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

• URL: http://arxiv.org/abs/2212.10844v1
• Date: Wed, 21 Dec 2022 08:36:02 GMT
• Title: Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning
• Authors: Jeremi Assael (BNPP CIB GM Lab, MICS), Thibaut Heurtebize, Laurent Carlier (BNPP CIB GM Lab), Fran\c{c}ois Soup\'e
• Abstract summary: As of 2022, greenhouse gases (GHG) emissions reporting and auditing are not yet compulsory for all companies. We propose a machine learning-based model to estimate scope 1 and scope 2 GHG emissions of companies not reporting them yet.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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