Artificial Intelligence and Environmental, Social, and Governance: A Hybrid Bibliometric Approach

Abstract

This study provides a comprehensive overview of research on artificial intelligence (AI) and Environmental, Social, and Governance (ESG) by creating a knowledge map of the field. Using a systematic–bibliometric approach, we quantitatively analyzed a total of 129 documents, which collectively were cited 4276 times (2017–2024). Following performance analysis, we conducted a rigorous systematic literature review, critically evaluating each document to identify key research methods, geographic focus, theories, and frameworks. We then performed citation network and co-citation analyses to uncover the intellectual foundations of the field. We also employed bibliographic coupling to identify emerging research streams, while thematic evaluation provided insights into the evolution of research areas over time, highlighting both the most and least explored themes. Finally, our complementary systematic–bibliometric analyses enabled us to derive an integrated framework outlining key antecedents, mediators, moderators, and outcomes. This in turn allows us to develop a structured conclusion for each of the six identified research clusters, which will guide future research directions in AI and ESG. We also discuss practical and theoretical implications, offering valuable insights for scholars and practitioners in the field.