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.

This study examines the impact of Environmental, Social, and Governance (ESG) disclosures on the firm valuation of non-financial firms listed in South Africa, using Tobin’s Q as a firm value proxy. Using a panel data approach of 642 firm-year observations from 2017 to 2022, the study applies Fixed Effects, Random Effects, and Generalized Method of Moments (GMM) estimators to address possible endogeneity concerns. The results consistently show that, for the whole sample, ESG disclosures are positively and significantly related to firm value, thus supporting the view that markets reward transparency and sustainability initiatives. Firm size and liquidity also have positive impacts, while financial leverage has an inverse relationship with firm value. Subgroup regression analysis shows significant sectoral differences: ESG disclosure in non-manufacturing companies has a positive and significant relationship with firm value, in line with stakeholder and signaling theories, emphasizing the premium for intangible assets like reputation and trust. However, in manufacturing companies, ESG disclosure is negatively and significantly associated with firm value, implying concerns among investors regarding compliance costs, strategic misalignment, or possible “greenwashing.” The study contributes to the emerging-market literature by (i) introducing a PCA-based ESG index specific to JSE-listed non-financials, (ii) triangulating results across static and dynamic specifications to ensure robustness, and (iii) uncovering sectoral heterogeneity that has been largely overlooked. The research also has practical implications for corporate managers, policymakers, and investors on the alignment of ESG practices to industry attributes for long-term value optimization.

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, particularly in non-financial sectors where ESG performance is experiencing significant growth. This study employs a panel dataset from 2018 to 2024, and it analyses publicly listed non-financial firms across five major sectors: manufacturing, energy, telecommunica-tions, consumer goods, and industrials. This study employs AI-powered multimodal analysis to examine the relationships between ESG and firm valuation in emerging markets. The research combines Fixed-Effects Regression and Machine Learning (ML) algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest to identify both linear and non-linear relationships between ESG scores 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 show more inconsistent effects. Firms identified in ESG controversies tend to face valuation penalties, which stresses market sensitivity to reputational risks. ML algo-rithms outperform conventional techniques in predictive accuracy, revealing complex, non-linear interactions within ESG data. The findings contribute to both academic lit-erature and practitioner understanding of the implications of how AI-driven ESG re-porting can improve robust firm valuation models and complex interdependencies in the ESG dataset.

Environmental, Social and Governance (ESG) risk is increasingly influenced by inter-firm relationships embedded in global supply chains. Challenging firm-level approaches that treat ESG exposure as independent across companies. This study examines whether firms’ structural positions within supply-chain networks are associated with ESG risk exposure and whether incorporating network information improves ESG risk prediction. The analysis draws on an international dataset integrating validated supplier-buyer relationships, shipment-level trade data. ESG incident records and sentiment derived from ESG-related news. Network-based econometric models and graph-oriented learning approaches are evaluated against conventional firm-level benchmarks. The results indicate that ESG risk clusters within connected groups of firms, with higher exposure observed among firms occupying central or intermediary positions in supply networks. In addition, ESG-related media sentiment exhibits predictive power for subsequent ESG incidents, supporting its role as an early warning signal. Overall, models that explicitly account for network structure deliver more accurate and better-calibrated predictions than standard econometric and machine-learning approaches. These findings highlight the value of a network-informed perspective for ESG risk assessment in complex international production systems.

Recent sustainability regulations extend firms’ sustainability risk responsibilities beyond their internal operations, embedding risk within supply-chain relationships. To investigate this, the study employs a panel of publicly listed firms from emerging markets over the period 2015 to 2024. The analysis employs network-informed econometric specifications and exploits regulatory tightening as a quasi-exogenous source of variation transmitted through pre-existing supply-chain links to examine how supply-chain positioning influences the valuation-relevance of sustainability risk. The results show that firms occupying central or brokerage positions within production networks exhibit higher ESG exposure materiality and heightened valuation sensitivity following regulatory intervention, suggesting that investors incorporate indirect, network-based sustainability risks into firm value. These findings contribute to accounting research by showing that sustainability regulation extends the effective informational boundary of the firm, with implications for sustainability reporting, risk assessment, and value-chain accountability. Complementary machine-learning analyses are applied exclusively for out-of-sample predictive validation and do not inform causal inference. JEL Classification: M41; G14; G32; Q56.

Environmental, Social, and Governance (ESG) risks increasingly propagate across interconnected supply chains, yet conventional ESG assessment methods remain largely reliant on firm-level disclosures and static ESG ratings that often overlook indirect risk transmission among trading partners. This study develops a network-aware artificial intelligence (AI) framework for forecasting ESG risk by integrating Graph Neural Networks (GNNs), transformer-based natural language processing (NLP), explainable AI, and conventional machine-learning techniques. The proposed framework combines supply-chain network structures, shipment-level trade information, ESG controversy records, governance indicators, and transformer-derived ESG sentiment extracted using FinBERT and RoBERTa. Using a dataset of 11,386 firms across 27 industries from 2015 to 2025, the proposed GNN achieved the highest predictive performance, outperforming conventional machine-learning models with an ROC-AUC of 0.913. The results further demonstrate that supply-chain network centrality and transformer-derived ESG sentiment substantially improve the early identification of firms exposed to future ESG controversies. By integrating network relationships with textual ESG intelligence, the proposed framework advances FinTech-enabled ESG analytics and provides a scalable approach for proactive risk monitoring, sustainable investment decision-making, and supply-chain risk management.