Revealing the influencing mechanism of the government response to the public online petition on domestic waste classification in China

Abstract

Domestic Waste Classification (DWC) is an effective measure to address the urban garbage siege. Based on text data of public online petitions and government responses regarding DWC from 278 cities across 31 provinces (cities, autonomous regions) in China, as well as corresponding city-level panel data, this paper employs text mining methods to evaluate the topics, sentiments, and quality of expression in public online petitions about DWC, and regression models are used to explore the factors influencing the government's response to public petitions on DWC. The results indicate that, the government's response quality to the petition topic of “Health threats from garbage incineration” and the sentiment of fear are relatively high. The quality of public online petitions can promote the government's response quality to related topics and sentiments expression. Additionally, there are significant differences in the government response to different topic petitions between eastern and western regions, while in cities with different DWC performance, there are significant differences in the government responses to various sentiments. Also, as the level of government decreases, the trend of selective response to petitions with different topics and sentiments becomes more pronounced. Finally, recommendations were provided for both the government and the public.