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

Jérémi Assael (), Thibaut Heurtebize, Laurent Carlier and François Soupé
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Jérémi Assael: Quantitative Finance, MICS Laboratory, CentraleSupélec, Université Paris-Saclay, 91190 Gif-Sur-Yvette, 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

Sustainability, 2023, vol. 15, issue 4, 1-28

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.

Keywords: sustainability; disclosure; greenhouse gas emissions; machine learning; interpretability; carbon emissions; scope 1; scope 2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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