Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/207139 
Year of Publication: 
2019
Series/Report no.: 
Economics Working Paper Series No. 19/317
Publisher: 
ETH Zurich, CER-ETH - Center of Economic Research, 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 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.
Subjects: 
Carbon tax
Carbon pricing
Electricity
Coal
Natural gas
United Kingdom
Carbon Price Surcharge
Policy evaluation
Causal inference
Machine learning
Climate policy
JEL: 
C54
Q48
Q52
Q58
L94
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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