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This study provides a systematic literature review on the topic of sustainable fashion (SF) research through the lens of the United Nations Sustainable Development Goals (SDGs). The study employs the Theory–Context–Characteristics–Method to analyze 502 peer-reviewed journal articles published from 2015 to 2024 using BERTopic, an unsupervised machine learning approach that identifies latent thematic structures and research trends in the SF literature. The analysis centers on six main research themes Sustainable Fashion–Design Education, Consumption Dynamics, Consumption Pioneers, Circular Fashion–Industry Analysis, Eco-Textile Innovation, and Social Media Influence. Mapping these against the 17 SDGs showed strong associations with SDG 12 (Responsible Consumption and Production), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action), while SDGs 14 and 15 remain relatively unexplored. Regional disparities between the Global North and South are also illuminated as well as an over-reliance on conceptual and quantitative methods with a dearth of longitudinal, simulation-based, and biometric approaches. The results yield a systematic research agenda over TCCM dimensions. This review deepens and integrates existing sustainable fashion scholarship by systematically mapping dominant research themes and methodological patterns to the SDGs. By combining machine-learning-assisted topic modeling with expert validation and organizing insights through the TCCM framework, the study offers a comprehensive, SDG-anchored synthesis and a future-oriented research agenda for scholars, practitioners, and policymakers.
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Stored only when both papers are in the corpus — 7 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.