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Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning

Author

Listed:
  • Jeremi Assael

    (BNPP CIB GM Lab, MICS)

  • Thibaut Heurtebize

    (BNPP CIB GM Lab)

  • Laurent Carlier

    (BNPP CIB GM Lab)

  • Franc{c}ois Soup'e

Abstract

As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies and methodologies of measurement and estimation are not unified. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet. Our model, specifically designed to be transparent and completely adapted to this use case, is able to estimate emissions for a large universe of companies. It shows good out-of-sample global performances as well as good out-of-sample granular performances when evaluating it by sectors, by countries or by revenues buckets. We also compare our results to those of other providers and find our estimates to be more accurate. Thanks to the proposed explainability tools using Shapley values, our model is fully interpretable, the user being able to understand which factors split explain the GHG emissions for each particular company.

Suggested Citation

  • Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & Franc{c}ois Soup'e, 2022. "Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning," Papers 2212.10844, arXiv.org.
    • Handle: RePEc:arx:papers:2212.10844
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    File URL: http://arxiv.org/pdf/2212.10844
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    References listed on IDEAS

    as
    1. Martijn Boermans & Rients Galema, 2017. "Pension funds carbon footprint and investment trade-offs," DNB Working Papers 554, Netherlands Central Bank, Research Department.
    2. Jérémi Assael & Laurent Carlier & Damien Challet, 2023. "Dissecting the Explanatory Power of ESG Features on Equity Returns by Sector, Capitalization, and Year with Interpretable Machine Learning," JRFM, MDPI, vol. 16(3), pages 1-22, March.
    3. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    4. Thomas O. Wiedmann & Manfred Lenzen & John R. Barrett, 2009. "Companies on the Scale: Comparing and Benchmarking the Sustainability Performance of Businesses," Journal of Industrial Ecology, Yale University, vol. 13(3), pages 361-383, June.
    5. Jeremi Assael & Laurent Carlier & Damien Challet, 2022. "Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning," Working Papers hal-03791538, HAL.
    6. Paul A. Griffin & David H. Lont & Estelle Y. Sun, 2017. "The Relevance to Investors of Greenhouse Gas Emission Disclosures," Contemporary Accounting Research, John Wiley & Sons, vol. 34(2), pages 1265-1297, June.
    7. Bernhard Goldhammer & Christian Busse & Timo Busch, 2017. "Estimating Corporate Carbon Footprints with Externally Available Data," Journal of Industrial Ecology, Yale University, vol. 21(5), pages 1165-1179, October.
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