Structural Breaks in Carbon Emissions: A Machine Learning Analysis
IMF Working Papers, January 21, 2022
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- Structural Breaks in Carbon Emissions: A Machine Learning Analysis
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Bibliographic details
- Authors: Jiaxiong Yao, Yunhui Zhao
- Published: January 21, 2022
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798400200267.001
Objective
- To identify and analyze structural breaks in carbon emission patterns to better understand forces that could bring about the rapid emissions declines needed to reach the global net-zero goal.
- Emphasis on the rarity of historically rapid declines and the need to detect country-specific structural breaks.
Methodology
- Use of machine learning methodologies to identify structural breaks in carbon emissions.
- Interpretation of detected breaks using a decomposition of carbon emission (Kaya Identity).
- Application of a user-friendly machine-learning tool to identify country-specific structural breaks for the top 20 emitters.
Key Findings
- Downward trend shifts in carbon emissions since 1965 are rare.
- Most trend shifts are associated with non-climate structural factors (such as a change in the economic structure) rather than with climate policies.
- The findings highlight the importance of nonclimate policies in reducing carbon emissions, although the paper does not explicitly analyze the optimal mix between climate and non-climate policies.
Contributions
- Methodological: adds to the climate toolbox by identifying country-specific structural breaks using machine learning and by interpreting results via the Kaya Identity.
- Practical: provides a user-friendly machine-learning tool for detecting structural breaks in emissions for the top 20 emitters.
Policy Implications
- Non-climate structural factors can play a dominant role in emission trend shifts.
- Policymaking should consider the role of economic-structure changes and other non-climate policies alongside climate policies to achieve rapid emission reductions.
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- Working Paper