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© 2022 International Monetary Fund
WP/22/9
IMF Working Paper
Strategy, Policy, and Review Department
Structural Breaks in Carbon Emissions: A Machine Learning Analysis
Prepared by Jiaxiong Yao and Yunhui Zhao
Authorized for distribution by Stephan Danninger
January 2022
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
ABSTRACT: To reach the global net-zero goal, the level of carbon emissions has to fall substantially at speed rarely seen in history, highlighting the need to identify structural breaks in carbon emission patterns and understand forces that could bring about such breaks. In this paper, we identify and analyze structural breaks using machine learning methodologies. We find that downward trend shifts in carbon emissions since 1965 are rare, and most trend shifts are associated with non-climate structural factors (such as a change in the economic structure) rather than with climate policies. While we do not explicitly analyze the optimal mix between climate and non-climate policies, our findings highlight the importance of the non-climate policies in reducing carbon emissions. On the methodology front, our paper contributes to the climate toolbox by identifying country-specific structural breaks in emissions for top 20 emitters based on a user-friendly machine-learning tool and interpreting the results using a decomposition of carbon emission ( Kaya Identity).
| JEL Classification Numbers: | C23, C50, Q54, Q58 |
| Keywords: | Climate Policies, Carbon Emissions, Machine Learning, Structural Break, Kaya Identity |
| JEL Classification Numbers: | C23, C50, Q54, Q58 |
| Keywords: | Climate Policies, Carbon Emissions, Machine Learning, Structural Break, Kaya Identity |
Contents
Abstract
I. Introduction
II. Literature Review
III. Country-Specific Machine-Learning Analysis
A. Data and Methodology
B. Unconditional Analysis: Results and Interpretation
C. Kaya Identity
D. Conditional Analysis: Results and Interpretation
IV. Conclusion and Policy Implications
Appendices
References