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Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies

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  • Thomas Archer

    (Rotterdam School of Management, Rotterdam, Netherlands)

Abstract

This study examines the role of Green Innovation in predicting ESG ratings across developed and emerging economies. Among 292 firms, Green R&D Intensity is identified as a key predictor of ESG ratings. Results indicate that companies currently make minimal investments in Green Innovation, meaning modest increases in investments could enhance ESG ratings. Findings support Signaling Theory, suggesting Green Innovation can immediately boost ratings, though long-term impacts may require time to mature. The study also shows integrating Green Innovation into ML models reduces prediction error by 2% rising to 11.5% for firms without prior ESG ratings. Ultimately, the study's implications underscore the importance of ESG factors for firms, investors, and policymakers, as higher ESG ratings are linked to increased firm value, improved performance, and economic growth.

Suggested Citation

  • Thomas Archer, 2025. "Assessing the influence of green innovation on ESG ratings: A machine learning approach across developed and emerging economies," Maandblad Voor Accountancy en Bedrijfseconomie Articles, Maandblad Voor Accountancy en Bedrijfseconomie, vol. 99(3), pages 145-154, July.
    • Handle: RePEc:arh:jmabec:v:99:y:2025:i:3:p:145-154
    DOI: 10.5117/mab.99.135692
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