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How Effective Was the UK Carbon Tax? — A Machine Learning Approach to Policy Evaluation

Author

Listed:
  • Jan Abrell

    () (ZHAW Winterthur and ETH Zurich, Switzerland)

  • Mirjam Kosch

    () (ZHAW Winterthur and ETH Zurich, Switzerland)

  • Sebastian Rausch

    () (ETH Zurich, Switzerland)

Abstract

Carbon taxes are commonly seen as a rational policy response to climate change, but little is known about their performance from an ex-post perspective. This paper analyzes the emissions and cost impacts of the UK CPS, a carbon tax levied on all fossil-fired power plants. To overcome the problem of a missing control group, we propose a novel approach for policy evaluation which leverages economic theory and machine learning techniques for counterfactual prediction. Our results indicate that in the period 2013-2016 the CPS lowered emissions by 6.2 percent at an average cost of € 18 per ton. We find substantial temporal heterogeneity in tax-induced impacts which stems from variation in relative fuel prices. An important implication for climate policy is that a higher carbon tax does not necessarily lead to higher emissions reductions or higher costs.

Suggested Citation

  • Jan Abrell & Mirjam Kosch & Sebastian Rausch, 2019. "How Effective Was the UK Carbon Tax? — A Machine Learning Approach to Policy Evaluation," CER-ETH Economics working paper series 19/317, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
  • Handle: RePEc:eth:wpswif:19-317
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    File URL: https://www.ethz.ch/content/dam/ethz/special-interest/mtec/cer-eth/cer-eth-dam/documents/working-papers/WP-19-317.pdf
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    References listed on IDEAS

    as
    1. Bretschger, Lucas & Lechthaler, Filippo & Rausch, Sebastian & Zhang, Lin, 2017. "Knowledge diffusion, endogenous growth, and the costs of global climate policy," European Economic Review, Elsevier, vol. 93(C), pages 47-72.
    2. Steve Cicala, 2017. "Imperfect Markets versus Imperfect Regulation in U.S. Electricity Generation," NBER Working Papers 23053, National Bureau of Economic Research, Inc.
    3. Abrell, Jan & Rausch, Sebastian, 2016. "Cross-country electricity trade, renewable energy and European transmission infrastructure policy," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 87-113.
    4. repec:dau:papers:123456789/11055 is not listed on IDEAS
    5. Julien Chevallier & Erik Delarue & Emeric Lujan & William D'haeseleer;, 2012. "A counterfactual simulation exercise of CO 2 emissions abatement through fuel-switching in the UK (2008-2012)," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 35(5), pages 311-331.
    6. Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2017. "Machine Learning from Schools about Energy Efficiency," NBER Working Papers 23908, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Marion Leroutier, 2019. "Carbon Pricing and Power Sector Decarbonisation: Evidence from the UK," Policy Papers 2019.03, FAERE - French Association of Environmental and Resource Economists.
    2. Klaus Gugler & Adhurim Haxhimusa & Mario Liebensteiner, 2019. "Effective Climate Policy Doesn’t Have to be Expensive," Department of Economics Working Papers wuwp293, Vienna University of Economics and Business, Department of Economics.
    3. Aleksandar Zaklan, 2020. "Coase and Cap-and-Trade: Evidence on the Independence Property from the European Electricity Sector," Discussion Papers of DIW Berlin 1850, DIW Berlin, German Institute for Economic Research.

    More about this item

    Keywords

    Climate Policy; Carbon Tax; Carbon Pricing; Electricity; Coal; Natural Gas; United Kingdom; Carbon Price Surcharge; Policy Evaluation; Causal Inference; Machine Learning;

    JEL classification:

    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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