Xiao Gu / China University of Mining and Technology
Feiyu Chen / China University of Mining and Technology
Clarifying the causal chain between a waste separation pilot policy that was introduced in China and emotional feedback from residents is important if the policy measures are to accurately match public needs and optimize the effectiveness of the policy. This study used big data mining technology to obtain 749,601 comment texts from residents about their responses to the new waste separation policy, measured daily resident sentiments using the MLPClassifier algorithm model, and constructed a difference in differences model to assess the policy effect of the waste separation policy on resident sentiments. The findings showed that (1) the waste separation pilot policy had a significantly negative impact on resident sentiment in the pilot cities and the impact was especially pronounced in resource-based cities; (2) resident attention to the issue of waste separation plays a crucial role in predicting resident sentiment reactions and increasing their attention can buffer the negative impact of the pilot policy on resident sentiment; and (3) while the waste separation pilot policy dampened the sentiment of local residents, it raised resident sentiment levels in neighboring areas through a negative spatial spillover effect.