Machine Learning Tools for Evaluating Corporate Environmental Performance Metrics

Abstract

This paper introduces a novel, cross-disciplinary methodology that applies machine learning techniques to the emerging domain of corporate environmental performance evaluation.
Moving beyond traditional regression-based ESG (Environmental, Social, and Governance)
scoring models, we propose a hybrid framework that integrates unsupervised learning for
metric discovery, graph neural networks for modeling inter-corporate environmental influence, and a bio-inspired optimization algorithm for weighting disparate environmental indicators. The core innovation lies in reformulating corporate environmental performance not
as a static score, but as a dynamic, multi-dimensional vector within a learned latent space,
where similarity reflects shared underlying environmental strategies and outcomes, rather
than superficial score proximity. Our methodology uniquely processes unstructured corporate disclosures, satellite-derived environmental data, and traditional financial filings to
construct a holistic performance profile. We address the critical, yet under-explored, research
question of how to quantify the ’environmental strategy coherence’ of a firm—the alignment
between its stated environmental goals, its operational data, and its peer-influenced actions.
Results from applying this framework to a novel dataset of 1,200 global corporations demonstrate its ability to identify latent environmental performance clusters that traditional ESG
ratings fail to discern, predict regulatory compliance events with 34