CAREER: Sustainable Textile Industry Through Circular Handling (STITCH)

Most unwanted clothing is discarded, even though much of it could be reused or recycled. The benefits of clothing donation, resale, repair, repurposing, and recycling depend on local infrastructure. Tradeoffs between potential benefits and energy use or environmental impacts often pose challenges for businesses, communities and policymakers. This CAREER project will develop evidence-based tools to help decisionmakers find strategies that keep textiles out of landfills, reduce gaseous emissions and conserve water and other resources. The project will demonstrate these tools on test cases involving reuse pathways in Massachusetts and a reuse system at the University of Massachusetts Lowell. The project will also combine research with engineering education, engage students in data collection and analysis, and participate in public events focused on thrifting and repair. The Sustainable Textile Industry Through Circular Handling project is a comprehensive modeling and assessment approach and initiative that aims to transform the textile industry. The project will integrate circular-economy principles with advanced industrial engineering models. The research will combine life-cycle assessment with machine learning and probability-based modeling to estimate, compare, and stress-test the environmental benefits and trade-offs of reuse, repair, and recycling strategies. The project will create time-dependent inventories that track repeated use, changes in product function, and regional differences in collection and processing. A probability-based state-transition model will estimate garment lifetimes and the likelihood that items move from reuse to repair, recycling, or disposal. To ensure that claims about circular solutions reflect physical limits, the research will incorporate thermodynamic measures that capture unavoidable losses in material quality and energy usefulness across repeated processing. Machine learning methods will infer difficult-to-measure quantities, such as how quickly garments degrade and the likelihood of a given pathway. Machine learning will also quantify uncertainty and improve predictions of garment lifetimes and end-of-life routing under different policies and business scenarios. The method will be demonstrated through two case studies: statewide reuse pathways shaped by the Massachusetts Textile Disposal Ban and an institutional reuse system at the University of Massachusetts Lowell. These case studies will produce publicly available models, datasets, and decision-support guidance that can be transferred to other resource-intensive products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.