IMF Working Papers

Structural Breaks in Carbon Emissions: A Machine Learning Analysis

Author/Editor:

Jiaxiong Yao ; Yunhui Zhao

Publication Date:

January 21, 2022

Electronic Access:

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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).

Series:

Working Paper No. 2022/009

Subject:

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

Frequency:

regular