How effective was the UK carbon tax? A machine learning approach to policy evaluation - Jan Abrell; Miriam Kosch; Sebastian Rausch (ETH Zurich, CER-ETH - Center of Economic Research, 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 e18 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: C54; Q48; Q52; Q58; L94; 330; Carbon tax; Carbon pricing; Electricity; Coal; Natural gas; United Kingdom; Carbon Price Surcharge; Policy evaluation; Causal inference; Machine learning; Climate policy