Integration of green supply chain and logistics practices in textiles and their impact on united nations sustainable development goals

The textile sector is a high-impact polluter with long, opaque supply chains, making credible measurement and coordinated action difficult; green supply chain management offers concrete levers to cut material and energy intensity while strengthening resilience. Yet decision makers lack a sector-specific, SDG-explicit map that shows which GSCM mechanisms matter most and where evidence is thin. This study addresses that gap by producing an SDG-linked cartography of textile GSCM. A PRISMA-style screen reduced 6,372 Scopus records to 1,224 eligible publications (2013–2025); bibliometric and science-mapping techniques (keyword co-occurrence, clustering, SDG tagging) were applied using VOSviewer and SciVal, with transparent parameters and sensitivity checks. Results resolve three thematic clusters—policy/management, Industry 4.0, and coordination/operations—and quantify goal salience: SDG 12 accounts for 55.7% of tagged papers, SDG 9 for 41.8%, and SDG 13 for 36.6%. Field activity expanded from 47 papers in 2013 to 198 in 2023 (≈ 321% increase), with a marked shift toward circularity and digitalisation after 2017. Contributions are geographically concentrated, with China accounting for 25.5% of items. Evidence indicates that digital traceability, analytics, and closed-loop logistics are associated with greater impact and tighter alignment with SDG 9 and SDG 12, while climate-action framing tends to follow digital adoption with a one-to-two-year lag. The map can guide firms toward investments in product passports, carbon-aware planning, and reverse flows, and can help policymakers target disclosure mandates, EPR design, and incentives for SME digitalization. The synthesis highlights underrepresented social goals and regions, motivating pilot studies that pair digital enablement with labor and equity indicators; future work should triangulate automated SDG tagging with manual multi-label coding and test causal links between specific GSCM levers and SDG targets using quasi-experimental designs.