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◆ Sustainability2026-04-16· Sustainability

Development of a Multi-Dimensional Framework for Interpreting the Sustainability of Textile Materials

Eui Kyung Roh

原始摘要(英文原文)· Original abstract
Sustainability assessment of textile materials has traditionally relied on origin-based classifications and indicator-driven life cycle assessment (LCA), often treating sustainability as an inherent or material-intrinsic property. However, materials sharing similar biological origins or “bio-based” labels frequently exhibit substantially different sustainability outcomes when processing pathways, composite structures, and end-of-life (EoL) compatibility are taken into account. To address this limitation, this study develops a qualitative, multidimensional analytical framework that conceptualizes textile material sustainability as a pathway-dependent and system-mediated outcome rather than an inherent material attribute. The framework integrates four interrelated dimensions—renewability, process sustainability, EoL options, and material source—derived from a structured review of academic, policy, and technical literature. To demonstrate the analytical scope and internal logic of the framework, a selected set of 65 innovative textile materials was systematically analyzed using a three-tier qualitative coding scheme (favorable, conditional, and unfavorable) under conservative data validation criteria. The analysis shows that sustainability performance is primarily shaped by pathway configurations—particularly processing intensity, binder chemistry, and EoL compatibility—rather than material origin alone and that similar bio-based materials can exhibit fundamentally different sustainability profiles depending on these factors. By reframing sustainability from a material-centered perspective to a pathway-oriented and system-based perspective, the proposed framework provides a structured basis for integrating material innovation, process design, and end-of-life planning in sustainability-oriented textile research and development and establishes a conceptual foundation for future empirical and quantitative extensions.
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