Identification of Key Predictors and Configurational Pathways of Rural Residents’ Compliance with Household Waste Classification Policies: Based on Explainable Machine Learning and fsQCA

Based on survey data from the National Ecological Civilization Pilot Zone (Jiangxi), this study divides rural residents’ self-reported policy compliance in household waste classification (PC) into habit-based policy compliance in waste classification (HPC) and decision-based policy compliance in waste classification (DPC). The integration of a random forest model with Shapley Additive Explanations (SHAP) is utilized to identify the key predictors of each compliance type and to reveal the non-linear predictive contributions and marginal effects through which these factors respectively affect HPC and DPC. Furthermore, fuzzy-set qualitative comparative analysis (fsQCA) is used to uncover the multiple configurational pathways driving each type of compliance. Research indicates the following: (1) The key predictive factors of HPC and DPC are different. (2) Subjective norms (SUN), information interaction (II), and social norms (SON) exhibit significant nonlinear predictive contributions to HPC; policy identification (PI), SON, and procedural fairness (PF) show nonlinear trends in their contributions to the predictive probability of DPC. (3) There are five configuration paths for HPC and four for DPC. This study not only deepens the understanding of rural residents’ HPC and DPC but also provides practical guidance for governments to formulate differentiated and efficient measures to encourage compliance with waste classification policies.