Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 3 https://isni.org/isni/0000000404811396, International Monetary Fund

Annex 1. Model Specification and Results

A computable general-equilibrium model is used to simulate the impact of different policy packages. The Envisage model (van der Mensbrugghe 2019) computes the carbon price needed to achieve a certain emission target and its economic impact.1 In a nutshell, it solves the dynamic general equilibrium for 16 country groups spanning more than 180 countries, incorporating a detailed input-output matrix with 27 economic sectors, including 13 energy sectors.2 Sectors are aggregated through a nested constant elasticity of substitution (CES) structure with heterogeneous elasticities. The level of sectoral granularity allows to capture substitution between different energy sources as well as the overall intensity of energy usage as carbon prices move. The model also features temporary wage rigidities, frictions in capital reallocation, trade costs, energy efficiency gains over time, progressive electrification of the economy, and a declining cost of renewable generation.

The policy simulations focus on the impacts on emissions, carbon prices and macroeconomic variables. Annex Table 1 describes each simulation, with policy scenarios designed to meet the EU’s current target to cut GHG emissions by 40 percent by 2030 relative to 1990 levels or a more ambitious goal of 50 percent.3 For each scenario, Annex Table 2 reports the emission reduction, carbon price needed, real income, unemployment and capital impact, carbon leakage generated, and share of renewable energy, all by 2030 and for the EU aggregate.4,5 It also shows the dispersion of the income impact across EU countries or country groups. Policy scenarios are measured against an unchanged policies baseline calibrated with policies currently in place (that is, excluding announced future policy changes). Annex Figure 1, panel 1, shows the time series of emissions, carbon taxes, and aggregate income under selected scenarios for the period 2020–30. The last panel shows the aggregate income impact across EU countries of those same scenarios. The reported income loss estimates reflect transitional dynamics due to wage rigidities, which cause temporary unemployment and capital reallocation frictions. Steady state income losses would be substantially smaller.

Annex Table 1.

Simulation Scenarios

article image
Annex Table 2.

EU: Simulation Results, 2030

article image
Note: Carbon prices are in 2019 euros and calculated as the economy wide average. For the “Unchanged policies” scenario, the variables income and unemployment are expressed as the cumulative percent growth relative to 2020. For the rest of scenarios, these variables are expressed as percentage point differences with respect to “Unchanged policies” in 2030. Carbon leakage is defined as the emission increase in the rest of the world relative to the decline in EU emissions. Cross-country standard deviations are calculated over the percentage impact in each scenario relative to the baseline. The renewable share is computed over energy production but excludes most of the energy produced by biofuels and biomass, which makes the share smaller than the value reported by Eurostat. BaU = ???; EU = European Union; NA = ???.
Annex Figure 1.
Annex Figure 1.
Source: Authors’ calculations.Note: Data labels in the figure use International Organization for Standardization (ISO) country codes.

Model results are inevitably subject to considerable uncertainty. With a highly parametrized model and forecasts extending over a decade, the results are surrounded by large uncertainty bands. Specific assumptions have to be made regarding the values of substitution and transformation elasticities based on available empirical estimates. Among others, this includes the elasticity of substitution between different energy generation technologies, which has a major impact on abatement cost estimates. Reassuringly, the implied marginal abatement cost curve from Envisage falls in the middle of the range across several other computable general-equilibrium models (Böhringer and others, forthcoming). Envisage model results on required carbon prices are also compared to a spreadsheet tool developed by Parry and Mylonas (2018), which is highly streamlined but provides more transparent intuition.6 In any case, the relative impact of different policy options is broadly invariant to the required level of carbon prices. It is also important to note that the current version of Envisage does not have forward-looking behavior and does not incorporate the economic value of positive environmental effects, such as avoided damages from climate change and air pollution, which would point to a more positive welfare impact from cutting emissions.

The unchanged policies baseline is calibrated broadly based on current policy settings. The starting economy-wide average price is about €9 per ton (a weighted average of the current EU ETS price and non-ETS carbon prices). In the baseline, the associated fiscal revenue is transferred as a lump sum to households. GDP and population growth forecasts are from the IMF April 2020 World Economic Outlook, whereas energy efficiency is assumed to improve by 1.5 percent annually, in line with gains in the previous two decades.7 Electrification increases by 50 percent by 2030 (in proportion to the 2014 electrification rate) in all sectors except transportation, which sees a 75 percent rise. Modest renewable energy share growth is assumed with country-specific targets.8 The elasticity of substitution between electricity generation technologies is set at 3. The rest of parameters are borrowed from the Organisation for Economic Co-operation and Development Green model (Lee, Martins, and van der Mensbrugghe 1994), and the data is from the GTAP Power 10 database (Chepeliev 2020; Peters 2016). Under these assumptions, EU emissions fall by 35 percent relative to 1990, broadly consistent with the European Environment Agency existing policies scenario.9 This scenario is labeled “Unchanged policies” in Annex Table 1.

A series of policy scenarios are then compared to the baseline. In the first two policy scenarios, the emission target is set at a 40 percent reduction of EU emissions by 2030 (the current target), and in the next five scenarios, at a more stringent target of 50. These scenarios are described in more detail in the following.

National Targets Scenario

Without comprehensive trade in emissions between EU countries, the current emission goal would require large carbon prices increases in some countries. The current EU cap-and-trade system only covers the power sector and energy-intensive industries. For the rest of sectors, emission reduction goals are defined nationally, and trade is extremely restricted. The “national targets” scenario is a simplified scenario which assumes that each country has to meet the 50 percent reduction target by 2030 (relative to 1990) individually. Based on the model results, this would imply an increase in average carbon prices across EU countries to €136 per ton by 2030.10 However, countries with less technological margin left to cut emissions, such as France, would see higher carbon prices of up to €216 per ton of CO2 if they were not allowed to trade emission permits. In contrast, Eastern European countries would benefit from smaller-than-average required prices, reflecting their cheaper abatement options. This scenario would lead to a fall in EU aggregate income of 1.2 percent compared to the baseline, as higher carbon prices distort the decisions of both consumers and producers.11

Emission Trade Scenario

A cap-and-trade system covering all sectors would reap the benefits of intra-EU trade and reduce the required carbon price considerably. If all EU countries and sectors were allowed to trade permits in order to meet a joint 50 percent target, mitigation efforts would be directed to countries/sectors with a lower marginal abatement cost. In Envisage, this would lower the required uniform carbon price to €101 per ton in 2030 and the associated aggregate income loss to (0.9 percent), underlying the income gains from an EU-level approach (see scenario “Lump-sum transfers”).12 For comparison, other computable general-equilibrium models estimate a range of prices between €65 and €140 for a similar emission reduction (Böhringer and others, forthcoming). The associated cumulative increase in gasoline and electricity prices would be €0.24 per liter and €0.02 per kWh by 2030.13 Note that a €101 price would be below the current carbon tax in Sweden or the planned tax in the Netherlands. The current more modest emission reduction target of 40 percent would require a much lower increase in carbon prices to €40 (scenario “Lump-sum transfers (40%)”) and an income loss of 0.3 percent of GDP. The more ambitious target requires a more-than-proportional increase in carbon prices because the cost of additional emission reductions grows as cheaper abatement margins (such as shifting away from coal power) are exhausted.

Labor Tax Cut and Renewable Investment Subsidies Scenarios

Carbon pricing revenue can be used to lower distortionary taxation or incentivize green investment. Carbon pricing revenue (either from carbon taxes or emission permit auctions) is significant, at about 1.1 percent of EU GDP by 2030 in the “Lump-sum transfers” scenario. The scenarios discussed so far all assumed that domestically collected revenue is rebated as a lump-sum transfer to households. The following two scenarios show the impact of alternative uses of the revenue for the 50 percent emission reduction target. The policies considered are cuts in labor tax rates and subsidies to investment in renewable energy sources. We also discuss the required carbon prices under other investment policies. Investment in green technologies and energy efficiency reduces emissions and therefore the required equilibrium carbon price. The impact of such policies on emissions in turn affects the required carbon price in equilibrium.

  • Labor tax cut. If all revenue from carbon taxes is used to reduce labor tax rates (scenario “Labor tax cut”), income is only 0.4 percent below the baseline. In other words, recycling carbon pricing revenue to reduce labor taxes can offset a significant share of the negative impact on real income from higher carbon price. This is because with high initial labor tax rates, rigid wages, and unemployment, labor taxes are roughly as distortionary as carbon taxes.14 Indeed, neutralizing the increase in EU average unemployment in the “Lump-sum transfers” scenario contributes to improve the income impact.15 Interestingly, Annex Figure 1, panel 3, shows that during the first years of the policy, income is above the baseline, as small carbon taxes are less distortionary and there is more margin to reduce unemployment. As carbon taxes grow, the income path crosses below the baseline.

  • Renewable investment subsidy. In this scenario, the revenue is used to subsidize investment in solar, wind, and other renewable sources (scenario “Renewable subsidy”). The scope of this policy is limited by the current small size of the renewables sector and the technological limits to the speed at which capacity can be expanded. To avoid unrealistic growth, a 75 percent cap is assumed on the subsidy rate for investment costs, and any remaining revenue is transferred to households.16 A renewable subsidy implies lower required carbon prices at €59 per ton, as it hastens the shift toward cleaner energy sources, making it less necessary to increase taxes. The renewable share in total energy supply increases to 48 percent in the EU on average, compared to 33 percent in the baseline. Income in 2030 is 0.2 percentage points higher than in the scenario with lump sum transfers, as the subsidy increases the aggregate capital stock, but is not as high as in the labor tax cut scenario (which allows for more revenue to be used in cutting taxes).17 Of note, the “Renewable subsidy” scenario is akin to a feebate scheme for the energy sector, coupled with an economy-wide carbon tax.

  • Supplementary investment policies. A different model, developed by Parry and Mylonas (2018), is used to calculate the potential carbon price reduction from three additional investment policies: electrification of cars, housing energy efficiency, and renewable energy R&D.18 The model allows the estimation of the required carbon prices in each scenario, but not the overall output effects. Policy scenarios are applied homogeneously across the EU. The cumulative impact on carbon prices of each policy is as follows:

    • Raising the share of electric cars to 20 percent (consistent with the target set by Germany) of the on-road car fleet by 2030 could lower by 10 percent the required carbon price to reach the current emissions target of 40 percent reduction.

    • In addition, increasing the energy efficiency of residential housing to the second highest efficiency rating would allow a 32 percent reduction in the carbon price relative to the baseline.

    • Supplementing these two measures with R&D subsidies delivering a 1 percent annual productivity growth (on top of the baseline growth) in renewable energy production would lower the needed carbon price by 40 percent relative to the baseline.

EU Compensatory Transfers Scenario

Introducing a uniform carbon price across sectors and countries can lead to disproportionate economic costs in some EU countries. The income cost in the 50-percent emission reduction scenario with lump-sum transfers ranges from 0.5 percent of GDP in France to 1.9 percent in Eastern Europe.19 Intra-EU compensatory transfers could be considered to lift the burden on lower-income Eastern European countries, which feature larger emissions per unit of output, and thus would see a larger increase in the tax burden. A system of transfers neutralizing the income impact of the policy across countries would not change the aggregate income effect at the EU level (relative to the “lump-sum transfers” scenario).

Border Adjustment and Global Carbon Price Scenarios

Strict emission policies at the EU level without international coordination can lead to carbon leakage to the rest of the world. According to the model simulation, a uniform carbon tax cutting emissions by 50 percent in the EU would increase emissions by 15 percent in the rest of the world for each unit of EU emissions avoided, as the EU would turn to importing or outsourcing energy-intensive goods instead of producing them domestically. A carbon border adjustment would contain carbon leakage by setting a levy on the carbon content of imports (based on the carbon intensity of the country of origin) equal to the carbon price applied to EU production (scenario “Border adjustment”).20

A global emission reduction effort would be more efficient. If the entire world implemented policies to reduce global emissions by 50 percent relative to 1990 (scenario “Global carbon price”), allowing for international trade in ETS permits, the global price of carbon required would be €93 per ton.21 For the EU, this would raise income by 0.2 percentage points relative to the scenario without international policy action.

References

  • Andersson, Julius J., 2019. “Carbon Taxes and CO2 Emissions: Sweden as a Case Study.” American Economic Journal: Economic Policy, American Economic Association 11 (4): 130.

    • Search Google Scholar
    • Export Citation
  • Aghion, Philippe, Antoine Dechezleprêtre, David Hemous, Ralf Martin, and John Van Reenen. 2016. “Carbon Taxes, Path Dependency and Directed Technical Change: Evidence from the Auto Industry.” Journal of Political Economy 124 (1): 151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aldy, E. Joseph, and Robert Stavins. 2011. “The Promise and Problems of Pricing Carbon: Theory and Experience.” NBER Working Paper 17569, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Altenburg, Tilman, Christina Rosendahl, Andreas Stamm, and Christian von Drachenfels. 2008. “Industrial Policy: A Key Element of the Social and Ecological Market Economy.” In The Social and Ecological Market Economy: A Model for Asian Development?, edited by Deutsche Gesellschaft für Technische Zusammenarbeit, 13453. Eschborn: GTZ.

    • Search Google Scholar
    • Export Citation
  • Arregui, Nicolas, Christian Ebeke, Jan-Martin Frie, Daniel Garcia-Macia, Dora Iakova, Andy Jobst, Louise Rabier, James Roaf, Chen Ruo, Anna Shabunina, and Sebastian Weber. Forthcoming. “EU Climate Change Mitigation: Sectoral Policies.” IMF Departmental Paper, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Avner, Paolo, Jun Rentschler, and Stephane Hallegatte. 2014. “Carbon Price Efficiency: Lock-in and Path Dependence in Urban Forms and Transport Infrastructure.” Policy Research Working Paper Series 6941, The World Bank, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Batini, Nicoletta, Ian Parry, and Philippe Wingender. Forthcoming. “Scaling Up Climate Mitigation in Denmark.” IMF Selected Issues Paper, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Bento, Antonio M., Mark R. Jacobsen, and Antung A. Liu. 2018. “Environmental Policy in the Presence of an Informal Sector.” Journal of Environmental Economics and Management 90: 6177.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Black, Simon, and Ian Parry. 2020. “Implications of the Global Economic Crisis for Carbon Pricing: A Quantitative Assessment for Coalition Member Countries.” Issues note, Coalition of Finance Ministers for Climate Action, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Bollinger, Bryan, and Kenneth Gillingham. 2014. “Learning-by-Doing in Solar Photovoltaic Installations.” https://environment.yale.edu/gillingham/BollingerGillingham_SolarLBD.pdf

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Böhringer, Christoph, Sonja Peterson, Jan Schneider, and Malte Winkler. Forthcoming. “Carbon Pricing after Paris: Overview of Results from EMF 36.” https://jgea.org/resources/res_display.asp?RecordID=6067

    • Search Google Scholar
    • Export Citation
  • Branstatter, Lee, and Yoshiaki Ogura. 2005. “Is Academic Science Driving a Surge in Industrial Innovation? Evidence from Patent Counts.” NBER Working Paper 11561, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Burniaux, Jean-Marc, Jean Chateau, and Romain Duval. 2013. “Is There a Case for Carbon-based Border Tax Adjustment? An Applied General Equilibrium Analysis.” Applied Economics 45 (16): 223140.

    • Search Google Scholar
    • Export Citation
  • Burtraw, Dallas, Amelia Keyes, and Lars Zetterberg. 2018. “Companion Policies under Capped Systems and Implications for Efficiency – The North American Experience and Lessons in the EU Context.” RFF Working Paper, Resources for the Future, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Coady David, Ian Parry, Nghia-Piotr Le, and Baoping Shang. 2019. “Global Fossil Fuel Subsidies Remain Large: An Update Based on Country-Level Estimates.” IMF Working Paper 19/89, International Monetary Fund, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Chepeliev, Maksym. 2020. “GTAP-Power 10 Data Base: A Technical Note.” GTAP Research Memorandum 31, Global Trade Analysis Project, West Lafayette, IN.

    • Search Google Scholar
    • Export Citation
  • Cosbey, Aaron, Susanne Droege, Carolyn Fischer, and Clayton Munnings. 2019. “Developing Guidance for Implementing Border Carbon Adjustments: Lessons, Cautions, and Research Needs from the Literature.” Review of Environmental Economics and Policy 13 (1): 322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dechezleprêtre, Antoine, Ralf Martin, and Myra Mohnen, M. 2017. “Knowledge Spillovers from Clean and Dirty Technologies: A Patent Citation Analysis.” Working Paper 135, Grantham Research Institute on Climate Change and the Environment and the Centre for Climate Change Economics and Policy, London, UK.

    • Search Google Scholar
    • Export Citation
  • DeFries Ruth, Ottmar Edenhofer, Alex Halliday, Geoffrey Heal, Timothy Lenton, Michael Puma, James Rising, Johan Rockström, Alex C. Ruane, Hans Joachim Schellnhuber, David Stainforth, Nicholas Stern, Marco Tedesco, and Bob Ward. 2019. “The Missing Economic Risks in Assessments of Climate Change Impacts.” Policy insight, The Grantham Research Institute on Climate Change and the Environment, London, UK.

    • Search Google Scholar
    • Export Citation
  • Delbeke, Jos, and Peter Vis (eds.). 2017. EU Climate Policy Explained. Abingdon, UK: Routledge.

  • Evans, Peter B. 1995. Embedded Autonomy: States and Industrial Transformation. Princeton, NJ: Princeton University Press.

  • European Commission. 2014a. “A Policy Framework for Climate and Energy in the Period from 2020 up to 2030.” Brussels, Belgium. https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=SWD:2014:0015:FIN:EN:PDF

    • Search Google Scholar
    • Export Citation
  • European Commission. 2014b. “Enhancing Comparability of Data on Estimated Budgetary Support and Tax Expenditures for Fossil Fuels.” Brussels, Belgium. https://ec.europa.eu/environment/enveco/taxation/pdf/201412fs_final_report.pdf

    • Search Google Scholar
    • Export Citation
  • European Commission. 2017. “Analysis of the use of Auction Revenues by the Member States, Final Report.” Brussels, Belgium. March 2017.

    • Search Google Scholar
    • Export Citation
  • European Commission. 2018a. “Report from the Commission to the European Parliament and the Council EU and the Paris Climate Agreement: Taking Stock of Progress at Katowice COP.” Brussels, Belgium. https://eurlex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52018DC0716

    • Search Google Scholar
    • Export Citation
  • European Commission. 2018b. “A Clean Planet for All: A European Long-term Strategic Vision for a Prosperous, Modern, Competitive and Climate Neutral Economy.” Brussels, Belgium. https://ec.europa.eu/clima/sites/clima/fles/docs/pages/com_2018_733_analysis_in_support_en_0.pdf

    • Search Google Scholar
    • Export Citation
  • European Commission. 2019. “Publication of the Total Number of Allowances in Circulation in 2018 for the Purposes of the Market Stability Reserve under the EU Emissions Trading System established by Directive 2003/87/EC.” Brussels, Belgium. https://ec.europa.eu/clima/sites/clima/files/ets/reform/docs/c_2019_3288_en.pdf

    • Search Google Scholar
    • Export Citation
  • Eurostat. 2020. “Concepts for Households Consumption – Comparison Between Mico and Macro Approach.” https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Concepts_for_household_consumption_-_comparison_between_micro_and_macro_approach

    • Search Google Scholar
    • Export Citation
  • Finardi, Ugo. 2011. “Time Relations between Scientific Production and Patenting of Knowledge: The Case of Nanotechnologies.” Scientometrics 89 (1): 3750.

    • Search Google Scholar
    • Export Citation
  • Fischer, Carolyn, and Alan Fox. 2012. “Comparing Policies to Combat Emissions Leakage: Border Carbon Adjustments versus Rebates.” Journal of Environmental Economics and Management 64 (2): 199216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, Carolyn, Leonie Reins, Dallas Burtraw, David Langlet, Asa Lofgren, Michael Mehling, Stefan E., Weishaar, Lars Zetterberg, Harro van Asselt, and Kati Kulovesi. 2019. “The Legal and Economic Case for an Auction Reserve Price in the EU Emissions Trading System.” CESifo Working Paper 7903, CESifo Network, Munich, Germany.

    • Search Google Scholar
    • Export Citation
  • Flachsland, Christian, Michael Pahle, Dallas Burtraw, Ottmar Edenhofer, Milan Elkerbout, Carolyn Fischer, Oliver Tietjen, and Lars Zetterberg. 2018. “Five Myths about an EU ETS Carbon Price Floor.” CEPS Policy Insights 2018/17, December 2018, CEPS, Brussels, Belgium.

    • Search Google Scholar
    • Export Citation
  • Goulder, Lawrence H., and Andrew Schein. 2013. “Carbon Taxes vs. Cap and Trade: A Critical Review.” NBER Working Papers 19338, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Hepburn, Cameron, Brian O’Callaghan, Nicholas Stern, Joseph Stiglitz, and Dimitri Zenghelis. 2020. “Will COVID-19 Fiscal Recovery Packages Accelerate or Retard Progress on Climate Change?Oxford Review of Economic Policy 36.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Howell, Sabrina T. 2017. “Financing Innovation: Evidence from R&D Grants.” American Economic Review 107 (4): 113664.

  • International Monetary Fund (IMF). 2019a. Fiscal Monitor: How to Mitigate Climate Change. Washington, DC, October.

  • International Monetary Fund (IMF). 2019b. “Sustainable Finance: Looking Farther.” Global Financial Stability Report: Lower for Longer. Washington, DC, October.

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF). 2020. “Greening the Recovery.” Special Series on Fiscal Policies to Respond to COVID-19, Fiscal Affairs Department, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Intergovernmental Panel on Climate Change (IPCC). 2018. “Global Warning of 1.5C.” Geneva.

  • Kahn, Matthew E., Kamiar Mohaddes, Ryan N. C. Ng, M. Hashem Pesaran, Mehdi Raissi, and Jui-Chung Yang. 2019. “Long-Term Macro-economic Effects of Climate Change: A Cross-Country Analysis.” IMF Working Papers in Economics 19/215, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuralbayeva, Karlygash. 2019. “Environmental Taxation, Employment and Public Spending in Developing Countries.” European Association of Environmental and Resource Economists 72 (4): 877912.

    • Search Google Scholar
    • Export Citation
  • Lee, Hiro, Joaquim Oliveira Martins, and Dominique van der Mensbrugghe. 1994. “The OECD Green Model: An Updated Overview.” OECD Development Centre Working Papers 97, Organisation for Economic Co-operation and Development, Paris.

    • Search Google Scholar
    • Export Citation
  • Ley, Marius, Tobias Stucki, and Martin Woerter. 2016. “The Impact of Energy Prices on Green Innovation.” The Energy Journal, International Association for Energy Economics, 0 (1).

    • Search Google Scholar
    • Export Citation
  • Metcalf, Gilbert E., and James H. Stock. 2020. “The Macroeconomic Impact of Europe’s Carbon Taxes.” NBER Working Paper 27488, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Mowrey, David C., Richard R. Nelson, and Ben R. Martin. 2010. “Technology Policy and Global Warming: Why New Policy Models are Needed (or Why Putting New Wine in Old Bottles Won’t Work).” Research Policy 39 (8): 101123.

    • Search Google Scholar
    • Export Citation
  • Nemet. 2012. “Subsidies for New Technologies and Knowledge Spillovers from Learning by Doing.” Journal of Policy Analysis and Management 31 (3): 60122.

    • Search Google Scholar
    • Export Citation
  • Noailly, Joëlle, and Victoria Shestalova. 2017. “Knowledge Spillovers from Renewable Energy Technologies: Lessons from Patent Citations.” Environmental Innovation and Societal Transitions 22: 114.

    • Search Google Scholar
    • Export Citation
  • UN Environment Programme (UNEP). 2018. “Emissions Gap Report 2018.” Nairobi, Kenya.

  • Organisation for Economic Co-operation and Development. 2015. “Curbing Corruption Investing in Growth.” 3rd OECD Integrity Forum Background Document. Paris.

    • Search Google Scholar
    • Export Citation
  • Parry, Ian W. H. 2020. “Increasing Carbon Pricing in the EU: Evaluating the Options.” European Economic Review 121: 123.

  • Parry, Ian W. H., and Antonio M. Bento. 2000. “Tax Deductions, Environmental Policy, and the ‘Double Dividend’ Hypothesis.” Journal of Environmental Economics and Management 39 (1): 6796.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parry, Ian W. H., Dirk Heine, Shanjun Li, and Eliza Lis. 2014. Getting Energy Prices Right: From Principle to Practice. Washington, DC: International Monetary Fund.

    • Search Google Scholar
    • Export Citation
  • Parry, Ian, and Victor Mylonas. 2018. “Canada’s Carbon Price Floor.” IMF Working Paper 18/42, International Monetary Fund, Washington, DC.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parry, Ian, Victor Mylonas, and Nate Vernon. 2020. “Mitigation Policies for the Paris Agreement: An Assessment for G20 Countries.” Journal of the Association of Environmental and Resource Economists, forthcoming.

    • Search Google Scholar
    • Export Citation
  • Perino, Grischa. 2018. “New EU ETS Phase 4 Rules Temporarily Puncture Waterbed.” Nature Climate Change 8 (4): 26264.

  • Perino, Grischa, Robert A. Ritz, and Arthur van Benthem. 2019. “Understanding Overlapping Policies: Internal Carbon Leakage and the Punctured Waterbed.” NBER Working Paper 25643, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Peters, Jeffrey C. 2016. “The GTAP-Power Data Base: Disaggregating the Electricity Sector in the GTAP Data Base.” Journal of Global Economic Analysis 1 (1): 20950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Popp, David. 2002. “Induced Innovation and Energy Prices.” American Economic Review 92 (1): 16080.

  • Popp, David. 2016. “Economic Analysis of Scientific Publications and Implications for Energy Research and Development.” Nature Energy 1: 18.

    • Search Google Scholar
    • Export Citation
  • Popp, David. 2017. “From Science to Technology: The Value of Knowledge from Different Energy Research Institutions.” Research Policy 46 (9): 158094.

    • Search Google Scholar
    • Export Citation
  • Popp, David, and Richard Newell. 2012. “Where Does Energy R&D Come From? Examining Crowding out from energy R&D.” Energy Economics 34 (4): 98091.

    • Search Google Scholar
    • Export Citation
  • Quemin, Simon, and Raphael Trotignon. 2019. “Intertemporal Emissions Trading and Market Design: An Application to the EU-ETS.” Grantham Research Institute on Climate Change and the Environment Working Paper 316, London.

    • Search Google Scholar
    • Export Citation
  • Ramsey, Frank P. 1927. “A Contribution to the Theory of Taxation.” The Economic Journal 37 (145): 4761.

  • Rodrik, Dani. 2004. “Industrial Policy for the Twenty-First Century.” Faculty Research Working Papers Series, University of Harvard, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Roques, Fabien, and Helene Laroche. 2020. “Combined Retrospective Evaluation and Prospective Impact Assessment Support Study on Emission Trading System (ETS) State Aid Guidelines.” Final Report to the European Commission. https://ec.europa.eu/competition/consultations/2020_ets_stateaid_guidelines/consultance_report.pdf

    • Search Google Scholar
    • Export Citation
  • Söderholm, Patrik, and Tomas Sundqvist. 2007. “Empirical Challenges in the Use of Learning Curves for Assessing the Economic Prospects of Renewable Energy Technologies.” Renewable Energy 32 (15): 255978.

    • Search Google Scholar
    • Export Citation
  • Springel, Katalin. 2018. “Network Externality and Subsidy Structure in Two-Sided Markets: Evidence from Electric Vehicle Incentives.” Memo, Resources for the Future, Washington, DC.

    • Search Google Scholar
    • Export Citation
  • Stavins, Robert. 2019. “The Future of US Carbon-Pricing Policy.” NBER Working Paper 25912, National Bureau of Economic Research, Cambridge, MA.

    • Search Google Scholar
    • Export Citation
  • Stern, Nicholas. 2006. The Stern Review on the Economic Effects of Climate Change. Cambridge: Cambridge University Press.

  • Stern, Nicholas, and Joseph E. Stiglitz. 2017. “Report of the High-Level Commission on Carbon Prices.” World Bank, Washington, DC.

  • van der Mensbrugghe, Dominique. 2019. “The Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) Model: Version 10.01.” The Center for Global Trade Analysis, Purdue University, West Lafayette, IN.

    • Search Google Scholar
    • Export Citation
  • Wei, Max, Shana Patadia, and Daniel M. Kammen. 2010. “Putting Renew-ables and Energy Efficiency to Work: How Many Jobs Can the Clean Energy Industry Generate in the US?Energy Policy 38 (2): 91931.

    • Search Google Scholar
    • Export Citation
  • Weyant, John P. 2011. “Accelerating the Development and Diffusion of New Energy Technologies: Beyond the ‘Valley of Death.’” Energy Economics 33 (4): 67482.

    • Search Google Scholar
    • Export Citation
  • Wingender, Philippe, and Florian Misch. 2020. “Emission Spillovers from Carbon Taxation: New Evidence and Policy Questions.” Unpublished manuscript.

    • Search Google Scholar
    • Export Citation
  • World Bank. 2019. State and Trends of Carbon Pricing 2019. Washington, DC: World Bank.

  • World Bank. 2020. State and Trends of Carbon Pricing 2020. Washington, DC: World Bank.

2

The current annual reduction rate of 1.7 percent will increase to 2.2 percent from 2021 to 2030.

3

It is currently unclear how the United Kingdom and the EU will collaborate in reducing global emissions after 2021. This paper assumes that the United Kingdom will continue to participate in the EU climate policy until 2030.

4

Sectors at risk of leakage are defined as those where ETS pricing would increase production costs by at least 5 percent of total gross value added and trade intensity with non-EU countries is above 10 percent. The benchmarks set the amount of free permits for each industrial product. See Delbeke and Vis (2017) for further discussion. Small emitters are allowed to opt out from the EU ETS if they are subject to equivalent measures, in order to minimize administrative costs.

5

Sectors that are not deemed to be at risk of leakage will receive up to 30 percent free allocations from 2021 to 2026, decreasing linearly to zero from 2026 to 2030. Sectors on the carbon leakage list will continue to receive 100 percent of their allowances up to benchmark levels for free. The benchmark levels will be updated every five years to take technological progress into account.

6

Crucial for the pace at which surpluses are reduced is the share which is placed in the MSR. This share is set at 24 percent until 2023 and will be halved after that. Currently, the permits in MSR will be placed back into the ETS if surplus falls below a certain threshold. Starting in 2023, MSR reserves in excess of the previous year’s auction volume will be permanently invalidated.

7

More specifically, 90 percent of allowances to be auctioned will be distributed to member states based on their share of verified emissions. The remaining 10 percent of the auctioning rights are distributed to member states with low per capita income.

8

Moreover, France has taken significant steps to integrate environmental goals in the budgetary process.

9

Iceland, Luxembourg, and Norway are allowed up to a limit of 4 percent.

1

The net fiscal cost of policies is kept at zero every year in the simulations to allow a clean comparison of the economic efficiency of different policy packages.

2

The model assumes that Iceland, Norway, Switzerland, and the United Kingdom (that is, countries that are currently in the EU ETS) participate in EU climate packages.

3

Despite high levels of taxation for road transport fuel, fuel prices still appear to be below their economically efficient levels, reflecting multiple negative externalities (Arregui and others forthcoming; Coady and others 2019). Thus, including the transportation sector in the ETS should not be used as an argument for a generalized reduction of existing fuel duties. Fuel duties should only decrease by the amount that was attributed to emission externalities; duties targeted to other purposes such as road congestion or revenue collection should remain in place.

4

An implicit reserve price already exists in the EU ETS. Article 7(6) of the Auctioning Regulation stipulates that an auction shall be canceled if the clearing price is significantly below the secondary market price. Nonetheless, this system has not prevented the carbon price from being low and volatile.

5

While the absence of a price floor may provide relief during cyclical downturns, the social cost of emissions is roughly unchanged, so it is not optimal for the carbon price to drop to the extent observed after the global financial crisis.

6

For example, in 2017, some member states transferred part of their ETS revenue to the industry, ranging from less than 5 percent in Lithuania to 30 percent in France and 50 percent in Luxembourg (EC 2019). These compensations have not always been driven by an economic rationale (Roques and Laroche 2020).

7

Beyond 2030, carbon pricing revenue would start to decrease as the EU approaches its 2050 goal of net zero emissions, because the revenue base will shrink. Hence, the use of revenue will provide support during the transition, not permanently.

8

The income losses from a carbon price increase reflect distortions in economic efficiency and transitory frictions in labor and capital markets. Labor market frictions are modeled as rigid wages, and capital market frictions as the inability to reallocate old capital vintages between sectors. In reality, job reallocation frictions may also play an important role. Note that the model does not take into account income gains from avoided environmental damages.

9

Metcalf and Stock (2020) estimate that the impact on output and employment of carbon pricing in the EU is not statistically significant.

10

Carbon leakage is defined as the increase in rest-of-the-world emissions caused by an increase in the cost of EU emissions. This can be due to either import substitution or outsourcing of industries.

11

As discussed earlier, uniform carbon prices reduce aggregate income losses (at the individual country level, income losses would decline especially sharply for countries with high marginal abatement costs combined with more ambitious targets). Thus a uniform carbon tax combined with cross-country transfers are in everyone’s economic interest.

12

There is a potential third factor as well. Energy consumption is likely to be reflected fairly accurately in the surveys, but total consumption may be underestimated. Aggregate consumption measured through household survey data is typically smaller than the aggregate final consumption of private households in the national accounts. This is due to several measurement and conceptual issues. An important difference is the imputation of rents for owner-occupied housing and financial services included in the national accounts data. See Eurostat (2020) for details.

13

Any type of BCA would eliminate only the leakage embodied in trade. It would not address the potential increase in global fossil fuel consumption induced by the reduction in fossil fuel prices due to lower demand by the EU.

14

Examples of such certification institutions include the World Resource Institute/World Business Council on Sustainable Development GHG Protocol or the ISO 14064 standard.

15

Note that the label “green” in this context refers to activities that have broadly neutral impact on the environment or help reduce GHG emissions.

16

Popp (2002) estimates that a 10 percent increase in energy prices leads to a 3.5 percent rise in the number of patents in renewable energy and energy efficiency technology. Ley, Stucki, and Woerter (2016) find that a 10 percent increase of energy prices results in a 3.4 percent increase in the number of green innovations.

18

See Soderholm and Sundqvist (2007) for renewable energy technologies, Nemet (2012) for wind turbines, and Bollinger and Gillingham (2014) for solar photovoltaic installations.

20

However, there is a risk that investors without environmental goals undo part of the effect by chasing higher yields in high-emitting companies.

1

One of the key strengths of the Envisage model is a consistent and complete representation of the global economy, including interactions between different economic agents. Tough the level of the technological details is lower than in energy-system models, and productivity growth is exogenous, energy demand is represented endogenously. Combined with a sufficient level of sectoral details, the model provides a comprehensive framework for a multiregion energy and environmental policy assessment.

2

The country groups are Germany, France, Italy, EU Eastern Europe, other Western Europe, the United Kingdom, the United States, other OECD, China, Russia, OPEC, advanced Asia, other East Asia, South Asia, other Latin America, and rest of the world. The energy sectors are coal, oil, gas, refined oil, nuclear power, coal power, gas power, hydro power, solar power, wind power, oil power, other, and electricity transmission and generation.

3

The agreement covers three types of gas emissions (carbon dioxide, nitrous oxide, and perfuorocarbons), but the focus here is on carbon dioxide from fossil fuel combustion, which accounts for most GHG emissions.

4

The term EU is used loosely throughout this section as the model data cover all European Economic Area countries. All European Economic Area countries participate in the EU ETS.

5

Real income is defined as the equivalent variation in income derived from the consumer’s expenditure function. It differs from GDP given the geometric aggregation of heterogeneous sectors into a final consumption good via a nested CES function.

6

The tool simulates the use of fossil fuels in three sectors (power generation, road transport, and “other energy”) until 2030, assuming a fixed energy supply. Energy demand is mainly driven by income growth, which is exogenous, and energy prices, which increase with a carbon tax.

7

No changes in energy efficiency are assumed for coal power generation, oil power generation, and oil refining.

8

This is achieved by assuming costs decline by 13 percent for wind power generation, by 35 percent for solar power, and by 18 percent other renewables from 2014 to 2030.

9

This baseline is not directly comparable to the EU Reference Scenario (EC 2018b), which includes policies to achieve legislated targets, including on renewable shares and energy efficiency, and thus implies a faster emission reduction.

10

All prices are expressed in 2019 euros. By assumption, nuclear energy and hydro power are not allowed to grow in the simulation.

11

This estimate likely underestimates the income loss from a lack of full emissions trading, because the stylized scenario allows for trade between ETS and non-ETS sectors within a country, and between different countries in a country group (such as Eastern Europe). On the other hand, it does not allow for trade in ETS sectors between country groups.

12

Note that a comparison of the estimated carbon prices across different models is complicated because the prices depend on the assumed baseline (among other things). For example, the model developed by Parry and Mylonas (2018) calculates a higher carbon tax of €78 per ton of CO2 in 2030 needed to achieve the EU goals, but that model has much smaller reduction in emissions in the baseline (about 25 percent compared to 33 percent in the baseline used in this paper).

13

The latter implies a €120 increase in the annual electricity bill for an average EU citizen (given 2014 electricity consumption values).

14

A well-known result in public economics is that the welfare deadweight loss from taxation is more than proportionally increasing in the tax rate (Ramsey 1927). Parry and Bento (2000) provide empirical support for the result that labor taxes can be more distortionary than carbon taxes, as labor taxes distort not only labor markets but also the choice between ordinary and tax-preferred spending (for example, on housing, fringe benefits, informal production).

15

The income impact is sensitive to the calibration of unemployment. Initial unemployment is assumed to be equal to the natural unemployment rate, sourced from the IMF World Economic Outlook. The minimum unemployment rate that can be achieved is capped for each country at either 70 percent of the distance between natural unemployment and 2.5 percent, or 2.5 percent, whatever is the larger value, consistent with historical data.

16

Such a subsidy rate leads to an expansion of the renewable energy share to 73 percent in Germany, which is slightly above its national target of 65 percent.

17

For a fairer comparison with the labor tax scenario, an alternative scenario where labor taxes are lowered by the same amount as the renewable subsidy is run (not shown). Even in that case, cutting labor taxes generates a more benign income impact, as the current labor tax rates are more distortionary than the effect of higher energy prices.

18

The Envisage sectoral aggregation does not provide the granularity needed to model these policies explicitly. However, the model by Parry and Mylonas (2018) does not calculate the full general equilibrium effects on macroeconomic variables. In the latter model, the increase in carbon price is relative to a baseline with no new or tightening of existing policies beyond those implicit in recently observed fuel use.

19

This range would be even wider if all individual countries were disaggregated in the simulation.

20

In the simulation, carbon exports are not exempt from carbon pricing, although this could be modeled as well (see section in the main text).

21

This scenario illustrates the efficiency gains from global carbon emissions trading. A practical global solution should take other factors into consideration as well, such as level of economic developments and mitigation possibilities, and may involve different targets for different countries.

EU Climate Mitigation Policy
Author: Mr. Jiaqian Chen, Maksym Chepeliev, Mr. Daniel Garcia-Macia, Ms. Dora M Iakova, Mr. James Roaf, Ms. Anna Shabunina, Dominique van der Mensbrugghe, and Mr. Philippe Wingender