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In this study, we explore the adoption of Environmental, Social, and Governance (ESG) practices in the Bangladeshi textile and apparel (T&A) industry which is a global leader in green certifications yet challenged by limited transparency. Leveraging Artificial Intelligence (AI) and Machine Learning (ML) methodologies, the research examines ESG disclosures from 220 LEED-certified factory websites. The analysis employs web scraping, Natural Language Processing (NLP), and topic modeling to classify ESG practices into the ESG categories. Findings reveal that only 37% of these factories disclose sustainability-related information online, with a focus on environmental (46%) and social (44%) aspects, while governance practices remain underrepresented (10%). This study bridges a critical gap in stakeholder theory by providing a data-driven framework to assess ESG reporting, offering actionable insights for brands, policymakers, and industry stakeholders. The results aim to enhance supply chain transparency practices, supporting continuous improvement in the Bangladeshi T&A sector's global competitiveness.
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Magotra et al. (2025) studied this question.