Decoding food waste behaviours among Chinese consumers: Machine learning insights into the intention–behaviour gap

Abstract

Identifying the factors contributing to the intention–behaviour gap is pivotal for reducing food waste. Existing research has largely concentrated on the antecedents of food-waste intention, while neglecting not only the discrepancy between intention and actual wasteful behaviour but also the determinants underlying this discrepancy. Drawing on survey data from China, this study employs five machine learning models, Gradient Boost, Random Forest, XGBoost, K-nearest neighbours and Decision Tree to investigate key predictors of this gap. Gradient Boost and Random Forest outperformed the others in predictive accuracy. Moral disengagement emerged as the most influential determinant; a finding consistently supported by the two best-performing models. Among its mechanisms, three neutralization techniques, namely moral justification, diffusion of responsibility and advantageous comparison, were found to significantly contribute to the gap. Additionally, dining culture was identified as another critical factor, with over-ordering and food discarding behaviours playing a central role. Based on these findings, policymakers should consider practical interventions, including traceability tools, accountability reminders and culturally sensitive campaigns, to effectively reduce household and food-service waste. With the development of machine learning models, this research broadens the perspective of food waste research and provides new solutions for using complex data in this field.