Data-Driven Insights into Sustainability: An Artificial Intelligence (AI) Powered Analysis of ESG Practices in the Textile and Apparel Industry
Abstract
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.
Keywords: artificial intelligence, AI, ML, ESG, sustainability, textile and apparel industry
How to Cite:
Magotra, A., Rana, M., Shishir, F. & Shomaji, S., (2025) “Data-Driven Insights into Sustainability: An Artificial Intelligence (AI) Powered Analysis of ESG Practices in the Textile and Apparel Industry”, International Textile and Apparel Association Annual Conference Proceedings 81(1). doi: https://doi.org/10.31274/itaa.18659
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