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

Abrell, J., Kosch, M., Rausch, S. (2022): How effective is carbon pricing? - A machine learning approach to policy evaluation. - Journal of Environmental Economics and Management, 112, 102589.
https://doi.org/10.1016/j.jeem.2021.102589

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 Creators:
Abrell, Jan 1, Author
Kosch, Mirjam2, Author              
Rausch, Sebastian 1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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Free keywords: Carbon pricing Carbon tax UK Policy evaluation Machine learning Electricity Carbon Price Support Climate policy Emissions abatement Effectiveness
 Abstract: While carbon taxes are generally seen as a rational policy response to climate change, knowledge about their performance from an ex-post 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 whether a higher carbon tax leads to higher emissions reductions and higher costs depends on relative fuel prices.

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Language(s): eng - English
 Dates: 2020-11-262021-12-172021-12-172022-03
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.jeem.2021.102589
Organisational keyword: RD3 - Transformation Pathways
PIKDOMAIN: RD3 - Transformation Pathways
Research topic keyword: Carbon Pricing
Research topic keyword: Energy
Research topic keyword: Decarbonization  
Research topic keyword: Climate Policy
Regional keyword: Europe
Model / method: Machine Learning
MDB-ID: No data to archive
 Degree: -

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Title: Journal of Environmental Economics and Management
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 112 Sequence Number: 102589 Start / End Page: - Identifier: Publisher: Elsevier
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journal-of-environmental-economics-and-management