The Impact of AI-Driven ESG Compliance Monitoring on Corporate Sustainability Risk: Evidence from Publicly Listed Corporations (2016-2024) - Ehsan, Nabeel (International Journal of Economics and Financial Issues, 2026)
This study investigates how Artificial Intelligence (AI) can improve Environmental, Social, and Governance (ESG) compliance monitoring and its effect on corporate sustainability risks. Specifically, it examines the extent to which AI enhances the accuracy, timeliness, and reliability of ESG disclosures, thereby reducing sustainability risk exposure. By using a quantitative research design, data was gathered from 320 publicly listed corporations across North America, Europe, and Asia-Pacific between 2016 and 2024, yielding a balanced panel dataset of 2,880 firm-year observations. The study used the Generalized Least Squares (GLS) regression model to examine the relationship between AI-driven ESG compliance monitoring and corporate sustainability risks. Results indicated a highly significant negative relationship: the AI_ESG coefficient was −6.84 (p<0.01), suggesting that a one-unit increase in AI-driven ESG monitoring adoption is associated with a 6.84-unit reduction in corporate sustainability risk, equivalent to approximately 0.53 standard deviations of the CSRISK distribution. Similarly, ESG performance scores were negatively associated with sustainability risk (coefficient = −0.39, p<0.01). Factors such as firm size and financial leverage were also found to have significant effects on levels of sustainability risks. Robustness checks, including lagged independent variables and subsample analyses, confirmed the stability of these findings. The implications of the findings are that AI integration in ESG reporting processes represents a strategic imperative for corporations seeking alignment with evolving regulations. Policymakers are encouraged to facilitate AI adoption, particularly among smaller firms, to promote broader ESG compliance and risk management across the corporate landscape.