Bridging the Gap: Estimating Scope 3 Emissions at Company’s Level

The 21st century is facing climate change challenge, which has rapidly intensified, impacting global systems in various ways. The need to mitigate climate change necessitates deep, fast and sustainable reductions in greenhouse gas (GHG) emissions. Efforts should go through several channels including Scope 3 emissions, which encompass indirect emissions from a company’s entire value chain. However, accurately estimating Scope 3 emissions at the company level remains challenging due to data scarcity and reliability issues. This paper presents a new empirical methodology designed to estimate Scope 3 emissions at the company level, taking into account the dynamics of value chains and company-specific factors. Using input-output tables and sectoral emissions data, we reconstruct company value chains and calculate emissions from upstream and downstream sectors. We address the challenge of missing data by using parametric and machine learning techniques, to predict both reported and unreported emissions. Our model, applied to French companies’ data, shows that company-specific characteristics play a key role in Scope 3 emissions, and sectors’ emissions in the value chain as a whole significantly influence Scope 3 emissions. The results suggest that machine learning models, particularly Random Forest, outperform traditional models in predicting Scope 3 emissions. The study also highlights the importance of expanding data reporting and designing comprehensive climate policies to better manage emissions across all sectors.