How Effective Was the UK Carbon Tax? - A Machine Learning Approach to Policy Evaluation
CER-ETH – Center of Economic Research at ETH Zurich Working Paper 19/317
51 Pages Posted: 14 May 2019
Date Written: April 15, 2019
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
JEL Classification: C54, Q48, Q52, Q58, L94
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