Low-carbon boosting and clothing recycling intentions in China: mediating roles of cognitive attitudes and social norms

Amidst the global low-carbon transition and China`s “dual carbon” strategy (aiming for carbon peak by 2030 and carbon neutrality by 2060), the textile and garment industry confronts a significant tension between its substantial carbon emissions and low recycling rates. Prior research has predominantly examined the isolated impact of single factors on consumers’ recycling intentions, neglecting the synergistic mechanisms involving cognitive attitudes, social norms, and low-carbon nudges. Furthermore, these studies lack a comprehensive analysis integrating both linear and nonlinear effects. This research aims to address this gap methodologically and provide empirical support for policy optimization. Employing stratified sampling, the study combined field research with a nationwide online survey, collecting 1680 valid questionnaires. Methodologically, it integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis: PLS-SEM is suited for path verification, while ANN supplements by capturing nonlinear relationships, enhancing analytical comprehensiveness. Empirical findings demonstrate that cognitive attitude exerts the strongest influence; both cognitive attitude and social norms not only directly drive low-carbon behavioral intentions but also exert significant indirect effects through nudge responses. These conclusions offer theoretical and practical implications for relevant governance strategies.