How effective is carbon pricing? A machine learning approach to policy evaluation

Jan Abrell, Mirjam Kosch and Sebastian Rausch

No 21-039, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

Abstract: While carbon taxes are generally seen as a rational policy response to climate change, knowledge about their performance from an expost perspective is still limited. 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 policy evaluation approach which leverages economic theory and machine learning 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 in the short run a higher carbon tax does not necessarily lead to higher emissions reductions or higher costs.

JEL-codes: C54 L94 Q48 Q52 Q58 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-cmp, nep-ene, nep-env and nep-reg
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Journal Article: How effective is carbon pricing?—A machine learning approach to policy evaluation (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:21039

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