PT  - JOURNAL ARTICLE
AU  - Thibaut Heurtebize
AU  - Frederic Chen
AU  - François Soupé
AU  - Raul Leote de Carvalho
TI  - Corporate Carbon Footprint: A Machine Learning Predictive Model for Unreported Data
AID  - 10.3905/jesg.2022.1.059
DP  - 2022 Nov 30
TA  - The Journal of Impact and ESG Investing
PG  - 36--54
VI  - 3
IP  - 2
4099  - https://pm-research.com/content/3/2/36.short
4100  - https://pm-research.com/content/3/2/36.full
AB  - The authors propose a model based on statistical learning techniques to predict unreported corporate greenhouse gas emissions that generates considerably better results than existing approaches. The model uses one linear learner and one nonlinear learner only, which reduces its complexity to the minimum required. An iterative approach to detecting and correcting data significantly improves the model predictions. Unlike mainstream approaches, which tend to construct one model for each industry, we construct one single global model that uses industry as a factor. This addresses the problem of lack of breadth or lack of reported data in some sectors and generates practical results even for industries where other approaches have failed. We show results for Scope 1 and Scope 2 corporate carbon emissions. Adapting the framework to Scope 3 will be the focus of a future article.