Big data and AI in Esg performance measurement: A bibliometric analysis - Ekaristi, Clara Yully Diana; Utomo, Dwi Cahyo; Rohman, Abdul (Edelweiss Applied Science and Technology, 2025)
This study looks at how Big Data and Artificial Intelligence (AI) are used to measure Environmental, Social, and Governance (ESG) performance by analyzing 17 articles from Scopus and WoS published in the last ten years. The study adopts a systematic methodology using VOSviewer and Publish or Perish to map thematic clusters, citation networks, and emerging research trends. Findings reveal that AI, particularly machine learning and natural language processing, enhances ESG transparency by enabling anomaly detection, greenwashing identification, and real-time sustainability analytics. However, the lack of common guidelines, unclear algorithm processes, and mismatched regulations make it hard to effectively use AI in ESG reporting. The study concludes that interdisciplinary collaboration is essential to developing accountable, interpreted, and harmonized ESG evaluation systems. From a practical perspective, this research offers actionable insights for regulators, firms, and investors to refine ESG strategies by leveraging technological innovation. The study also highlights the need for integrating alternative data sources—such as IoT, blockchain, and remote sensing—to strengthen data reliability. By advancing a unified research agenda, this work contributes to bridging the methodological and conceptual divide between sustainability, accounting, and AI domains.