Work

Adapting machine-learning estimators to account for the downward bias of reported greenhouse gas Scope 3 emissions

Year: 2025

Type: article

Abstract: Companies are facing increased reporting demands regarding greenhouse gas (GHG) emissions as climate change evolves into a material topic for them. However, the estimation of corporate Scope 3 GHG emissions (S3E) remains challenging up to date. At the same time, we recognize the potential of machine-learning algorithms in the measurement and reduction of corporate GHG emissions. We empirically replicate the downward bias of reported S3E against true S3E. Based on such insight, it does not appear helpful to minimize the prediction risk of machine-learning estimators. Instead of point estimates, we derive that asymmetric confidence intervals based on estimators with a low bias–standard-deviation ratio (bias–SD ratio) are worthwhile to approximate true S3E from below. We develop a machine-learning algorithm with a bias–SD ratio at least 82% smaller than the bias–SD ratio of comparable methods.

Source: Journal of Cleaner Production

DOI: 10.1016/j.jclepro.2025.145781

Authors: Georg Benjamin Fiedler, Sven Lundie

Institutions: Universität Hamburg, UNSW Sydney

Language: English

Topic: Environmental Impact and Sustainability

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $4,660