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

Author

Listed:
  • Jeremi Assael

    (BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab, MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay)

  • Thibaut Heurtebize

    (BNP Paribas Asset Management, Quantitative Research Group, Research Lab)

  • Laurent Carlier

    (BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab)

  • François Soupé

    (BNP Paribas Asset Management, Quantitative Research Group, Research Lab)

Abstract

As of 2022, greenhouse gas (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

  • Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse gas emissions: estimating corporate non-reported emissions using interpretable machine learning," Post-Print hal-03905325, HAL.
    • Handle: RePEc:hal:journl:hal-03905325
    DOI: 10.3390/su15043391
    Note: View the original document on HAL open archive server: https://hal.science/hal-03905325v4
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    sustainability; disclosure; greenhouse gas emissions; machine learning; interpretability; carbon emissions; scope 1; scope 2; interpretable machine learning;
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