiation target on the one hand and a comparatively efficient economic policy measure on the other, we adopt it as a policy model to explore and quantify these strategic issues.

The effects of varying numbers and sizes of regions that might commit to such a tax are considered by conducting simulations of a global economic model and combining these with the results from a meta-study of the temperature and economic welfare impacts of alternative levels of global carbon emissions. Three IPCC temperature rise cases are considered: “low”, “best” and “high”. These yield quite different economic gains from moderating the rise in average global surface temperatures. In the “best” case, the results suggest that the absence of further carbon mitigation will see the average surface temperature rise by four degrees Celsius, bringing with it a loss to the global economy of 15 per cent of its GDP. In the IPCC “high” temperature case this impact is almost doubled. More modest results emerge in the “low” temperature case. When mitigation is added via carbon taxation at 20 USD per tonne, five key conclusions emerge.

First, the more widespread is the implementation of the tax the more the global terms of trade shift in favour of just a few comparatively energy-efficient regions, including the EU and Japan. Gains to these regions stem both from the abatement and hence lower temperatures as well as from these terms of trade improvements. Second, in the “best” and “high” IPCC temperature scenarios, in present value terms the US and China would derive positive net economic gains from their unilateral implementation of the tax, irrespective of the behaviour of other regions.

Third, in the “best” scenario, the large carbon-emitting regions, namely the US, the EU, and China, have sufficient individual effects on the global climate that the gains each would derive from their joint implementation of the tax, and hence their unilateral effects on the global surface temperature, exceed their collective economic costs of implementing it. Together, they face a purely economic incentive to implement the carbon tax that does not depend on whether other regions choose to do so. This finding contradicts the analysis using the DICE-Coalition model by Nordhaus (2015), with their pessimistic result ruling out a coalition without penalties. It does, however, confirm the “small paradox” theory of Barrett (1994;

2003). It also highlights the crucial role of China, rather than Japan, in global climate policy.

Fourth, in the “best” IPCC temperature scenario, were they able to coordinate, China, the US and the EU would choose to implement the tax collectively. The question then arises as to whether the additional gains they would derive (via lower temperatures and further terms of trade changes) were the remaining regions also to implement the tax would be sufficient for them to afford side payments large enough to induce the other regions to do so. Our results show that, so long as future benefits and costs are discounted at the 2017 ten year Treasury bond yield, their additional gains are quite sufficient to finance such side payments. At higher rates of discount, only the IPCC “high” temperature scenario yields net gains to the big three that are large enough to compensate other regions for participation. Indeed, at these higher discount rates, strategic interaction between the US and China is heightened in the “best” temperature scenario, with only one facing unilateral net gains from implementation. When one commits to abatement policy the other will free ride.

Fifth, and finally, it is shown that carbon abatement policies will be politically difficult to implement by all countries, even the US and China, which are the only unilateral gainers under the “best” temperature scenario. This is because the annual net gains do not turn positive for at least two decades. This finding suggests pessimism about the potential for implementation in the “anchor” regions but, moreover, since no side payments would be affordable in the first two decades, the potential for wider coalition building also looks bleak. Nonetheless, even though net benefits are negative in the early decades, implementing regions would be generating considerable public revenue from the taxes and these could be a source of inducement through global redistributions. Yet, even this would require strong forward looking behaviour by governments.

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Annex to Chapter 2

Figure A.2.1 Regional GDP Growth Baseline Projection

Source: Baseline Simulation of the model described in the text.

Table A.2.1 Regional Aggregation, Mapping and Investment Target Investment Aggregated NO GTAP Region Region Aggregation Target % Map (2015-2050) 1 Indonesia Indonesia 5.5 to 4 Indonesia 2 Singapore Singapore 4 to 1 ASEAN 3 Malaysia Malaysia 4 to 2 ASEAN 4 Cambodia, Lao, Myanmar, Philippines, Other ASEAN Countries 5 to 3 ASEAN Thailand, Vietnam, Rest of Southeast Asia 5 Taiwan, Rest of East Asia, Bangladesh, Other Asia Countries 5 to 4 ROW Pakistan, Sri Lanka, Rest of South Asia 6 Australia Australia 2.8 to 1.5 Australia 7 New Zealand and Rest of Oceania New Zealand & Oceania 3 to 2.5 ROW 8 China and Hong Kong China China 8.5 to 1.5 China 9 Japan Japan 2 to 0.2 Japan 10 South Korea Korea 3 to 0.5 ROW 11 India India 7 to 4 ROW 12 Brazil Brazil 3.5 to 1.5 ROW 13 USA USA 2.8 to 1.5 USA 14 Canada Canada 3 to 1.5 ROW 15 Mexico, Costa Rica, Guatemala, Latin America Countries 4 to 2 ROW Nicaragua, Panama, Rest of Central America, Caribbean, Rest of North America, Argentina, Bolivia, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela, Rest of South America 16 Austria, Belgium, Bulgaria, Croatia, EU28 2.5 to 1 EU28 Cyprus, Czech Rep., Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom* 17 Russia Federation Russia 3 to 1 ROW 18 Switzerland, Norway and Rest of FTA FTA Europe 2 to 1.5 ROW 19 Albania, Belarus, Ukraine, Rest of Ex. Soviet Union and Other 3 to 1.5 ROW Eastern Europe, Rest of Europe, Europe Kazakhstan, Kyrgyzstan, Rest of Former Soviet Union, Armenia, Azerbaijan, Georgia, Turkey 20 Iran, Rest of Western Asia, Egypt, Middle Eastern and North 4 to 2.5 ROW Morocco, Tunisia, Rest of North Africa Africa 21 Nigeria, Senegal, Rest of West Africa, Other Africa 5 ROW Central Africa, South Central Africa, Ethiopia, Madagascar, Malawi, Mauritius, Mozambique, Tanzania, Uganda, Botswana, South Africa, Rest of South African Custom Unions

Table A.2.2 Sectoral Aggregation

NO Aggregated Sectors Explanation GTAP commodities 1 Agriculture Agriculture and Food pdr, wht, gro, v_f, osd,c_b, pfb, Processing ocr, ctl, oap, rmk, wol, cmt, omt, vol, mil pcr, sgr, ofd, b_t 2 Other Primary Sectors Forestry, Fisheries and frs, fsh, omn Mineral Extraction 3 Coal Coal coa 4 Crude Oil Crude Oil oil 5 Gas Gas and Gas Products gas, gdt 6 Oil Products Petroleum and coal p_c Products 7 Energy Intensive Energy Intensive and Non crp, nmm, i_s, nfm Metallic Mineral Industries 8 Electricity Electricity ely 9 Manufacturing Manufacturing Industries tex, wap, lea, lum, ppp, fmp, mvh, otn, ole, ome, omf 10 Transportation Water, Air and Other otp, wtp, atp Transport 11 Service Water, Construction, Trade wtr, cns, trd, cmn, ofi, isr, obs, and Other Services ros 12 Non Trading Service Public administration osg, dwe Source: GTAP Database.

Table A.2.3 Sectoral Productivity Growth

China input productivity growth rate is assumed to be positive yet declining throughout forecast period. It grows by 6 per cent in 2005 yet declines to only 3.5 per cent in 2050. The same assumptions applied for India (aggregated in ROW) where input productivity growth declines from 5 to 4 per cent by the end of the forecast period.

NO Sectors Indonesia Singapore Malaysia ASEAN Asia Australia 1 Agriculture 3.5 2.0 3.5 2.7 2.5 1.4 2 Other Primary 3.5 0 3.5 2.7 2.5 1.4 3 Coal 3.5 0 0 0 0 1.4 4 Oil 0 0 0 0 0 0 5 Gas 3.5 0 3.5 0 0 1.4 6 Petroleum Products 0 0 0 0 0 0 7 Energy Intensive 3.5 2.0 3.5 2.7 2.5 1.4 8 Electricity 1.75 1.0 1.75 1.35 1.25 0.7 9 Manufacturing 3.5 2.0 3.5 2.7 2.5 1.4 10 Transportation 3.5 2.0 3.5 2.7 2.5 1.4 11 Service 1.75 1.0 1.75 1.35 1.25 0.7 12 NTR Service 1.75 1.0 1.75 1.35 1.25 0.7

NO Sectors NZ_OC China Japan Korea India Brazil 1 Agriculture 1.4 6.0 to 3.5 1.5 2.0 5 to 4 2.5 2 Other Primary 1.4 6.0 to 3.5 1.5 0 5 to 4 2.5 3 Coal 0 6.0 to 3.5 0 0 5 to 4 0 4 Oil 0 0 0 0 0 0 5 Gas 0 6.0 to 3.5 0 0 5 to 4 0 6 Petroleum Products 0 0 0 0 0 0 7 Energy Intensive 1.4 6.0 to 3.5 1.5 2.0 5 to 4 2.5 8 Electricity 0.7 3.0 to 1.75 0.75 1.0 2.5 to 2 1.25 9 Manufacturing 1.4 6.0 to 3.5 1.5 2.0 5 to 4 2.5 10 Transportation 1.4 6.0 to 3.5 1.5 2.0 5 to 4 2.5 11 Service 0.7 3.0 to 1.75 0.75 1.0 2.5 to 2 1.25 12 NTR Service 0.7 3.0 to 1.75 0.75 1.0 2.5 to 2 1.25

N Sectors USA Canada Latin America EU28 Russia FTA EU O 1 Agriculture 1.8 1.8 2.0 1.3 3.0 1.3 2 Other Primary 1.8 1.8 2.0 1.3 3.0 1.3 3 Coal 1.8 1.8 0 0 3.0 0 4 Oil 1.8 1.8 2.0 0 3.0 0 5 Gas 1.8 1.8 2.0 0 3.0 0 6 Petroleum Products 0 0 0 1.3 0 0 7 Energy Intensive 1.8 1.8 1.0 1.3 3.0 1.3 8 Electricity 0.9 0.9 1.0 0.65 1.5 0.65 9 Manufacturing 1.8 1.8 2.0 1.3 3.0 1.3 10 Transportation 1.8 1.8 2.0 1.3 3.0 1.3 11 Service 0.9 0.9 1.0 0.65 1.5 0.65 12 NTR Service 0.9 0.9 1.0 0.65 1.5 0.65

NO Sectors Ex. Soviet MENAF Africa 1 Agriculture 2.0 2.7 2.5 2 Other Primary 2.0 2.7 2.5 3 Coal 0 00 0 4 Oil 0 2.7 0 5 Gas 2.0 2.7 0 6 Petroleum Products 0 2.7 0 7 Energy Intensive 2.0 2.7 2.5 8 Electricity 1.0 1.35 1.25 9 Manufacturing 2.0 2.7 2.5 10 Transportation 2.0 2.7 2.5 11 Service 1.0 1.35 1.25 12 NTR Service 1.0 1.35 1.25

Table A.2.4 Technological Improvement in Energy Usage in Production

Technological improvement rate (year on year) applies to coal usage in manufacturing, energy intensive and electricity sectors in selected countries. This energy intensity improvement is also applied for petroleum usage in transportation sector. The rates are based on IEA/ OECD, Energy Technology Perspective Report 2010 and Japan Energy Efficiency Report (Feb 2012). The value are measured based on energy used in 1990-2007 and 1990 to 2010 for Japan.

NO Sectors Japan USA EU28 China India 1 Energy Intensive (coal usage) 0.9 1.5 0.6 5.8 0.75 2 Electricity (coal usage) 0.9 1.5 0.6 5.8 0.75 3 Manufacturing (coal usage) 0.9 1.5 0.6 5.8 0.75 4 Transportation (petroleum Usage) 0.9 1.5 0.6 5.8 0.75

Table A.2.5 Regional Carbon Emission in 2015 and Projection to 2050

2015 2050 NO Regions Emission % of Global Emission % of Global (MT) (MT) 1 Indonesia 588.37 1.58 2673.41 2.70 2 Singapore 60.82 0.16 175.18 0.20 3 Malaysia 233.78 0.63 1040.03 1.00 4 Other ASEAN Countries 530.26 1.42 1641.94 1.60 5 Other Asia Countries 863.49 2.32 4058.94 4.00 6 Australia 518.11 1.39 1184.46 1.20 7 New Zealand & Oceania 74.18 0.20 173.89 0.20 8 China 6958.22 18.70 16704.58 16.70 9 Japan 1337.11 3.59 2124.24 2.10 10 Korea 604.40 1.62 1669.29 1.70 11 India 2037.85 5.48 12126.02 12.10 12 Brazil 404.20 1.09 910.19 0.90 13 USA 7484.64 20.11 13415.56 13.40 14 Canada 754.46 2.03 1643.33 1.60 15 Latin Americas 1643.72 4.42 4651.82 4.60 16 EU28 4955.84 13.32 8860.28 8.80 17 Russia 2945.47 7.91 8925.11 8.90 18 FTA Europe 136.27 0.37 254.97 0.30 19 Former Soviet Unions 1413.84 3.80 3545.30 3.50 30 Middle East & North Africa 2873.39 7.72 10165.43 10.10 21 Other Africa 796.28 2.14 4342.63 4.30 TOTAL 37214.70 100.00 100286.60 100.00 Source: GTAP 7 Database and Estimation as described in the text.

Table A.2.6 Studies on Temperature and Welfare Impacts

No Literature Temperature Welfare Effect Rise (o C) (% Global GDP) 1 Nordhaus (1994a) 3.0 -4.8 2 Nordhaus (1994b) 3.0 -1.3 3 Fankhauser (2013) 2.5 -1.4 4 Hope (2006) 2.5 0.9 5 Rehdanz & Maddison (2005) 1.0 -0.4 6 Nordhaus & Yang (1996) 2.5 -1.7 7 Plambeck & Hope (1996) 2.5 2.5 8 Nordhaus (2006) 2.5 -0.9 9 Tol (1995) 2.5 1.9 10 Nordhaus & Boyer (2003) 2.5 1.5 11 Mendelsohn et al. (2000a) 2.5 0.0 12 Mendelsohn et al. (2000b) 2.5 0.1 13 Tol (2002a) 1.0 2.3 14 Tol (2002b) 1.0 3.0 15 Maddison (2003) 2.5 -0.1

Table A.2.7 The Net Aggregate Effects per Degree Celsius Temperature Rising

The region-specific GDP incidence per degree increase in the average surface temperature from 1 to 5 degrees Celsius by four causal effects of Sea Level Rise (SLR), Agriculture (AGR), and Labour Productivity due to heat (HEAT) and Vector Borne Disease (HEALTH) are listed below.

A. The Net Aggregate Effects One Degree Celsius

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 1oC) 1 Indonesia -0.0001 -0.0539 -0.6708 -0.0527 -0.78 2 Singapore -0.0007 -0.0037 -0.5455 -0.0975 -0.65 3 Malaysia 0.0000 -0.0586 -1.0848 -0.0705 -1.21 4 Other ASEAN -0.0001 -0.2886 -0.6161 -0.0405 -0.95 5 Other Asia -0.0001 -0.3813 -0.3663 -0.2101 -0.96 6 Australia 0.0000 -0.2770 0.0000 -0.0773 -0.35 7 New Zealand & Oceania -0.0005 -0.7798 -0.0394 -0.0697 -0.89 8 China (PRC) -0.0001 -0.6481 -0.0677 -0.1968 -0.91 9 Japan -0.0001 -0.2266 -0.0215 -0.0335 -0.28 10 Korea -0.0001 -0.2441 -0.0159 -0.0265 -0.29 11 India 0.0000 -0.5181 -0.5096 -0.2788 -1.31 12 Brazil 0.0000 -0.3053 -0.0684 -0.1082 -0.48 13 USA 0.0000 -0.2234 0.0000 -0.0955 -0.32 14 Canada 0.0000 -0.5177 0.0000 0.0000 -0.52 15 Other America -0.0001 -0.3207 -0.0754 -0.0607 -0.46 16 EU_28 0.0000 -0.5117 0.0000 -0.0163 -0.53 17 Russia 0.0000 -0.6414 0.0000 -0.0190 -0.66 18 FTA Europe 0.0000 -0.3014 0.0000 -0.0191 -0.32 19 Ex. Soviet & Other EU 0.0000 -0.9302 0.0000 -0.0883 -1.02 20 Middle East 0.0000 -0.4197 -0.1009 -0.1283 -0.65 21 Africa 0.0000 -0.2855 -0.2634 -0.2446 -0.79

B. The Net Aggregate Effects Two Degree Celsius

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 2oC) 1 Indonesia -0.0003 -1.2071 -1.6828 -0.1054 -3.00 2 Singapore -0.0013 -0.2047 -1.5926 -0.1950 -1.99 3 Malaysia 0.0000 -0.7211 -2.5631 -0.1411 -3.43 4 Other ASEAN -0.0002 -1.2152 -1.4320 -0.0810 -2.73 5 Other Asia -0.0002 -0.7955 -0.8850 -0.4202 -2.10 6 Australia 0.0000 -0.5085 0.0000 -0.1545 -0.66 7 New Zealand & Oceania -0.0008 -0.9903 -0.1018 -0.1393 -1.23 8 China (PRC) -0.0003 -0.8970 -0.1827 -0.3936 -1.47 9 Japan -0.0002 -0.3334 -0.0728 -0.0670 -0.47 10 Korea -0.0002 -0.3362 -0.0735 -0.0531 -0.46 11 India 0.0000 -1.1874 -1.1696 -0.5576 -2.91

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 2oC) 12 Brazil 0.0000 -0.8151 -0.2238 -0.2164 -1.26 13 USA 0.0000 -0.2934 0.0000 -0.1909 -0.48 14 Canada 0.0000 -0.3606 0.0000 0.0000 -0.36 15 Other America -0.0001 -0.9306 -0.4258 -0.1215 -1.48 16 EU_28 0.0000 -0.4845 -0.0003 -0.0326 -0.52 17 Russia 0.0000 -0.4558 0.0000 -0.0380 -0.49 18 FTA Europe 0.0000 -0.2593 0.0000 -0.0381 -0.30 19 Ex. Soviet & Other EU 0.0000 -0.9707 -0.0007 -0.1766 -1.15 20 Middle East 0.0000 -0.5281 -0.2307 -0.2566 -1.02 21 Africa 0.0000 -1.1493 -0.6238 -0.4891 -2.26

C. The Net Aggregate Effects Three Degree Celsius

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 3oC) 1 Indonesia -0.0005 -2.8437 -2.9463 -0.1581 -5.95 2 Singapore -0.0019 -0.4909 -2.7987 -0.2925 -3.58 3 Malaysia -0.0001 -1.6562 -4.4011 -0.2116 -6.27 4 Other ASEAN -0.0004 -2.4845 -2.3474 -0.1215 -4.95 5 Other Asia -0.0003 -1.3162 -1.5048 -0.6304 -3.45 6 Australia 0.0000 -0.7879 -0.0258 -0.2318 -1.05 7 New Zealand & Oceania -0.0011 -1.1457 -0.2086 -0.2090 -1.56 8 China (PRC) -0.0005 -1.1324 -0.3558 -0.5904 -2.08 9 Japan -0.0003 -0.4440 -0.1989 -0.1005 -0.74 10 Korea -0.0003 -0.4225 -0.1686 -0.0796 -0.67 11 India 0.0000 -2.0466 -1.8991 -0.8364 -4.78 12 Brazil 0.0000 -1.4869 -0.4938 -0.3246 -2.31 13 USA 0.0000 -0.3518 -0.0074 -0.2864 -0.65 14 Canada 0.0000 -0.0398 0.0000 0.0000 -0.04 15 Other America -0.0001 -1.7418 -0.4835 -0.1822 -2.41 16 EU_28 0.0000 -0.3505 -0.0008 -0.0490 -0.40 17 Russia 0.0000 -0.0717 0.0000 -0.0571 -0.13 18 FTA Europe 0.0000 -0.1429 0.0000 -0.0572 -0.20 19 Ex. Soviet & Other EU 0.0000 -0.8556 -0.0030 -0.2649 -1.12 20 Middle East -0.0001 -0.6049 -0.3885 -0.3849 -1.38 21 Africa 0.0000 -2.3297 -1.1081 -0.7337 -4.17

D. The Net Aggregate Effects Four Degree Celsius

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 4oC) 1 Indonesia -0.0008 -4.3595 -4.3871 -0.2109 -8.96 2 Singapore -0.0024 -0.7517 -4.0388 -0.3900 -5.18 3 Malaysia -0.0001 -2.5447 -6.2995 -0.2821 -9.13

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 4oC) 4 Other ASEAN -0.0005 -3.8601 -3.3824 -0.1620 -7.41 5 Other Asia -0.0004 -2.0936 -2.2774 -0.8405 -5.21 6 Australia 0.0000 -1.2634 -0.0685 -0.3091 -1.64 7 New Zealand & Oceania -0.0014 -1.9174 -0.3491 -0.2787 -2.55 8 China (PRC) -0.0006 -1.8686 -0.5963 -0.7872 -3.25 9 Japan -0.0004 -0.7268 -0.4377 -0.1340 -1.30 10 Korea -0.0004 -0.6977 -0.3589 -0.1062 -1.16 11 India 0.0000 -3.2387 -2.7094 -1.1152 -7.06 12 Brazil 0.0000 -2.3382 -1.1145 -0.4328 -3.89 13 USA 0.0000 -0.5854 -0.0239 -0.3819 -0.99 14 Canada 0.0000 -0.1697 0.0000 0.0000 -0.17 15 Other America -0.0002 -2.7316 -0.8000 -0.2430 -3.77 16 EU_28 0.0000 -0.6442 -0.0045 -0.0653 -0.71 17 Russia -0.0001 -0.2448 0.0000 -0.0761 -0.32 18 FTA Europe 0.0000 -0.2824 0.0000 -0.0762 -0.36 19 Ex. Soviet & Other EU 0.0000 -1.5052 -0.0230 -0.3532 -1.88 20 Middle East -0.0001 -1.0136 -0.5746 -0.5131 -2.10 21 Africa 0.0000 -3.6231 -1.6919 -0.9782 -6.29

E. The Net Aggregate Effects Five Degree Celsius

CONTRIBUTED PARAMETER TOTAL PERCENTAGE NO COUNTRY/ REGION SLR AGR HEAT HEALTH CHANGE GDP (+ 5oC) 1 Indonesia -0.0010 -5.5761 -5.8763 -0.2636 -11.72 2 Singapore -0.0030 -0.9569 -5.3141 -0.4876 -6.76 3 Malaysia -0.0001 -3.2768 -8.2503 -0.3526 -11.88 4 Other ASEAN -0.0007 -5.1319 -4.4612 -0.2025 -9.80 5 Other Asia -0.0005 -2.9651 -3.1368 -1.0506 -7.15 6 Australia 0.0000 -1.8271 -0.1655 -0.3863 -2.38 7 New Zealand & Oceania -0.0017 -3.0627 -0.5145 -0.3484 -3.93 8 China (PRC) -0.0008 -2.8948 -0.8957 -0.9840 -4.78 9 Japan -0.0004 -1.1054 -0.7748 -0.1675 -2.05 10 Korea -0.0005 -1.0826 -0.5838 -0.1327 -1.80 11 India 0.0000 -4.5284 -3.5514 -1.3940 -9.47 12 Brazil 0.0000 -3.2140 -1.9550 -0.5410 -5.71 13 USA 0.0000 -0.9232 -0.0822 -0.4773 -1.48 14 Canada -0.0001 -0.6337 0.0000 0.0000 -0.63 15 Other America -0.0002 -3.7257 -1.1257 -0.3037 -5.16 16 EU_28 0.0000 -1.2307 -0.0119 -0.0816 -1.32 17 Russia -0.0001 -0.8291 0.0000 -0.0951 -0.92 18 FTA Europe 0.0000 -0.6017 0.0000 -0.0953 -0.70 19 Ex. Soviet & Other EU -0.0001 -2.6613 -0.0704 -0.4415 -3.17 20 Middle East -0.0001 -1.6253 -0.7861 -0.6414 -3.05 21 Africa 0.0000 -4.8287 -2.4007 -1.2228 -8.45 Source: Estimation as described in the text.

CHAPTER 3 RENEWABLE PRODUCTIVITY AND GLOBAL ABATEMENT EQUILIBRIA

3.1 Introduction

Renewable energy refers to energy resources that are constantly replenished by nature. It includes wind, marine, solar, hydro, geothermal and bioenergy. Renewable energy technology (RET) enables these alternate resources to be harnessed on a sustainable basis. Recent trends show very rapid growth in their productivity, particularly of wind and solar energy, and associated declines in their cost. The new affordability of renewable resources could fill the exponential growth of energy demand and the anticipated instability of supply due to the anticipated depletion of conventional fossil fuels. Moreover the global concern with balancing economic demand with environmental concern has made these energy sources attractive and controversial.

The Paris Accord, which many experts see as a fundamental shift in the diplomatic landscape, has put the role of renewable energy in the spotlight (Kern & Rogge 2016). It provides a mechanism to translate the Paris Agreement into a national agenda for each signatory, via associated commitments to ensure that the growth of energy use in developing countries is supported by renewables and therefore will not accelerate carbon emissions. The Renewable and Energy Efficiency Initiative (REEI) of the “least developed countries” could be implemented as an integrated part of poorer countries' policy platforms in ways that would not retard their development. Of course, there is some way to go in achieving this, since the share of renewables in the total energy mix remains insignificant in some countries. Nonetheless, this commitment exhibits the potential role of renewables in producing sustainable power and combating GHG emissions.

Global electricity generation from renewable energy is expected to grow 2.7 fold between 2010 and 2035, with its share growing from 20 per cent to 31 per cent of total electricity generation (Ellabban et al. 2014). As a proportion of installed power generation capacity, the contribution of renewables grew more than 2.5 fold, from

437 GW in 2010 to 1,236 GW in 2017.1 This growth trend is strong, particularly in China, the US, Europe, India and Japan (Arent et al. 2011).

There is, therefore, a common perception that renewables will assist greatly in limiting GHG emissions. Works by Clarke (2009), and by Dietz & Stern (2015), offer varying estimates of their potential impact. These depend on many factors, including policy regimes, the models employed, the evolution of fossil fuel supplies and their prices and the development of availability of emission reduction technology. Notwithstanding numerous studies emphasising renewable contributions to emission abatement, comparatively few studies focus on the long-term contributions of renewable energy to abatement at the global level (Özbuğday & Erbas 2015).

All this suggests the possibility that the findings of Chapter 2, in which it was shown that China, the US, and the EU would derive positive economic gains from the unilateral implementation of carbon pricing, might be different if the recent extraordinary growth of renewable productivity is to continue. It is possible, for example, that this could reduce the likelihood of unilateral gains and make more likely the non-cooperative equilibrium promulgated by Nordhaus (2015). It is therefore important to examine how the renewable productivity growth rate could change the strategic behavior of these large players. This chapter is structured to serve this purpose.

First, renewable energy prospects are reviewed and their significance in mitigating global emissions examined. Second, a representative productivity growth rate is chosen to construct a new baseline projection to 2050. A similar strategic analysis to that presented in Chapter 2 is then offered, embodying modifications to the integrated assessment model to incorporate the renewables sector, revised mitigation cost experiments and temperature impact welfare assessments, and finally game theoretic analysis in search of new global Nash equilibria.

For reasonable changes in climate impact, when renewable productivity is growing, the individual contributions of the US and China again emerge as determining factors. The continued expansion of their clean energy sectors will significantly

1 https://www.power-technology.com/comment/renewable-energy-reach-22-5-share-global-power- mix-2020/

reduce their emissions, making them both reluctant to further commit to a carbon abatement policy. Less expectedly, when the renewables productivity shock is global, the EU and Japan, which are comparatively efficient users of fossil fuels and which were gainers when others implemented abatement policies, now lose competitiveness and are more motivated to defect.

The remainder of this chapter is structured as follows. Section 3.2 briefly surveys developments in renewable energy, and Section 3.3 gives a comprehensive overview of the modifications to the model required to incorporate the renewables sector. It includes some review of current renewable energy models, the differences embodied in this one, the database and the simulation approach. The results are then presented in Section 3.4, followed by concluding comments in Section 3.5.

3.2 Global Renewable Energy Trends

3.2.1 Declining Costs

Measured renewable energy output is almost all used in electricity production. Despite rapid growth in supply from other technologies, at least half of all renewable production is from hydropower. It is popular as a renewable source because it can contribute to base load as well as to peak load supply.2 While supply from geothermal sources and biomass has the potential to contribute to base load, that from solar and wind is temporally constrained and its full potential requires the development of better storage technologies. Nonetheless, production costs from solar and wind have been declining rapidly. Solar photovoltaic (PV) is increasingly competitive as its levelized cost of electricity (LCOE)3 decreased by more than half between 2010 and 2014. Cited from the International Renewable Energy Report (IRENA 2018), Figure 3.1 shows the global renewable LCOE compared to that of conventional fossil fuel generation.

2 Base load is defined as the electricity demand for daily requirement. It is also called continuous load, as it is needed to provide the power components that runs all times. The peak load is the electricity level regained to fulfil the moment of high demand. 3 Levelized Cost of Electricity (LCOE), normally given as unit dollar per kilowatt-hour (kwh) or megawatt-hour (mwh) gives a consistent basis of different methods of electricity generation. The cost consists of capital costs, which is tend to be relatively high for renewables yet very low for the fossils fuels power stations. It also includes fuel cost which is definitely zero for renewables, and other cost such as insurance and maintenance. LCOE is calculated based on the net present value of electricity cost over lifetime of an electricity generation assets.

Figure 3.1 Global LCOE from Utility-Scale Renewable Power Generation Technologies, 2010-2017

Source: International Renewable Energy Agency (IRENA) 2018

At the same time, the renewable energy share in total primary energy consumption shows an incremental trend that started after the year 2000. As shown in Figure 3.2, the global renewable share in the overall energy consumption has doubled in the current decade, with the most significant growth in solar and wind energy consumption.4 Solar consumption expanded at an annual rate of 50 per cent, and wind was growing 23 per cent per year between 2005 and 2015. On the other hand, the long run technological improvement for hydroelectric power, geothermal and biomass has contributed to steady utilization growth since the early 1990s, with seven per cent annual growth.

In 2015, the International Energy Agency (IEA) predicted that the electricity generation from renewable resources will double to 7705 TWh in 2030 compared to the 2006 level. Renewable electricity is also predicted to overtake gas as the second largest electricity resource after coal, increasing from 18 per cent in 2006 to 23 per cent in 2030. Cumulative global renewable power in electricity installed capacity

4 A panel co-integration study of Sadorsky (2009) estimates that positive trend in the renewable energy consumption has been driven by increases in real GDP per capita and CO2 emission per capita.

grew by eight per cent in 2014 and is predicted to grow at a steady rate of 6.6 per cent over the next decade.

Figure 3.2 Renewable Share of Global Primary Energy Consumption (MToE)

Source: Author re-estimation from BP Statistical Review, BP (2018).

China is leading the renewable transformation, followed by the USA, Brazil, Germany, and Canada. Despite the complex and intermittent nature of wind, this energy source has become particularly popular. The US, China and India are experiencing enormous growth in their wind power industries while Europe has 50 per cent of the global share of wind capacity. Germany, Spain, Japan and the US have dominated the quantity of solar electric generation by photovoltaic energy installation. Technological improvement has lowered the price of the crystalline silicon modules that are essential to the expansion of the solar photovoltaic industry.

3.2.2 Renewables and GHG Emission

Most analytical reviews of clean energy’s potential contribution to emission abatement depend on Integrated Assessment (IA) models. The results are, however, highly variable given the great variety of scenarios and analytical focus in those models. The impression is therefore given that much scope remains for the evaluation of the potential contributions of renewable energy to emission abatement. One of the most comprehensive reviews of this literature, by the integrated EMF22

project, supports this conclusion.5 Nonetheless, that study gives weight to the obvious conclusion that there is considerable potential for abatement via increased production from low and negative emission energy resources.

Among the eleven integrated studies considered, the analysis of Guerney et al. (2009) using the GTEM model, van Vliet et al. (2009) with the IMAGE 2.4 model and Calvin et al. (2009) with the SGM model, confirm the significant potential contribution of clean technology. To achieve their “medium” scenario of 550 ppm

GHG concentration in 2050, global CO2 emissions must be reduced by 25 per cent from the 2005 level. Assuming full implementation of abatement policy by all regions these studies conclude that renewables need to supply between 100 and 500 Energy Joule (EJ) fossil equivalents, implying that the share of low carbon energy production in the energy supply mix should expand to 75 per cent in 2050.6

There are also numerous region-specific studies that use empirical methods, including panel co-integration, to show the negative correlation between the 7 incremental share of renewable energy and the impacts on CO2 emission. These too suggest that growth in renewables will reduce emissions to an extent that would matter for policy. Others find evidence of a positive correlation between renewables and emissions (Lantz & Feng 2006; Menyah & Wolde-Rufael 2010; and Zoundi 2017), indicating that renewable energy consumption will not expand to the extent needed to make a significant contribution to emissions reduction.

The differences between these groups of studies come down to empirical method and database range.8 The empirical models are of the reduced form type, lacking structural detail, implying the omission of country-specific energy and

5 EMF22 is an integrated project to analyse the certain feasibility of climate policy to achieve certain climate target with full or delayed participation from countries using various IA Models. The climate target is the radiative forcing level which was based on CO2 equivalent (CO2e) or GHG concentration target under Kyoto (2.6 W/m2 or equivalent to 450 ppm, 3.7W/m2 or 550 ppm and 4.5 or 650 ppm). Refer to Clarke et al. (2009) for the overview. The project produces 11 integrated literatures that used more than 10 energy or climate models. 6 While to reach more than 50 per cent CO2 reduction to achieve 450ppm GHG concentration (the ideal/ low risk scenario), the low carbon energy has to increase seven to ten times from the 2005 level. 7 These include Shafiei & Salim (2014), Robalino-López (2015); Özbuğday & Erbas (2015); Biglili et al. (2016), Wesseh & Lin (2016) and Jafroullah & King (2017). 8 Jafroullah & King (2017) contradict with Menyah & Wolde-Rufael (2010), despite both studies focusing on the impact on the US economy. The former’s modified co-integration and Granger Causality Test of Menyah and Wolde-Rufael finds different implications of the renewables on the emissions. Zoundi’s (2017) analysis on Africa also contradicts with Wesseh & Lin (2016).

environmental policy detail. Abatement performance is shown by de Arce et al. (2016), not only to be sensitive to this policy detail but also to depend significantly on the capture of competition across industries.

Likewise, Furlan & Montarino (2018) find that competition on price between renewables and fossil fuels could, initially at least, slow the spread of renewables. This is seen as the “renewables paradox” (Blazques et al. 2018 ; Shah et al. 2018). The levels of this competition are very different between the emerging economies such as China and India on the one hand and the US and Europe on the other. For the moment both fossil fuel and renewable energy have positive trends in the US and steeply positive trends in China. Fossil fuel use is declining in Europe and there is moderate growth in renewables. Only in India is the growth of fossil fuel use outpacing renewables.

The indefinite literature on renewables and their impact globally, combined with the equivocal results of the regional studies leave room for a careful analysis offered in this chapter. In capturing the relevant interaction between the two different energy types, a substantial modification is made to the integrated assessment model used in Chapter 2, offering a representation with clear distinctions between renewable and fossil fuel energy within highly disaggregated sectors in each region. As a consequence, the long-term potential contribution of renewable energy to emission levels can be integrated into a welfare analysis of the type presented in Chapter 2 in a way that clarifies the links between assumptions and outcomes.

3.2.3 Intermittence and Competition with Fossil Fuels

Raising the renewable share in the global primary energy mix will be challenging for two reasons. The first is the intermittence issue of renewable power, where the convertibility of renewable energy is highly dependent on site and source-specific conditions. Wind, solar, tidal and wave energy require back up and storage to guarantee reliable power supply (Weitemeyer et al. 2015). This is a major concern in the major economies, as indicated by the assessment of Europe’s power system by Mosele et al. (2010).9 Yet the relative abundance of fossil-based energy sources,

9 Europe’s 20-20-20 energy policy includes a 20 per cent reduction in fossil energy, a 20 per cent increase in renewables and a 20 per cent reduction in GHG by 2020.

especially gas, ensures that flexible back up will continue to be available for intermittent renewable energy, thus facilitating the latter’s expansion.

Yet the prospect that renewables will lead to a significant reduction in GHG emissions appears to remain weak. The affordability of fossil-based electricity, especially from gas, in providing flexible back up for intermittent renewable creates a long standing dependency of renewable power on gas-fired generation. At the same time, the substitutability of renewables with still-abundant natural gas is a challenge to abatement policy. The analysis of Hamrin et al. (2007)10 includes 35 scenarios for global energy development, finding that renewables sustain a relatively modest share in all scenarios over the next four decades. In particular, they find that, despite favourable policy interventions and innovations, the fossil fuel share of the energy portfolio will still be 80 per cent in 2050. Hamrin’s finding is consistent with other studies, including that by Sims et al. (2003), who find that coal will still have the largest share of electricity production by 2020, at 38 per cent. It will be followed by renewables at 20 per cent, nuclear at 17 per cent, and natural gas peaking at 16 per cent. Indeed, the subsequent study by Sims (2004) contends that significant policy changes are required to support the rapid uptake of renewables so that fossil fuels are displaced, and emissions reduced. Renewable sources are projected to grow but, without changes to government intervention, their supply will expand at only two per cent per year. The intertemporal general equilibrium analysis of Kalkhul et al. (2013) offers similar pessimism about the contribution of renewables. They content that, even if the price of renewable energy falls due to subsidies and “learning by doing”, sunk costs will make it difficult to crowd out fossil fuel energy supply.

The current dependency of fossil fuels and the very recent surge in the productivity of renewable sources highlights the need to capture the interaction between energy types in understanding the trend in global emissions (Omer 2008; Covert et al. 2016). As fossil fuel resource markets are integrated, and renewable sources expand, domestic energy policies will alter prices by source, with the chance that there will be adverse supply responses in fossil fuel or renewable markets. For this reason, an

10 Hamrin et al. (2007) analysed 11 reports, in each of which one indicator investigated is the share of renewable energy in electricity generation. All scenarios give pessimistic projections of renewable shares in 2050, in the range 25 to 35 per cent. The lowest projection was given by IEA World energy outlook baseline that anticipates weak government intervention to promote renewables. The EREC/ Greenpeace scenario gives a comparatively optimistic projection of 70 per cent in 2050 with strongly favourable government intervention.

integrated assessment model that disaggregates clean and fossil fuel energy types is needed to capture the dynamic interaction between these energy commodities so as to more accurately predict carbon abatement effects.

3.3 Modelling Approach

This section reviews the structure of the Integrated Assessment Model first presented in Chapter 2 and details the extensions to it that enable the analysis of renewable energy technologies. As in that chapter, the global abatement equilibrium is determined based on game theoretic analysis using the present value of the net economic gain to each region from abatement. The policy choice facing each region is to join a global mitigation agreement or to free ride. The net economic gain is obtained by subtracting the economic cost of mitigation from the economic benefit estimated to stem from reduced GHG emissions. The renewable energy input is introduced into the Integrated Assessment Model so as to see how important renewables can be in affecting the potential for agreement and future economic welfare.

3.3.1 Adding Renewables Source to the Model

In projecting GHG emissions and the economic cost of implementing abatement policy, this application further adapts and extends the model used in Chapter 2, which had its origins in the Dynamic GTAP Energy (GDyn-E) Model of Golub (2013).11 Renewable energy sources are added to both production and indirect demand. Since almost all renewables generate electricity, this is the only source of indirect demand. Figure 3.3 illustrates the CES nested electricity production structure in the expanded model. In it, output depends on non-capital primary factors, other inputs and a capital-energy composite, which in turn depends on a composite of energy products.

11 The Gdyn-E model merges the dynamic version of the GTAP model (Ianchovina & Walmsley 2012) and the static energy model of GTAP-E (Burniaux & Truong 2002). This original version omits renewable energy technologies and the changes made as described in Chapter 2. Here the dynamic features of international capital mobility and accumulation with lagged adjustment to investment are preserved. As before, there are three economic agents: production by firms, a representative household and a government.

Figure 3.3 CES nested Electricity Production Structure

Output

σ = 0

Value-Added Energy Non Energy Input

σVAE

Land Labour Natural Capital-Energy σD Resources Composite Domestic Foreign

σLAB σKE σM

Skilled Unskilled Capital Energy Composite Region 1 Region i

σENER Renewable Non-Renewable

σNRENEW

Coal Non Coal

σNCOAL

Oil Gas Oil Products

Source: Adaptation of Gdyn-E Model (Golub 2013), as described in the text.

The new energy composite draws on non-renewable energy sources and renewables, where the latter only enter via the electricity sector. The added branch in the CES nest is that between non-renewables (NR) and renewables (R), represented as:

 11 1  Yenergy_ comp  R v NR   R v R (3.1) 

Electricity production thus depends on net substitution between the non-renewable energy composite (VNR) and renewable input (VR) while 휎 depicts the elasticity of substitution between them. The non- renewable energy composite incorporates inter- fuel substitutability among conventional fossil energy types (coal, gas, oil and petroleum products) which is close to unit elastic, while in the upper nest the choice between renewable and the non-renewable composite is highly substitutable. The

elasticity between the non- renewable composite and the renewable energy sources (σ) is five, indicating high substitutability, following Papageorguiou et al. (2013).12

Renewable energy sources are aggregated into a single, integrated commodity/ sector, while the non-renewable energy nest remains the same as in the original model. Although this single renewable commodity/industry is a simplification, most renewable energy supplied today serves peak load or intermittent complementary.13 Electricity consumers are assumed to have no preference over the technology used that would justify different consumption prices for power from renewables. Electricity is therefore a homogeneous product and the amount produced, in Gigawatts per hour (GWh), is always equal to the electricity consumed.

Renewable industry has also a nested structure. The key inputs in the energy composite nest of renewable production, however, are renewable raw inputs. Fossil fuel inputs are negligible in this industry. As for other sectors, the uppermost level production of renewable can be presented in the CES form:

  1  1  1   yi A  ij  ij  VAE vae ij (3.2)  j

Final output depends on net substitution between an energy value added sub- nest, 푣푎푒, and a sub nest of intermediate inputs 푣. The value of 휎 depicts the elasticity of substitution between them, and variable A represents the efficiency parameter or Total Factor Productivity (TFP) coefficient.

The expenditure structures for both the government and the representative household use CES nesting, and household behaviour is characterised by the non-homothetic CDE utility function as in the original model. Renewable energy is assumed to be used only in electricity production and so it is not consumed directly, either by the government or private households. The contribution of renewables to energy use arises only from their substitutability with fossil fuel generating technologies in the electricity sector.

12 The Electricity Power Model of the JGCRI-Phoenix (Wing et al. 2011), the GEM-E3 (Capros et al. 2013) and the GTEM/CTEM (Arora & Cai 2015) separate transmission and distribution from generation(fossil versus renewable) in electricity production. Transmission and distribution are captured as a service input in this application. 13 Generalisation is possible, however, as highlighted in the GTAP-E Power Model of Peters (2016). The MIT- Joint Program Model (Paltsev et al. 2005) and the OECD_ENV Linkage Model (Chateau et al. 2014) also use the same techniques.

3.3.2 Database and Data Aggregation

The core data used is from the Global Trade Analysis Project (GTAP-9),14 as described in Aguilar et al. (2016). The database provides consistent data on consumption, production and international trade, along with the energy data and CO2 emissions for 140 regions, 57 sectors, and eight factors of production, with the year 2011 as the base year. Further calibration is undertaken for this application to match observed income, population and labour growth through 2015. The factor endowments available in the original database are condensed into land, natural resources, low and high skill labour and capital. The regions are consolidated to ten: China, the USA, European Union (EU), Japan, India, Russia, Indonesia, Australia, the Middle Eastern Countries and the Rest of the World (ROW). The full set of 57 commodities is aggregated into 12 industries.15

This application keeps the four fossil energy products (coal, gas, oil, petroleum and its derivatives) but it adds an additional integrated renewable energy product. The share between fossil fuels and the renewable input in electricity production for each region follows the estimated share in electricity use by energy type from the World Bank’s World Development Indicators (2014).16 Unlike conventional fossil fuel types, which can be consumed directly, renewable energy is consumed in the model by all agents only in the form of electric power. For this reason, there is no consumption of renewable energy by other than the electricity industry.

3.3.3 The Baseline Simulation

As in Chapter 2, the baseline projection from 2015 to 2050 is constructed, first, by incorporating assumptions about the drivers of economic growth, namely anticipated changes in populations, labour forces and technology. Since technology, and its effect on productivity, is the least easy to project the approach is to construct preliminary estimates of productivity growth for each industry and country and to then use the model to simulate the global economy through 2050 assuming no

14 The GTAP Database is constructed from input-output tables and other data from members of the member of GTAP Network. The GTAP-9 is the latest version of the global database for three benchmark years of (2004, 2007 and 2011). This application uses the 2011 base year. 15 New regional, sectoral and endowment factor aggregations are listed in Annex Tables A.3.1, A.3.2 and A.3.3 respectively. 16 The Summary of the Share of Fossil and Non-Fossil in World Electricity Production is listed in Appendix Table A.3.4. Input to Renewable Energy Sectors is following Peters (2016).

changes in policy interventions. The results are usually at variance with standard projections from such bodies as the World Bank and the IMF. Since the baseline projection itself is not the focus of the analysis, but rather the changes to it caused by abatement policies, the modeller’s preference is to ensure that it is non-controversial. To achieve consistency with other reputable projections a “pre-base” simulation is constructed that has exogenous GDP and investment growth in each region, set consistently with IMF (2017).

For each region a region-wide component of productivity growth and a premium on regional investment returns are made endogenous. This simulation calculates time trends in supplemental productivity growth and consistent changes in “risk” premia. These then become exogenous shocks in the final baseline, in which, once again, investment and GDP levels are endogenous. The baseline projection has India growing most strongly and China continuing to be one of the most rapidly expanding economies, with projected GDP growth above six per cent. Europe and the US have modest growth, at two per cent per year, though they remain the largest of the represented regions alongside China.17

The emissions behaviour embodied in the baseline projection is also calibrated for consistency with the recent IPCC emission forecast of CO2 by industrial and fossil fuel usage (Climate Synthesis Report, IPCC 2014). This is achieved by adjusting assumptions regarding end-use efficiency improvements at the industry level, particularly in major emitter countries like China and India.18 China’s current ambitious clean energy targets change its future technical structure, reducing forecast emissions. Its emission control rate is doubled in this application, to be consistent with IEA (2015) and the ERI-2009 National Development Forecast (Qi & Li 2011). India’s emission control rate is tripled, to capture improvements in its energy source diversification that will achieve 20 to 25 per cent emission reductions by in 2020 (Pal et al. 2014).

In the baseline projection China is the number one emitter in the world through 2050, followed eventually by India, whose emissions expand most rapidly. By 2050,

17 GDP growth rate and the share nominal GDP projection is summarized in Annex Table A.3.5 18 This application uses the same controlling emission rate as the previous chapter (Chapter 2) for the USA, Europe and Japan. Compared with Chapter 2, the updated database and new emission control rates for the other regions have China surpassing the USA a bit earlier. This forecast is consistent with the latest IPCC Report of 2014.

China is projected to contribute almost 25 per cent of the global CO2 emissions, with India’s share around 15 per cent. Emissions by the USA and Europe still increase but their share of global emissions declines moderately. Figure 3.4 summarises the projected CO2 level from industrial and fuel energy usage. This amount represents 75 per cent of the overall GHG level, assuming all other greenhouse gases are growing at the same rate.

Figure 3.4 Regional Emission Projections in GT of CO2

Source: Baseline simulation to 2050, as described in the text.

Renewable energy output is predicted to grow in all regions, most significantly in emerging economies such as Indonesia, India and China, where growth rates reach more than five per cent per year on average. As renewables provide lower emission energy, this growth indicates lower emissions contributed by industrial activities. As before, however, China’s and India’s emissions remain high.

The renewables growth that emerges from the baseline simulation in some emerging countries is likely caused by their economic expansion rather than any extraordinary factor productivity improvement in their renewables industries. Table 3.1 reveals that their non-renewable energy industries also grow at the same rate. The gas industry grows at six per cent per year in Indonesia and India, and close to five per cent per year in China. Even coal shows a positive trend, particularly in India.

Table 3.1 Average Growth Rate of Energy Commodities in Baseline Scenario (Per cent)

Energy Type N Region Renewable Coal Gas Oil Petroleum Products O 1 Indonesia 9.24 4.90 6.53 0.32 3.07 2 Australia 3.66 0.93 1.32 -0.25 1.80 3 China 5.93 4.80 4.58 -0.59 3.59 4 USA 2.61 2.40 2.77 2.50 1.63 5 EU28 4.24 0.94 0.20 0.06 1.47 6 Japan 1.36 -0.41 -1.25 0.05 0.76 7 India 8.50 7.31 6.96 -1.05 3.81 8 Russia 1.58 1.34 1.65 2.87 1.90 9 Middle East 6.38 1.83 3.39 3.33 3.00 10 Rest of the World 5.65 2.08 1.07 0.27 2.22 Source: Baseline simulation to 2050, estimated as described in the text.

Figure 3.5 Renewable Energy Share in Energy Input of Electricity Production

Source: Baseline simulation to 2050, estimated as described in the text.

Strong growth in the non-renewable energy sectors is linked to slow growth in the share of renewables in the mix of energy sources. As shown in Figure 3.5, under the baseline scenario the renewables share overall averages at around 20 per cent of the total energy mix.19 The projection also implies slow growth in the renewables share

19 Our baseline projection is consistent with Hamrin et al. (2007).

in the advanced economies. After Fukushima, Japan’s energy mix shifted toward fossil fuels (mainly gas) while non-nuclear renewables have been sluggish. The US renewable share also remains low at 14 per cent. A significant renewable contribution is only predicted in Europe, reaching close to 40 per cent of the total energy mix for power generation in 2050.

3.4 Renewable Productivity Scenarios

In capturing the significance of renewables for global emissions, this application introduces a positive productivity shock to the renewables industries throughout the global economy. It is assumed that there is a technological improvement, represented by variable A in equation 3.2, that makes renewable inputs more efficient. The same factors and inputs produce more renewable energy. This efficiency reduces the cost of renewable power and hence of electricity production, while at the same time increasing the demand for renewable inputs.

Simple though this seems, choosing a representable productivity rate is another challenge. Since the renewable energy industry predominantly supplies the European energy market, the literature focusing on renewables technological improvement is segmented only to European power plant industries.20 While the literature analysing the global scope of renewable transformation remains scant, this analysis follows the study of Boumol (1986) who used the current growth rate on the global renewable energy consumption to represent global renewables productivity.

3.4.1 Renewable Productivity Shock: Implication Analysis

In Scenario 1, productivity in the renewable energy industries is projected to grow by 2.5 per cent per year, from the year 2015 to 2050. In Scenario 2 it doubles to 5 per cent, Scenario 3 7.5 per cent and finally Scenario 4, 10 per cent.21 These productivity

20 Among these few, the prominent studies of Raez & Vestergaard (2016) and Andreas et al. (2017) use the Malmquist index to measure productivity growth in the renewable industry. This index reflects the contributions of technical, efficiency and scale change using stochastic frontier and data development analysis for renewable industries in Denmark and Austria. The average annual productivity growth rate according to Raez & Vestegaard’ study is 2.5 per cent, while Andreas et al. gives the lower rate of 1.1 per cent. 21 This analysis captures the current growth rate on the consumption of renewable energy. Global renewable consumption has grown 6.4 per cent from year 2015 to 2016. Hydropower is growing 2.9 per cent per annum while geothermal and biomass energy types grow at 7.7 per cent. Other renewable sources, solar and wind has grown more than 15 per cent per annum from 2005 to 2015 (BP 2018).

growth rates are applied in all regions simultaneously in each scenario; with a view to analysing how sensitive is the change in global emissions. Hypothetically, different productivity rates should result in different emission levels, and in the long- term, lower global temperatures and increased welfare.

Figure 3.6 shows the effects of renewables productivity scenarios on total emissions

(GT of CO2) from fossil fuel use in power production and industrial processes. The 2.5 per cent growth rate would result in significantly reduced global emissions by 2050 from 69.2 to 53.4 GT. With a five per cent productivity growth rate in renewables, the reduction would be by 30 per cent to 48.3 GT. Then from Figure 3.7 it is clear that to achieve by 2050 the 75 per cent renewable share implied by the Kyoto target of 550 ppm, as projected by EMF22 (Clarke et al. 2009) renewables productivity has to grow by at least 2.5 per cent per year.

Figure 3.6 Emission Forecast from Renewable Productivity Shocks

Source: Estimated as described in the text.

Figure 3.7 Renewable Input Shares in Electricity Production

Source: Estimated as described in the text.

Interestingly, however, once renewables productivity expands faster than five per cent, there is a tendency for emissions to strengthen toward the end of the forecast period. The higher productivity rates of 7.5 and 10 per cent yield almost the same emissions level in 2050. While the 2050 final level of emissions is about 48 GT in both cases, lower emissions during the lead-up mean lower cumulative emissions over the full simulation period. In the ten per cent productivity scenario, the lowest emission level would be achieved in the year 2026, at 27.7 GT, before increasing subsequently.

This finding suggests the green paradox effect, which refers to moderate emission reduction despite considerable policy effort (Sinn 2012; Jensen et al. 2015). The productivity shock to the renewables industries increases the renewable share but creates an excess supply of conventional fossil fuels energy. This excess supply drives down the price and attracts secondary industries to substitute toward fossil fuels. Figure 3.8 exhibits the average growth of fossil fuel demand in secondary sectors due to renewables productivity shocks, relative to the baseline forecast.

Emissions contributed by other production sectors still show increasing trends. These are the main drivers to the overall increasing trend in emission from released by fossil fuels and industrial processes (Table 3.2). While renewable technology generation pushes down the contributed emission from electricity sector to zero,

other sectors still face trade-offs between energy commodities with changing relative price. These other sectors tend to choose fossil fuels to reduce production cost. In particular, the emissions from energy-intensive sectors such as transportation show an increasing emission trend, due to growing consumption of fossil fuels.

Figure 3.8 Average Growth of Global Fossil Fuel Demand in Secondary Industries Relative to Baseline

Source: Estimated as described in the text.

Table 3.2 Sectoral CO2 Emission Various Renewable Productivity Scenarios

Sectoral Sectoral Emission Sectoral Emission Emission (GT) (GT) (GT) 10% Renewable in 5% Renewable NO Sectors Baseline Productivity Productivity Shock Shock Year Year Year Year Year Year 2015 2050 2015 2050 2015 2050 1 Electricity 13.57 21.92 13.39 0.07 13.05 0.00 2 Agriculture 0.64 1.28 0.65 1.41 0.65 1.45 3 Other Primary Sector 0.28 0.73 0.28 0.79 0.28 0.71 4 Energy Intensive 3.74 14.46 3.75 15.30 3.75 15.26 5 Manufacturing 0.78 2.38 0.78 2.70 0.78 2.50 6 Transportation 5.08 8.86 5.09 9.86 5.10 10.47 7 Service 1.31 4.10 1.31 4.70 1.31 5.02 Source: Estimated as described in the text.

3.4.2 Re-Examining Strategic Interactions with High Renewable Productivity

As renewable productivity growth significantly reduces global emissions, it is of value to re-examine the Chapter 2 findings on the global abatement equilibria. Hypothetically, if the productivity growth in renewable energy is sufficiently high to depress fossil fuel consumption and reduce emissions, each country will be more reluctant to join in carbon mitigation agreements. The large emitters will be less likely to enjoy unilateral gains from abatement, so zero participation is more likely. Here, the regional cost and benefit measurement from Chapter 2 are adapted to construct new payoff matrices for different combination of participation. As before, the strategic analysis is also conducted for the three IPCC’s scenarios: “low”, “best” and “high” temperature.

The productivity of the renewables sector is assumed to be consistently growing by 5 per cent with the emission projection is growing following Scenario 2. A “general agreement on carbon taxation” is then introduced at 20 USD per tonne of carbon. The strategic interaction analysis follows, involving the big three emitters of China, the USA, and Europe, followed, as in Chapter 2, by its expansion to five regions including Indonesia and Australia as likely free riders. The summary of the outcome of participation of China, USA, and Europe on the global agreement is shown in Table 3.3. For the comparative statistic, the table also includes the participation outcome when there is no productivity shock on renewable sectors.

As in Chapter 2, the low-temperature scenario yields the non-cooperation equilibrium in which all the three regions choose to defect. With modest temperature rising, this result is predictable. The unilateral benefit from stabilizing the global temperature with carbon abatement is smaller than the cost committing to the agreement and imposing the tax. The incentive to defect for all regions is positive, reaching the equilibrium with zero participation.

Table 3.3 Net Gains from Abatement to the Three Large Emitters a

No Renewable Productivity With Renewable Productivity Acceleration Acceleration IPCC Extra Country Net Gains Extra Net Net Gains Temperature Net Gain / Region Gain by Scenario by Participate Defect Defect Participate Defect Defect (A) (B) C=B-A (D) (E) F=E-D LOW USA 9.71 12.08 2.37 9.38 12.67 3.29 China 11.32 14.44 3.12 14.02 25.59 11.57 EU 9.84 14.23 4.39 5.63 9.37 3.74 BEST USA -21.24 -26.62 -5.38 18.37 22.07 3.7 China -14.28 -50.79 -36.51 25.38 37.63 12.25 EU -30.46 -28.84 1.62 16.46 20.33 3.87 HIGH USA -196.01 -217.48 -21.47 -41.83 -44.09 -2.26 China -220.35 -336.92 -116.57 -29.42 -60.05 -30.63 EU -241.77 -245.87 -4.1 -56.71 -54.97 1.74 a Net gains are the outcome of Normal form Static Games on 2015 present values, measured in Trillions USD. The results are shown with and without accelerated renewables productivity. Countries will participate in global mitigation agreement once the extra net gain by defect (difference on net gain between defecting and participating) is negative. Source: Estimated as described in the text.

Likewise, the “best” temperature scenario also results in non-participation equilibrium, thus confirming the hypothesis of countries would be less willing to join a climate agreement if they enjoy high renewable productivity growth. This contrasts with the Chapter 2 finding where, in the absence of renewable productivity growth, the US and China enjoy unilateral gains from committing to abatement.

The strategic behaviour changes, however, in the high scenario where China and the US decide to join while Europe still chooses to defect. As the country with the highest GDP as of 2040, and the largest emissions, China’s present value of the net benefit from abatement is now significant. Its dominant strategy is now to participate in the global agreement and undertake the abatement. The US also chooses to participate as its extra gain by defecting in present value terms is negative.

The analysis involving five players confirms the same result. All regions (especially low emitters) have dominant strategies to defect in almost all temperature scenarios. As shown in Table 3.4, the Nash equilibrium is reached only for China and US participation in the high-temperature scenario. All other players choose to defect. Again, the equilibrium for smaller emitters, like Indonesia and Australia is as before. In the absence of a renewable productivity shock, these two countries have consistently chosen to freeride in dealing with the agreement.

The extra incentives to defect due to renewable productivity growth are the difference between the extra net gain with and without renewable productivity acceleration (Column F and C in Table 3.4). Table 3.5 summarizes these values, also represents the renewable productivity effect for each country. It reveals that renewable productivity growth in China seems to counter the climate impact effectively in the “low” and medium (“best”) temperature projection. The extra incentives are positive and strong in China, making its best strategy to defect and to avoid further mitigation cost.

Table 3.4 Net Gains from Abatement for Five Regions b

No Renewable Productivity With Renewable Productivity Acceleration Acceleration IPCC Country/ Net Gains Net Gains Extra Temperature Extra Net Region Net Gain Scenario Gain by Participat By Participate Defect Defect Defect e Defect (A) (B) C=B-A (E) (D) F=E-D USA 9.71 12.08 2.37 9.38 12.67 3.29 LOW China 11.32 14.44 3.12 14.02 25.59 11.57

EU 9.84 14.23 4.39 5.63 9.37 3.74 Indonesia -0.17 0.33 0.5 -0.97 -0.65 0.32 Australia -0.12 0.12 0.24 -0.2 -0.05 0.15 BEST USA -21.24 -26.62 -5.38 18.37 22.07 3.7 China -14.28 -50.79 -36.51 25.38 37.63 12.25 EU -30.46 -28.84 1.62 16.46 20.33 3.87 Indonesia -1.8 -1.35 0.45 -0.69 -0.36 0.33 Australia -0.55 -0.32 0.23 -0.1 -0.04 0.06 HIGH USA -196.01 -217.48 -21.47 -41.83 -44.09 -2.26 China -220.35 -336.92 -116.57 -29.83 -60.05 -30.22 EU -241.84 -245.95 -4.11 -56.73 -54.99 1.74 Indonesia -8.81 -8.44 0.37 -3.1 -2.8 0.3 Australia -2.61 -2.4 0.21 -0.81 -0.67 0.14 b Net gains are the outcome of Normal form Static Games on 2015 present values, measured in Trillions USD. The results are shown with and without accelerated renewables productivity. Countries will participate in global mitigation agreement once the extra net gain by defect (difference on net gain between defecting and participating) is negative. Source: Estimated as described in the text.

For the extreme temperature case, despite China’s extra incentives to defect being positive (86.35 T USD), committing to a global carbon pricing agreement reduces further loss. The negative net gains by participation are smaller regardless of the renewable productivity acceleration scenarios, so its dominant strategy is to join. The US also has a strong motivation to defect in the low and medium scenarios, though not as strong as China’s. In contrast, the EU’s domestic term of trade is altered more

due to global renewable productivity growth as they lose competitiveness.22 Joining the agreement means greater EU losses, so its best strategy is to defect in all IPCC temperature scenarios, in the manner of the smaller emitters like Indonesia and Australia.

Table 3.5 Extra Incentives to Defect due to Renewable Productivity Acceleration

IPCC Extra Incentives Temperature Country/ Region (2015 PV in Trillion USD) Scenario LOW USA 0.92 China 8.45 EU -0.65 Indonesia -0.18 Australia -0.09 BEST USA 9.08 China 48.76 EU 2.25 Indonesia -0.12 Australia -0.17 HIGH USA 19.21 China 86.35 EU 5.85 Indonesia -0.07 Australia -0.07 Source: Estimated as described in the text.

3.4.3 Re-Evaluating Transfer Payment and Unilateral Incentives to Abate

The universal implementation of carbon abatement makes the world better off, even with the accelerated productivity in renewables. With full participation, the global agreement with the uniform tax reduces GHG emissions from 68.50 GT to 50.46 GT in 2050 and improves global welfare from 0.57 per cent to 2.11 per cent of global GDP in 2015 following the IPCC “best” temperature estimate.23 Yet, the findings suggest that only China and the US have unilateral incentive to join the agreement. And their participation only in the extreme temperature case creates more difficulties to achieve full participation in global mitigation agreement.

22 The analysis of the GDP impact of the five per cent global renewable productivity shocks shows that EU domestic output is below the baseline, indicating their loss due to term of trade, particularly for energy intensive commodities. 23 Refer to Figure 2.4 of Chapter 2 on the estimation of global welfare reduction per giga tonne of emissions.

Side payments by China, the US and Europe to induce full participation are affordable from the net benefits they receive due to the lower temperatures that would result, but only in the IPCC extreme temperature case. The present value of the additional net welfare benefits to China, the US, and the EU, which arises from the implementation of the tax by all other regions, is estimated at roughly 29.71 trillion USD, more than sufficient to compensate the 7.52 trillion USD the total of present value incremental net loss from all other regions if they change their strategy and join the carbon pricing agreement.24

As noted in Chapter 2, there remains a major problem with timing. The abatement cost must be borne early while the net benefits accrue after two decades. As shown in Figure 3.9, the big three do not derive a net gain until after 2035, which make this compensation mechanism especially difficult to implement.

Figure 3.9 Side Payments to Induce Universal Participation in IPCC High- Temperature Scenario (Renewable Productivity Acceleration)

Source: Estimated as described in the text.

Under the same IPCC high temperature scenario, this time lag is longer compared with no renewable productivity growth (Figure 3.10). Renewable productivity

24 By comparison, the big three obtain present value incremental net loss by universal implementation of 25.52 and 57 trillion USD in “low” and “best” temperature estimate, so hardly affordable to compensate another loss from all countries (9 and 12 trillion USD respectively)

growth makes an agreement more effortful in respect of timing with side payments. Another behavioral deterrence mechanism such as a punitive agreement might be more effective to motivate full participation.

Figure 3.10 Side Payments to Induce Universal Participation in IPCC High- Temperature Scenario (No Renewable Productivity Acceleration)

Source: Estimated as described in the text.

3.5 Conclusions

Pressure from the science and the high costs of abatement have directed greater attention to the role of renewable energy sources. In recent decades, the utilization of renewable energy has become an integrated part of energy policy in almost every country. This chapter extends the integrated global model used in Chapter 2 to address the contribution of renewables in reducing the global emissions, and to re- examine the strategic interactions associated with global abatement policy. Three important findings arise for further policy consideration.

First, under baseline conditions rapid economic growth is spread across all industrial and service sectors, including renewable energy. Its share as the energy input tends to be constant in some countries, thus yielding little change to emissions growth. This finding is consistent with the sceptical literature on renewable prospects. It

further suggests the need for government intervention to promote the full utilization of clean energy.

Second, experimenting with different rates of renewable productivity growth establishes that renewables can make a significant contribution to limiting future emissions. Nonetheless, high productivity growth could potentially have a retarding effect on efforts to reduce global emissions. Because renewables are employed only in power generation, renewables productivity has a direct impact on the electricity sectors. Unused fossil fuel energy creates an excess supply that drives down the price of gas, coal, and petroleum. This triggers higher demand for fossil fuels by other industries and results in the moderation of emission reductions. While this finding depends on the transport sector, in particular, not employing renewables directly (say by electrification) because fossils fuels become cheaper, it does foreshadow facing renewable transformation of energy industries.

Finally, in low temperature climate scenarios, where renewable productivity grows, the global mitigation agreement cannot succeed. There is only a non-cooperative equilibrium. Regions such as Europe and Japan, which are efficient users of fossil fuels and relatively low emitters, lose competitiveness with stronger renewables productivity growth and are motivated to defect. The strategic analysis finds the equilibrium in which China and the US cooperate by adopting carbon taxation, but then only in the IPCC ‘high” temperature scenario. The individual contributions of the US and China again emerge as determining factors, and underline their roles in global abatement strategy. And in present value terms, while side payments that induce other regions to commit to carbon abatement are affordable by them from the benefit they would derive, these benefits do not arise for two decades, thus front loading the costs and making the choice to undertake such payment politically unpalatable.

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Annex to Chapter 3

Table A.3.1 Regional Aggregation in New Database

NO Region Aggregation GTAP Region 1 Indonesia Indonesia 2 Australia Australia 3 China China and Hong Kong China 4 USA USA 5 EU28 Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Rep., Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom** 6 Japan Japan 7 India India 8 Russia Russia Federation 9 Middle Eastern and North Africa Iran, Bahrain*, Israel*, Jordan*, Kuwait*, Oman*, Qatar*, Saudi Arabia*Rest of Western Asia, Egypt, Morocco, Tunisia, Rest of North Africa 10 Rest of the World Singapore, Malaysia, Cambodia, Lao, Philippines, Thailand, Vietnam, Brunei Darussalam*, Rest of Southeast Asia, Taiwan, Rest of East Asia, Nepal*, Bangladesh, Pakistan, Sri Lanka, Rest of South Asia, South Korea, New Zealand and Rest of Oceania, Canada, Rest of North America, Mexico, Costa Rica, Guatemala, Nicaragua, Panama, El Salvador*, Rest of Central America, Dominican Republic*, Jamaica*, Puerto Rico, Trinidad & Tobago*, Caribbean, Argentina, Brazil, Bolivia, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela, Rest of South America, Albania, Belarus, Ukraine, Rest of Eastern Europe, Rest of Europe, Switzerland, Norway and Rest of FTA, Kazakhstan, Kyrgyzstan, Rest of Former Soviet Union, Armenia, Azerbaijan, Georgia, Turkey, Benin*, Burkina Faso*, Cameroon*, Cote d’Ivorire*, Ghana*, Guinea*, Nigeria*, Nigeria, Senegal, Togo*, Rest of West Africa, Central Africa, South Central Africa, Ethiopia, Kenya*, Madagascar, Malawi, Mauritius, Mozambique, Rwanda*, Tanzania, Uganda, Zambia*, Zimbabwe*, Botswana, Namibia*, South Africa, Rest of South African Custom Unions. *New countries in GTAP 9 Database **United Kingdom is still part of EU for this study

Table A.3.2 Sectoral Aggregation in New Database

NO Aggregated Sectors Explanation GTAP commodities 1 Agriculture Agriculture and Food pdr, wht, gro, v_f, osd,c_b, pfb, ocr, ctl, Processing oap, rmk, wol, cmt, omt, vol, mil pcr, sgr, ofd, b_t 2 Other Primary Sectors Forestry, Fisheries and frs, fsh, omn Mineral Extract 3 Coal Coal coa 4 Crude Oil Crude Oil oil 5 Gas Gas and Gas Products gas, gdt 6 Oil Products Petroleum and coal p_c Products 7 Renewable Energy* Renewable Energy renew 8 Energy Intensive Energy Intensive and Non crp, nmm, i_s, nfm Metallic Mineral Industries 9 Electricity Electricity ely 10 Manufacturing Manufacturing Industries tex, wap, lea, lum, ppp, fmp, mvh, otn, ole, ome, omf 11 Transportation Water, Air and Other otp, wtp, atp Transport 12 Service Water, Construction, Trade wtr, cns, trd, cmn, ofi, isr, obs, ros, osg, and Other Services, Public dwe administration *Renewable Energy is created by splitting Electricity Sector into fossil based versus renewable based as described in Table A.3.2. The input of renewable sector is following the input share of GTAP Power Database (Peter 2016)

Table A.3.3 Endowment Factors Aggregation in New Database

NO Aggregated Sectors Explanation GTAP commodities 1 Land sluggish Land 2 Natural Resources sluggish Natural Resources 3 Skilled Labour mobile Technician, Professionals, Officials and Managers 4 Unskilled Labour mobile Clerks, Service/ Shop Workers, Agricultural and Unskilled Workers 5 Capital mobile Capital

Table A.3.4 Summary World Electricity Production from All Energy Sources (GWh)

2004 2014 No Region Share Fossil Share Non Share Fossil Share Non Fossil (%) Fossil (%) (%) (%) 1 Indonesia 88.27 11.73 91.48 8.52 2 Singapore 97.33 2.67 97.50 2.50 3 Malaysia 92.58 7.42 91.34 8.66 4 Other ASEAN 82.19 17.81 77.70 22.30 5 Other Asia 74.94 25.06 73.69 26.31 6 Australia 91.71 8.29 88.13 11.87 7 New Zealand & Oceania 27.31 72.69 31.85 68.15 8 China 81.23 18.77 73.69 26.31 9 Japan 62.16 37.84 85.53 14.47 10 South Korea 62.77 37.23 69.36 30.64 11 India 82.42 17.58 91.48 8.52 12 Brazil 10.35 89.65 15.44 84.56 13 USA 71.21 28.79 68.47 31.53 14 Canada 25.63 74.37 23.67 76.33 15 Latin Americas 53.32 46.68 55.77 44.23 16 EU 28* 53.96 46.04 43.27 56.73 17 Russia 64.56 35.44 65.43 34.57 18 EFTA Counties 0.85 99.15 1.97 98.03 19 Ex. Soviet Union 62.54 37.46 67.94 32.06 20 Middle Eastern 95.37 4.63 95.39 4.61 21 Other Africa 69.16 30.84 66.92 33.08 22 ROW* 54.91 45.09 56.10 43.90 *ROW energy share is the weighted average of other countries (aggregated regions) outside the first nine countries/ regions listed in table A.3.1 Source: Re-estimation from World Bank Development Indicator 2014

Table A.3.5 GDP Growth Rate and Nominal GDP Share in Baseline Projection

2015 2050 Share of No Country GDP Growth Share of GDP Growth Nominal (%) Nominal GDP (%) GDP 1 Indonesia 4.96 0.01 4.04 0.02 2 Australia 3.02 0.02 1.92 0.02 3 China 6.28 0.12 3.07 0.19 4 USA 2.27 0.21 1.89 0.17 5 EU 2.12 0.24 1.53 0.18 6 Japan 1.03 0.08 0.56 0.05 7 India 6.79 0.03 4.31 0.05 8 Russia 2.56 0.03 2.02 0.03 9 Middle Eastern 4.09 0.05 3.10 0.08 10 Rest of the World 3.21 0.22 2.48 0.22 Source: Estimated as described in the text.

CHAPTER 4 GLOBAL MITIGATION POLICY: DOMESTIC IMPLICATIONS FOR INDONESIA

4.1. Introduction

Indonesia’s recent growth resurgence has seen an increase in the energy intensity of its economic activity. Over the last four decades, its total consumption of energy has grown almost five-fold, and it has doubled over the past twenty years. The total energy demand has grown even faster, at nearly six per cent in 2016, slightly above the rates of demand growth in emerging China and India according to BP (2018), who indicate that energy and industrial activities contribute a third of the country’s overall emissions and predict it to expand. It has increased by 7.6 per cent in 2016, twice its ten year average.1 Because of the country’s comparatively high population growth, its expanding economy and the centrality of its energy and industrial sectors, these will be the largest contributors of Indonesian emissions (Shahbaz et al. 2013; Azam et al. 2014; and Alam et al. 2016).

It is important to recall that Indonesia remains a developing country whose electricity grid remains incomplete. The delivery of cheap electricity to a growing proportion of its citizens remains a policy priority. Its comparatively abundant coal reserves make it very costly for its government to consider expansion paths that are low in emissions. Nonetheless Indonesia’s government is actively involved in global mitigation discussions. It has ratified the Kyoto Protocol and the recent Paris Accord, announcing a new target for emission reductions of 29 per cent by 2030 relative to a Business as Usual (BAU) scenario (Wada et al. 2012; Arshad 20162). Indonesia’s commitment and its strategies and policies to achieve this, are laid out in its 2010 Climate Change Sectoral Roadmap (ICCSR). These are to be funded through the Indonesia Climate Change Trust Fund (Caravani et al. 2012). This fund

1 https://www.bp.com/content/dam/bp/en/corporate/pdf/energy-economics/statistical-review-2017/bp- statistical-review-of-world-energy-2017-indonesia-insights.pdf. 2 The target was 26-41 per cent by 2020 (Presidential Regulation No.61/2011) before being revised into 29 per cent by 2030 in the new government (Arshad 2016).

is intended to assist with the adaptation and mitigation in nine sectors including energy, industry and transportation.

Thus far, however, the mitigation roadmap in energy consumption has been dominated by regulatory policies, performance standards and voluntary action programs. These programs have been seen to lack effectiveness in the past (Lye et al. 2009), and overall GHG emissions are still growing with ever stronger contributions from energy consumption. Even the newly adopted national energy plan (Government Regulation No.79/2014) which aims to rebalance the energy mix, has yet offered only minor attention to limit emissions. Fossil fuels still have the largest share, and the renewables target is only 23 per cent of the total energy mixed by 2025. Reducing emissions contributed from Land Use Change and Forestry (LUCF) is the main priority, while the abatement strategy through controlling energy consumption is progressively slow and submissive.

To reduce emissions from energy consumption, pricing carbon by imposing a tax on emissions has been under consideration since 2009 through the Ministry of Finance Green Paper: Economic and Fiscal Policy Strategies for Climate Change Mitigation. Carbon pricing is widely known as an effective carbon abatement tool. Its effective tax has been shown to be more transparent than regulatory measures, leading to faster changes to emitter behavior, and hence more prompt cuts to emissions (Parry et al. 1999; Baranzini et al. 2000; and Garnaut 2008). However, the detrimental effects of such taxation on the economy have weighed heavily on the government since then, leading thus far to hesitation in its implementation.

Admittedly, for countries of Indonesia’s economic size, global abatement analyses have tended to suggest the best strategy is to free ride (Chapter 2, Nordhaus 2015). Indonesia’s emissions are less significant compared to China, the US, and Europe, so Indonesian abatement would have considerably less impact on the rate of global mitigation.3 Moreover, incentives to defect are stronger once global renewable productivity grows. By participating, and thereby distorting their domestic

3 The expense of global mitigation lies in the contribution of the top emitters, thus positioning Indonesia as a free rider. In 2014, China, the US and Europe total GHG emission are 11.6 GT, 6.3 GT and 3.6 GT respectively, while the world total was 49 GT. Indonesia reached 2.5 GT in which only 0.8 GT contributed from energy used and industrial processes. This is less significant relative to China with 11 GT of its emission contributed from energy or 5.6 GT in US Emission (CAIT data climate watch, World Resources Institute 2018).

economies via higher energy prices, competition loss in trade is unavoidable and this only results in a greater mitigation cost without welfare improvement.

Motivated by these general positions in Indonesian abatement, this chapter takes the method of previous chapters and applies it with a spotlight on the domestic implications of carbon abatement policy for the Indonesian economy. It aims to explore the consequences of a carbon tax and of alternates for Indonesia to meet its abatement target. The chapter assesses three different policies: carbon taxation, renewable productivity growth via the importation of frontier technology and a production subsidy on renewables, in case by evaluating the potential CO2 reduced, the share of the clean energy, and the implications to the domestic economy. For this purpose in particular, the chapter adapts the dynamic model used in Chapter 3. The adaptation highlights the Indonesian and other economies across industry groups, Indonesia’s major trading partners and countries that are central to climate change policy. It draws on the core elements of the GTAP-9 global database to simulate global economic performance out to 2050. This approach differs from the earlier works on Indonesian abatement policy as it widens the scope of primary analysis by incorporating global interaction in the context of international trade.

The analysis finds that the unilateral implementation of carbon pricing does significantly retard Indonesia’s overall economic performance, but the impacts on sectoral productivity are mixed. Significant emission reductions should be traded off against their impact on economic performance, especially in the electricity sector. All approaches to the abatement are not equally damaging, however. Renewable subsidies offer relatively modest implications without the detrimental effects on electricity production. But the subsidy rate considered results in a weaker contribution to emission reduction compared to a unilateral tax, and the long-term implementation of it could cause a burden to the economy. The best outcomes are achieved through renewable productivity growth based on imported technology, via encouragement to adopt frontier technologies rather than substantial government intervention. This option offers continued economic growth with low CO2 emissions.

The chapter is organized as follows. Section 4.2 discusses the Indonesia’s energy profile. The related literature is surveyed in section 4.3 covering the economic context of mitigation policy, with emphasizes on carbon tax implementation, and the impacts on Indonesian economic growth. Section 4.4 describes the foundational,

“business as usual” baseline projection to 2050, which is then compared in Section 4.5with corresponding projections that incorporate carbon taxation renewable subsidy and productivity. Section 4.6 offers general conclusions.

4.2. Recent Developments in Indonesian Energy and Emissions

Indonesian energy consumption depends predominantly on fossil fuels, and in particular, on petroleum. It supplies 41 per cent of overall primary consumption, followed by coal at 36 per cent and natural gas at 19 per cent. However, Indonesia only produces 55 per cent of its oil consumption, thus depending for almost half of its consumption on imports. Its proven reserves are constantly declining, amounting as of 2016 to 3.3 thousand million barrels, and production growth is low, at about 2.7 per cent per year.4

The coal share, on the other hand, is expanding steadily. Its consumption has grown by more than 20 per cent per year, supported by a national policy of Domestic Market Obligation (DMO), prioritizing sales to the domestic market. As it is cheap and plentiful, coal is the easiest energy source from which to expand Indonesia’s electricity grid.5 Indonesia also plays significant roles in the global coal market, supplying half of Asia’s steam coal imports. Its production almost doubled between the years 2008 to 2013, mainly to fill import demand from China and India. With export and domestic consumption set to continue growing coal is expected to come to dominate Indonesia’s future energy mix. By contrast, natural gas consumption fell by seven per cent in 2016, reaching its lowest level since 2007. A policy favouring coal has weakened the demand for gas. To implement Indonesia’s 35-GW programme, the Indonesian State-Owned Electricity Company (PT PLN) attempts to minimise the cost of electricity expansion by raising the share of coal, which eventually reduces the demand for gas.6

The least developed energy resources in Indonesia are renewables, and the overall consumption share reached only 3.3 per cent in year 2016 (BP 2018). Indonesian

4 This is 8.2 per cent lower than the year 2015 or 1.5 per cent annual declining rate in one decade. 5 Today, almost one fifth of the total population still does not have access to electricity, and the government has launched a fast track program to add 35 GW of power capacity in 2019. 6 https://www.woodmac.com/reports/gas-markets-indonesia-gas-and-lng-h2-2017-summary- 51879056

renewable sources are primarily from hydroelectric power and geothermal. The consumption of hydro is more than a half on the total renewable consumption, while geothermal contributes the rest. Solar and wind are recent entrants, contributing less than two per cent to the renewable share. The prospect of the expansion from geothermal sources is promising as Indonesia holds 11.8 per cent share of the cumulative global capacity, the second largest after the Philippines. The development of geothermal energy started in the 1990s, but its suitability for the base-load power escalated its popularity. This key strength offsets the major concern associated with solar and wind, its capacity to supply uninterrupted power supply. As Figure 4.1 shows, geothermal consumption shows a steady upward trend relative to comparatively undulating supplies from hydroelectric sources. It is expected that this renewable type will play a determinant role in future energy policy.

Figure 4.1 Indonesia’s Energy Consumption: Historical Trends

Source: Author Re- Estimation from BP (2018)

To progress electrification, Indonesia still depends on the intensification of fossil energy, especially coal and gas. In 2016 for example, 55 per cent of the new 59.6 GW installed power generation plants used coal. Gas provided the other 26 per cent while renewables and petroleum supplied only 12.45 per cent and seven per cent, respectively.7 This dominant role of coal and gas in power generation reflects

7 Directorate General of Electricity (2016).

Indonesia’s significant available reserves of both and the relatively low cost of supplies from them.

Coal is a low-cost fuel, and by this virtue, it will continue its ongoing dominance in future energy share. Gas will also remain favorable in the future given Indonesian extensive gas reserves.8 Given the dwindling reserves of petroleum it is expected that oil will be gradually phased out in power generation by 2026. The current refinery oil share in Indonesian electricity production is, in any case, 6.7 per cent. Table 4.1 exhibits the evolution of the fuel mix of Indonesian power generation and its target in 2026.

Table 4.1 Fuel Mix of Power Generation (Per cent)

Target Energy Type 2009 2010 2011 2012 2013 2014 2015 2016 2017 2026 Coal 39.00 38.00 44.06 50.27 51.58 52.87 56.06 54.69 55.60 50.4 Gas 25.00 25.00 21.00 23.41 23.56 24.07 24.89 25.89 25.80 26.7 Oil 25.00 22.00 22.95 14.97 12.54 11.81 8.58 6.97 6.70 0.4 Hydro 8.00 12.00 6.80 6.39 7.73 6.70 5.93 7.88 6.40 12.3 Geothermal 3.00 3.00 5.13 4.85 4.42 4.44 4.34 4.33 4.70 9 Others 0.00 0.00 0.07 0.11 0.16 0.11 0.20 0.24 0.80 1.2 Total 100.00 100.00 100.01 100.00 99.99 100.00 100.00 100.00 100.00 100.00 Source: Performance Report of Directorate General of Electricity (“DGE”) 2016.

Hydropower accounts for 6.4 per cent of national power generation, and it is expected to grow to 12.3 per cent by 2026. Indonesia still has many prospective yet unexploited hydro sites with good water flow and head. The geothermal share is smaller but is expected to triple by 2026. Solar and wind remain very minor contributors. Until clearer sources are better developed in Indonesia, its dependency on fossil fuels will lead to further growth in energy-related emissions. As Figure 4.2 reveals, energy-related emissions grow similarly to primary energy consumption. With a fast increasing demand and policy priority to increase its electrification rate, the near future holds considerable expansion in its emissions from the fossil fuel use.9

8 Indonesia’s current reserve to productivity ratio (R/P) for its gas is 41.1 which indicate the time-span of the availability in distant future. This 41.1 R/P level is medium, but with decreasing trend in gas price, favourability in gas consumption is high (BP 2018). 9 The national average of Indonesia’s electrification rates in 2016 was 91.2 per cent, and plan to increase to 97.4 per cent by 2019 and 99.7 per cent by 2025. Electricity consumption in 2016 was

Figure 4.2 Indonesia’s Energy Consumption and Emission Growth

Source: CAIT Climate Watch, 2017, World Resources Institute 2018.

Notwithstanding its engagement with global negotiations on emissions, Indonesia has no specific target for lowering energy sector emissions (Jafari et al. 2012). In reconciling with the commitment to reduce total emissions, the government gives emphasis to clean coal technology and increases in the share of renewables in its energy mix to almost a third in 2050 (Cornot-Gandolphe 2017). Yet the recent commitment to add 27 GW of coal-fired capacity in the next ten years will strain this target. Short of a significant policy change, the immediate economic imperative driven by the cheapness of coal is likely to see Indonesia continuing a high carbon emissions path.

4.3. Abatement Strategy: Carbon Tax or Renewable Intensity

4.3.1 An Indonesian Carbon Tax Study

At the global level, the intensive consumption of fossil fuels has been linked to negative externalities that include carbon emissions. Most elementally, by the fundamental welfare theorems, these lead to a failure of unfettered markets to allocate resources optimally. The early literature on policy formation in the presence of such externalities was led by Pigou (1932) and subsequently extended by, among others, Baumol (1972). They provided the means to design taxation regimes that

0.96 Megawatt hours per capita, still well behinds other ASEAN economies (Directorate General of Electricity 2016).

could internalize these external costs and so to maximize social welfare. In the meantime, studies by Yusuf (2011), Resosudarmo (2011) and Arnold (2012) have been central to the Indonesian government’s commitments to mitigation, advocating carbon taxes to deliver a faster decline in emissions. Nonetheless, such corrective taxes introduce the world of the second best and a choice must emerge amongst alternative interventions that maximizes collective welfare.

To minimize deadweight losses, it is proposed that pollution tax regimes be designed to be revenue neutral. In this case, the resulting revenue is recycled, allowing more distortionary revenue sources to be abated, and so yielding the “double dividend”, the improvement of environmental and economic efficiency (Goulder 1995; Seung- Joon et al. 2015) The idea stems originally from Duesenberry (1949), and its wider implementation is due to (De Mooij, 2000). The most recent applications are by De Miguel & Manzano (2011), Lu et al. (2010) and Wang et al. (2011), each of which confirms that Pigouvian taxation can yield non-negative impacts on raw economic performance with revenue neutrality.

Studies focussing on carbon taxation in Indonesia have, thus far, been based on this concept. The earliest, by Resosudarmo et al. (2011) employed a medium-term dynamic inter-regional general equilibrium model, entitled “IRSA-Indonesia 5”.

They simulated a hypothetical carbon tax of IDR 10,000 per ton of CO2, implemented from 2010 onwards, where the whole of the revenue from the tax is returned to the economy by increasing government spending. The results revealed that the carbon tax would have an overall positive affect on the economy while reducing CO2 emissions in 2020 below the baseline. Household consumption per capita would also increase in both rural and urban areas. More recently; Yusuf and Resosudarmo (2015) examined the distributional impact of a hypothetical carbon tax on Indonesian Households. They employed a comparative static general equilibrium analysis, adopting a modified ORANI-G model with a database constructed from an Indonesian Social Accounting Matrix (SAM) for 2003. Their principal claim was that the introduction of a tax equal to 280,000 IDR (20 USD) per ton of CO2, along with revenue recycling, is not necessarily regressive.

A downside to revenue recycling is that it can be biased in favor of affected industries thereby reducing the emissions impact of carbon taxation. Moreover, numerous criticisms arise from affected industries about the implementation of

carbon taxes. These tend to over-emphasize both the immediate and long-term detrimental impacts of carbon tax policy. Poterba (1991) offered early cost assessments that were given further emphasis by Baker & Koehler (1998), and Kosonen (2012). All point out the effects on production costs and the associated regressive distributional impacts on real household purchasing power. Bassi et al. (2009), point out that carbon taxation can reduce international competitiveness as well as measures of overall economic welfare that ignore the externality, and that it must also alter the industrial structure of trade.10

In the current global climate of widespread support for policies to reduce carbon emissions, the major expressed concern relates to the long-term growth performance of developing countries like Indonesia. The literature on Indonesian policy and effects is quiet on this issue. Most Indonesian studies address only short-term effects, presenting results that do not capture the medium and long-term effects on investment and risk, especially as they affect businesses operating in advantaged and disadvantaged industrial sectors. In addition to this, no literature specifically address the implication on the Indonesian performance when other countries create a coalition for a carbon pricing agreement. The analysis that follows offers an initial focus on these issues.

4.3.2 Carbon Tax or Renewables Subsidy

Recent literature, not only on global abatement but related issues in Indonesia, has tended to emphasise regulatory and tax mechanisms for reducing fossil fuels use. The comparative analyses of Gerlagh & Van der Zwaan (2006), Fisher & Newell (2008), and Van der Ploeg & Withagen (2014) all suggest a carbon tax as the most efficient policy for reducing emissions, compared to subsidising renewable use. Gerlagh & Van der Zwaan also find tax is more efficient to induce energy saving and to create a faster transition to less carbon intensive energy resources. Achieving this most efficient outcome also relies on the recycling of the revenue fromcarbon taxes to support renewable deployment.

10 This point is raised in the study of Rentschler & Kornejes (2017) of the Indonesian firm level evidence; energy prices have small but significant adverse long run effects on competitiveness.

In the same way, Fisher & Newell assess different policies to reduce carbon dioxide emissions as well as to promote innovation and diffusion of renewables, focusing on the US electricity sectors. They find emission pricing as the most efficient policy as it gives incentives for fossil fuel producers to reduce emission intensity, for the consumer to conserve, and for renewable energy producers to expand their production and productivity. The Van der Ploeg and Withagen study gives more attention to reserves and imperfect substitutability between oil and renewables. Their analysis draw the same conclusion: that the transition to renewables is quicker by a tax, compared to subsidy.

The implementation of carbon taxes or renewables subsidies is likely, however, to yield different effects on emission reduction and economic performance depending on the country. Which is most efficient depends on many factors. Taxing emissions may be efficient for the US economy given its high energy consumption, sufficient oil reserves, its mostly urban population and existing tax infrastructure, but probably not for an emerging economy such as Indonesia. Unfortunately, however, the Indonesian literature on its renewable prospects is limited. This is noted by two relevant studies: Marpaung et al. (2007) and Retnanestri & Outhred (2013). An early assessment of the potential for renewables to reduce regional emissions is given by Kumar (2015) via the LEAP energy model. His assessment finds full deployment of renewables in electricity generation is needed to reach 81 percent emission reduction by 2050. Since the contribution of renewables is now around three per cent, reducing emissions with renewables is costly unless the government takes more proactive steps to promote it (Hasan et al. 2011).

The limited literature on Indonesian renewables leaves room for a careful analysis. To develop a mitigation roadmap, the Indonesian government must consider potential dependency on renewables, in combination with a carbon tax in its abatement policy package. This chapter offers such an analysis, evaluating Indonesian abatement using both policies and doing so in the context of potential global agreements.

4.4. The Baseline Projection to 2050

4.4.1 Database and Baseline

The simulation uses the GTAP 2009 database, defined on 140 regions and 57 sectors. Following Chapter 3, the regions are aggregated into 10, focussing on Indonesia and its major trading partners. The 57 sectors are grouped into 12 sectoral commodities, where energy sectors are divided into coal, crude oil, gas, petroleum products and one renewable energy commodity. The sectoral aggregation also separates electricity, manufacturing, and the energy-intensive industries for their intensity in energy consumption and their significant role in boosting growth in the wider economy. The factor endowments are physical capital, skilled and unskilled labour, land and natural resources.

To ensure that, in the long run, the economy will reach a steady state growth path, a specific investment target is imposed on each region. This is done by the same means as in Chapter 3: constructing a “pre-base” simulation that includes exogenous population and labour force growth but in which the paths of real GDP and investment are made exogenous and productivity on the one hand, and risk premia over interest rates on the other, are made endogenous. The baseline simulation and all subsequent experiments then impose the productivity and risk premium changes as time paths of exogenous shocks.

The population and labour force projections follow CEPII by Fouré et al. (2013), with some updating. Indonesia is assumed to have positive population growth but at a declining rate. Its population grew 0.9 per cent in the year 2015 but is predicted to grow by 0.5 per cent in 2050.As labour is divided between skilled and unskilled, the forecast growth rates of each are different. Indonesia’s skilled labour is predicted to grow at a rate that slows from around 3.5 per cent per year to 2.0 per cent per year in 2050. Unskilled labour growth declines from 1.2 per cent per year to be negative by the 2040’s, ending up at -0.70 per cent by 2050.

4.4.2 Indonesian Growth: Baseline Projection 2015-2050

The Indonesian economy is projected to grow steadily, at around four to five per cent per year. Its rate peaks around five per cent and flattens after that. Real income per capita grows by less than four per cent annually, and the real effective exchange rate

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has a slight depreciating trend. The baseline imposes constant real investment growth of five per cent per year which sees a slight decline in the expected rate of return on Indonesian financial assets to 2020 but growth at a stable rate on the periods after.

The high capital growth and comparatively low wage growth keep marginal costs of both capital and labour comparatively low. Most industries grow continuously with the strongest rate of expansion in the renewable energy industry. This high renewable growth is from a low base so its share in electricity production remains less than ten per cent throughout the period. Fossil energy inputs dominate baseline Indonesian future energy composition. Gas grows by 6.5 per cent and contributes more than 30 per cent of the total energy output. Coal use grows at 4.9 per cent and its contribution declines slightly but remains above 25 per cent throughout. By contrast, the crude oil share is significantly reduced with a negative growth rate by 2050.Table 4.2 summarizes the baseline growth trajectories of key variables, and Figure 4.3 shows the distribution of baseline total energy output.

Secondary industries also expand, but mostly to fulfil domestic demand. Manufacturing, electricity, energy intensive industries and transportation grow above four per cent per year. Manufacturing contributes 12 per cent of total domestic sales followed by the energy-intensive industries at nine per cent. The major exports, however, are still primary commodities. Besides coal and gas, positive net exports are agricultural and other primary commodities. Major export contributions, which contribute to a continuing trade surplus, are gas, coal and primary commodities, partially offsets by imports of crude oil, petroleum products, and all secondary industries. With seven per cent export growth, the contribution of gas is substantial. Coal export is also significant yet declining after two decades relative to gas.

Table 4.2 Projected Annual Growths in Baseline Indicators

Growth Rate 2015 Growth Rate 2050 Average Growth No Indicator (%) (%) Rate (%) Quantity Price Quantity Price Quantity Price I Macro Indicator Real GDP 4.96 -0.02 4.04 0.22 4.45 0.13 Real Income per capita 3.85 3.38 3.58 Capital Goods 5.30 0.01 5.36 0.23 5.30 0.15 Capital Allocation 6.02 5.35 5.56 Expected Rate of Return 2.21 -0.92 -0.07 Population 0.91 0.05 0.52

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Growth Rate 2015 Growth Rate 2050 Average Growth No Indicator (%) (%) Rate (%) Quantity Price Quantity Price Quantity Price II Sectoral Output Energy Sector Coal 6.28 -0.35 3.71 -0.49 4.90 -0.38 Crude Oil 1.63 1.59 -0.79 1.12 0.31 1.50 Gas 8.24 0.40 4.93 -0.06 6.53 0.22 Petroleum Products 3.56 1.30 2.91 0.70 3.07 1.15 Renewable Energy 10.82 -0.34 7.17 -0.37 9.24 -0.31 Secondary Industries Energy Intensive 4.88 -0.10 3.51 0.12 4.09 0.05 Electricity 4.40 0.69 3.92 0.18 4.06 0.53 Manufacturing 4.79 -0.20 3.68 0.04 4.12 -0.05 Transportation 4.62 0.45 3.46 0.42 3.96 0.50 Other Industries Agriculture 3.87 -0.08 2.80 -0.09 3.34 -0.11 Other Primary Sectors 5.34 0.31 4.18 0.54 4.79 0.53 Service 5.22 0.03 4.41 1.97 4.77 0.20 III Endowment Land 0.00 4.01 0.00 2.05 0.00 2.98 Natural Resources 0.00 6.44 0.00 4.46 0.00 5.50 Skilled Labour 3.52 1.65 2.00 2.45 2.83 2.00 Unskilled Labour 1.21 3.23 -0.67 4.39 0.35 3.71 Capital 6.02 -0.78 5.35 -0.67 5.56 -0.64 Emission (CO2 Energy & IV Industrial) Regional Emission 4.52 3.88 4.12 Fuels Emission Coal 5.24 4.26 4.68 Crude Oil 3.96 3.84 3.79 Gas 5.11 4.31 4.70 Petroleum Products 3.90 3.31 3.49 Source: Estimated as described in the text

Figure 4.3 Projected Energy Output Share

Source: Estimated as described in the text.

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Figure 4.4 Emission Historical Trends and Projections

Source: Historical Data of CAIT Climate Watch, 2017 (World Resources Institute 2018), and Baseline projection is as described in the text.

In the absence of new environmental policies and energy regulations, this expanding economy contributes increasingly significant emissions growth. CO2 emissions grow 4.1 per cent per year and reach 1.86 GT by 2050, almost five times the 2015 level.

Assuming other gases are growing at the same rate as CO2, the total GHG from energy consumption and industrial activities is projected to reach 1.4 GT in year 2030, and 2.9 GT in 2050 (Figure 4.4), three per cent and six percent respectively of baseline global emissions.11 This baseline level is consistent with the projections on energy emission of Secretariat General of National Energy Council (2016) and WRI (2017). By this, the baseline is used as the Indonesia’s Business As Usual (BAU) trajectory in the analysis.

11 The figure also shows the total GHG emission including the land and deforestation which is assumed to grow at constant 2016 level of 1.71 GT. By this assumption, Indonesian emission depends on the CO2 emission from energy consumption, thus controlling the growth of energy related emission means controlling overall emission. And it is essential to achieve 29 per cent target in 2030.

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4.5. Abatement Scenarios: Implications on Emission and Domestic Economy

The baseline projection is then superimposed by three arbitrary policy shocks of a 20 USD carbon tax, two per cent renewable subsidy and a surge in renewable productivity growth. In the analysis, the scale of potential emissions reduction is measured, and the domestic implications are evaluated. Since there is no specific mitigation target for the CO2 emissions from the energy consumption, this study uses the 29 per cent overall reduction target by 2030, and the 2025-2050 renewable share target as the potential emission reduction indicators. As indicated by the new adopted energy plan in 2014, Indonesia aims to reduce emissions by 29 per cent from BAU by 2030 and achieve a 23 per cent renewable share in its energy mix by 2025. The three arbitrary shocks are compared to evaluate which policy could proceed towards these mitigation targets. Evaluation of the domestic implications, on the other hand, focuses on the deviation of macro indicators and the sectoral analysis.

The first scenario introduces a unilateral carbon tax in the Indonesian economy, while no policy changes are made elsewhere. The tax is imposed downstream for energy fuels, on the consumption for both domestic and imported products of coal, crude oil, gas, and petroleum products.12 To be consistent with previous chapters, the 20 USD carbon tax rate is implemented from year 2015 onward.13 Different from Indonesian literatures, the implementation of carbon tax is not structured to meet the government’s double dividend objectives, yet focuses on the potential of tax to reduce emissions, and its impact on domestic economy.14 The evaluation of this first scenario is also expanded, to see the impact on the Indonesian economy of unilateral the carbon taxes in China and the US.

Development of the next two scenarios aims to assess of what it takes for Indonesia to meet the abatement target without a carbon tax, while no policy changes are

12 See equation (12) in the Appendix Chapter for the carbon tax imposition. 13 The 20 USD per tonne is a closer rate to 280,000 IDR used by latest Indonesian carbon tax study of Yusuf & Resosudharmo (2015). 14 The adaptation of the model still keeps the assumption that the revenue from tax (including carbon taxation) raises domestic government expenditure and national saving. This is considered to be limitation for revenue neutral analysis where the carbon revenue will be return as government transfers. Offsetting the carbon tax with another tax breaks is possible and is considered for future research.

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implemented in the rest of the world. These policies are designed to reduce emissions through intensification of renewables use. The analysis of Chapter 3 suggests that, globally, renewables make significant contributions to future emissions control. To achieve this, renewables need government intervention to increase their supply, to reduce their price, and to promote the competitiveness.

For this purpose, the second scenario introduces a two per cent production subsidy to renewables per year. With a specific rate (to), the subsidy boosts the revenue received by the renewables firms, (Ps Q), but it lowers the market price (Pm), thus eventually increases the demand (Equation 4.1). The total subsidy amount is simply the difference between the value of total output at the market price and at the production price ()PQPQsm   . And the subsidy ratio, (rto), reflects the share of the subsidy amount relative to the total renewables output value at the market price. This scenario aims to double the subsidy ratio by 2026 by increasing the production subsidy two per cent per year.15

Ps t o P m (4.1)

()PQPQsm   rto  (4.2) ()PQm 

In the third scenario, the renewable competiveness is achieved through TFP shocks. As in Chapter 3, this option implies that the government actively promotes the adoption of frontier by private firms without subsidy cost. This might be achieved by regulation or by underwriting investment projects. Indonesian renewable productivity is, in this scenario, projected to grow by 5 per cent per year, higher than the productivity rates of the other countries.16 The result is a reduction in renewables production cost, lowering their price and increasing their use.

4.5.1 Potential Emissions Reduction and Renewable Shares

The three abatement strategies result in lower CO2 emissions by energy used. As projected on Figure 4.5, the 20 USD carbon tax rate gives a relatively stable

15 The subsidy ratio in the current databases is 23.11 per cent. Doubling this ratio by year 2026 is equivalent to two per cent subsidy rate (to) per year. 16 The five per cent productivity growth rate is slightly higher than Indonesian growth rate of renewable consumption of 3.3 per cent in year 2016.

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reduction rate after 2015. The tax reduces emissions by 18 per cent per year, with the projected emission of 1.46 GT in year 2050. The effects of the two renewable scenarios, in contrast, are relatively modest in the early stages but accelerate faster through the end of the forecast period. Because of the low renewables production base, the projected emissions growth under the renewables production subsidy shows insignificant reduction relative to the baseline forecast before the year 2030. The average reduction rate is 14 per cent, slightly lower than using the carbon tax. But in 2050, emissions reach 1.21 GT, 35 percent lower relative to the baseline. Similarly, a significant emissions reduction is achieved in the five per cent renewable productivity growth case. Emissions are 27 per cent lower, reduced to 1.06 GT in 2050.

Figure 4.5 Projected CO2 Emissions from Energy Used and Industrial Activity: Various Scenarios

Source: estimated as described in the text.

As a consequence of these findings, achieving the 29 per cent emission reduction target by 2030 will only be feasible under the productivity shock scenario (Table 4.3). The five per cent renewable growth also could meet the 23 per cent renewable target in 2025. Despite giving stable and potentially lower cumulative emissions, carbon tax may not be a feasible option. Since the target is set relative to the business

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as usual trajectory, an abatement policy with a carbon tax may miss this target. 17 As seen, emissions are only reduced 18 per cent to 0.7 GT, and still higher than the 30 per cent target of 0.6 GT CO2 emissions in 2030. In addition, the 20 USD per tonne rate is relatively higher than rates proposed by Indonesian literatures, which makes politically acceptance difficult.

Table 4.3 Emission Deviation Relative to Baseline and Renewable Share

Year Indicators Averag 2020 2025 2030 2035 2040 2045 2050 e CO2 Emission Reduction (% from Baseline) 20 USD Carbon Tax -16.8 -17.6 -18.5 -19.3 -20.2 -21.1 -22.0 -19.0 Renewables Subsidy -1.2 -3.7 -8.2 -15.0 -22.7 -29.8 -35.1 -14.1 Accelerated Renewables Productivity -4.5 -16.8 -31.0 -37.9 -40.5 -41.9 -43.2 -27.6 Renewable Share of Energy Mix (%) 20 USD Carbon Tax 8.2 9.8 11.8 13.9 16.2 18.6 21.1 Renewables Subsidy 7.7 14.5 25.8 41.4 58.3 72.7 82.7 Accelerated Renewables Productivity 16.4 47.0 80.4 95.1 98.9 99.8 99.9 Source: Estimated as described in the text.

The renewable subsidy scenario also misses this emission target, and the renewable share in 2025 is still below 23 per cent. But renewable deployment is steadily increasing in this scenario, far exceeding the 31 per cent share in 2050. Compared to tax, abatement strategy through productivity and subsidy are seen to be effective to promote intensification of renewables.

4.5.2 Abatement Scenarios: Economic Performance

This subsection compares the implications of three abatement scenarios for the domestic economy. The focus is on the changes of four macro indicators: real GDP, real per capita income, general price indices and real investment. Government interventions to control emissions through a carbon tax, a renewables subsidy and induced productivity all alter the energy consumption pattern, and eventually change energy demand. These changes on the demand and supply sides affect the first three indicators as well as rates of return on investment and so Indonesia’s share of the

17 It leads to a different story once the reduction target is set relative to a particular year, such as Japan (26 percent by 2030 compared to 2013 level). A stable emission rate results with carbon tax, make it feasible to meet the target.

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global pool of savings to finance new capital. Figure 4.6 illustrates the deviation of these indicators relative to the baseline.

Figure 4.6 Abatement Implications on GDP and Other Macro Indicators

Source: Estimated as described in the text.

The carbon tax implementation generates the highest cost to the Indonesian economy. It leads to real GDP growth that is 1.2 per cent per year lower rather than the original baseline.18 Giving subsidies to renewables also creates additional costs to the economy. Real GDP is still consistently lower, and declines even further in the final projection periods. Renewable productivity, achieved by regulation or underwriting, on the other hand, offers overall strong GDP growth performance with superior real income and smaller price increased at the end of projection periods.

Indeed, a slowing economy is also reflected through the decline of the real per capita income in the cases of the carbon tax and the renewables subsidy. The declining rates are constant and relatively insignificant for the carbon tax, but substantial for

18 Real GDP reductions resulted from carbon tax still lay in range of Hoerner & Bosquet (2001) study of -5 per cent to 2.5 per cent.

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the subsidy case as its price indexes increasing more significant. Real investment is declining with the carbon tax, but escalating with subsidy. Real investment also significantly increases for accelerated productivity growth. Productivity growth with frontier technology has no cost that boosts GDP growth, investment and income.

4.5.3 Domestic Implications: Sectoral Analysis

In verifying deviations on the macro indicators, this subsection focuses on the implications of the three abatement scenarios sectorally. It covers the analysis via changes in production, market prices and the shifts in factor endowment. The analysis also covers the implications for Indonesia export and the capital outflow.

Production and Market Price

Table 4.4 summarizes the average changes in production and commodity prices for the three cases. With a carbon tax, producers face higher production costs, causing a decline relative to the baseline in production in almost all sectors except renewables. The most affected is the electricity industry, as its production inputs are dominated by fossil fuels. The electricity price is raised by 15 per cent and electricity output is lower by 11 per cent relative to the baseline. Higher electricity prices cause other secondary industries to cut their production, which results in lower output with higher market prices.

On the other hand, electricity production increases with subsidies to renewables. The subsidy limits the use of fossil fuel energy in power generation but intensifies the use of renewables with their cheaper price. This rebalancing of inputs results in lower electricity prices. The impacts on the output of other sectors, however, are modest relative to the carbon tax case. Besides renewables, the service sector also experiences slightly higher output growth due to lower electricity prices.

Table 4.4 Changes in Output Production and Market Price (Percentage Change)

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Quantity Produced Market Price Relative to GDP Accelerated Accelerated Sectors Carbon Renewables Carbon Renewables Renewables Renewables Tax Subsidy Tax Subsidy Productivity Productivity Petroleum -3.70 -0.43 -0.60 0.51 0.00 -0.03 Renewables 80.29 10.69 13.77 0.77 -2.38 -6.42 Secondary Industries Energy Intensive -2.01 -0.22 0.18 0.93 0.17 -0.03 Electricity -11.40 1.08 4.72 14.86 -0.99 -4.56 Manufacture -1.52 -0.15 0.19 0.49 0.18 -0.01 Transportation -1.80 -0.13 0.14 1.95 0.16 0.01 Primary & Other Agriculture -0.25 -0.18 0.02 -0.12 0.17 0.09 Other Primary Industries -0.46 -0.08 0.06 -0.41 0.08 0.07 Service -0.95 0.15 0.21 0.18 0.25 0.02 Source: Estimated as described in the text.

Like the renewable's subsidy case, the accelerated renewables productivity increases electricity production significantly. This scenario yields generally improved conditions, in part because the economic costs of the regulation and underwriting involved are not modelled. Production in secondary industries also expands, and prices also tend to fall. The relative price of electricity is even lower than the subsidy scenario could achieve.

In addition to the electricity sector, these three scenarios impact differently on Indonesia’s coal industry. In the carbon tax scenario, both coal production and price are falling, confirming excess supply due to limited domestic consumption. These could potentially lead to the carbon leakage through the trade channel if coal exports also rise. Both production and price also fall in the case of a renewables productivity surge. Since all commodity prices also fall, an associated decline in coal production indicates that the sector loses its competitiveness to renewables.

In the renewables subsidy case, however, coal production falls but its price is higher than baseline. This suggests that coal demand less is distorted despite the renewable subsidy scheme. The coal demand for electricity production might be lower but it may not be as low as under the tax policy scheme. To verify this, Figure 4.7 shows the changes in the energy input share in electricity production.

As seen under the subsidy scheme, reductions of fossil fuels used in electricity production are all under 10 per cent. The total energy input is still positive, which suggests that the decline in fossil fuels demand may not be substantial, and overall

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electricity production is not significantly affected. The renewables productivity case gives the same story, though with a stronger contribution of renewables to replace fossil fuels in electricity production.

Returning to the carbon tax case, coal use in electricity production is reduced by 60 per cent and gas by almost 40 per cent. Renewables are intensified, reaching almost 80 per cent higher than the baseline scenario. The carbon tax scheme, in the absence of policies to support a transition to renewables, does pressure the renewable sector to fulfil the energy demand, but electricity production still declines.

Figure 4.7 Energy in Electricity Production: Various Scenarios

Source: Estimated as described in the text

Primary Factor Use Changes

As production slows under a carbon tax scheme, the demand for labour declines in almost all sectors (Table 4.5). The biggest cut happens in the fossil fuel industries, from where labour moves into renewables, electricity, energy intensive and transportation industries. Capital use in renewables rises more than 70 per cent,

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though from a low base. This indicates nonetheless that carbon taxation would spur development in this clean energy sector. Capital use in electricity production more broadly expands notwithstanding the cuts to coal use. The crude oil and mineral sectors contract their capital use slightly, though their export-orientation stabilises their production.

Table 4.5 Deviation (Percentage Change) on Demand for Factor Endowments

Accelerated Renewables Carbon Tax Renewables Subsidy NO Sectors Productivity Labour Capital Labour Capital Labour Capital 1 Agriculture -0.53 -0.64 -1.00 -0.79 0.16 0.24 2 Other Primary -0.48 -0.14 -0.50 -0.50 0.12 0.12 3 Coal -4.40 -6.92 -4.88 -3.16 -0.28 0.40 4 Crude Oil -0.10 -0.30 -0.79 -0.61 -0.09 -0.02 5 Gas -6.39 -7.76 -3.73 -3.28 0.19 0.35 6 Petroleum -2.97 -3.69 -2.00 -0.81 -0.20 -0.10 7 Energy Intensive 0.17 -1.25 -1.34 -0.70 0.02 0.31 8 Electricity 5.82 1.41 -1.62 -0.36 -2.46 -0.45 9 Renewable 78.71 77.15 4.72 5.59 -4.05 -3.24 10 Manufacture -0.61 -1.63 -0.90 -0.30 0.08 0.35 11 Transportation 2.82 -0.21 -1.82 -0.66 0.12 0.37 12 Service 0.03 -0.94 0.11 0.76 -0.03 0.25 Source: Estimated as described in the text *) The rate is average between skilled and unskilled labour.

In the renewable subsidy case the endowment reallocation is more obvious, as both labour and capital move to the subsidised sector. A different view appears under the renewable productivity surge. Productivity is higher, electricity prices are lower and all energy using industries expand. At the margin labour and capital use switches to primary industries such as agriculture and secondary industries such as manufacture and transportation.

Export Pattern

In the unilateral carbon tax case, the excess supply of fossil fuels and their lower prices stimulate Indonesian exports. With weakening domestic demand, energy producers find new opportunities in foreign markets. Table 4.6 shows that aggregate exports are higher for coal and crude oil in this case. This represents carbon leakage through the trade channel, confirming the earlier findings of Burniaux & Martin (2012). With unilateral carbon taxation fossil fuel energy industries alter their

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market orientation and export more. This only shifts the associated emissions to other regions, weakening progress toward the global emission reduction target.

Table 4.6 Deviations (Percentage Change) on Aggregate Export

Accelerated Renewables Sectors/ Commodities Carbon Tax Renewables Subsidy Productivity Energy Coal 0.18 -0.71 -0.05 Oil 8.87 0.77 1.24 Gas -2.00 -0.80 -0.03 Petroleum -1.95 -0.05 0.08 Renewables 9.56 14.93 43.82 Secondary Industries Energy Intensive -3.20 -0.63 0.12 Electricity -53.98 5.77 31.29 Manufacture -2.30 -0.90 0.05 Transportation -9.48 -0.83 -0.08 Primary & Other Agriculture 0.51 -0.74 -0.39 Other Primary Industries 1.27 -0.25 -0.20 Service -0.67 -0.98 -0.07 Source: Estimated as described in the text.

In the rest of the economy almost all secondary industries lose competitiveness, with lower production and declining exports. The slowing growth of these sectors drives the substantial deviation of real GDP from the baseline, as previously discussed. The carbon tax scheme changes the export pattern, with Indonesia focussing more on primary commodities that are not large users of fossil energy.

Different effects show up in the export pattern in the subsidy scenario. There, expansion relative to the baseline occurs only in renewables and electricity, while the exports of primary industries decline. As the production subsidy shifts factor use from all industries to renewables, other industries including the primaries reduce their production and exports. The renewable productivity surge scenario, on the other hand, offers the economy low cost energy, making secondary industries more competitive for export market.

Capital Outflows

In addition to the trade channel, carbon emissions leakage under the unilateral carbon tax could potentially be channeled through investment. Global investment is now moving to other regions that are free of the tax. This capital could possibly be invested in carbon intensive sectors. As indicated in Figure 4.8 the new capital

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allocation to Indonesia declines to two per cent, while the income from foreign asset ownership significantly increases.19 Leakage through this finance channel is only significant in the case of the carbon tax.

Figure 4.8 Deviations on Capital Allocation and Indonesian Income from Foreign Equity

Source: Estimated as described in the text.

4.5.4 China and US Unilateral Carbon Policy: Implication on Indonesia

This section gives further evaluation of the unilateral carbon tax policy described previously. It illustrates the implications for the Indonesian economy of China and the US unilaterally implementing the carbon tax. As established in Chapter 3, China and the US enjoy unilateral gains in present value terms from implementing carbon pricing. Indeed, their unilateral net benefits are sufficient for them to afford side payments that would make carbon taxation in such countries as Indonesia economically viable. At the same time, China and the US are Indonesia’s major trading partners, and their unilateral implementation of a carbon tax could have substantial effects on the structure and performance of the Indonesian domestic economy. China and the US, as well as Japan, each hold 11 per cent shares of total Indonesian exports.

The first and immediate effect of a China-US commitment to carbon taxation is on the Indonesian coal industry. Around 70 to 80 per cent of Indonesian coal production is exported, mostly as thermal coal of low to medium quality. The demand for

19 There is a positive capital outflow into other regions especially China and Australia. The highest capital reallocation happens in most Indonesian major trading partners. See Annex Figure A.4.1

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thermal coal for steel making skyrocketed during China’s growth surge and continues in support of China’s electrification program. Indonesia contributes 30 percent of China’s coal imports, second after Australia.20 And for Indonesia, China is the first export destination before India, Japan, and South Korea. Indonesia’s coal industry is therefore very sensitive to China’s carbon policy. A carbon tax in China causes Indonesian coal production to decline significantly as indicated in Figure 4.9.

Figure 4.9 Effects of a Unilateral Carbon Price in China and USA: Deviations from Baseline in Indonesia Aggregate Export

Source: Estimated as described in the text.

Second, unilateral implementation in both China and the US also affects the Indonesian crude oil industry. Crude oil production declines slightly while oil exports fall steeply. This export is mainly to Japan, but the exports to China and the US are also significant. China has a 10 per cent share of Indonesian total crude oil exports, and six per cent goes to the US.21 A slowing in the oil refinery industry in China and the US reduces import demand from all their trading partners, including Indonesia.

Export declines also occur in other primary industries, covering the mineral commodities such as iron, nickel, bauxite, copper, and silver. But this fall is caused more by lower demand from China than the US.22 China limits its import demand for

20 https://www.statista.com/statistics/271043/countries-of-origin-for-chinas-imported-coal/. [August 19, 2018]. 21 https://www.hellenicshippingnews.com/eia-indonesia-oil-market-overview/. [August 19, 2018] 22 China is the third major mineral Indonesian mineral export destination after Japan and the Philippines. https://wits.worldbank.org/CountryProfile/en/Country/IDN/Year/LTST/TradeFlow/Export/Partner/by -country/Product/25-26_Minerals.

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mineral commodities as raw inputs, following the slowing down of production in its secondary industries. Mineral exports to the US are less significant and hence less influential over Indonesian mineral production.

The implications of a China-US carbon pricing policy for Indonesia economy are dominated by the trade channel, through exports. There is no significant impact on Indonesia’s domestic demand and real GDP moderates only slightly until 2025.23 While there are no significant changes in domestic capital allocation, in-coming foreign investment is reduced. Of some importance is that reduced exports cause the path of the domestic coal price to be lower and this results in more consumption and higher Indonesian emissions than occur in the baseline forecast. This is a general pattern in response to carbon taxes that are imposed by some but not all countries. These cheapen fossil fuels elsewhere, raising their use and associated emissions. The importance of side agreements to induce participation outside the large emitter club is highlighted by this.

4.6. Conclusion

Achieving global abatement equilibrium with full participation has been proven to be difficult. Addressing the economic interest of countries in abatement policy in the prior studies only found the equilibrium with China and the US participation while the rest chooses to free ride. For Indonesia, doing abatement by carbon pricing is costly, thus best positioning itself as a free rider in global mitigation agreement. Since global mitigation needs collective action, this chapter evaluates the implication of unilateral implementation of carbon tax to Indonesian economy. It is followed by a comparative study of abatement policy through renewable subsidy and productivity, to offer an alternate of abatement that Indonesian government could consider. There are five significant finding that could be summarized.

First, correcting negative externalities through implementing carbon taxation comes with an immediate effect. The production cost increases which results in lower output supply for all secondary industries. The effect on the electricity is detrimental, retarding the overall economic performance and the effort to improve the electrification rate. On the other hand, abatement policy through the renewable

23 Refer to Annex Figure A.4.2.

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subsidy and the adoption of frontier technology to boost renewables productivity growth, gives positive effect to electricity sector, but long-term subsidy also creates additional burden to the economy.

Second, all policies bring positive impacts for the renewable industry. The renewables improve as most of the endowment factors will be reallocated into this sector. But the renewables price is rising in carbon taxation case which makes electricity production fall. Conversely, renewables production subsidy and technological improvement control renewables price make it a sustainable input for electricity production.

Third, the analysis confirms the carbon leakage through trade and investment channel by unilateral tax. The export of coal, gas and crude oil is higher relative to the non-carbon pricing policy scenario, justifying the market reorientation from domestic consumption to the foreign market. Likewise, the capital investment is moving to other regions which are free of tax constraint. Indonesian net foreign income grows higher, validating the carbon leakage through investment. There are no significant effects for renewable subsidy and productivity scenarios.

Fourth, for emissions reduction, the carbon tax scheme gives an immediate and a constant reduction rate per year. By 20 USD per tonne rate, the CO2 emissions from energy and industrial process are 18 per cent lower from business as usual trajectory. Abatement strategy through intensification of renewables, however, gives gradual impacts on emissions reduced. By two per cent production subsidy to renewables, the emissions reduction rate is less significant but improved throughout the years. The average emissions reduced is 14 per cent per year, and still lower than using carbon tax. The renewable productivity improvement gives the highest reduction rate. The five per cent productivity growth in renewables reduces emission by 27 per cent.

The latest finding is when Indonesia’s decision to free ride being evaluated. The impacts of unilateral carbon tax implementation of China and the US are moderate to Indonesian economy. The export of certain commodities will fall but it gives a positive impact on the domestic consumption. As a major trading partner, China mitigation policy will affect Indonesian coal and minerals export, and the US

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decision impacts on the crude oil. Regardless of these implications, the overall effect tends to be insignificant for Indonesian growth.

These findings emphasize that Indonesia’s unilateral mitigation cost by carbon pricing is too high, affecting the productivity of the electricity sector and the domestic consumption. Then too, the emissions from energy consumption and the industrial process are relatively small compared to China and the US, thus less influential in affecting the global climate. Consequently, Indonesia’s best strategic position in dealing with global mitigation agreement that involves pricing the carbon is non-commitment. For controlling its own emission and contributing to the global collective action, Indonesia could reorientate their energy and mitigation policy through development of its renewable sector.

References

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Annex to Chapter 4

Figure A.4.1: Capital Outflows: Indonesia Unilateral Carbon Tax Implementation

Source: Estimated as described in the text

Figure A.4.2: Implications of Unilateral Carbon Price in China and USA: Deviations from Baseline of Indonesia Macro Indicators

Source: Estimated as described in the text

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Figure A.4.3 Implications of Unilateral Carbon Pricing in China and USA: Deviations from Baseline in Sectoral Output

Source: Estimated as described in the text

Figure A.4.4 Implications of a Unilateral Carbon Price in China and USA: Deviations from Baseline of Capital Allocation and Indonesian Income from Foreign Equity

Source: Estimated as described in the text

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CHAPTER 5 CONCLUSIONS AND POLICY IMPLICATIONS

5.1 Summary of Major Findings

The recent deadlock in climate negotiations stems from the moderate nature of the responsibilities for emission targets agreed to and the temporal mismatch between costs and benefits from abatement. This thesis re-examines the strategic interaction among countries over abatement policy, aiming to map country positions and to seek avenues by which effective agreements might be reached. Its core chapters comprise three essays examining GHG emission mitigation outcomes using a new integrated global model, with the analysis centering on the carbon pricing through uniform tax implementation. The essays point out countries’ economic interests in abatement, analyse their strategic interaction and identify global abatement equilibria. Five major findings emerge from the three analytical chapters.

First, the empirical analysis using the integrated assessment model confirms that China and the US would derive net positive economic gains from unilateral implementation of carbon taxes. This contradicts the findings of most related literature, which tends to find that no country or region has sufficient influence over the climate for there to be a net gain from unilateral abatement. Strategic analysis with three different IPCC temperature scenarios shows that both China and the US enjoy net economic advantage, in present value terms, from unilateral carbon abatement policy. They are each large enough to affect the global climate and to benefit significantly from the moderation of future temperature increases. Indeed, in combination with the EU, they have a particularly powerful collective impact.

Second, if the “big three” commit to abatement moderation of global temperature growth will be significant, but the analysis confirms the “small paradox theorem”, signifying that the best strategic option for all other countries considered in the study is to free-ride. Their contributions to global emissions are individually less influential, and, for them, mitigation policy has costs that outweigh the benefits derived from their own mitigation alone. At the same time, however, the gains to the “big three” are made considerably larger if all other countries contribute to abatement via carbon taxation, so much so that the additional gains are sufficient to

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compensate all other countries for net losses and hence to induce universal abatement. The downside is that there is an intertemporal mismatch between the costs, which begin immediately, and the benefits, which are delayed by at least two decades.

Third, the contribution of renewable energy in limiting future climate change is significant. A world-wide acceleration in the rate of productivity growth in renewable energy, akin to the continuation of gains achieved in the most recent five years, is, by itself, effective in achieving a significant expansion in the renewable share of the global energy sufficient to meet the Kyoto medium target for carbon emissions. It is shown, however, that higher rates of renewable productivity could potentially lead to “green paradox” effects whereby fossil fuels are cheapened to such a degree that emissions are not further moderated.

Fourth, renewable productivity growth affects country strategic behavior and global mitigation equilibria. Productivity shocks in renewable energy reduce countries’ incentives to adopt such mitigation measures as a carbon tax and can lead to equilibria in which all countries attempt to free ride. With high renewables productivity growth, China and the US change strategy and can choose not to implement carbon taxation but, instead to rely on their clean energy sectors. Indeed, the equilibrium with carbon taxation, involving both the US and China, is only attained in the highest IPCC temperature projection case. Only then does their commitment to carbon pricing avoid further welfare loss. In and of itself, if clean energy reduces China-US emissions sufficiently, the failure to tax carbon in these large emitters is not a bad thing. It means, however, that the benefits of a side- payment driven global movement to tax carbon would be lost. In free-riding regions, where renewable productivity grows more slowly, commitment to such a taxation regime may be essential to their contributions to emission control.

Fifth, in the final analysis it is confirmed that the effects of carbon taxation on secondary industries are critical in determining country decisions to commit. Chapter 4 considers the important case of Indonesia, which is a large and growing country just below the “large emitter” threshold. There carbon taxation causes a loss of competitiveness in both domestic and global markets, and an associated impairment of labour market performance. These are substantial factors in such countries’ decisions to free ride. A key element of the conflict is that electricity production is

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impaired, raising costs throughout the economy but, more importantly for such developing countries, slowing advances in electrification. It is shown that, if renewables productivity can be accelerated in such countries through regulation or investment underwriting, and this is a big if, this approach would be cost efficient and therefore thus more feasible for emerging countries to support and be part of collective action of global mitigation.

5.2 Policy Implications

The analysis of strategic interaction in mapping the country climate position highlights the crucial role of China in global climate policy. With its very large emissions from fuel and energy consumption, more than for any other country, China’s mitigation policy will significantly affect the achievement of the global emission abatement target. From the perspective of mitigation benefits, China’s climate risk factors are high, indicating its vulnerability to climate variation. In the analysis of this thesis, these factors make China’s best strategy one of commitment to carbon abatement. Such a commitment would be advantageous not only to China but to the world as a whole, due to the non-excludability of temperature change benefits.

Following the US withdrawal from the Paris agreement, China has by far the greatest potential to lead global negotiations on carbon mitigation. If it can induce the US and Europe to follow, this thesis shows that side payments are possible that could possibly induce all countries to participate in policies that have the equivalent effect to a USD 20 per tonne carbon tax. It is also shown, however, that a major stumbling block in any global negotiation will be the intertemporal mismatch between the costs, which begin immediately, and the benefits, which are delayed by at least two decades.

Finally, in future climate negotiations the thesis shows that oft-expressed pessimism about the potential for renewables to offer significant assistance in meeting mitigation objectives may be misplaced. Significant carbon abatement effects are possible from this source if productivity growth in renewables sectors is fostered by governments, not necessarily by direct subsidy but even by regulation and underwriting. Moreover, global aid budgets can assist in this by comprising

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compensatory payments in the form of technological transfers as strategic collaborative tools directed to universal clean energy development.

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CHAPTER: APPENDIX THE MODEL

A.1 Structure of Gdyn-E and the Modification

As a part of the Integrated Assessment Model developed for the thesis, the estimation of mitigation cost and the projected emission are based on an adaptation and a substantial modification of the Gdyn-E model, developed by Golub (2013). The original of the model was constructed by merging the comparative static, energy-focussed Global Trade Analysis Project (GTAP-E) model with the standard version of the dynamic GTAP model (GDyn). GTAP-E extends the standard global, comparative static GTAP model by incorporating more detailed representation of energy industries and fuel substitution in its structure, and a complete set of emission coefficients for each industry. By comparison, GDyn is a recursive dynamic extension of the standard GTAP model, with specific enhancement in investment theory that incorporates international capital mobility and ownership (Ianchovichina and Walmsley, 2012). By combining the features of GTAP-E and GDyn, Gdyn-E is specifically designed to analyse the dynamic effects of energy and climate change policy.

All essential features of the original GTAP model (Hertel, 1998) are preserved, namely the essential neoclassical structure of the model and its embodiment of perfect competition, Armington product differentiation, non-homothetic consumer demands, international trade, transport and endogenous international financial flows. The model has three economic agents, which are the firm that produce and consume the commodities at the same time, a representative households and a government. The modification extends the electricity production to include the renewable energy alternatives, as the main feature in the integrated model that used in Chapter 3 and 4. Subsequent sections elaborate further on the theoretical structure and features of the precursors GDyn-E and the modified version.

A.1.1 Firm

In production, the firm follows GTAP-E production structure with Constant Return to Scale (CRS). It involves nested Constant Elasticity of Substitution (CES) function

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where it incorporates inter-fuel and fuel-factor substitution involving technical change terms Ai . Firms use endowment factors, fuels and intermediate goods to produce the output. The production nest is illustrated in Figure A.1.

Figure A.1 General Production Structure

Output

σ = 0

Value-Added Energy Other Input (energy as

σVAE feedstock)

Land Labour Natural Capital-Energy σD Resources Composite Foreign

σLAB Domestic

σKE σM

Skilled Unskilled Region 1 Region i

Capital Energy Composite

σENER Non Electric Electric

σD σNELY Coal Non Coal Domestic Foreign

σM σD σNCOAL

Region 1 Region i

Domestic Foreign Oil Gas Oil Products

σD

σM

Domestic Foreign Region 1 Region i

σM

Region 1 Region i

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Compare to original GTAP-E model, the adaptation includes capital and energy complementarity that sees a capital-energy composite as a primary factor which contributes to the value added component of total output in each industry. There is also a more complex pattern of inter-fuel substitutions that include energy industries by type in the value added hierarchy and fuel raw materials on the expendable input side. Indeed, estimating the adjustment of aggregate output following energy price changes requires a structure such as this which embodies energy and capital complementarity, a necessary condition for output produced to rise, assuming no other major distortions in economy and flexible wage rates (Vinals, 1984).

Inter-fuel and fuel-to-factor substitutions are embodied in all nests in the model’s production structure. The energy composite was divided to coal and non-coal in the Gdyn-E model that still used in Chapter 2. The modification extends the energy composite into the choice of renewable and non-renewable energy, as the basis of the analysis of Chapter 3 and 4, as illustrated in Figure A.2.

Figure A.2 Expansion in Energy Composite in Electricity Production

Energy Composite

σENER

Non-Renewable Renewable

σNRENEW

Coal Non Coal

σD

σNCOAL

Oil Gas Oil Products

Domestic Foreign

σM

Region 1 Region i

These energy aggregates are then combined with capital to produce a CES energy- capital composite which, in turn, contributes to the value-added energy composite.

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Final production levels also use expendable intermediate inputs that include fuels as feedstock.1

In general, firm chooses input Qi to minimize PQ subject to i ii

 1   YAQ   1 where Y is the total output production; variable α is the i i i i  i  input share and parameter σ represent elasticity of substitution. By fist order derivative rule, the optimum quantity supplied could be estimated as:

AP QYA  ii  (1) i i i Pf

And the optimum supply price: 1 1 1  Pi Psii   (2) i A i

In terms of choice between productions inputs, firm’s choice between domestic and import followed Armington substitution.

A.1.2 Representative Household Expenditure

The household private consumption sub-utilities apply the CES function, while on the top of consumption structure uses the Constant Difference Elasticity (CDE) demand system.2 The sub-utilities in household expenditure refer to the choice of energy and non-energy expenditure, where the first consist of all types of energy and electricity. The representative household faces a single electricity commodity and its consumption of crude oil and renewable is insignificantly small. Household final demand is illustrated in Figure A.3.

1 Inter-fuel substitution is critical to the electricity sector. The interaction between renewable and fossil fuels energy type is perfectly captured in electricity production. Other sectors consume electricity in addition to fossil fuels as featured in the original Gdyn-E model, and their renewable share in other industry is insignificant. 2 Within the energy composite group, consumption expenditures adopt Rutherford and GREEN Models where CES sub-structure applied. This treatment is intended to allow flexible substitution between individual energy commodities (Burniaux et al. 1992; Rutherford & Montgomery 1997)

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Figure A.3 Household Expenditure Structure

Household Demands for Private Goods

CDE

Energy Composite Non Energy Commodities

CES σD σPEN =1 Electricity Oil Oil Coal Products Renewable Gas σD Domestic Foreign

σM

Domestic Foreign Region i σM Region 1 Region 1 Region i

*The share of renewable expenditure is insignificant

The CDE approach is realistically non-homothetic to ensure that consumption change as incomes grows. The general expenditure function of CDE demand could be presented as: E G  P , U  min p' x : f x  U (3)

The minimum expenditure of E is the product of N dimensional vector of prices ()p and demand of ()x in which has to be at least equal to utility. As G() is homogenous function of degree 1 in prices, the function allows normalization price and expenditure such as: G  E1P , U  G z , U  1 (4)

The normalized price of z is determined by per capita private expenditure Cpir, or mathematically described as:

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Pir, zir,  (5) Cpir,

The CDE expenditure function follows the approach of Hanoch (1975) by restricting a number of substitution effects into this normalized price ()z , and it takes the form as:

N G z, U   Uzi  i  i (6)   i1 i

Parameter of i is the non-negative distribution/ scale parameter need to specify the function, and i parameter represents expansion/ income parameter which is the identity of non-homothetic consumption function.3 Income parameter is strictly positive to ensure utility grows as income grows. The substitution parameter of  i determines the substitutability among commodities in consumption, which takes the value between zero to one (0 i 1)

Demand for each good in each region:

i  i  i 1 iUz i i X i  N (7) Uzk  k  k  K 1 k k k

While the average budget share is calculated by:

PXii Si  N (8) P X k1 kk

And marginal budget share estimated by:

i S i i (9)

, where i is income elasticity of demand. The thesis preserves the originality of CDE expenditure approach, used in Gdyn-E model.

3 Variable Ui represent per-capita utility level which is calculated using a range of 25 to 250 per cent of benchmark level of expenditure in each region. CDE is standard function of GTAP Model, and per- capita utility estimation follows Hertel (1997).

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A.1.3 Government Expenditure

Government expenditure is assumed to be a standard CES function, based on optimizing utility of consuming good X i subject to the government budget schedule. Mathematically represented as:

  1  1 Minimize YUXX()   subject to PX iii i i ii 

It follows that the government optimum demand function is:

 P XY  i (10) ii Pg

1 PP  1 1 (11) gi i i 

Government’s choice among commodities is the same as the private demand of the representative households. The government expenditure structure is illustrated in Figure A.4.

Figure A.4 Government Expenditure Structure

Demand for Composite

σGENNE=0.5

Energy Composite Non Energy Composite

σGNE=1 σGEN =1

Electricity Oil & Gas Coal Renewable Pcts σD …. ….

Domestic Foreign

σM

Region 1 Region i

*The share of renewable expenditure is insignificant

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A.1.4 Taxation and Armington Substitution

The model track CO2 emission by agent and source, for both domestic and imported, and assumes that emissions are proportional to emitting input use by firm or emitting commodity consumption by households. Real carbon tax   in the other hands, applies on fuel consumed as an additional component after the ad-volarem tax   to market pricePm  . The magnitude of will depend on the emission intensity in each consumption / expenditure.

PP1   (12) cmi,,,,,, j r  i j r i j r

All economic agents are assumed to maximize their utility, or minimize the cost, for the option between domestic and import commodities. The choice between the two is following Armington Elasticity of Substitution. Composite price of domestic and imported goods is estimated by:

1 11   1 PPPi dom 1   imp  (13)

1 PP  1 1 dom, impi i c 

The  represents Armington elasticity between domestic and imports goods. It follows the consumption demand for aggregate domestic products ()X dom and import ()Ximp , are defined as:

 Pdom  Pimp  XYXYdom ; imp  (1  )   (14) PPii   

A.1.5 Capital, Global Trust, Saving and Investment Theory

As a global recursive dynamic model, Gdyn-E offers better treatment of long run projections by allowing current account imbalances in regions and thus codifying international capital mobility, capital accumulation and combining them with an adaptive theory of investment. In capturing international capital mobility the model tracks investment flows and international factor income payments, indirectly

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simulating portfolio management in each region where portfolios comprise the home and foreign financial assets, representing indirect claims on firm physical capital in the home economy and abroad. Portfolios expand each year with the addition of purchases financed by home saving.

The model therefore includes a rudimentary representation of financial assets, where regions accumulate not only physical capital stocks, but also claims over ownership of physical capital at home and abroad. To minimize portfolio complexity, the model has all foreign assets held via a global trust. This way each portfolio has only two elements, home capital and foreign capital in the form of units in the global trust. The following subsections elaborate the concept of capital accumulation and the adaptive theory of investment of the model.

Capital and Asset Accumulation

One distinct feature on dynamic model is the treatment on time. Time is not treated as discrete index, but as continuous variable. Ianchovichina & Walmsley (2012) claim continuous treatment could minimize inaccuracy of linearization, in measuring real wealth in term of size of capital stockQk  . Further, continuous time approach will be applied in capital accumulation equation within the models that embodied investment and financial assets theory. The capital accumulation equation in deriving capital stock Qk is formulated by:

t Q Q Q dt (15) k k0 t cgds 0

Since the net level of capital goods Q is equal to net investment level ()I and cgdsNET allowing region generic shift factor w  and regional specific shift of capital stocks

()r the capital stock’s estimation is:

t Q () r Q  I  dt (16) k w r k0 t0

In capturing the international capital mobility, the model introduces financial assets with differentiation between assets location and ownership. It is assumed that firms have physical capital, renting land and natural resources from household by issuing financial assets, in for of equity shares, which represent indirect claim on their

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capital. To minimize parsimonious data requirement for foreign investment, the model introduce the intermediary fictional agent of global trust.4 The Annex Figure A.5 illustrates the role of global trust in the model.

Figure A.5 Global Trust Scheme

House -holds

Local Global Firms Trust

Foreign Entity

For simplicity of accounting purpose, there are no assets except fixed capital, and only firm own it. So, the ownership value of each firm in each region f ()r is equal to the value of the fixed capital.

f()()()()r  V k r  P cgds r  Q k r (17)

Since firm issue financial assets backed up by those fixed capital assets, and some were purchased by regional households and another by global trust, the assets accumulation for each firm could be formulated:

f()()()r   f__ hh r   f tr r (18)

4 Full portfolio representation would require full bilateral data on foreign assets, which is poor even for the OECD countries and would have to be very speculative in a global database.

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,where  f_ hh is the ownership claims by regional households on firm physical capital as they bought firm financial assets (equity) and  f_ tr is foreigner claim through purchase from global trust.5 It follows that, the regional household’s domestic and foreign assets ownership consist of the one from domestic firm ()Qf_ hh and from the global trust ()Qtr_ hh .

Qhh()()() r  f__ hh r   tr hh r (19)

Equivalently, household ownership also could be traced from the value of total equity, in which equal to:

tt ()()()r  P r r dt  P dt (20) hh cgdstt hh__ f trust hh tr 00

As  depicts the quantity of equity which is growing overtime, total value of financial assets ()v is simply the products of the equity price.

vhh__ f()()() r P cgds r hh f r , and vhh__ tr ( r ) P trust hh tr ( r )

Further, the model assumes that regional saving (S) is invested back in term of financial assets, or S()()() r vhh__ f r v hh tr r

Reformulating equation above, the household ownership assets accumulation could be described as the sum of initial ownership and saving growth:

t ()()()r   r    S r (21) hh f__ hh00 tr hh t 0

Net value of foreign assets in each region is equivalently determined by this equation:

tr__ hh()()()()r  f tr r   hh r  f r (22)

To avoid negative value in both foreign assets and gross foreign liabilities, the standard Gdyn model developed cross entropy minimization theory. It is further

5 Variable  represents ownership. The  f is firm ownership of fixed asset to back up the equity issued. Consequently, the  f_ hh could literally be translated into household claims on firm physical capital (fixed assets) as they have purchased domestic equity issued by the firm, or foreign physical capital by buying foreign equity from global trust Qtr_ hh  .

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developed by introducing the rigidity parameter of capital allocation openness of  d

and  f . If  d is assigned to be high, and  f low, it will keep the allocation of household wealth nearly fixed, and put most of capital adjustment on the source shares of equity to local firm. If the value are in other way around, equity will remain the initial value and household wealth allocation do most of the adjusting.

Introducing the rigidity parameter will guarantee that split between equity in local firm and foreign firm stay as close as possible to the split in initial database as the reginal household’s equity is changing overtime. Add to this, split between capital belonging to foreigner and local households closer to initial database.

In determining three wealth variables of household, firm and the trust, the next step is minimizing the following function:

()()()()r  r    r  r  F ( r )logf____ hh   ( r )log tr hh   ( r )log f hh   ( r )log f tr dff____ hh tr hh   f hh f tr  f____ hh()()()()r  f hh r  f hh r  f tr r 0 0   0 0 

,subject to f()()()r   f__ hh r   f tr r and hh()()()r   f__ hh r   tr hh r

First order derivative using Lagrangian method with respect to foreign equity in domestic capital,  f_ tr ()r obtain level of equity holding of trust in the firm (foreign ownership of domestic equity):

 ()r X( r )  ( r ) logf_ tr  1 ff ()r f_ tr0

And domestic ownership of foreign equity

 ()r X( r )  ( r ) logtr_ hh  1 hh d  ()r tr_ hh0

So, the domestic ownership of domestic equity

 f_ hh ()r Xhh() r  X  f () r  d () r  f ()log r   1  ()r f_ hh0

Different with standard Gdyn, the developed research papers do not fully adopt the a- theoretic rule to determine capital composition and wealth allocation. Hence the

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rigidity parameter  d and  f are set to be equal to 1 across region to represent higher substitutability of investment. It assumes equal adjustment in household wealth share and regional’s firm capital shares.

In terms of intermediary agent of global trust, value of assets owned by the trust is equal to the sum across region of foreign ownership of firm:

   ()r (23) trr f_ tr

Then it also equal to the sum of region equity in trust, or the ownership of foreign assets in regional households:

   ()r (24) trr tr_ hh

Income Financial Asset

As firm own fixed capital, buy intermediate input, hire labour and rent land, with zero profit assumption, the total amount of income payment by firm Y f  is equal to after tax capital earning minus depreciation.

Yf (),() r  VOA capital r  DEP r (25)

Firm pays to its shareholders in proportion of their shareholding. The payment to regional household Y f_ hh  is equal to:

 f_ hh ()r Yf_ hh()() r  Y  f r (26)  f ()r

And firm’s payment to global trustY f_ tr  :

 f_ tr ()r Yf_ tr()() r  Y  f r (27)  f ()r

Total income of the trust Ytr  is the sum of equity receipt in each region.

Y()() r  Y  r (28) trr f_ tr

And this income is distributed among the household shareholders across region

Ytr_ hh  :

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tr_ hh ()r Ytr_ hh()() r  Y  tr r (29) tr ()r

Finally, the total income of regional households is gained from the domestic equity purchased from firm and foreign stock from global trust Yhh  is equal to:

Yhh()()() r  Y  f__ hh r  Y  tr hh r (30)

Rate of Return and Investment Theory

The gross of capital return requires the earning and the capital gain component.

()r RORgross () r    Prcgds ()

As the model doesn’t capture the capital gain , the gross rate of return is

estimated only by rental   and the price of the capital goods Pcgds 

()r RORgross () r  (31) Prcgds ()

In case of positive profitability shock, increase in capital will escalate the capital price and reduce the return rate and capital gain. This brings unrealistically increase on the price of capital goods to equalize the effect.

In reality the underlying capital, and the claims over it, are differentiated across regions so yields on assets differ, often markedly. Perfect capital mobility is therefore an inappropriate assumption that would yield unrealistic variability in the prices of capital goods across regions and across time. Perfect capital mobility assumption also tends to overstate supply of capital. The original Gdyn approach involves a further stabilising influence over investment that arises from the incorporation of a number of lags in adjustment, combined with explicit adjustment costs. The behaviour of portfolio managers is driven by both expected and actual rates of return.

The actual rate is just the capital rental rate per dollar of a region’s capital goods price. Expected rates of return are determined so as to account for the likely diminishing effect of intended new investment on each region’s actual rate of return. In addition, the lagged adjustment approach recognizes a global target rate of return

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that is consistent with equality between saving and investment for the world as a whole. In the short run actual rates of return vary from this target in each region, but only in ways that allow saving and investment always to be bound together globally. They converge on the target level when the model is un-shocked for extended periods so that it approaches a steady state global growth path.

In the lagged adjustment approach, the targeted rate of return is defined as:

RORgross ROR targ et 0

It follows that:

RORtarg et()()() r ROR world  ROR risk r  ROR dep r

Or, in percentage form, the target return rate  t  consists of region generic  w  and region specific component r  , in which the later embodies the regional return risk and depreciation.

t()()rr  t  r (32)

The second step relates the target return rate with level of investment through expected return, E ROR , where investor will invest based on this rate. Higher capital stock should be followed by lower expected rate of return.

 E( ROR )( r ) Qr()  k RORref()() r Q kf r

Where the variable  is a positive parameter reflecting the magnitude of elasticity of expected return with respect to the size of capital stock. If the level of actual capital stock Qrk () is same as the reference level Qrkf () the expected return will be at the same rate as the reference return rate RORref () r . Reference capital size is assumed to grow at natural rate of  , or mathematically:

 t Qkf Q k _0 e

Parameter  is defined as the rate when capital can grow without affecting rate of return, while t is the real time variable. Replacing the reference capital level with the formula above, we obtain:

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  E( ROR )( r ) Qrk ()   t (33) RORref() r Q k_ o e

Differentiating with respect to time t:

ERGrorg  RG QK () r  

dQk dt Qcgds R dep Q k() r Q cgds As RGQK    R dep , it follows that: Qk()()() r Q k r Q k r

Qrcgds () ERGrorg   R dep  (34) Qrk ()

And its first order differentiation:

erg()()() r  r I q  q   r  d  (35) rorg cgds k  where Ї is defined as investment capital ratio. Rearranging the equation 1.34 above, the investment level Qgds  could be formulated as:

Qrk () QrQrRcdgs()()()()() k  dep  Qrr k    ERG RORG (36) 

Gdyn Model adopts adaptive theory of investment since the lagged investment theory contains inconsistency between level calculated and the theory. The new theory now allows an error correction treatment to be incorporated in the model. It implies the expected rate of return would be estimated by:

  E( ROR )( r ) Qrk ()  Er()kt RORref() r Q k _0 e and Er() is the shift factor of the expected rate of equation.

Alternative of Investment Theory The thesis used specific investment target instead of the investment theory described above. This is more limited objective approach by imposing a specific target on regional share in world investment and allows usual saving theory to determine its level. Assets are assumed to be differentiated across regions so their yields on assets

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differ, with a tendency for those yields to converge due to return-seeking investment flows. Country risk effects are exogenous, presented by exogenous premia on yields that are calibrated from investment projections via a pre-base simulation. Thus, instead of being determined by equation 1.35, the growth in the investment level is the product of region specific factor R  and region generic factor W  that was exogenously targeted. It is further defined by the following equation:

qcdgs()() r R r W (37)

A.1.6 International Emission

The carbon dioxide (CO2) emissions are assumed only from the fossil fuel energy consumption, hence burning of coal, gas, oil and petroleum products. Each region emits proportionally to the sum of energy used for all domestic and imported goods, adjusted via process specific emission data. This follows study of Lee (2008) which is also consistent with IPCC guidelines. In the application, the CO2 emissions are estimated as follow:

44 FCijr,,,,,, CC ijr (1  CST ijr )  EF i  FOC i ( ) CO  12 (38) 2i,, j r 1000

Where: FC : Fuel Consumption in 1000 tonnes of oil equivalent (toe) CC : Conversion Coefficient (Tera Joules (TJ) /1000 toe) CST : Ratio of Carbon Storage. It follows (1-CST) is ratio of carbon released EF : Emission Factor (Tonne/ TJ) FOC : Fraction of Carbon Oxidized.6 : type of energy (coal, oil, petroleum products, gas, gas product and ‘I’ electricity) ‘j’ : sectors/ industries ‘r’ : all regions in the model The total regional emission is then summing over energy commodities for all economic agents:

QCO()()()() r CO rfirm  CO r hh  CO r gov (39) 2i, j 2i , j  i 2 i  i 2 i

The original application of Gdyn-E model assumed that emission grows are proportional on energy input demand. This treatment did not really capture

6 See Lee (2008) for commodity specific value of CC, EF and FOC. The value of CST derived from IEA/OECD Energy Balance Report 2006.

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technological improvement especially in production sectors for some countries such as China, India or Japan. Consequently, the prediction of the total emission growth would have been unrealistically high. For this reason, the paper uses additional variable ()Ae to represent the technological improvement rate on energy used in each sector.

 APii AYii  Qj() Pf CO() j i  (40) 2 Ae()() j Ae j

By linearization, the CO2 growth in percentage form:

co2 ()()() j qi j ae j In case of mitigation policy through carbon tax per tonne emission, total revenue obtained is the product of tonne CO2 released and nominal tax rate Tc 

REVCO22()()() r Tc r QCO r (41)

A.1.7 Regional Household and Balance of Payment

Total income for the regional households consists of the income from endowment supply (land and natural resources), indirect taxes T  and carbon tax revenue. The factor income FINC  also includes the income of equity holding in which represent the claim of firm physical capital/assets.

F()()() r P  Q  P  Q  r  Y  r (42) INCek me00 e  m k k   hh  e endowment commodities, k  capital endowment commodities and ()r is change in ratio of tax on capital to income. Regional Household (National) Income is calculated as:

Y()()()() r FINC r  T r  REVCO2 r (43)

Revenue gain from mitigating carbon through tax should be accounted as additional component on Balance of Payment. EX( r ) IM ( r )  REVCO ( r )  0 (44) iiii 2

A.1.8 Equilibrium Conditions

This equation assures market clearing for domestic output

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QQQQ   (45) domt,,,,, r p dom___ f t p r dom hh t r gov hh t r

This equation assures market clearing for traded good market (margin commodities) QQQQ   (46) 0m,,,,, rdom m r st m rs xmd m r s

This equation assures market clearing for traded good market (non-margin commodities) QQQQ   (47) 0nm,,,,, rdom nm r st nm rs xmd nm r s

This equation assures market clearing for tradeable commodity entering each region

QQQQQ    (48) IMts,,,,,,,rp IMS trs IM___ f tpr IM hh tr IM gov tr

This equation assures market clearing in the markets for endowment goods which is perfectly mobile among uses

QQ (49) 0,e r p fe,, p r

A.2 Welfare Estimation of Net Climate Benefit

A.2.1 Present Value of Estimated Welfare

In determining the global abatement equilibria of Chapter 2 and 3, the welfare is estimated using these following functions.

As YCIGXMt t  t  t  t  t , GDP growth over time could be derived as:

t Y y t dt (50) t 0 Yt

Where YYYt  t  t1 . It follows that, deviation on GDP growth because of economic shock of tax, in which reflecting direct mitigation cost, could be denoted as dy() , and dy() indicates deviation due to temperature change or mitigation benefit. The payoff matrices for game strategic analysis is constructed based on the Present Value of the net welfare impact of abatement through taxes for region i,

PV_ Yi , or mathematically estimated as:

t dy()() dy Y PV_ Y  t t t (51) i t t 0 1 r

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Variable Yt is projected value of GDP (USD) under baseline scenario and r denotes the discount rate. The time t starts at year 2016, ends at year 2050.

A.2.2 Welfare Improvement for Side Payment Analysis

On the other hands, the global welfare improvement to analyse the affordability of side payment uses slightly different approach. Benefit of mitigation through temperature is now calculated relative to non-abatement case. This phase ensures positive contribution on each unilateral mitigation policy.

dy dy  dy  (52)  NET  AB  NON_ AB

t dy()()t dy NET Y t PV_ SP  t (53) i t t 0 (1 r )

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