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Inconsistent Environmental, Social, and Governance (ESG) reporting and widespread greenwashing have undermined transparency, trust, and comparability in global sustainability assessments. This study proposes an AI-centric framework to support automated analysis and improved standardization of ESG reporting by integrating Natural Language Processing (NLP) and centralized data management systems. Using sustainability reports from 440 companies listed on the Johannesburg Stock Exchange (JSE) and the National Stock Exchange of India (NSE), a DistilRoBERTa transformer model was fine-tuned to classify ESG indicators. The model achieved an accuracy of 99.1% for environmental indicators, 99.3% for governance, and 63.6% for social indicators, reflecting the qualitative and heterogeneous nature of social disclosures. These results demonstrate that AI can reduce subjectivity, increase comparability, and minimize human error in ESG disclosures. By enabling real-time, standardized ESG insights, this framework supports regulatory foresight, enhances global data governance, and advances policy-oriented sustainability planning.
Telukdarie et al. (Wed,) studied this question.