Structural Breaks in Carbon Emissions: A Machine Learning Analysis

January 21, 2022

Preview Citation

Format: Chicago

Jiaxiong Yao, and Yunhui Zhao. Structural Breaks in Carbon Emissions: A Machine Learning Analysis, (USA: International Monetary Fund, 2022) accessed September 20, 2024

Disclaimer: 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.

Summary

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 nonclimate 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).

Subject: Carbon tax, Climate change, Climate policy, Environment, Greenhouse gas emissions, Machine learning, Taxes, Technology

Keywords: Carbon Emissions, Carbon tax, Climate change, Climate Policies, Climate policy, Emission level, Energy intensity, Global, Greenhouse gas emissions, Kaya Identity, Machine learning, Machine Learning, Machine learning analysis, Machine learning methodology, Structural Break, User-friendly machine-learning tool

Publication Details

Pages:

47
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/009
Stock No:

WPIEA2022009
ISBN:

9798400200267
ISSN:

1018-5941

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