WPS8392 Policy Research Working Paper 8392 Paris Climate Agreement and the Global Economy Winners and Losers Muthukumara Mani Zekarias Hussein Badri Narayanan Gopalakrishnan Deepika Wadhwa South Asia Region Office of the Chief Economist April 2018 Policy Research Working Paper 8392 Abstract The 2015 Paris Climate Agreement was the first instance It uses a computable general equilibrium framework to of countries adhering to take a collective action against model global, regional, and country impacts. The analysis global warming. More than 190 countries came forward suggests that the economic impacts will be mostly felt in and submitted their contributions in the form of Intended the European Union if the Paris Agreement is fully imple- Nationally Determined Contributions, reflective of their mented. The European Union is likely to suffer a welfare ability and capacity to reduce greenhouse gas emissions, loss of 1.0 to 1.5 percent by 2030. Among non-European as each country set its own targets and actions. For some countries, Australia, New Zealand, and Mexico will also countries, it meant a significant decline in their emissions be affected, with an expected welfare loss of about 1.5 by 2030, while others, like China, the United States, and percent. Some of the major emitters, such as China and India, decided on a more gradual phasing out extending India, will experience minimal impacts to their welfare, beyond 2030. This paper estimates the economic impacts and the United States will experience a welfare loss of only of implementation of the Paris Climate Agreement in about 0.7 by 2030. The sectoral analysis of production and terms of its implications for welfare, gross domestic prod- trade suggests a significant loss to fossil fuel–based sectors, uct, investments, and trade for major countries and regions. while clean energy sectors can experience significant gains. This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mmani@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Paris Climate Agreement and the Global Economy: Winners and Losers Muthukumara Mani1 Zekarias Hussein Badri Narayanan Gopalakrishnan Deepika Wadhwa . Keywords: international climate negotiations, global stock taking, Carbon taxation, Paris Agreement, INDC, computable general equilibrium (CGE) modelling JEL codes: C60, F47, F64, Q40, Q50, Q34, Q54, Q58                                                              1  Corresponding author. Muthukumara Mani is with the office of the Chief Economist for South Asia. Zekarias Hussein is with the US Environmental Protection Agency. Badri Narayanan Gopalakrishnan is with the University of Washington, Seattle. Deepika Wadhwa is with Infinite Sum Modeling Inc. The authors would like to thank Martin Rama, Anil Markandya and Govinda Timilsina for their very thoughtful comments and suggestions on an earlier version. 1. Introduction In the past, there has been a general reluctance on the part of countries to take voluntary and unilateral action on climate for the fear that if only some countries act, there was a risk that their efforts will be rendered futile as some emissions could simply move to countries with less strict climate-change regulations, thereby resulting in so-called carbon leakage. It was also believed that this could result in distortions to competitiveness, particularly for energy- and carbon- intensive industries, as producers in mitigating countries would face costs for reducing emissions and compete against firms not burdened or burdened less by such costs. In this context, the Paris Agreement is an improvement over its predecessor, “the Kyoto Protocol” which differentiated between the developed and developing countries. Under the Kyoto Protocol, the developed countries (also known as Annex 1 countries under the UNFCCC Convention) had commitments to reduce their GHG emissions by 5.2 percent from the 1990 level; the developing countries did not have any commitment. The Paris Agreement on the other hand is unprecedented since it nullifies that differentiation by eliciting commitment from all countries (see Annex 1). Around 190 countries got-together in Paris and adopted an historic international climate agreement at the U.N. Framework Convention on Climate Change (UNFCCC) Conference of the Parties (COP21) in December 2015. Through this agreement, the countries acknowledged the seriousness of the climate change issues and outlined the post-2020 climate actions they intended to take. The commitments came to be known as their Intended Nationally Determined Contributions (INDCs). These INDCs were communicated by UNFCCC Parties to its Secretariat much before the Paris Agreement. Countries negotiated on them and agreed on the Nationally Determined Contributions (NDCs) “on a voluntary basis.”  INDCs were primarily designed for governments to communicate the steps they will undertake to address climate change in their own countries. INDCs were supposed to reflect in some sense each country’s ambition for reducing emissions, taking into account its domestic circumstances and capabilities. Moreover, some countries also took the opportunity to highlight the ways they will adapt to climate change impacts, and the support they will need in terms of climate finance, or will provide to other countries to adopt low-carbon pathways, in order to build climate resilience (UNFCCC, 2016). The climate actions communicated in these INDCs, thus, will largely determine if the world will be able to achieve the long-term goals of the Paris Agreement- to restrict the increase in global average temperature to well below 2°C, to pursue efforts to limit the increase to 1.5°C, and to achieve net zero emissions in the second half of this century. Under the provisions of the Paris Agreement, countries are expected to submit an updated NDC every five years, which will then represent a progression beyond the country’s then current NDC to reflect its highest possible ambition.2 A well-designed NDC is a signal to the world that the country is doing its part to combat climate change and limit future climate risks. NDCs should also emphasize a country’s efforts in                                                              2  The word “intended” was used initially because countries were communicating proposed climate actions ahead of the Paris Agreement being finalized. However, as countries formally join the Paris Agreement and look forward to implementation of these climate actions – the “intended” is dropped and an INDC is converted into a Nationally Determined Contribution (NDC).  2    integrating climate change into other national priorities, such as sustainable development and poverty reduction, and for fostering effective partnerships with the private sector to contribute to these efforts. The Paris Agreement reflects an acceptance among countries that climate change is happening and it poses a significant threat to the global economy in the long-term. This has resulted in an understanding among countries that the long-term benefits of combating climate change will far outweigh the economic costs of taking action now. A report by UNFCCC (2016) has highlighted that INDC commitments cover 86 percent of global greenhouse gas emissions – almost four times the level of the first commitment period of the Kyoto Protocol and will bring global average emissions per capita down by as much as 8 percent by 2025 and 9 percent by 2030, compared to business as usual (Figure 1). It is also expected that more ambition will be built into the commitments as the countries revise their NDCs every five years. Figure 1: INDCs will lead to reductions in GHG emissions by 2030 Source: UNFCCC (2016) However, the recent withdrawal of the United States from the Paris Agreement has once again brought to light the concern among countries about the economic impacts of taking measures to combat climate change. Another lingering question is if other countries will follow suit and weaken the premises of the treaty. In this backdrop, the paper assesses the economic impacts of INDC commitments on the global economy and major countries in terms of their GDP growth, investments, and exports. Section 2 provides a brief review of the recent literature, while section 3 highlights the data sources and methodology used in the paper. Section 4 describes simulation scenarios for the Computable General Equilibrium (CGE) analysis. Section 5 shows the findings with a focus of analyzing the impacts on GDP, welfare, investments and exports of major signatory countries. 3    Finally, the conclusions highlight the potential of investment in renewable energy which can make the economic costs of implementation of the Paris Treaty far more manageable. 2. Review of Recent Literature There have been a few studies in the existing recent literature which have examined the economic impacts of the Paris Agreement. A recent study by Bohringer and Rutherford (2017) focuses on the carbon tariffs that the United States might face in the context of its withdrawal from the Paris Accord (the paper was written before the actual US withdrawal). The authors examine the economic impacts of a US withdrawal from the Paris Agreement for four policy- relevant scenarios against a reference scenario in which all signatory parties to the Paris Agreement including the United States comply with their INDC targets. Scenario 1 is where the United States defects from the Paris Agreement without any sanctions by trading partners. In scenario 2 the remaining regions to the Paris Agreement levy carbon tariffs on US imports at their respective domestic emission prices and in scenario 3, the United States retaliates with optimal tariffs. In scenario 4, the three strategic players, the United States, China, and Europe, end up in a tariff war. The empirical results suggest that carbon tariffs are not an effective instrument to prevent US withdrawal from the Paris Agreement, as carbon tariffs do not come as a credible threat to the United States when accounting for retaliatory tariffs and the possibility of a tariff war. Also, the United States performs slightly better under carbon tariffs compared to the Paris Agreement. Moreover, the prospect of a tariff war where China and Europe levy tariffs on US imports would not come as a credible threat to the United States, rather it would be China (the most trade-intensive region) that could incur a drastic economic loss from such a tariff war. Hof et al (2017) estimate the annual abatement costs of achieving the NDC reduction targets, if countries would take additional targets beyond the Paris Agreement to keep global warming well below 2°C and pursue efforts towards 1.5°C by 2030. The authors find the abatement costs to be highly sensitive to socioeconomic assumptions. The global abatement costs of achieving the unconditional NDCs are projected between 58–135 billion USD in 2030 representing the differences between the three baselines, i.e. under SSP3 (with assumptions of slow economic growth, rapidly growing population, and high inequality) the global abatement costs are estimated at USD135 billion by 2030 which is more than twice the level as under the more sustainable socioeconomic assumptions of SSP1. These findings are consistent with Fujimori et al. (2016), who calculate the abatement costs for unconditional NDCs and a more ambitious greenhouse gas emission reduction scenario consistent with the 2°C goal using a general equilibrium model. The authors then assess the benefit of carbon emission trading on achieving the NDCs and show that welfare losses could be reduced by 75 percent when emissions trading was allowed. Sokolov et al (2016) focus on the climate impact of the Paris Accord in the next few decades. Using the MIT Integrated Global System Model, the authors consider a ‘no climate policy’ scenario and three other scenarios that effectively extend the NDCs to 2100: 1) no additional climate policy after 2030, but the proposed cuts are extended to 2100; 2) reductions in emissions and emission intensities after 2030 at the same rate as in the 2020-2030 period; and 3) no country increases its GHG emissions after 2050. The empirical results indicate that the Paris 4    Agreement can reduce the global mean surface air temperature (SAT) in 2100 between 0.63 and 1.07°C relative to the no climate policy case. However, since the climate system takes many years to respond to emissions reductions, in 2050, the SAT is reduced only by about 0.12°C under all three scenarios. Moreover, the rise in SAT relative to preindustrial times (1861-1880) exceeds the 2°C goal in 2053, and in 2100, reaches between 2.7 and 3.6°C under all three scenarios. Another study by Vandyck et al (2016) assesses the energy and economic implications of the climate mitigation policies in the NDCs. The study presents 3 scenarios: first, the Reference scenario, which includes only the existing climate policies particularly for 2020, without adding new additional policies. The second scenario represents the climate change mitigation pledges made by individual countries in the run-up to the COP21 in Paris. The third scenario considers a pathway of global greenhouse gas emissions that is likely to be consistent with limiting global temperature increase to 2°C by the end of the century. The results of simulations indicate that the NDCs have little impact on global demand for oil and gas. Rather, considerable demand reductions of energy and solid fuels would lead to lower greenhouse gas emissions. However, they argue that a substantial gap will remain between the global GHG emissions in the NDCs and the 2°C scenario in 2030, one-third of which could be bridged by decarbonizing the power sector. Moreover, the NDCs imply modest reductions in GDP for most regions (less than 1 percent compared to the Reference in 2030), whereas some regions will experience an increase in GDP due to gains in competitiveness. Overall, the analysis shows that global economic growth rates would be only marginally below the levels of the Reference with emerging and lowest-income economies maintaining high rates of economic growth, while fossil-fuel exporting countries could face larger impact. This paper builds on the given literature and looks specifically at how the Paris commitments will impact the signatory countries. 3. Model, methodology and data Model To analyze the economic impacts of the Paris Agreement on countries, the study uses a comparative static Computable General Equilibrium (CGE) model. CGE models are powerful tools for tracing how changes in one sector are propagated through the rest of the economy, affecting dependent sectors, patterns of trade, income and consumption and the fiscal and international financing needed for macroeconomic stability and growth goals (Figure 2). CGE models are widely used to analyze the aggregate welfare and distributional impacts of policies whose effects may be transmitted through multiple markets. They have also been deployed to analyze the effects of specific instruments or a combination of instruments. Examples of their application may be found in areas as diverse as fiscal reform and development planning (see, e.g., Perry et al 2001; Gunning and Keyzer 1995), international trade (Shields and Francois 1994; Martin and Winters 1996; Harrison et al 1997), and, increasingly, environmental regulation (Weyant 1999; Bovenberg and Goulder 1996; Goulder 2002). The paper uses a GTAP which is a publicly available multi-sectoral multi-regional CGE model. The model follows the standard approach of most CGE models in the sense that it models 5    competitive market behavior via producers, consumers, and government and their interaction with and within a set of countries. This has several standard features of a global CGE model such as Constant Elasticity of Substitution (CES) nests for production and trade and firms supply commodities for private and government consumption and to other firms in the form of intermediate use. Some of the special features of the GTAP model relate to the use of a flexible Constant Difference Elasticity (CDE) functional form for allocating consumption across commodities,3 Cobb-Douglas utility function for the aggregate regional household, perfect competition, constant returns to scale, conversion of all global savings into all global investment and Armington assumptions to capture product heterogeneity between domestic and imports as well as among imports from different sources (see Figure 2). This model is well-documented at length in Hertel (1997).                                                              3  In each region of the GTAP model, a regional household allocates income to maximize per capita aggregate utility per a Cobb-Douglas utility function. The utility function is described as “aggregate” utility because it comprises both government and private sector behavior. The arguments in the utility function are per capita utility from private consumption, per capita utility from government consumption, and per capita real saving. Real saving is a single commodity, defined as saving deflated by a saving price. Utility from government consumption is a Cobb-Douglas aggregate of government consumption of individual commodities. Per capita utility from private consumption is aggregated from per capita private consumption of individual commodities using a constant difference elasticity (CDE) demand system (Hertel, 1997).   6    Figure 2: Description of the workings of a CGE Model Domestic savings Factor Markets Value added Wages, rents Direct taxes Indirect taxes Producers Households  Government  Savings  Intermediate Demand Social Transfers Private consumption Government consumption Product Market Sales Final Demand Exports Imports Foreign Savings Rest of the World  Source: Breisinger et al (2009) The paper uses a newly developed special version of the GTAP model, named the GTAP- POWER model. This is documented in Peters (2016). In this model, there is a substitution between capital and energy products as a composite commodity; there is a multi-level nesting system among different energy products, including both renewable and non-renewable energy. This accounts for substitution among these sources in response to either carbon taxes for cutting emissions or to any other economic and technological policy changes in the economy. Further to these changes, another modification is made to this model, which enriches the labor supply specification; in the standard GTAP model, one assumes a horizontal or vertical labor supply curve, whereas in this model, the labor supply curve for different countries is defined using the estimated labor supply elasticities. This helps in capturing a more realistic labor supply situation. Further in reality, the actions on climate change may affect technological innovations as well as investment levels. Therefore, the assumption is that investment and savings both are endogenous in each region in the model. The model does capture the reduction in investment due to reduced output and savings. Substitutability across energy sources does vary across sectors and regions, as the parameters and shares of different variables that influence substitutability have huge variations across sectors and regions. 7    One of the main policy instruments to achieve the INDC targets is through pricing of carbon dioxide emissions. The level of the carbon value/price is calculated endogenously in the model to achieve the targeted reductions in emissions. A simple supply and demand economics is used here to illustrate some of the key concepts embedded in the models used to assess mitigation targets/costs. Figure 3 shows the supply and demand for energy/carbon. If a tax is imposed on carbon, this creates a gap between the supply and demand price and a reduction in carbon emissions. One can then plot the tax/carbon reduction relationship as a marginal cost curve for carbon emission reductions as shown in Figure 4. This tells us how much emission reduction a market-based incentive (carbon tax) achieves or it can be used to assess the implication of a targeted emission reduction on the implicit price of carbon (on the y-axis). Figure 3: Carbon supply and demand Carbon price  ($/ton)  Supply     Tax          Pe  Demand  Qe  Figure 4: Emission Reduction and Marginal Abatement Cost Carbon tax  ($/ton)  Marginal Abatement Cost  Carbon tax  Carbon Emission Reduction (tons)  Reduction   The introduction of an emission restriction, first and foremost, makes energy expensive. However, not all sources of energy are affected similarly as different types of energy have varying degrees of carbon content. The paper uses the power generation sector to illustrate how emission restriction policies propagate through the economy. This sector, like many others in 8    the model, uses labor, capital, intermediate goods, and energy to produce power. By their very nature, energy inputs are interchangeable in production but differ in terms of their carbon intensity. Therefore, in the baseline scenario, production is a mix of fossil fuels and other power generation technologies and it happens to be the case that high-carbon intensive sources like coal are cheaper than low-carbon alternatives. When emission restriction policy is applied, the cost of all types of fuel used in power generation rises as there are no carbon free inputs. However, the rise in input price makes low carbon energy sources relatively cheaper than, say, coal or other high carbon fuels. This change in energy mix of inputs in favor of low carbon fuels derives the economy-wide decline in carbon emissions (Harris et al, 2017; Peters, 2016). In the model, electricity is an intermediate good whose production cost also depends on the cost of all other inputs including energy. The extent of the price increase will ultimately depend on the elasticity of demand for electricity. In the immediate run, firms will have little time to substitute away from electricity in the face of the price rise, hence demand will likely be inelastic for all intended purposes. Hence, the production of electricity will be increased slightly but the now higher cost of production will be passed through to prices i.e. other sectors buying and final consumers of electricity will face higher prices. Other things remaining constant, expensive electricity leaves less income in the hands of consumers leading to a fall in overall economy-wide demand. On the supply side, increase in the price of electricity demands more resources i.e. with little opportunity to shift away from electricity in the short run, firms now spend more to get the same level of output. This leaves less resources for producing other goods. Hence, one would expect aggregate supply to decline following the rise in electricity price. Reduced output and demand mean lower employment of factors of production like capital, labor, and other intermediate inputs. The decline in total output will not be the same across sectors as the degree to which sectors use electricity differs markedly. Those that use less electricity will see modest change while those that rely on electricity will see noticeable decline in output. The example used so far about the impact of the emission restriction on the power generating sector can be applied to any sector of the economy that is carbon intensive. The qualitative nature of the story will remain the same while the magnitudes will differ across sectors. The basic process of transmission will start with an initial increase in production cost which will be partly passed through to prices and the rest of the economy leading finally to decrease in aggregate demand and supply (Harris et al, 2017; Peters, 2016). Data The data source for this paper is the GTAP-POWER data set documented by Peters (2016). The data used in GTAP-POWER come from numerous IEA publications. For example, data on electricity production (in GWh) by fuel source for each region, levelized (i.e., annualized cost per GWh) capital, operating and maintenance (O&M), fuel, and effective tax costs of electricity for select generating technologies and regions are all collected from IEA and then processed and combined with the standard GTAP database. The standard GTAP Data Base is documented in Narayanan et al (2015). Version 9 of the Global Trade Analysis Project (GTAP) model, with 9    2011 as the base year, is used to analyze the economic implications of the Paris Climate agreement.4 4. Scenarios Two scenarios modeled here are: Scenario 1-Implementation of INDCs by all member countries that signed the Paris Accord. Scenario 2 - Scenario A along with exogenous reduction in the prices of renewables by 25 percent. Starting from 2011, a baseline model is run until 2030 for GDP, labor force, and population, which would expand the whole global economy, including aspects such as production, trade, and investment. The baseline is based on IIASA's Sustainable Socioeconomic Pathways (SSP). The emission reduction commitment is treated in the model as an endogenous tax on carbon on sectors that use carbon-intensive inputs. It is important to keep in mind that the experiments reported are conducted under the counterfactual or “what if” analysis. The INDCs are unlikely to be implemented at the same time in all the regions reported and even more uncertain is the exact time frame these policies will likely get implemented and therefore a simpler approach is taken for imposing INDC shocks on the global economy for the base year 2011. The analysis assumes no change in the trade account: thus, the variable representing the current account balance (DBALCAR) is set to be exogenous and equal to zero in all countries/regions except one. The slack variable, cgdslack, is made endogenous. These variables are usually exogenous in the standard GTAP closure. The implication here is that investment is calculated as a residue in order to guarantee no change of the trade account. The quantitative restrictions applied to carbon emissions are introduced by making the real carbon tax RCTAX (i.e. the nominal carbon tax deflated by the GDP deflator) endogenous and the emission growth rates gco2t exogenous and equal to the Paris commitments. It is also possible to impose an exogenous real or nominal carbon tax (RCTAX or NCTAX, respectively) and leave the emission growth rates to be determined endogenously. 5. Main findings All the results are cumulative as happening in the final year of analysis, i.e. 2030 and not annualized or year-by-year. The results from Scenario 1 are discussed below and highlighted in Figures 5 through 8, which show both welfare and macroeconomic impacts of the Paris commitments across countries. Four other hypothetical alternative sub-scenarios were also examined, besides the original INDC commitment scenario. They include: (i) INDC_50MR scenario that looks at the impact of increasing the INDC commitments by 50 percent more than what countries initially intended to achieve. This is an extreme case, however, having such a scenario helps in giving benchmark values to alternative policy measures (ii) INDC20 looks at the impact of achieving just 20 percent of the INDC targets. (iii) INDC50 shows percent                                                              4 https://www.gtap.agecon.purdue.edu/ GTAP (1997), T. Hertel Ed., Global Trade Analysis Modeling and Applications, NY, USA. 10    achievements of the original INDC commitments. (iv) INDC80 assumes 80 percent achievements of the original INDC commitments. As shown in figure 5, for a few OPEC countries, the results indicate that overall these countries are likely to experience negative economic consequences following the global implementation of INDCs in all the five scenarios. The costs, as measured in terms of utility of the representative regional household, range from about 1 percent in Qatar to 2.1 percent in Saudi Arabia in the base INDC implementation scenario. The welfare loss in Saudi Arabia happens even though it has no carbon reduction commitments to comply with under the Paris deal. Overall, the higher cost in the OPEC region is partly explained by deterioration in terms-of-trade since the majority of the countries in the region are net energy exporters. Thus, even non-committed countries can experience welfare decline following the implementation of the INDCs. However, the welfare implications of the implementation of INDCs seem to be positively correlated with the size of ambition to reduce emissions. For instance, a 50 percent increase in commitments to reduce CO2 emissions across the globe can contribute to a welfare loss of about 9 percent in Ecuador and 4 percent in Saudi Arabia. The welfare impacts of achieving only about 20 percent of the INDC targets are mixed. Welfare costs seem to be marginally negative and even positive in some countries. Likewise, for some of the top net emission importers, the welfare costs of the INDC commitments are negative across the board (measured by MtCO2 per year), as shown in Figure 6. The more ambitious are the commitments, the higher is the cost on society. However, the welfare loss is smaller in magnitude compared to the impact on OPEC member countries. Unlike OPEC countries, the terms of trade effect for these countries are in fact positive as they benefit from declining energy prices following the implementation of the carbon emission reduction policy. Moreover, some of these major countries have binding commitments to reduce emissions, by at least 40 percent from 1990 emissions levels. Figure 7 shows the impact of INDC commitments across selected countries of the G20. The G20 group represents a mixed representation in terms of INDC commitments, some member countries have binding and ambitious INDC commitments (e.g. Australia and Canada), some have modest targets (the Republic of Korea, Japan and Argentina), while some countries (e.g. India, the Russian Federation, and South Africa) have no commitments to reduce emissions at all in the near-future. Overall, the Paris commitments would result in decline of welfare across these countries. Figure 8 shows the marginal cost of achieving the Paris Agreement based on the alternative INDCs scenarios. The European countries face a high cost for meeting their aggressive Paris Agreement target which ranges from a low of $54 per ton of CO2 in Spain to $372 per ton of CO2 in France if they manage to reduce emissions by about 40 percent as planned. The cost is reduced if the target was set at only reaching 20 percent of the INDCs. In this case the marginal cost ranges from a high of $35 per ton of CO2 in France to $7 per ton of CO2 in Spain. A more stringent target, say achieving 50 percent more than what is stated in the INDCs leads to a much higher price of carbon in Europe. The price hike ranges from $914 per ton of CO2 in France to $101 in Spain. 11    Figure 5: The cost alternative scenarios of achieving the INDCs, OPEC region Source: Authors’ computations. Note on the legend. The bars indicate the five different scenarios. INDC=the INDC commitments; INDC_50MR=Countries achieving 50 percent more than the original INDC commitments; INDC20=Countries achieving 20 percent of their original INDC commitments; INDC80=Countries achieving 80 percent of their original INDC commitments 12    Figure 6: The cost alternative scenarios of achieving the INDCs, Major net importers of emission5 Source: Authors’ computations. Note on the legend. The bars indicate the five different scenarios. INDC=the INDC commitments; INDC_50MR=Countries achieving 50 percent more than the original INDC commitments; INDC20=Countries achieving 20 percent of their original INDC commitments; INDC80=Countries achieving 80 percent of their original INDC commitments Figure 7: The cost alternative scenarios of achieving the INDCs, some G20 countries Source: Authors’ computations. Note on the legend. The bars indicate the five different scenarios. INDC=the INDC commitments; INDC_50MR=Countries achieving 50 percent more than the original INDC commitments; INDC20=Countries achieving 20 percent of their original INDC commitments; INDC80=Countries achieving 80 percent of their original INDC commitments                                                              5 As measured in MtCO2 per year, Figure 5 in Davis and Caldeira (2010). 13    Figure 8: Marginal Costs of Achieving the INDC targets Source: Authors’ computations. Note on the legend. The bars indicate the five different scenarios. INDC=the INDC commitments; INDC_50MR=Countries achieving 50 percent more than the original INDC commitments; INDC20=Countries achieving 20 percent of their original INDC commitments; INDC80=Countries achieving 80 percent of their original INDC commitments. Norway was removed from this figure as the marginal cost of abatement was an outlier (the USD per ton CO2 ranged from 1,230 to 5,645) for the different scenarios, significantly affecting the scaling of the figure. Table 1 highlights some of the major macro-economic results for the global economy (by major regions and countries) following implementation of the Paris Agreement. The results suggest that countries in Europe including the European Union are likely to be the most impacted if the Paris Agreement is fully implemented. They can witness a GDP loss of 1.0 to 1.5 percent by 2030 (Norway being the extreme with a loss of 4.6 percent). The GDP of France can decline by about 1.6 percent, while Germany, Italy and the United Kingdom can experience about 1.0 percent decline in their respective GDPs. Among non-European countries, Australia, New Zealand and Mexico will be impacted the most, with an expected GDP loss of about 1.0 percent. Interestingly since commitments of some of the major emitters such as China and India will not take into effect by 2030, they will experience minimal impact in their GDPs. GDP in the United States can decline by about 0.6 percent by 2030. The majority of the countries in Asia, Latin America and MENA will witness a marginal decline in their GDPs. Among African countries, Sub-Saharan Africa will experience a decline in GDP of about 1.0 percent. Overall, the analysis does not indicate that adhering to the Paris commitments will entail any major global economic downturn. 14    Table 1: Implications of Paris Treaty for GDP, Investment and Trade percent percent percent percent Change Change Change Change Regions/Countries in in in in Investment GDP    Imports Exports Australia -0.84 -0.47 -3.08 -1.93 New Zealand -1.06 0.65 -3.14 -4.3 East Asia China(Mainland) -0.06 -0.01 1.19 0.55 Japan -0.26 -0.28 -1.67 -2.99 Korea, Rep. 0.04 0.99 -1.04 -1.89 Indonesia -0.02 -0.23 -0.28 0.58 South Asia India -0.01 0.16 0.28 -0.91 Rest of South Asia -0.01 -0.09 0.45 -0.14 North America Canada -0.27 0 -0.6 0.18 United States -0.62 0.8 -1.19 -2.81 Mexico -1.18 -0.27 -0.65 -0.07 Latin America and Caribbean Brazil 0 -0.01 -0.22 -0.1 Ecuador 0.32 5.97 1.1 2.7 Venezuela, RB -0.1 -0.68 -5.13 0 Argentina -0.01 -0.04 0.02 0.19 Other Latin 0.01 -0.11 -0.26 0.31 American Central America 0.01 -0.11 0.14 -0.06 and Caribbean Europe and Central Asia France -1.63 0.38 -4.3 -6.04 Germany -1.06 0.97 -3 -3.11 Italy -1.13 0.08 -3.46 -4.54 Spain -0.23 -0.29 -1.6 -2.38 United Kingdom -1.06 -0.52 -2.63 -3.35 Other European -1.61 0.05 -3.9 -4.53 Union 27 Rest of Europe -1.27 0.68 -3.85 -4.56 15    Russian Federation -0.83 -0.28 -5.58 -1.61 Norway -4.6 -2.23 -8.13 -5.01 Turkey -0.35 -0.22 0.17 -0.14 Central Asia -0.07 -0.24 0.83 0.98 Middle East and North Africa Qatar -0.05 -0.43 -3.23 -0.31 Saudi Arabia 0.05 0.06 -4.44 -0.51 United Arab -0.15 -0.75 -1.75 0.88 Emirates Iran, Islamic Rep. -0.2 -0.69 -3.65 -0.58 Iraq -0.35 -2.19 -2.37 0.81 Kuwait -0.05 -0.81 -2.7 0.23 Algeria and Libya -0.21 -2 -6.81 -2.3 Rest of Middle East and North -0.05 -0.33 0.47 1.57 Africa Africa Nigeria -0.33 -1.22 -5.54 -1.07 Angola and Gabon -0.1 -1.38 -3.81 -0.07 South Africa 0.02 -0.09 -0.54 -0.52 Sub-Saharan -0.03 -0.31 -0.19 0.55 Africa Rest of the World 0.1 -0.22 -0.17 0.22 Source: Authors’ computations. In terms of investments, the analysis suggests that the impact is likely to be minimal with most countries experiencing either a small positive or negative effect by 2030. Among European countries, France and Germany are likely to experience a positive increase in their investments, while Norway, Spain and the United Kingdom can register a small decline in their investments. Some of the oil exporting countries such as the United Arab Emirates, Iraq, Russia, Nigeria and Kuwait can also show modest declines in their investments. The United States and Korea are likely to benef positively in terms of their investments. The analysis also suggests that changes in trade could be a negative consequence of the implementation of the Paris Treaty. Most countries in Europe are likely to experience a decline of 3.0 to 5.0 percent in their imports and exports. Countries in MENA can experience a bigger percent change in their imports than the exports. Australia, New Zealand, Japan, Korea, the United States and African countries will also be negatively impacted to varying degrees. China and to some extent India will not experience any significant shift either in their exports or imports. In terms of the implications for various industries/sectors, the analysis suggests that fossil fuel- based sectors such as coal, gas, and oil will be adversely affected by the implementation of the 16    Paris Agreement. Both basic production as well as power generation using coal, oil and gas and trade are likely to experience a significant decline as countries ramp up their efforts to reduce greenhouse gas emissions. While China, India, Russia and Turkey will witness an increase in coal-fired power generation, most other countries will witness a significant decline in coal- powered generation by 2030. On the other hand, clean energy industries such as nuclear, wind and solar will experience a significant increase in their production and trade. This will have little implications for basic manufacturing industries or services. Overall, the dirty industries are likely to be majorly impacted, while clean industries show a flourishing trend as the Paris Agreement is implemented without any major implications for industrial competitiveness. Table 2: Implication of Paris Treaty for Global Trade and Output in Various Sectors in in Output Trade Crops -0.2 -0.3 Processed Food -0.3 -0.7 Textiles -0.2 -0.6 Meat -0.4 -1.1 Fish -0.1 -0.5 Forest 0.1 2.6 Coal -5.8 -23.5 Oil -4 -5 Gas -13.2 -13.4 Petroleum and Coal -4.7 -10.2 Electricity Transmission -5.8 -9.4 Nuclear Power 16.5 20.2 Coal-fired Power -19.7 -42.9 Gas-fired Power -11.8 -23.6 Wind Power 18.7 18.4 Hydro Power 13.2 16.6 Oil-fired Power -3.5 -27.9 Other Power 11.6 12 Gas-fired Power (Peak -8.5 -16.6 Load) 17    Hydro Power (Peak Load) 8.7 1.8 Oil-fired Power (Peak -1.9 -3.7 Load) Solar Power -1.2 2 Light Manufacturing -0.4 -0.7 Heavy Manufacturing -0.3 -0.9 Services -0.4 -0.9 Source: Authors’ computations. The results here consider the implementation of the basic INDC scenario discussed above. Figure 9: Coal-fired Power Generation across countries by 2030 Source: Authors’ computations. The results here consider the implementation of the basic INDC scenario discussed above. It is well recognized now that renewable energy deployment must be accelerated, if one were to limit global temperature rise to 2°C. To achieve the Paris Agreement goals, governments must address the most severe disincentives and energy market distortions by improving policies to level the playing field between fossil fuels and nuclear power on the one hand, and renewable energy on the other. Reduced costs for renewable energy technologies will not only make global energy transition possible, but also less expensive than the alternatives. Per a recent IRENA report (IRENA, 2015), with the right regulatory and policy frameworks, solar and wind technologies can still unlock significant additional cost reductions by 2025 and beyond. The analysis finds that the cost of electricity for solar and wind power technologies will fall between a quarter and around two-thirds by 2025. 18    The results for Scenario 2 are presented in Table 3. The findings suggest that the reduction in GDP resulting from the implementation of the Paris Treaty would be considerably nullified by a reduction in the price of renewables by 25 percent. For example, France, Germany, Italy and the United Kingdom are likely to experience a much smaller decline in their GDPs compared to the business as usual scenario (Scenario A). Similarly, the United States will witness a decline in GDP of 0.28 percent in scenario 2 as compared to 0.62 percent in the baseline scenario. Table 3: Scenario 2: Implication of Paris Treaty for Global Trade and Output in Various Sectors with 25 percent reduction in price of renewables percent percent percent percent Region/Country Change in Change in Change in Change in Investment GDP Imports Exports Australia -0.64 -0.27 -3.01 -1.83 New Zealand -0.53 1.43 -2.76 -3.98 East Asia China (Mainland) 0.17 0.18 1.19 0.62 Japan -0.01 -0.27 -1.47 -2.74 Korea, Rep. 0.34 1.53 -0.79 -1.58 Indonesia 0.22 0.47 0.27 -1.29 South Asia India 0.01 -0.13 -0.33 0.62 Rest of South Asia 0.33 0.99 0.39 -0.67 North America Canada 0.3 0.45 -0.1 0.77 United States -0.28 1.39 -0.9 -2.46 Mexico -0.93 0.27 -0.56 -0.02 Latin America and Caribbean Brazil 0.47 0.45 -0.04 -0.15 Ecuador 0.61 6.72 1.22 2.49 Venezuela, RB 0.23 -0.61 -5.35 -0.15 Argentina 0.03 0.14 0.05 0.01 Other Latin American 0.37 0.26 -0.27 0.51 19    Central America and Caribbean 0.15 0.78 0.05 -0.27 Europe and Central Asia France -1.01 0.72 -3.94 -5.55 Germany -0.55 1.99 -2.42 -2.52 Italy -0.65 0.86 -2.82 -4.02 Spain 0.35 -0.16 -1.15 -1.96 United Kingdom -0.72 -0.02 -2.24 -3.03 Other European Union 27 -0.91 1.04 -3.31 -3.92 Rest of Europe 0.06 1.99 -2.46 -3.11 Russian Federation -0.57 1.42 -5.13 -1.35 Norway -4 -1.77 -7.45 -4.41 Turkey -0.17 -0.17 0.26 -0.45 Central Asia 0.1 0.1 0.78 0.91 Middle East and North Africa Qatar -0.05 -0.45 -3.41 -0.32 Saudi Arabia 0.06 0.1 -4.35 -0.5 United Arab Emirates -0.15 -0.72 -1.66 0.89 Iran, Islamic Rep. -0.16 -0.47 -3.64 -0.61 Iraq -0.16 -0.78 -2.11 0.91 Kuwait -0.05 -0.78 -2.64 0.24 Algeria and Libya -0.21 -1.98 -6.78 -2.31 Rest of Middle East and North -0.02 -0.2 0.52 1.55 Africa Africa Nigeria -0.3 -1.31 -5.56 -1.13 Angola and Gabon -0.03 -1.09 -3.75 -0.06 South Africa 0.03 0.04 -0.45 -0.55 Sub-Saharan Africa 0.29 0.45 0.01 0.65 Rest of the World 0.15 -0.01 0.03 0.17 Source: Authors’ computations. Results from Scenario B. 20    Similarly, in terms of investments, countries are likely to benefit much more with the reduction in the price of renewables than otherwise. Most European countries will experience a spurt in their investments. Countries like Australia, the United States, the United Kingdom, and Spain will register a small decline in their investments. In trade, the majority of the countries show a decline in their exports and imports in scenario 2 but to a smaller extent as compared to scenario 1. In other words, trade disruptions are likely to be minimum with the reduction in the price of renewables. Table 4: Implication of Paris Treaty for Global Trade and Output in Various Sectors with reduction in price of renewables percent change in percent change in Sector/Industry Trade Output 1 Crops -0.18 -0.04 2 Processed Food -0.44 -0.06 3 Textiles -0.32 0.05 4 Meat -0.66 -0.12 5 Fish -0.05 0.14 6 Forest 2.92 0.43 7 Coal -29.06 -5.34 8 Oil -4.21 -3.58 9 Gas -15.24 -11.16 10 Petroleum and Coal -9.20 -3.90 11 Electricity Transmission 0.84 2.15 12 Nuclear Power 76.52 68.42 13 Coal-fired Power -62.72 -39.64 14 Gas-fired Power -41.46 -27.77 15 Wind Power 68.28 75.95 16 Hydro Power 65.24 59.04 21    17 Oil-fired Power -47.90 -13.47 18 Other Power -10.90 -7.43 19 Gas-fired Power (Peak Load) -19.74 -9.98 20 Hydro Power (Peak Load) 45.72 30.63 21 Oil-fired Power (Peak Load) 0.32 1.16 22 Solar Power 50.42 45.89 23 Light Manufacturing -0.24 -0.02 24 Heavy Manufacturing -0.39 0.07 25 Services -0.54 -0.07 Source: Authors’ computations. Results from Scenario B. In scenario 2, in terms of the impact on various industries/sectors, the analysis suggests that fossil fuel-based power generating sectors such as coal, gas, and oil are expected to be hit negatively with the drop in the price of renewables. On the other hand, clean energy sectors will gain positively with significant increase in their production and trade. The impact on manufacturing sectors will also be less pronounced than in the business as usual scenario. Thus, the reduction in the prices of renewables is expected to result in the decline of fossil fuel-based power generation, with most countries except Russia and Turkey witnessing significant to modest declines in their coal-fired power generation (Figure 10). 22    Figure 10: Change in Coal-fired Power Generation across countries by 2030 with reduction in price of renewables Source: Authors’ computations. Results from Scenario B. With implementation of the Paris Agreement, the global average emissions are expected to reduce by 167 metric tons of CO2 in 2030, based on the model results. The Paris Accord has a potential of total reduction of emissions by 4,489 metric tons of CO2. Even with conservative carbon pricing estimates, the benefits from emissions reduction may far outweigh the losses in GDP. 6. Conclusions This paper is one of the first comprehensive attempts to estimate the economic impacts of implementation of the Paris Climate Agreement in terms of its implications for welfare, GDP, investments and trade for major countries and regions including the G-20 countries and oil- exporting countries. The analysis suggests that the economic impacts will be mostly felt in Europe including the European Union if the Paris Agreement is fully implemented. European countries are likely to undergo a welfare loss of 1.0 to 1.5 percent by 2030. Among non-European countries, Australia, New Zealand and Mexico will also be impacted with an expected welfare loss of about 1.5 percent. Interestingly, since the commitments of some of the major emitters such as China and India will not take effect by 2030, they will experience minimal impacts in their welfare, while the United States is expected to witness a decline of about 0.7 percent in its welfare by 2030. Most countries in Asia, Latin America, and MENA will have negligible impacts. The sectoral analysis suggests a significant loss for fossil fuel-based sectors in terms of production and trade while significant gains are expected for clean energy sectors. 23    Moreover, the results also indicate that a fall in the prices of renewables would considerably soften the economic impact of the Paris Agreement. The economic impacts for Europe especially in terms of GDP loss will almost be halved for the majority of the countries. The associated investment gains would be significant and trade disruptions substantially minimized. In terms of specific sectors, a fall in the price of renewables will give further boost to clean energy sectors such as solar and wind at the cost of the fossil fuel-based sectors. Coal-fired power generation would also be significantly reduced. The findings therefore point in the direction of improving the investment regime for renewable energy which in turn would make the economic costs of implementation of the Paris Agreement far more manageable. Investments in R&D could also help in speeding up innovation in technology which in turn would reduce the price of renewables considerably. Overall, all this could also spur more ambitious NDC submissions by countries as a follow-up to the commitments agreed in Paris. It is important to highlight two major limitations of the current paper. First, the stylized nature of the policy experiment needs a careful interpretation of the results. In the paper, a “what if” scenario is developed to explore implications of INDCs, i.e., if INDCs were implemented in the global economy as represented by our 2011 base year. However, the INDCs, by their very nature, would be implemented over a period, which would undoubtedly take many policy reforms and approaches to achieve the goals. This contrasts very much with the approach used here and represents one of the major weaknesses of static CGE models that are mostly used in climate policy analysis. The use of a dynamic CGE model would have partially addressed some of these concerns. Second, implementation of the INDCs is likely to impact the workings of energy markets across the globe. However, such impacts are likely to be transferred in the form of welfare change which depends on a number of factors that are particular to the model used here. For example, the degree to which changes in higher energy cost of production are passed on to consumers depends on the relative price elasticities of supply and demand. These elasticities therefore play a critical role in evaluating the impact of INDCs. In addition to demand elasticity, inter-fuel substitution is key to the price responsiveness of firms’ demand for oil and other energy sectors. Thus, proper representation and validation of these parameters is paramount to proper documentation of the policy impacts of INDCs. Finally, one observes that the benefits from cutting emissions as per the Paris Accord may potentially outweigh the costs in terms of slowdown in GDP. 24    References Aldy, J.E. and Pizer, W.A. (2015). The Competitiveness Impacts of Climate Change Mitigation Policies. Journal of the Association of Environmental and Resource Economists 2015 2:4, 565- 595. Beckman, Jayson and Hertel, Thomas (2009) “Why Previous Estimates of the Cost of Climate Mitigation are Likely Too Low,” GTAP Working Papers, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University. Beghin, J., Bowland, B., Dessus, S., Roland-Holst, D., Van der Mensbrugghe, D., (2002). Trade integration, environmental degradation and public health in Chile: assessing the linkages. Environment and Development Economics 7 (2), 241– 267. Bergman, Lars, (1991). General equilibrium effect of environmental policy: a CGE modeling approach. Environmental and Resource Economics 1, 43– 61. Böhringer, C. and Rutherford, T. F. (2017). Paris after Trump: An Inconvenient Insight (June 21, 2017). CESifo Working Paper Series No. 6531. Available at SSRN: https://ssrn.com/abstract=3003930 Bovebourg A.L. and Goulder. L..H. (1996). Optimal Environmental Taxation in the Presence of Other Taxes: General-Equilibrium Analyses, American Economic Review 86(4):985-1000. Breisinger, C., Marcelle Thomas, M. and Thurlow. J. (2009). “Social Accounting Matrices and Multiplier Analysis: An Introduction with Exercises,” Food Security in Practice, International Food Policy Research Institute, Washington D.C. Dufournaud, M., Harrington, J., Rogers, P., (1988). Leontief’s environmental repercussions and the economic structure revisited: a general equilibrium formulation. Geographical Analysis 20 (4), 318– 327. Edwards, T. Huw, Hutton, John P., (2001). Allocation of carbon permits within a country: a general equilibrium analysis of the United Kingdom. Energy Economics 23 (4), 371–386. Forsund, F., Storm, S., (1988). Environmental economics and management: pollution and natural resources. Croom Helm Press, New York. Fujimori, S., Kubota, I., Dai, H., Takahashi, K., Hasegawa, T., Liu, J.Y., Hijioka, Y., Masui, T., Takimi, M., (2016). “Will International Emissions Trading help Achieve the Objectives of the Paris Agreement?” Environmental Research Letters, Vol.11. GGGI (2014). The impacts of environmental regulations on competitiveness. Policy Brief, November 2014. Seoul: Global Green Growth Institute. Goulder, L., ed. (2002). Environmental Policy Making in Economics With Prior Tax Distortions, Northampton MA: Edward Elgar. Grossman, G., Krueger, A., (1993). Environmental impacts of a North American free trade agreement. In: Peter, Garber (Ed.), The Mexico–US Free Trade Agreement. The MIT Press, Cambridge. 25    Gunning, J.W. and M. Keyzer (1995). Applied General Equilibrium Models for Policy Analysis, in J. Behrman and T.N. Srinivasan (eds.) Handbook of Development Economics Vol. III-A, Amsterdam: Elsevier, 2025-2107 Harris, M., Roach, B. and Codur, A. (2017) “The Economics of Climate Change,” Global Development and Environment Institute, Tufts University Medford, MA 02155 Harrison, G.W., T.F. Rutherford and D.G. Tarr (1997). Quantifying the Uruguay Round, Economic Journal 107: 1405-1430. Hazilla, M., Koop, R., (1990). Social cost of environmental quality regulations: a general equilibrium analysis. Journal of Policy Modeling 98 (4), 853– 873. Hertel, Thomas et al. (1997), Global Trade Analysis Modeling and Applications, T. Hertel Ed, NY, USA. Hof, A., den Elzen, M., Admiraal, A., Roelfsema, M., Gernaat, D., and Vuuren, D. (2017). “Global and Regional Abatement Costs of Nationally Determined Contributions (NDCs) and of Enhanced Action to levels well below 2 °C and 1.5 °C.” Environmental Science & Policy. Vol. 71, 30–40. Ianchovichina, Darwin, E.R., Shoemaker, R., (2001). Resource use and technological progress in agriculture: a dynamic general equilibrium analysis. Ecological Economics 38, 275– 291. Iyer, G.C., Edmonds, J.A., Fawcett, A.A., Hultman, N.E., Alsalam, J., Asrar, G.R., Calvin, K.V., Clarke, L.E., Creason, J., Jeong, M. (2015). “The Contribution of Paris to limit Global Warming to 2C.” Environmental Research Letters, Vol 10 (12). Jaffe, Adam B., Steven R. Peterson, and Paul R. Portney (1995). "Environmental Regulation and the Competitiveness of U.S. Manufacturing: What Does the Evidence Tell Us?" Journal of Economic Literature, March 1995. Jorgenson, D.W., Wilcoxen, P., (1990). Intertemporal general equilibrium modeling of U.S. environmental regulation. Journal of Policy Modeling 12 (4), 715– 744. Jorgenson, D.W., Wilcoxen, P., (1993). Reducing U.S. carbon dioxide emissions: an assessment of different instruments. Journal of Policy Modeling 15 (5), 491– 520. Kehoe, Timothy (2003) “An evaluation of the performance of applied general equilibrium models of the impact of NAFTA,” No 320, Staff Report, Federal Reserve Bank of Minneapolis. Lucas, R., Wheeler, D., Hettige, H., (1992). Economic development, environmental regulation and the international migration of toxic industrial pollution: 1960–1988. In: Patrick, Low (Ed.), International trade and the environment, World Bank Discussion Paper, vol. 159, pp. 67–88. Washington, DC. Madrid-Aris, M.E., (1998). International trade and the environment: evidence from the North America Free Trade Agreement(NAFTA). Presented in the World Congress of Environmental and Resources Economics, Venice, Italy, June 25–27. Martin, W. and L.A. Winters, eds. (1996). The Uruguay Round and the Developing Economies, New York: Cambridge University Press. 26    Perry, G., J. Whalley and G. McMahon, eds. (2001). Fiscal Reform and Structural Change in Developing Countries, New-York: Palgrave-Macmillan. Peters, J. (2016) “GTAP-E-Power: An Electricity-detailed Economy-wide Model,” Journal of Global Economic Analysis, Vol 1, pp.156-187. Pigott, J., Whalley, J., Wigle, J., (1992). International linkages and carbon reduction initiatives. In: Anderson, K., Blackhurst, R. (Eds.), The greening of world trade issues. University of Michigan Press. Porter, M. E. (1991). "Towards a Dynamic Theory of Strategy." Strategic Management Journal 12 (Winter 1991). Robinson, S., Gelhar, C., (1995). Land, water and agriculture in Egypt: the economy-wide impact of policy reform. Discussion paper. International Food Policy Research Institute, Washington, DC. Shields, C.R. and J.F. Francois, eds. (1994). Modeling Trade Policy: Applied General Equilibrium Assessments of North American Free Trade, New York: Cambridge University Press. Sokolov A., Paltsev S., Chen H., and E. Monier (2016). “Climate Impacts of the Paris Agreement”. Vol. 18, EGU General Assembly. UNFCCC (2015). Adoption of the Paris Agreement. Proposal by the President, Draft decision -/CP.21. Paris: UNFCCC. UNFCCC (2016). Aggregate effect of the intended nationally determined contributions: an update Synthesis report by the secretariat. Conference of the Parties, Twenty-second session Marrakech, 7–18 November 2016. Vandyck, T., Keramidas, K., Saveyn, B., Kitous, A., Vrontisi, Z., (2016).“A Global Stock Take of the Paris Pledges: Implications for Energy Systems and Economy.” Global Environmental Change, Vol 41, 46–63. World Bank (2008). International Trade and Climate Change: Economic, Legal and Institutional Implications. Washington DC: World Bank. Yang, Hao-Yen, (2001). Trade liberalization and pollution: a general equilibrium analysis of carbon dioxide emissions in Taiwan, China. Economic Modelling 18, 435– 454. 27    Annex 1. INDC Ambition by 2030 by Country INDC Target Per Emission by 2030 Capita Country Rank ( Emission (2010) percent (2010) change) Afghanistan 98 0 1.1 Angola 60 -24 3.7 Albania 145 2 3.1 Andorra 177 -27 6.5 United Arab Emirates 34 -14 28.8 Argentina 28 -3 7.9 Armenia 146 115 2.7 Antigua and Barbuda 174 26 7.5 Australia 17 -38 25.3 Austria 57 -33 10.2 Azerbaijan 82 -18 6 Burundi 155 783 0.5 Belgium 45 -37 12.1 Benin 130 9 1.4 Burkina Faso 103 108 1.8 Bangladesh 43 68 0.9 Bulgaria 75 -9 8.3 Bahrain 99 20 23.7 Bahamas 161 -13 9 Bosnia and 109 46 6.8 Herzegovina Belarus 56 17 9.7 Belize 171 -10 3.2 Bolivia 90 -15 3.7 Brazil 7 0 5.2 Barbados 168 -21 6.1 Brunei Darussalam 125 18 37 Bhutan 167 -7 2.9 Botswana 116 -38 10.4 Central African 96 -22 7.3 Republic Canada 11 -25 21 Switzerland 81 -58 7 Chile 53 18 5.7 28    China 1 30 8.4 Côte d'Ivoire 120 -15 0.9 Cameroon 104 94 1.4 Congo, Dem. Rep. 83 15 0.7 Congo, Rep. 142 3 2.2 Cook Islands 196 4 2.4 Colombia 38 2 4.3 Comoros 189 53 0.3 Cabo Verde 175 6 1.3 Costa Rica 126 -46 3.2 Cuba 78 40 5 Cyprus 139 -43 9.3 Czech Republic 44 -25 13.2 Germany 8 -37 11.9 Djibouti 169 21 1.9 Dominica 185 -73 3.2 Denmark 73 -40 11.6 Dominican Republic 95 -8 3.3 Algeria 39 -9 5.2 Ecuador 76 -45 4.1 Egypt, Arab Rep. 29 3 3.7 Eritrea 148 -29 1.4 Spain 27 -14 7.6 Estonia 118 -37 15.1 Ethiopia 46 -25 1.5 Finland 66 -35 14 Fiji 163 9 3.3 France 18 -36 8.5 Micronesia 191 -31 1.3 Gabon 151 30 4 United Kingdom 15 -38 10 Georgia 135 190 2.8 Ghana 117 59 0.9 Guinea 121 -35 1.7 Gambia, The 165 -39 1.5 Guinea-Bissau 166 -30 1.4 Equatorial Guinea 150 -39 8.5 Greece 48 -19 11 Grenada 181 -11 3.2 29    Guatemala 110 17 1.8 Guyana 159 -21 4.7 Honduras 122 29 2.2 Croatia 100 -1 6.8 Haiti 140 45 1 Hungary 71 2 6.9 Indonesia 9 1 3.5 India 4 48 2.5 Ireland 74 -30 13.9 Iran, Islamic Rep. 10 16 10.1 Iraq 41 -6 5.1 Iceland 156 -60 15 Israel 61 -28 10.6 Italy 20 -25 8.5 Jamaica 143 63 3.3 Jordan 102 18 4.5 Japan 6 -14 9.9 Kazakhstan 30 -18 18.1 Kenya 70 45 1.7 Kyrgyzstan 123 -38 3 Cambodia 97 -22 2.2 Kiribati 193 -37 0.9 Saint Kitts and Nevis 180 26 7.3 Korea, Rep. 16 -18 12.5 Kuwait 50 -4 36.6 Lao PDR 137 -18 1.9 Lebanon 114 28 5.1 Liberia 152 -42 1.5 Saint Lucia 178 24 2.9 Liechtenstein 184 -47 6.4 Sri Lanka 108 14 1.3 Lesotho 157 -17 2.3 Lithuania 115 16 6.9 Luxembourg 131 -48 24.3 Latvia 134 17 5.9 Morocco 63 39 2.4 Monaco 192 -46 2.5 Moldova 127 -21 3.5 Madagascar 92 -47 1.6 30    Maldives 170 83 3.5 Mexico 12 -27 5.6 Marshall Islands 188 -48 3.7 Macedonia 133 24 5.9 Mali 77 -20 3.8 Malta 162 -38 7.4 Myanmar 51 -6 2 Montenegro 158 -31 7.1 Mongolia 107 38 10 Mozambique 94 -35 1.4 Mauritania 138 0 3.3 Mauritius 154 51 3.9 Malawi 144 -15 0.6 Malaysia 32 30 10.2 Namibia 132 -10 5.6 Niger 87 -12 2.4 Nigeria 36 68 1.3 Nicaragua 124 -8 2.7 Niue 198 24 4.1 Netherlands 37 -41 12.8 Norway 80 -54 11.3 Nepal 91 -8 1.3 Nauru 195 52 6.1 New Zealand 62 -22 18 Oman 64 -34 26.2 Pakistan 26 -8 2.3 Panama 129 2 3.8 Peru 52 -44 3.3 Philippines 40 -12 1.9 Palau 194 -43 4.1 Papua New Guinea 128 26 2 Poland 23 -24 10.8 Portugal 68 3 6.9 Paraguay 93 577 5.4 Qatar 47 -9 72.1 Romania 49 12 5.9 Russian Federation 5 34 16.2 Rwanda 149 -31 0.6 Saudi Arabia 14 5 22.6 31    Sudan 113 -42 2.4 Senegal 111 21 2 Singapore 79 41 11 Solomon Islands 183 -49 0.5 Sierra Leone 153 -24 1 El Salvador 136 22 2 San Marino 186 -26 7.4 Somalia 105 -41 2.9 Serbia 67 10 8.1 São Tomé and 190 -16 1 Príncipe Suriname 160 20 6.7 Slovak Republic 84 -21 8.6 Slovenia 119 -17 9.6 Sweden 72 -30 7 Swaziland 164 -1 2.3 Seychelles 173 2 8.2 Chad 106 -17 2.3 Togo 147 57 1.2 Thailand 22 -3 6.5 Tajikistan 141 67 1.2 Turkmenistan 65 37 15.2 Tonga 187 -9 2.1 Trinidad and Tobago 58 12 62.8 Tunisia 88 44 3.6 Turkey 24 97 5.7 Tuvalu 197 -46 2 Tanzania 69 -44 1.5 Uganda 86 17 1.2 Ukraine 25 63 8.8 Uruguay 85 -25 11.9 United States 2 -39 22.5 Uzbekistan 35 -22 7.9 Saint Vincent and the 182 29 2.8 Grenadines Venezuela, RB 33 7 9.5 Vietnam 31 81 3.3 Vanuatu 176 -29 2.4 Samoa 179 2 2 32    Yemen, Rep. 101 -7 1.2 South Africa 19 -13 10.1 Zambia 89 -24 2.7 Zimbabwe 112 97 1.8 33