The economics of greenhouse gas mitigation in developing Asia

Aleluia Reis, Lara; Emmerling, Johannes; Tavoni, Massimo; Raitzer, David Working Paper The economics of greenhouse gas mitigation in developing Asia ADB Economics Working Paper Series, No. 504 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Aleluia Reis, Lara; Emmerling, Johannes; Tavoni, Massimo; Raitzer, David (2016) : The economics of greenhouse gas mitigation in developing Asia, ADB Economics Working Paper Series, No. 504, Asian Development Bank (ADB), Manila This Version is available at: https://hdl.handle.net/10419/169335 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org The Economics of Greenhouse Gas Mitigation in Developing Asia This study uses an economy–energy–climate model to assess the long-term effects of Paris Agreement pledges on developing Asia, in comparison with business as usual and more ambitious scenarios to limit warming to 2°C. It finds potential for modest macroeconomic costs of ambitious mitigation, but that clean energy investment needs are substantial. When costs, benefits of avoided climate change, and cobenefits are considered together, investment in mitigation policy is found to have substantial economic returns for the region—if action is taken rapidly and international carbon market mechanisms are implemented to allow mitigation to occur where it is least costly. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to a large share of the world’s poor. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ADB ECONOMICS WORKING PAPER SERIES NO. 504 December 2016 THE ECONOMICS OF GREENHOUSE GAS MITIGATION IN DEVELOPING ASIA Lara Aleluia Reis, Johannes Emmerling, Massimo Tavoni, and David Raitzer ADB Economics Working Paper Series The Economics of Greenhouse Gas Mitigation in Developing Asia Lara Aleluia Reis, Johannes Emmerling, Massimo Tavoni, and David Raitzer No. 504 | December 2016 Lara Aleluia Reis ([email protected]) is a junior researcher, Johannes Emmerling ([email protected]) is a senior researcher, and Massimo Tavoni (massimo.tav[email protected]) is the deputy coordinator of the Climate Change and Sustainable Development research program at Fondazione Eni Enrico Mattei and Centro EuroMediterraneo sui Cambiamenti Climatici. David Raitzer ([email protected]) is an economist at the Economic Research and Regional Cooperation Department of the Asian Development Bank. Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2016 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 632 4444; Fax +63 2 636 2444 www.adb.org Some rights reserved. Published in 2016. Printed in the Philippines. ISSN 2313-6537 (Print), 2313-6545 (e-ISSN) Publication Stock No. WPS168585-2 Cataloging-In-Publication Data Asian Development Bank. The economics of greenhouse gas mitigation in developing Asia. Mandaluyong City, Philippines: Asian Development Bank, 2016. 1. Climate change.2. Energy.3. Greenhouse gas.4. Mitigation.5. Paris Agreement. I. Asian Development Bank. The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. 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ADB does not endorse this work or guarantee the accuracy of the data included inthis publication and accepts no responsibility for any consequence of their use. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Notes: 1. In this publication, “$” refers to US dollars. 2. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda CONTENTS TABLES AND FIGURES iv ABSTRACT vi I. BACKGROUND AND OBJECTIVES 1 II. METHODOLOGY AND STUDY DESIGN 3 A. The Business-as-Usual Scenario 4 B. Scenario Matrix 4 C. Implementation 6 III. CLIMATE EFFECTIVENESS AND REGIONAL EMISSIONS GAPS 7 IV. THE TRANSFORMATION CHALLENGE: ENERGY SYSTEM AND LAND-USE CHANGE 11 V. POTENTIAL CARBON MARKET DEVELOPMENT 18 VI. POLICY COSTS 20 VII. AIR POLLUTION COBENEFITS 21 VIII. NET COSTS AND BENEFITS OF MITIGATION POLICIES 25 IX. CONCLUSIONS 28 APPENDIX 29 REFERENCES 33 TABLES AND FIGURES TABLES 1 Kaya Emissions and Drivers for 2011 1 2 Scenario Matrix Description 5 3 Sales (Positive) or Purchases (Negative) of Emissions Permits in the INDC Scenario with Carbon Trade 20 Appendix Table: The World Induced Technical Change Hybrid Model Regions 33 FIGURES 1 Average Annual Growth Rate of Carbon Dioxide Equivalent Greenhouse Gas Emissions, 1990–2012 1 2 SSP2‒BAU Characterization of GHG Emissions, GDP, and Population across WITCH Regions 4 3 Greenhouse Gas Emissions from Asia and the Rest of the World under the Business-as-Usual Scenario 7 4 Emissions Trajectories of the INDC Scenarios and of the IPCC AR5 Scenarios Consistent with 2°C at the End of the Century 8 5 Developing Asia Emissions by Scenario 9 6 Global Mean Temperature Increase across the Scenarios 10 7 Regional Emissions Reductions with Respect to the Business-as-Usual Baseline 10 8 Global CO2 Emissions from LULUC and Fossil Fuels and Industrial Processes across WITCH Regions 11 9 Decomposition of Mitigation Sources in the INDC Scenario versus the INDC to 2°C Scenario 12 10 Forest Cover in Developing Asia 13 11 Yearly Energy and Carbon Intensity Improvements, 2010–2050 14 12 Primary Energy Mix in Developing Asia 14 13 Future Electricity Mix in Developing Asia 15 14 Capital Costs for Energy Technologies by Scenario in 2050 17 15 Investment in Power Supply in Developing Asia, 2015–2050 17 16 Marginal Abatement Costs in the INDC Scenario 18 17 Sales (Positive) or Purchases (Negative) of Emissions Permits in Value in the INDC Scenario with Carbon Trade 19 18 Economic Cost of Emissions Reduction as a Percentage of GDP in Developing Asia 20 19 2050 Marginal Abatement Costs Discounted at 5% (Left) and Policy Costs by Region for 2030 and 2050 (Right) 21 20 Regional Emissions Profiles of Sulfur Dioxide in Asia 22 21 Sector Emissions of Sulfur Dioxide for the People’s Republic of China and India 23 22 Estimation of Premature Deaths Estimation in 2030 and 2050 due to Particulate Matter and Ozone Pollution (Left) and Change in 2050 Premature Deaths from Air Pollution Relative to a Scenario without New Pollution Control (Right) 24 23 Reduction in 2050 Crop Losses from Ozone Pollution in Developing Asia by Modeled Scenario 25 24 Economic Losses from Higher Temperature in Developing Asia 26 25 Gains from Mitigation in 2050 by Region under Modeled Scenarios 26 26 Net Annual Costs and Benefits of Modeled Mitigation Scenarios for Developing Asia 27 Appendix Figure: The World Induced Technical Change Hybrid Model Structure 32 BOX FIGURES 1 Cumulative Carbon Capture and Storage 16 2 Policy Cost (GDP) CCS 16 ABSTRACT Developing Asia has the world’s fastest greenhouse gas emissions growth. This study uses an economy–energy–climate model to assess the effects of Paris Agreement pledges on Asia, in comparison with business as usual (BAU) and more ambitious scenarios. Results confirm that pledges must be strongly increased in ambition to achieve the Paris Agreement’s goal of less than 2 degrees Celsius (2°C) warming. The policy costs of Asia’s pledges are found to be less than 1% of gross domestic product (GDP) through 2050, while 2°C scenarios may cost less than 2% of GDP. However, costs are sensitive to assumptions about international carbon markets and mitigation timing, with costs for 2°C scenarios doubling in the absence of carbon trade, and increasing the later that mitigation is initiated. Under the 2°C scenarios, annual average energy supply investments are about $300 billion above the BAU levels through 2050. Mitigation policy may substantially reduce air pollution mortality, with up to 600,000 fewer deaths in Asia annually by 2050. When costs, benefits of avoided climate change, and cobenefits are considered together, investment in mitigation policy is found to have substantial economic returns for the region—if action is taken rapidly and international carbon market mechanisms are implemented. Keywords: climate change, energy, greenhouse gas, mitigation, Paris Agreement JEL codes: C61, D58, Q52, Q53, Q54 I. BACKGROUND AND OBJECTIVES Among world regions, developing Asia has had the most rapid greenhouse gas (GHG) emissions growth globally (Figure 1). Between 1990 and 2012, emissions grew at more than 4% annually, whereas no other region had growth above 4% for the period. Much of this emissions growth has been driven by the energy system that is becoming more carbon intensive, even as economies in the region have grown quickly. Figure 1: Average Annual Growth Rate of Carbon Dioxide Equivalent Greenhouse Gas Emissions, 1990–2012 Source: CAIT Climate Data Explorer. 2015. Washington, DC: World Resources Institute. http://cait.wri.org (accessed August 3, 2016). Despite substantial GHG emissions of 20 gigatons of carbon dioxide equivalent (GtCO2e) in 2012, per capita income in developing Asia remained below the world average, as did many aspects of energy access (Table 1). With large natural resource-dependent poor populations living in volatile climates, it is also a region with high vulnerability to climate change. This means that the region has a large stake in climate change policy, both in terms of the transformation that emissions reduction may entail and the costs that the region may bear if climate change is not contained. Table 1: Kaya Emissions and Drivers for 2011 Emissions (GtCO2e) Population (billion) Income ($ ’000 per capita, PPP) Energy Intensity (MJ/$ PPP) Carbon Intensity (kg CO2/kg Oil equivalent) South Asia, including India 3.5 1.7 4.4 5.1 2.7 Rest of East Asia, Southeast Asia, and the Pacific (ESEAP), including Indonesia and the People’s Republic of China 14.9 2.0 9.5 7.6 3.3 World 52.0 7.0 13.4 5.9 2.6 CO2 = carbon dioxide, GtCO2e = gigaton of carbon dioxide equivalent, kg = kilogram, MJ = megajoule, PPP = purchasing power parity. Source: World Bank. 2015. “World Development Indicators.” http://data.worldbank.org/data-catalog/world-development-indicators -2 0 2 4 6 Developing Asia Middle East and North Africa Sub-Saharan Africa Latin America and the Caribbean Japan, Australia, and New Zealand North America Europe %, growth rate 8 | ADB Economics Working Paper Series No. 504 Figure 4: Emissions Trajectories of the INDC Scenarios and of the IPCC AR5 Scenarios Consistent with 2°C at the End of the Century °C = degree Celsius, AR5 = Fifth Assessment Report, GHG = greenhouse gas, GtCO2 = gigaton of carbon dioxide, INDC = intended nationally determined contribution, IPCC = Intergovernmental Panel on Climate Change. Source: Authors’ estimates. This ratio appears to be relatively constant over time which means that emissions gaps grow in absolute values during the period analyzed. Based on these results, the INDCs reduce emissions by 33% in 2050 (compared to almost 64% in the 2°C scenario) and 52% by 2100 (compared to a required 87% reduction in the 2°C scenario). In order to get a sense of how likely the INDC scenarios are compatible with the 2°C target, the percentage of IPCC AR5 2°C scenarios that fall at or above the INDC pathway are considered. For 2030, only about 15% of the IPCC scenarios show emissions equal or greater than those consistent with INDCs. By the middle of the century, this number is further reduced to around 0.5%. Patterns evident at the global scale are repeated within the region. Asia’s INDCs are a substantial indication of a potential commitment from the region to curtail GHG emissions. However, like the INDCs of other regions, they are insufficient to put the world on a pathway to a maximum of 2°C warming or to ensure that Asian emissions peak in the 2020s as the 2°C path demands. INDCs lead to emissions mitigation of less than half by 2050, which falls short of the 70% reduction or more that is necessary under the scenarios to limit temperature rise to 2°C (Figure 5). An economically optimal pathway to keep warming below 2°C would start action more quickly than the INDCs, as mitigation in 2030 needs to be more than double what the INDCs specify. 0 % 19 % 49 % 0 % 68 % 33 % 64 % 0 % 89 % 52 % 87 % AR5 2C 0 25 50 75 100 2010 2030 2050 2080 Kyoto gas (GtCO2) emission reductions (%) Policy Business as usual Optimal 2°C INDC to 2°C INDC to 2°C with trade INDC INDC with trade INDC position − World GHG − for 2°C The Economics of Greenhouse Gas Mitigation in Developing Asia | 9 Figure 5: Developing Asia Emissions by Scenario °C = degree Celsius, CO2 = carbon dioxide, INDC = intended nationally determined contribution. Source: Authors’ estimates. The MAGICC climate model is applied to estimate the global average temperature increase in 2100. This estimation takes into account the air pollution emissions pathways foreseen in the SSP2 scenario.3 The emissions pathways of the air pollutants will be discussed in more detail in section VI. Nevertheless, it is important to note that a decreasing pollution emissions pathway leads to higher efforts for mitigation in the 2°C scenarios, because the reduction of aerosols with negative radiative forcing provokes an increase in temperature. Therefore, the 2°C scenarios correspond to a range of GHG concentrations of 515–520 parts per million. The temperature patterns shown in Figure 6 indicate that the BAU baseline would lead to an end-of-century temperature increase of approximately 3.9°C. The INDC continuation scenarios reduce average global temperature by around 1°C to around 2.9°C, which is still far from the 2°C goal of the Paris Agreement. There is much heterogeneity in the magnitude of the reductions for countries in developing Asia. Figure 7 shows the regional emissions reductions in the different scenarios. India and East Asia, Southeast Asia, and the Pacific are the regions that have lower reductions from INDCs. On the other side of the spectrum is Indonesia, which has 2030 pledges that are closest to what would be optimal under the 2°C scenario. Overall, this analysis shows that the INDCs represent a significant effort in terms of short-term emissions reductions, but that the continuation of similar efforts post-2030 is not sufficient for the 2°C goal. A ramping up of efforts beyond the submitted INDCs is needed to keep global warming below 2°C. 3 For further information on the air pollutant emissions, please refer to Rao et al. (2016). 0 10 20 30 40 50 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 INDC INDC to 2°C Optimal 2°C with trade Business as usual Billion tons of CO 2 equivalent/year 10 | ADB Economics Working Paper Series No. 504 Figure 6: Global Mean Temperature Increase across the Scenarios °C = degree Celsius. Source: Authors’ estimates. Figure 7: Regional Emissions Reductions with Respect to the Business-as-Usual Baseline °C = degree Celsius; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution; PRC = People’s Republic of China. Source: Authors. 0 1 2 3 4 2010 2030 2050 2080 2100 Temperature (°C) Policy Business as usual Optimal 2°C INDC to 2°C INDC to 2°C with trade INDC INDC with trade 0 20 40 60 80 100 PRC ESEAP India Indonesia South Asia Reduction (%) INDC INDC to 2°C INDC_average INDC to 2°C average The Economics of Greenhouse Gas Mitigation in Developing Asia | 11 IV. THE TRANSFORMATION CHALLENGE: ENERGY SYSTEM AND LAND-USE CHANGE Globally, the BAU scenario finds an expected doubling of emissions by 2050. As discussed, aiming at a long-term stabilization of global warming at or below 2°C will therefore require rapid deceleration of this increase, a decrease of emissions in the medium term, and ultimately (almost) full decarbonization of the economy. The energy and industry sectors (fossil fuels and industry) bear the highest mitigation share in absolute terms, even though land use and land-use change and agricultural emissions will contribute negative emissions after 2040 (Figure 8). Figure 8: Global CO2 Emissions from LULUC and Fossil Fuels and Industrial Processes across WITCH Regions °C = degree Celsius; CAJAZ = Canada, Japan, and New Zealand; CO2 = carbon dioxide; GtCO2 = gigaton of carbon dioxide; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution; LACA = Latin America, Mexico, and the Caribbean; LULUC = land use and land-use change; MENA = Middle East and North America; PRC = People’s Republic of China; SSA = Sub-Saharan Africa; TE = transition economies; US = United States; KOSAU = Republic of Korea, South Africa, and Australia; WITCH = World Induced Technical Change Hybrid. Source: Authors’ estimates. The distribution of mitigation efforts between the different sectors is very region specific: several regions including Europe, India, and the PRC show negative emissions from land-use change today and in the future. In other regions, including many Asian economies, land-use change contributes significantly to GHG emissions: in 2005, land use and land-use change contributed about 74% of CO2 emissions in Indonesia, about 19% in the rest of East Asia, and 18% in South Asia excluding India. 12 | ADB Economics Working Paper Series No. 504 The specific means of mitigation for the INDC scenario and the additional effort to comply with 2°C can be seen in Figure 9, as derived from the decomposition using an additive mean Divisia index. Energy efficiency improvements contribute around a third to overall mitigation in both scenarios, while non-CO2 gases can yield emissions reductions especially in the shorter term. Land-use emissions reductions are an important abatement option, especially in Indonesia. The reduction of fossil fuel and industrial emissions and the use of carbon capture and storage, however, provide the largest potential under the 2°C scenario. Under the INDC to 2°C scenario, fuel substitution generates about a third of developing Asian 2050 mitigation, and carbon capture and storage (CCS) contributes about 14%. Figure 9: Decomposition of Mitigation Sources in the INDC Scenario versus the INDC to 2°C Scenario °C = degree Celsius, GtCO2 = gigaton of carbon dioxide, INDC = intended nationally determined contribution, PRC = People’s Republic of China. Notes: Emissions reductions from business as usual to the INDC (dark colors) and additionally the INDC to 2°C scenario (shaded colors). INDC to 2°C values are additional to those of INDC, such that they sum to total INDC to 2°C mitigation when considered together. Source: Authors’ estimates. The Economics of Greenhouse Gas Mitigation in Developing Asia | 13 Patterns of mitigation are similarly reflected in changes in forest cover outcomes, where changes are concentrated in a few regions. By 2050, the INDC to 2°C scenario produces 45 million additional hectares of forest, most of it in Indonesia and the rest of Southeast Asia. The INDC scenario produces a much smaller addition of 5 million hectares of forest by that time (Figure 10). Figure 10: Forest Cover in Developing Asia (million hectare) °C = degree Celsius; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution; PRC = People’s Republic of China. Notes: Forest cover changes under modeled mitigation scenarios relative to business as usual. Values are additive between the INDC scenario and the INDC to 2°C scenario. Source: Authors’ estimates. Expected energy intensity improvements in Asia under BAU are expected to be around half a percentage point per year (compared to a global historical average of about 1.1% per year), reflecting a comparably higher level of economic growth. Under the INDC and 2°C scenarios, this improvement is increased. Similar to the decarbonization in the energy sector, the carbon intensity improvement reaches around 0.4% per year in this scenario across regions (Figure 11). Structural change in the energy system is pivotal for achieving climate goals. Figure 12 shows the primary energy mix in the Asian regions. Historically, there is variability across regions, which persists over time. However, in the INDC, and even more so in the INDC to 2°C scenarios, renewable energy sources are increasingly phased in. In the PRC and India, a substantial reduction in coal consumption will be required starting by 2020 and all mitigation scenarios show a declining share of coal after 2030. The substitute in these countries is mostly renewable energy sources, notably wind and solar energy. In the PRC, the high availability of wind potential and its low investment cost make onshore and (later) offshore wind gain a significant share by 2030. In East Asia and South Asia, higher use of bioenergy and natural gas allows a somewhat slower restructuring of the energy system with expanded use of both energy sources. All scenarios still find continued use of oil, mainly in the transportation sector, where demand is projected to rise significantly and cost-effective substitutes are most likely to penetrate the market only later in the century. 14 | ADB Economics Working Paper Series No. 504 Figure 11: Yearly Energy and Carbon Intensity Improvements, 2010–2050 °C = degree Celsius; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution; PRC = People’s Republic of China. Source: Authors’ estimates. Figure 12: Primary Energy Mix in Developing Asia °C = degree Celsius; EJ = exajoule; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution, PRC = People’s Republic of China. Source: Authors’ estimates. The Economics of Greenhouse Gas Mitigation in Developing Asia | 15 Electricity demand decreases in the INDC scenario, but is not much lower in the INDC to 2°C scenario, due to more use of electricity in transport. Moreover, as mitigation progresses in the INDC to 2°C scenario, the electricity mix becomes less carbon intensive through the use of renewable energy sources, increasing biomass use, and to some extent the use of natural gas with CCS after 2030 and, later in the century, coal with CCS and biomass with CCS (Figure 13). Although under this scenario, CCS makes up a substantial share of the energy mix in some regions, results are not highly sensitive to CCS cost assumptions (see text box). Figure 13: Future Electricity Mix in Developing Asia °C = degree Celsius; CCS = carbon capture and storage; EJ = exajoule; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution; PRC = People’s Republic of China. Source: Authors’ estimates. 16 | ADB Economics Working Paper Series No. 504 Testing Cost Assumptions for Carbon Capture and Storage Carbon capture and storage (CCS) can have an important role in deep decarbonization as it can reconcile continued use of fossil fuels with greenhouse gas mitigation. At the same time, CCS is an unproven technology with high costs, due to energy penalties for carbon capture. There are also many uncertainties about the long-term cost of the technology were it to be deployed widely. In the intended nationally determined contribution (INDC) to 2°C scenario, global CCS amounts to about 12 gigatons of carbon dioxide (GtCO2) by 2050. To explore the sensitivity of results to assumptions on CCS, this study ran an additional scenario to consider more optimistic reductions of the costs of CCS for coal, gas, and biomass (implying roughly a reduction by 1% per year of the investment costs), denoted INDC to 2°C_CCS. In this scenario, for the People’s Republic of China, the capital cost per kilowatt of installed capacity for a coal-fired power plant with integrated gasification combined cycle and CCS decreases from $3,170 to $2,120 in 2050 and $1,280 by 2100, compared to the cost of a standard pulverized coal plant at about $970 per kilowatt, which is in line with projected total capital costs of CCS (Havlík et al. 2011; Rubin, Davison, and Herzog 2015). In terms of carbon prices and policy costs, this reduces the costs of climate mitigation (net present value of gross domestic product loss discounted at 5% until 2100) by roughly 10%, which indicates only modest WITCH sensitivity to CCS cost assumptions. Note that these annual figures translate into a cumulative amount of carbon dioxide to be permanently stored by the end of the century of around 2,050 GtCO2, compared with 1,100 GtCO2 in the standard case. This can be compared to a global storage capacity that has been estimated in the range of about 1,000–10,000 GtCO2 (see IPCC 2005; Hendriks, Graus, and van Bergen 2004). °C = degree Celsius; GDP = gross domestic product; ESEAP = East Asia, Southeast Asia, and the Pacific; NPV = net present value; PRC = People’s Republic of China; WITCH = World Induced Technical Change Hybrid. Source: Authors. Learning by doing for renewable energy sources, such as wind and solar, leads to a persistent decline of the costs of these two technologies, as shown in Figure 14. For wind, the investment costs are already much lower in leading countries with high potential. The intermittency of these sources means that storage capacity rises to deal with intermittency issues (Carrara and Marangoni 2016). For example, the INDC to 2°C scenario for the PRC requires an investment in electricity storage of about 800 gigawatts (roughly 10% of total installed capacity) by midcentury. Although storage technologies are currently expensive (around $6,000 per kilowatt), costs decline to about $1,000 per kilowatt by 2050.4 4 All values are in 2005 US dollars. 0 500 1,000 1,500 2,000 2025 2050 2075 2100 year GtCO2 Business as usual INDC to 2°C with trade INDC to 2°C with trade (low CCS) Box Figure 1: Cumulative Carbon Capture and Storage 2.4% 0.8% 1.6% −1.2% 5% 6% 3.4% 3.7% 2.3% −1.7% 2.7% 2.5% –2 0 2 4 6 % of GDP (NPV) INDC to 2°C (low CCS) INDC to 2°C with trade Box Figure 2: Policy Cost (GDP) CCS ESEAP India Indonesia PRC South Asia WORLD The Economics of Greenhouse Gas Mitigation in Developing Asia | 17 Figure 14: Capital Costs for Energy Technologies by Scenario in 2050 °C = degree Celsius, CC = combined cycle, CCS = carbon capture and storage, CSP = concentrated solar power, INDC = intended nationally determined contribution, PC = pulverized coal, PV = photovoltaic. Source: Authors’ estimates. A dramatic transition for developing Asia’s energy system depends on redirected investment in energy supply. Cumulative energy supply investments from 2015 to 2050 are just over $23 trillion (or $670 billion per year) under BAU and rise to nearly $34 trillion in aggregate for the INDC to 2°C and optimal 2°C scenarios (or about $300 billion more per year). Most of this is directed toward increased investment in renewables, use of carbon capture and storage, the energy grid, and energy storage, which account for $17 trillion under the INDC to 2°C scenario, but about $7 trillion of this is offset by reduced investment in fossil fuels, leaving a net increase of about $10 trillion, or nearly $300 billion per year on average over the period. The additional investment required under the INDC scenario is a bit over half of that of the INDC to 2°C scenario, with most of the additional investment in renewables and the grid. Figure 15: Investment in Power Supply in Developing Asia, 2015–2050 °C = degree Celsius, CCS = carbon capture and storage, INDC = intended nationally determined contribution. Source: Authors. 0 1 2 3 4 5 6 Coal GCC with CCS Coal PC without CCS Gas CC with CCS Gas CC without CCS Solar CSP Solar PV Wind 2010 INDC INDC to 2°C INDC to 2°C under low cost CCS $ per thousand kilowatt 0 10 20 30 40 Business as usual INDC INDC to 2°C Optimal 2°C Oil Extraction Fossil Fuels Fossils with CCS Grid and Storage Nuclear Renewables Research Real 2005 $ trillion 24 | ADB Economics Working Paper Series No. 504 These emissions scenarios are applied to estimate future concentrations of air pollutants using a fixed average future meteorological field, and concentrations are applied to estimate the number of premature deaths and effects on crop yields. Air pollution impacts on mortality are concentrated in the populated areas of India, the PRC, and, to a lower extent, other countries in Southeast Asia. Figure 22 portrays premature deaths due to PM in developing Asia in 2030 and 2050 in the absence of improved pollution control. Were pollution control not to improve, the number of annual deaths from pollution in developing Asia would nearly double from under 3 million to well over 5 million, with a majority of these in the PRC. Improved air pollution controls, or “end-of-pipe” measures achieve important reductions, but are insufficient to offset the mortality increase over time, as seen by the difference between the BAU‒FLE and the other scenarios. The 2°C scenarios add important additional mortality reductions of another 550,000 deaths by 2050. After 2030, climate policies can effectively attain significant health cobenefits. Figure 22: Estimation of Premature Deaths Estimation in 2030 and 2050 due to Particulate Matter and Ozone Pollution (Left) and Change in 2050 Premature Deaths from Air Pollution Relative to a Scenario without New Pollution Control (Right)  °C = degree Celsius; ESEAP = East Asia, Southeast Asia, and the Pacific; INDC = intended nationally determined contribution; PRC = People’s Republic of China. Notes: Mortality due to air pollution covers ozone and particulate matter pollution only. Source: Authors’ estimates. Mortality reductions can be valued using value of statistical life (VSL) approaches. A leading source of VSL estimates is the US Environmental Protection Agency, which periodically conducts surveys of estimates in the economic literature to inform VSL estimates used in regulatory cost– benefit analysis. The current agency-recommended VSL is $9.7 million for a mortality event in 2013 (EPA 2016). This VSL is adjusted in relation to GDP for regions in the model and is applied to value mortality changes arising from mitigation. Using this approach finds a mortality cobenefit value in the order of 2.5%–2.7% of GDP for developing Asia by 2050 as a benefit of the 2°C scenarios, over and above improved air pollution control from end-of-pipe measures. The INDC scenario leads to benefits in the order of 1% of GDP. Air pollution also causes vegetation damage, which reduces crop production and quality (Van Dingenen et al. 2009). As productivity growth in agriculture is slowing, the world faces challenges for 0 2 4 6 2010 2030 2050 South Asia PRC ESEAP Indonesia India Premature deaths annually 0 1,000 2,000 3,000 Optimal 2°C INDC to 2°C INDC Air pollution control Lives saved annually ('000) PRC ESEAP India Indonesia South Asia The Economics of Greenhouse Gas Mitigation in Developing Asia | 25 food production to keep pace with food demand, and additional crop losses due to pollution may exacerbate this. Ozone is the main pollution cause of crop damage, and leads to significant economic losses, since it can be transported over long distances (Van Dingenen et al. 2009; Fuhrer 2009; Avnery et al. 2011). The impact of mitigation scenarios on O3-caused crop losses is shown in Figure 23. Pollution has differentiated effects according to the type of crops, regions, and growing seasons. The highest losses are observed for wheat, both globally and in Asia, which accounts for approximately half of the global crop losses. In developing Asia, rice losses are nearly as large as wheat. Improved air pollution control can be effective in reducing a substantial share of these losses. Gains from climate policy principally occur under the 2°C scenarios, where, gains amount to about 4 million tons of rice and 2.5 million tons of wheat in 2050 over and above improved air pollution control. Figure 23: Reduction in 2050 Crop Losses from Ozone Pollution in Developing Asia by Modeled Scenario °C = degree Celsius, INDC = intended nationally determined contribution. Source: Authors’ estimates. VIII. NET COSTS AND BENEFITS OF MITIGATION POLICIES This primary direct intended benefit of climate change mitigation is to reduce losses from climate change. Lee, Villaruel, and Gaspar (forthcoming) performed econometric analysis of panel data to derive relationships between temperature change and GDP. Coefficients from this analysis were used to parameterize a damage function, and that function used temperature projections derived from these WITCH scenarios to assess potential future additional losses from climate change (Figure 24). The analysis finds that economic losses under BAU from climate change may exceed 10% of GDP by 2100 and may reach nearly 4% of GDP by 2050 (Figure 25). Mitigation achieved under the modeled scenarios has the potential to limit this damage. Under the 2°C scenarios, losses from climate change are limited to about 2% of GDP over the long term, while the INDC scenario leads to a 6% GDP loss by 2100. This means a gain of 8% of GDP under the 2°C scenarios and a gain of 4% under the INDC scenario in terms of reduced climate change impact. 0 10 20 30 40 Rice Wheat Maiza Soybean Air pollution control INDC INDC to 2°C Million tons annually 26 | ADB Economics Working Paper Series No. 504 Figure 24: Economic Losses from Higher Temperature in Developing Asia °C = degree Celsius, GDP = gross domestic product, INDC = intended nationally determined contribution. Source: Minsoo Lee, Mai Lin Villaruel, and Raymond Gaspar. 2016. Effects of Temperature Shocks on Economic Growth and Welfare in Asia. ADB Economics Working Paper Series No. 501. Most of these gains are concentrated in India, in East Asia, Southeast Asia, and the Pacific, and in Indonesia (Figure 25). In the PRC, gains are smaller and climate vulnerability is lower. As the PRC accounts for a large share of developing Asian GDP, this means that much of developing Asia’s population, especially in poorer areas, experiences more than the mean gains shown in the previous figure. Figure 25: Gains from Mitigation in 2050 by Region under Modeled Scenarios °C = degree Celsius; ESEAP = East Asia, Southeast Asia, and the Pacific; GDP = gross domestic product; INDC = intended nationally determined contribution; PRC = People’s Republic of China. Source: Authors’ estimates. -15 -10 -5 0 2020 2030 2040 2050 2060 2070 2080 2090 2100 GDP losses from climate change (%) INDC to 2°C Business as usual INDC Optimal 2°C 0 1 2 3 4 PRC South Asia Indonesia ESEAP India % of GDP Gains under INDC Additional gains under optimal 2°C The Economics of Greenhouse Gas Mitigation in Developing Asia | 27 These estimates of economic benefits from less climate change can be combined with air quality cobenefits and policy costs to explore how mitigation policy affects developing Asia on balance. When benefits and costs are combined, the annual net cost rarely reaches 1% of GDP, even in the most costly scenarios (Figure 26). Strikingly, benefits exceed costs most quickly in the optimal 2°C scenario with carbon trade. In the other scenarios, benefits exceed costs by the mid-2030s to 2040s. By 2050, the net effect on GDP is a 2%–3% gain for the 2°C scenarios with trade, with the highest gain in the optimal 2°C scenario. INDC mitigation has about half of these gains. The lowest gain is in the INDC 2°C scenario without carbon trade. Figure 26: Net Annual Costs and Benefits of Modeled Mitigation Scenarios for Developing Asia °C = degree Celsius, GDP = gross domestic product, INDC = intended nationally determined contribution. Note: Net annual costs and benefits as percentage of GDP (left) and in absolute amounts under the optimal 2°C scenario (right). Source: Authors’ estimates. As GDP grows over time, effects in levels are even larger than in terms of shares of GDP. In all scenarios, benefits exceed costs during the 21st century. In the optimal 2°C scenario, each dollar of economic cost generates $2.2 of benefits in present value terms (discounted at 5%). Moreover, benefits are greater than costs within a decade of strong mitigation action. Initially, cobenefits generate the largest share of gains, while the effects of avoided climate change dominate benefit streams after 2050. Compared with the optimal 2°C scenario, the INDC to 2°C scenario with trade effectively delays the onset of ambitious mitigation from 2020 to 2030, which reduces its payoff. In this scenario, each dollar of economic cost creates $1.5 of present value benefits. Removing trade further lowers this to just over $1 of benefits per dollar of economic cost because the economic costs of mitigation rise further for developing Asia. The INDC scenario, because of lower costs under declining marginal benefits from mitigation, has a higher relative benefit–cost ratio, with $2.6 of benefits per dollar of economic cost, but it ultimately generates less than half of the total benefits of the 2°C scenarios. The difference in absolute flows of benefits compared with costs is better reflected in an internal rate of return (IRR). When this is calculated on the flows of net costs and benefits in levels, the optimal 2°C scenario has an impressive IRR of 22%. The INDC to 2°C scenario with trade has an IRR of 11%, as does the INDC scenario. The INDC to 2°C scenario without trade has an IRR of 7%, far less than what can be achieved with trade and/or earlier action. These results underscore that coordinated, -2.0% -1.0% 0.0% 1.0% 2.0% 3.0% 2015 2020 2025 2030 2035 2040 2045 2050 Net annual costs/benefits of policy ( % of GDP) INDC INDC to 2°C INDC to 2°C with trade Optimal 2°C -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2015 2020 2025 2030 2035 2040 2045 2050 Economic costs of optimal 2°C Cobenefits of optimal 2°C Climate benefits of optimal 2°C Real 2005 $ trillion 28 | ADB Economics Working Paper Series No. 504 early, and ambitious action has the potential to more than double the returns to the region from investment in climate change mitigation. IX. CONCLUSIONS This study has assessed a range of possible future international climate policy architectures using the WITCH integrated economy–energy–climate model. It has simulated a continued bottom–up architecture based on gradual strengthening of the currently proposed INDCs as well as cooperative climate agreement compatible with limiting temperature rise to 2°C. In so doing, this has contrasted policy architectures with complete fragmentation with cases of full harmonization of marginal abatement costs via global carbon markets. Several insights emerge from this analysis. First, the currently proposed INDCs need to be aggressively ratcheted up in terms of their mitigation ambitions to achieve the emissions reductions for a temperature rise of 2°C or less. The INDC scenario is found to generate around half of the mitigation requirements for this goal. Even under INDCs, the energy and land-use systems of developing Asia would need a transformation toward low-carbon energy and phasing out of land-use emissions. All mitigation scenarios show coal energy peaking by the end of the 2020s. Further, renewable deployment responds rapidly to mitigation policy. INDCs under the Paris Agreement are found to generate a large range of marginal abatement costs. This price discrepancy is a source of economic inefficiency, which global carbon trade can reduce. Price harmonization would result in a significant trading volume of up to 4 GtCO2 in 2030, corresponding to monetary exchanges of $50 billion. This would require major institutional efforts to ensure emissions reductions are additional and verifiable. As a first step, regional trading systems of smaller size could be linked and have the potential to yield important efficiency gains within developing Asia. For developing Asia, policy costs of INDCs are found to stay below 1% of GDP. Achieving less than 2°C of warming would lead to a GDP cost of 2% by 2050 with trade in place and has a substantially lower cost if action starts earlier. In terms of air pollution, large reductions are possible through end-of-pipe pollution regulations, but ambitious climate policies further achieve a reduction of the main air pollutants, leading to up to 600,000 additional avoided cases of premature deaths in developing Asia by 2050. Valuation of this mortality suggests that this may equate to 2.5% of GDP. Effects on crops are more minor but still imply benefits of millions of tons of additional rice and wheat annually. While pollution cobenefits are largely concentrated in the PRC and India, benefits of avoided climate change are much more important in India, in Indonesia, and in East Asia, Southeast Asia, and the Pacific. At an aggregate level, avoided climate change benefits only exceed pollution benefits after about 2050. Considering climate benefits, cobenefits and the economic costs of climate policy together finds that ambitious coordinated climate policy is in the economic interest of developing Asia, with benefits vastly exceeding costs to the region. However, this outcome is sensitive both to delays in ambitious mitigation or to fragmentation of climate policy. This suggests that the region should support a rapid ramp-up of mitigation ambition far beyond that of the INDCs, with carbon market linkages in place among regions, if it is to capture substantial economic returns from investment in a low-carbon future. APPENDIX: THE WITCH MODEL–OVERVIEW WITCH (World Induced Technical Change Hybrid) is an integrated assessment model designed to assess climate change mitigation and adaptation policies.1 It is developed and maintained at Fondazione Eni Enrico Mattei and the Centro Euro-Mediterraneo sui Cambiamenti Climatici. WITCH consists of a dynamic global model that integrates in a unified framework the most important elements of climate change. The economy is modeled through an intertemporal optimal growth model which captures the long-term economic growth dynamics. A compact representation of the energy sector is fully integrated (hard linked) with the rest of the economy so that energy investments and resources are chosen optimally, together with the other macroeconomic variables. Land-use mitigation options are available through a soft link with a land-use and forestry model (GLOBIOM). A climate model (MAGICC) is used to compute the future climate. WITCH represents the world in a set of representative native regions (or coalitions of regions). For each, it generates optimal mitigation and adaptation strategies for the long term (from 2005 to 2100) as a result of a maximization process in which the welfare of each region (or coalition of regions) is chosen strategically and simultaneously accordingly to other regions. This makes it possible to capture regional free-riding behaviors and strategic interaction induced by the presence of global externalities. The noncooperative, simultaneous, open membership game with full information, is implemented through an iterative algorithm which yields the open-loop Nash equilibrium. In this game-theoretic setup, regional strategic actions interrelate through greenhouse gas (GHG) emissions, dependence on exhaustible natural resources, trade of oil and carbon permits, and technological research and development (R&D) spillovers. The endogenous representation of R&D diffusion and innovation processes constitute a distinguishing feature of WITCH, allowing to describe how R&D investments in energy efficiency and carbon-free technologies integrate the currently available mitigation options. The model features multiple externalities, both on the climate and the innovation side. The technology externalities are modeled via international spillovers of knowledge and experience across countries and time. In each country, the productivity of low-carbon mitigation technologies and of energy depend on the region stock of energy R&D and by the global cumulative installed capacity, two proxies for knowledge and experience, respectively. The R&D stock depends on domestic investments, domestic knowledge stock, and foreign knowledge stock through international spillovers. The spillover term depends on the interaction between the countries’ absorptive capacity and the distance of each region from the technology frontier. This formulation of technical change affects both decarbonization as well as energy savings. The energy sector is fully integrated with the rest of the economy. It distinguishes an electric sector, a transportation sector, and an aggregated nonelectric (industry, services, and residential) sectors. It has a rich array of fuel types and energy technologies (Figure A1.1). 1 Note that the appendix draws on the standard WITCH description at http://doc.witchmodel.org/general-framework.htm 30 | Appendix Appendix Figure: The World Induced Technical Change Hybrid Model Structure BACKnel = backstop for nonelectric energy, CCS = carbon capture and storage, COALnel = coal for nonelectric energy, EL = electric energy, EL2 = electricity generation without hydro, ELBACK = electricity generated with backstop, ELBIGCC = electricity generated with biomass with CCS, ELCOALBIO = electricity generated with coal and biomass, ELCSP = electricity generated with concentrated solar power, ELFF = fossil fuel electricity, ELFFREN = electricity generated with fossil fuels and renewables, ELGAS = electricity generated with gas, ELGASCCS = electricity generated with gas with CCS, ELGASTR = electricity generated with gas turbines, ELHYDRO = electricity generated with hydroelectric power, ELIGCC = sum of electricity generated through integrated gasification combined cycle (IGCC) with CCS based on coal and biomass, ELCIGCC = electricity generated with coal IGCC plus CCS, ELNUKE = electricity generated with nuclear, ELNUKE&BACK = electricity generated with nuclear and backstop, ELOIL = electricity generated with oil, ELPB = electricity generated with biomass, ELPC = electricity generated with pulverized coal, ELPV = electricity generated with photovoltaics, ELW&S = electricity generated with wind and solar, ELWIND = electricity generated with wind energy, EN = Energy, EPC = electricity generated with coal and biomass without CCS, ES = energy services, GASnel = gas for nonelectric energy, K = capital invested in the production of final good, KL = capital–labor aggregate, L = labor, NEL = nonelectric labor, OGB = oil, backstop, gas, and biofuel, OIL&BACK = oil and backstop for nonelectric energy, OILnel = oil for nonelectric energy without transportation (industrial and residential), OUTPUT = gross domestic product, RDEN = energy research and development capital, Trad Bio = traditional (1st generation) biofuels, TradBiom = traditional biomass, WINDOFF = electricity generated with offshore wind, WINDON = electricity generated with onshore wind. Source: Johannes Emmerling, Laurent Drouet, Lara Aleluia Reis, Michela Bevione, Loic Berger, Valentina Bosetti, Samuel Carrara, Enrica De Cian, Gauthier De Maere D'Aertrycke, Tom Longden, Maurizio Malpede, Giacomo Marangoni, Fabio Sferra, Massimo Tavoni, Jan Witajewski-Baltvilks, and Petr Havlik. 2016. The WITCH 2016 Model - Documentation and Implementation of the Shared Socioeconomic Pathways. https://ideas.repec.org/p/fem/femwpa/2016.42.html OUTPUT ES EN K L NEL EL RDEN ELHYDRO EL2 0.5 1 0.5 1.6 0.66 2 22 5 ҙ ҙ ҙ ҙ ҙ ҙ ҙ ҙҙ TradBiom COALnel OGB ELFFREN GASnel ELFF ELCOALBIO ELWIND EPC ELPC ELPB ELCIGCC ELBIGCC ELIGCC ELGASTR ELGAsCCS WINDOWS WINDOFF ELOIL ELGAS ELPV ELCSP OILnetlBACKnel ELBACK ELNUKE ELW&S ELNUKE&BACK OIL&BACK Trad Bio KL Appendix | 31 The original version of the model has 13 regions; however, the regional aggregation is flexible. For this paper, we separated out Indonesia and moreover considered the European Union (EU) as the present EU-28 to represent the EU intended nationally determined contribution (INDC) pledge. The PRC was also separated out, rather than considered in composite with other regions (Table A1.1.) Appendix Table: The World Induced Technical Change Hybrid Model Regions Region Countries Focus Region Name CAJAZ Canada, Japan, and New Zealand PRC People’s Republic of China PRC EAsia Southeast Asia and the Pacific (excluding Indonesia) Rest of East Asia, Southeast Asia, and the Pacific India India India Indonesia Indonesia Indonesia KOSAU Republic of Korea, South Africa, and Australia LACA Latin America, Mexico, and the Caribbean MENA Middle East and North Africa Europe European Union (EU) old and new member states and European Free Trade Association SAsia South Asia (excluding India) South Asia SSA Sub-Saharan Africa TE Non-EU Eastern European countries, including the Russian Federation US United States The intertemporal equilibrium is calculated as an open-loop Nash equilibrium, but a cooperative solution can also be implemented. 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