Circular Economy Transitions in Textile, Apparel, and Fashion: AI-Based Topic Modeling and Sustainable Development Goals Mapping

Raghu Raman (), Payel Das, Rimjhim Aggarwal, Rajesh Buch, Balasubramaniam Palanisamy, Tripti Basant, Urvashi Baid, Pozhamkandath Karthiayani Viswanathan, Nava Subramaniam and Prema Nedungadi
Additional contact information
Raghu Raman: Amrita School of Business, Kollam Campus, Amrita Vishwa Vidyapeetham, Kollam 690525, Kerala, India
Payel Das: Amrita School of Business, Amaravati Campus, Amrita Vishwa Vidyapeetham, Amaravati 522503, Andhra Pradesh, India
Rimjhim Aggarwal: School of Sustainability, Arizona State University, Tempe, AZ 85287, USA
Rajesh Buch: School of Sustainability, Arizona State University, Tempe, AZ 85287, USA
Balasubramaniam Palanisamy: Amrita School of Business, Coimbatore Campus, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India
Tripti Basant: School of Sustainability, Arizona State University, Tempe, AZ 85287, USA
Urvashi Baid: Amrita School of Business, Coimbatore Campus, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India
Pozhamkandath Karthiayani Viswanathan: Amrita School of Business, Kollam Campus, Amrita Vishwa Vidyapeetham, Kollam 690525, Kerala, India
Nava Subramaniam: Amrita School of Business, Coimbatore Campus, Amrita Vishwa Vidyapeetham, Coimbatore 641112, Tamil Nadu, India
Prema Nedungadi: Amrita School of Computing, Amritapuri Campus, Amrita Vishwa Vidyapeetham, Amritapuri 690525, Kerala, India

Sustainability, 2025, vol. 17, issue 12, 1-36

Abstract: This study focuses on the shift to a circular economy (CE) in the textile, apparel, and fashion (TAF) sectors, which generate tons of waste annually. Thus, embracing CE practices is essential for contributing to UN Sustainable Development Goals. This study employs a mixed-methods approach, integrating PRISMA for systematic literature selection, BERTopic modeling and AI-driven SDG mapping, and case study analysis to explore emerging CE themes, implemented circular practices, and systemic barriers. Machine-learning-based SDG mapping reveals strong linkages to SDG 9 and SDG 12, emphasizing technological advancements, industrial collaborations, and circular business models. Moderately explored SDGs, namely, SDG 8, SDG 6, and SDG 7, highlight research on labor conditions, water conservation, and clean energy integration. Reviewing 655 peer-reviewed papers, the BERTopic modeling extracted six key themes, including sustainable recycling, circular business models, and consumer engagement, whereas case studies highlighted regulatory frameworks, stakeholder collaboration, and financial incentives as critical enablers. The findings advance institutional theory by demonstrating how certifications, material standards, and regulations drive CE adoption, reinforcing SDG 12 and SDG 16. The natural resource-based view is extended by showing that technological resources alone are insufficiently aligned with SDG 9. Using the Antecedents–Decisions–Outcomes framework, this study presents a structured, AI-driven roadmap for scaling CE in the TAF industry, addressing systemic barriers, and supporting global sustainability goals, highlighting how multistakeholder collaboration, digital traceability, and inclusive governance can improve the impact of CE. The results recommend that CE strategies should be aligned with net-zero targets, carbon credit systems, and block-chain-based supply chains.

Keywords: sustainable development goal; circular economy; textile; institutional theory; circular business model; ADO framework (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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