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Structural Breaks in Carbon Emissions: A Machine Learning Analysis

Author

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  • Jiaxiong Yao
  • Mr. Yunhui Zhao

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 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).

Suggested Citation

  • Jiaxiong Yao & Mr. Yunhui Zhao, 2022. "Structural Breaks in Carbon Emissions: A Machine Learning Analysis," IMF Working Papers 2022/009, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2022/009
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    Citations

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    Cited by:

    1. Yasunori Ito & Hidemichi Fujii, 2024. "Trend Changes and the Driving Forces of Environmental Indicators in Countries Worldwide: A Structural Change Analysis of Variations in CO 2 Emissions and Eco-Efficiency," Sustainability, MDPI, vol. 16(12), pages 1-16, June.
    2. Payne, James E. & Lee, Junsoo & Islam, Md. Towhidul & Nazlioglu, Saban, 2022. "Stochastic convergence of per capita greenhouse gas emissions: New unit root tests with breaks and a factor structure," Energy Economics, Elsevier, vol. 113(C).

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