The Impact of AI-Integrated ESG Reporting on Firm Valuation in Emerging Markets: A Multimodal Analytical Approach

This study examines the impact of Artificial Intelligence (AI)-enhanced Environmental, Social, and Governance (ESG) reporting on firm valuation in emerging markets. It aims to explore how AI integration enhances the interpretability and predictive accuracy of ESG metrics in shaping market perceptions and investor decisions. This study employs a panel dataset from 2018 to 2024, analysing publicly listed non-financial firms across five major sectors: manufacturing, energy, telecommunications, consumer goods, and industrials. This study contributed by employing AI-powered multimodal analysis with conventional ESG scoring methods and integrating Fixed-Effects Regression with machine learning (ML) algorithms including Extreme Gradient Boosting (XGBoost) and Random Forest to identify complex, non-linear relationships within ESG data and firm valuation. The results show empirical evidence that integrating ML enhances the explanatory power of ESG data. Findings indicate that ESG performance is positively correlated with higher market valuations, particularly in Environmental and Social dimensions. Governance metrics are more inconsistent, which may be due to heterogeneity in governance practices, regulatory enforcement and the challenges of quantifying governance quality beyond compliance indicators across the focus emerging markets. Firms identified in ESG controversies tend to face valuation penalties, which stresses market sensitivity to reputational risks. ML algorithms outperform conventional techniques in predictive accuracy, revealing complex, non-linear interactions within ESG data. This study contributes to both the academic literature and practice showing how next-generation ESG reporting can robust valuation models, address limitations of conventional ESG scoring, and ensure a reliable outlook for investors and policymakers of industries in emerging markets.