Big data, artificial intelligence, and machine learning for measuring ESG performance

Peter Öhman and Tarek Rana

Chapter 17 in Handbook of ESG Investing, 2026, pp 428-449 from Edward Elgar Publishing

Abstract: This chapter proposes a tentative framework for measuring Environmental, Social, and Governance (ESG) performance through emerging technologies. Drawing on Innovation Diffusion Theory, Systems Theory, and Resource-Based Theory—and grounded in a comprehensive literature review—the chapter addresses key limitations of traditional ESG reporting. By leveraging big data for real-time analysis, along with artificial intelligence (AI) and machine learning (ML) for pattern detection and predictive risk management, the framework enables a more proactive approach to ESG performance measurement. Case examples illustrate the practical applications of big data, AI, and ML in ESG contexts while the proposed framework underscores the interconnections among its components. Finally, the chapter outlines potential avenues for future empirical research in accounting.

Keywords: ESG; Big Data; Artificial Intelligence; Machine Learning; Performance Measurement (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035322770
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