Clustering S&P 500 companies by machine learning for sustainable decision-making
DOI:
https://doi.org/10.18559/ebr.2025.3.1895Keywords:
sustainability, clustering algorithms, machine learningAbstract
This study examines the Environmental, Social, and Governance (ESG) performance of S&P 500 companies using three clustering algorithms: K-Means, Gaussian Mixture Model, and Agglomerative Clustering. ESG scores from leading data providers are analysed to uncover sectoral patterns and performance trends. The findings indicate that technology and healthcare firms achieve the highest ESG scores, particularly in the governance and social dimensions, while the industrial and energy sectors face the greatest environmental challenges. Among the methods compared, K-Means demonstrates superior clustering performance by forming compact and well-separated ESG groups. These results offer a robust foundation for sector-specific ESG benchmarking, supporting investors and policymakers in identifying sustainability leaders, assessing risk, and targeting areas for improvement.
JEL Classification
Sustainable Development (Q01)
Environment and Development • Environment and Trade • Sustainability • Environmental Accounts and Accounting • Environmental Equity • Population Growth (Q56)
Ecological Economics: Ecosystem Services • Biodiversity Conservation • Bioeconomics • Industrial Ecology (Q57)
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