Identifying the influencing factors and constructing incentive pattern of residents' waste classification behavior using PCA-logistic regression

With the acceleration of urbanization, domestic waste has become one of the most inevitable factors threatening the environment and human health. Waste classification is of great significance and value for improving urban environmental quality and promoting human well-being. Based on the theory of planned behavior, we added external and socio-economic factors to systematically examine how they affect residents' waste classification behavior (WCB). We collected 661 valid data through a questionnaire survey conducted in Jinan, a pilot city for waste classification in China. Key driving factors were identified by combining binary logistic regression and the principal component analysis. The results showed that the elderly, women, and people with higher education are more likely to participate in waste classification. Attitude, collaborative governance, and institutional pressure positively affect WCB, while subjective norm and infrastructure have a negative effect. Knowledge mastery and degree of publicity are positively and significantly related to WCB, but other perceived behavioral control sub-variables negatively affect WCB. Based on the results and status of waste classification in Jinan, we propose the multi-agent linkage governance pattern from various dimensions to explore a powerful guiding incentive that can enhance WCB and provide a reference for waste management policymakers.