AI-enabled ESG practices and financial performance in Asian firms: a regional scoping review and conceptual synthesis

Abstract This scoping review maps how artificial intelligence (AI) and closely related digital technologies are discussed in relation to Environmental, Social, and Governance (ESG) practices and financial performance in Asian firms. The review addresses the need to clarify how digital tools may support ESG data reliability, disclosure quality, risk monitoring, and regulatory alignment in emerging markets, without treating the reviewed evidence as causal proof. Guided by the Arksey and O’Malley framework, literature was retrieved from Scopus and Web of Science, yielding 1046 records and a final set of 31 studies for thematic synthesis following PRISMA-informed reporting. The literature suggests that AI tools, including machine learning, artificial neural networks, explainable AI, generative AI, and natural language processing, can support ESG practices through improved data processing, prediction, interpretation, and monitoring. However, the evidence is heterogeneous and most directly supports reporting, governance, innovation, financing constraint, and market valuation channels rather than a uniform direct effect on financial performance. Barriers include high implementation costs, weak digital infrastructure, limited technical expertise, data quality problems, and uneven regulatory capacity. The paper therefore offers a regional scoping synthesis and a provisional conceptual framework in which AI is interpreted as a possible mechanism linking ESG practices to financial-performance channels, conditional on technology readiness, data governance, and regulatory support. The contribution is deliberately positioned as a mapping and conceptual interpretation of the existing literature rather than empirical verification of causal impact.