Digital ESG Analytics: A Bibliometric Review of AI-Driven Sustainability Research (2010–2025)

Abstract

This study provides a comprehensive bibliometric mapping of Digital ESG Analytics by synthesizing evidence from 643 peer-reviewed publications published between 2010 and 2025. Using data extracted from Google Scholar and Scopus and analyzed with VOSviewer, the study demonstrates a sharp rise in research output beginning in 2023, reflecting growing global demand for transparent, technology-enabled ESG assessment. The keyword co-occurrence analysis maps eight major thematic clusters—ranging from ESG reporting, AI-driven analytics, technological innovation, machine learning models, and sustainability goals to sector-specific ESG applications—illustrating how the field has shifted from descriptive ESG discussions toward predictive, automated, and data-intensive approaches. Overlay visualization further highlights the temporal evolution of research, with recent years dominated by themes such as NLP-based sentiment analysis, generative AI, real-time analytics, and digital ESG integration. Co-author network examination indicates fragmented global collaboration, with a small number of regional clusters—particularly from East Asia—forming the primary collaborative nodes. Building on these findings, the study outlines three major research gaps: (1) conceptual limitations such as the lack of integrated theoretical frameworks; (2) methodological constraints, including limited high-quality datasets and insufficient use of advanced analytical techniques; and (3) contextual and sectoral gaps involving under-representation of SMEs, emerging markets, and resource-constrained environments. Correspondingly, the study proposes future research directions that encourage theoretical consolidation, methodological strengthening, greater transparency and explainability in AI-enabled ESG models, and broader exploration of sectoral and geographical contexts. Overall, the study enhances understanding of the structure, evolution, and research frontiers of Digital ESG Analytics, offering valuable insights for scholars, practitioners, and policymakers seeking to advance data-driven sustainability governance