Design–sorting mismatch limits circularity in fast-fashion garments

Sustainable Production and Consumption, vol. 69, pp. 122–133

Abstract

Automated textile sorting is increasingly promoted as a key enabler of fibre-to-fibre recycling, yet its effectiveness depends on whether garments are designed in ways that current near-infrared (NIR)-based systems can identify and route. This study uses retailer product data from H&M and Uniqlo in the United Kingdom and Australia to assess garment-level design compatibility with NIR-based automated textile sorting. We develop a rule-based framework that translates documented sorting constraints into measurable indicators of recognition scope, blend complexity, low-share components, black-colour proxy for optical risk, and surface–hidden material mismatch. Across 47,522 colour-specific garment variants, 50.3% violated at least one baseline sorting-relevant rule, with a sensitivity range of 45.0–64.0%. The largest barriers were unsupported recognition scope (36.4%), more-than-two-fibre blends (20.0%), and minor-component detectability (17.3%), followed by the black-colour proxy for optical risk (13.7%) and surface–hidden mismatch (8.2%). These findings highlight design-for-sorting, reliable component-level product information, and continued development of sorting capabilities as complementary prerequisites for scalable textile circularity.

Authors 2

1. Kai Li corresponding Aachen Chair of Operations Management Institute of Environmental Sciences (CML)

   RWTH Aachen University · Leiden University

   Affiliation as printed

   Chair of Operations Management, RWTH Aachen University, Kackertstr. 7, 52072, Aachen, Germany

   Institute of Environmental Sciences (CML), Leiden University, 2333 CC, Leiden, the Netherlands

2. Grit Walther Aachen Chair of Operations Management

   RWTH Aachen University

   Affiliation as printed

   Chair of Operations Management, RWTH Aachen University, Kackertstr. 7, 52072, Aachen, Germany