Impact of AI on ESG Performance of Firms of Haryana

The interplay between Artificial Intelligence (AI) and Environmental, Social, and Governance (ESG) performance is a growing area of academic and industry interest. This study examines how AI adoption influences ESG performance in firms across Haryana, with a particular focus on manufacturing and service sectors. Findings from existing literature suggest that AI adoption leads to significant ESG performance improvements by optimizing energy consumption, reducing emissions, and enhancing corporate governance mechanisms.  However, prior research predominantly focuses on correlation rather than causation. This study aims to bridge this gap by analyzing the bidirectional relationship between AI adoption and ESG performance, hypothesizing that AI not only drives ESG improvements but is also influenced by a firm's ESG strategies and regulatory environment. A significant data gap exists in the datasets used in this study, lacking direct indicators of AI adoption and robust social and governance parameters. To overcome this, this study employs proxy variables to estimate AI adoption and social and governance parameters. The research applies a game-theoretic framework, including the Stackelberg competition model and Bayesian evolutionary game theory, to assess strategic AI adoption decisions under competitive and regulatory pressures. Additionally, empirical methodologies such as a modified Difference-in-Differences (DiD) approach and Propensity Score Matching (PSM) are utilized to control for selection bias and establish causality. Furthermore, this study explores AI adoption disparities between formal and informal firms, highlighting policy gaps and financial and strategic constraints. The study contributes to existing knowledge by developing an evolutionary game theoretical model and by shifting the focus from correlation to causation, providing insights for policymakers and industry leaders on integrating AI into sustainable business practices. Future research will refine AI adoption metrics and expand longitudinal data analysis to further explore AI’s role in corporate sustainability.