Mandatory travel policy or soft travel policy? Evidence from Chinese social media data

Carbon emissions, a major contributor to global warming, have attracted significant international focus. In China, emissions from motor vehicles represent a substantial portion of this problem. In response, the Chinese government has implemented driving restriction policies on the one hand, and introduced policies to promote the development of the new energy vehicle (NEV) industry on the other, in an effort to reduce carbon emissions from motor vehicles. This study employs Latent Dirichlet Allocation (LDA) topic modeling and text network analysis to identify public concerns under different policy interventions, and uses sentiment analysis to assess public attitudes toward these policies. The results reveal widespread dissatisfaction with the driving restriction policy, with many individuals preferring to purchase electric vehicles as an alternative to public transportation. Despite the restrictions, private car travel remains favored. In contrast, policies involving soft interventions, such as subsidies for NEVs, receive higher public acceptance.