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

Jan Abrell, Mirjam Kosch () and Sebastian Rausch
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Mirjam Kosch: ZHAW Winterthur and ETH Zurich, Switzerland

No 19/317, CER-ETH Economics working paper series from CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich

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.

Keywords: Climate Policy; Carbon Tax; Carbon Pricing; Electricity; Coal; Natural Gas; United Kingdom; Carbon Price Surcharge; Policy Evaluation; Causal Inference; Machine Learning (search for similar items in EconPapers)
JEL-codes: C54 L94 Q48 Q52 Q58 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2019-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-env, nep-pub and nep-reg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

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