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Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning

Author

Listed:
  • Jérémi Assael

    (Quantitative Finance, MICS Laboratory, CentraleSupélec, Université Paris-Saclay, 91190 Gif-Sur-Yvette, France
    BNP Paribas Corporate & Institutional Banking, Global Markets Data & Artificial Intelligence Lab, 75009 Paris, France)

  • Thibaut Heurtebize

    (BNP Paribas Asset Management, Quantitative Research Group, Research Lab, 92000 Nanterre, France)

  • Laurent Carlier

    (BNP Paribas Corporate & Institutional Banking, Global Markets Data & Artificial Intelligence Lab, 75009 Paris, France)

  • François Soupé

    (BNP Paribas Asset Management, Quantitative Research Group, Research Lab, 92000 Nanterre, France)

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, 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, countries, or revenue buckets. We also compare the model results to those of other providers and find our estimates to be more accurate. Explainability tools based on Shapley values allow the constructed model to be fully interpretable, the user being able to understand which factors split explains the GHG emissions for each particular company.

Suggested Citation

  • Jérémi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse Gases Emissions: Estimating Corporate Non-Reported Emissions Using Interpretable Machine Learning," Sustainability, MDPI, vol. 15(4), pages 1-28, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3391-:d:1066568
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    5. 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.
    6. Martijn Boermans & Rients Galema, 2017. "Pension funds carbon footprint and investment trade-offs," DNB Working Papers 554, Netherlands Central Bank, Research Department.
    7. 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.
    8. Patrick Bolton & Marcin Kacperczyk, 2021. "Global Pricing of Carbon-Transition Risk," NBER Working Papers 28510, National Bureau of Economic Research, Inc.
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