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

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.

Carbon Emission Prediction in a Region ofHainan Province Based on Improved STIRPAT Model

“…To better understand the causal relationship between the five indicators and carbon emissions (C), and identify nonlinear relationships, this paper uses the SHAP method for interpreting the model (Assael et al 2023;Parsa et al 2020) to conduct an interpretable analysis of the impact parameters on carbon emissions. SHAP values the impact of each factor on carbon emissions (C) by calculating the Shapley value for each influencing factor and summarizes the Shapley values of all parameters to achieve global and local interpretation of carbon emissions.…”