Abstract
Using prefecture-level panel data and social media data, this study investigates how industrial agglomeration, environmental regulations, and technology affect the pollutant intensity and spillover channels of pollutant emissions by integrating social and economic networks into a Spatial Durbin Model. The results show that industrial agglomeration, environmental regulations, and technological inputs facilitate the emissions intensity abatement. The outcomes also confirm that these factors affect the intensity of pollutant emissions in neighboring regions through social, economic, and spatial networks. Agglomeration has a negative spillover effect on the intensity of pollutant emissions in surrounding cities via social and spatial networks, while environmental regulations affect pollutant emissions intensity in related cities through social networks. Technology can effectively lower pollutant emissions through economic networks. These findings highlight the network linkages and spillover channels affecting the intensity of pollutant emissions.
Highlights
Given the rapid urbanization and economic development in China, environmental issues have attracted increasing attention [1,2]
Regarding the spillover effect of industrial agglomeration on pollutant emissions intensity, we found that the coefficients of Wweibo*lnVA are significantly negative in relation to wastewater and sulfur dioxide emissions, and those of Wweibo*lnFirms shown in Table 3 are significantly negative in relation to SO2 and soot emissions
Previous studies have stressed the importance of the effects of industrial agglomeration, economic development, R&D, environmental regulations, and local economic structures on pollutant emissions
Summary
Given the rapid urbanization and economic development in China, environmental issues have attracted increasing attention [1,2]. Since environmental issues and local government environmental regulations vary across space, it is necessary to explore the mechanism of pollution emissions by considering the spatial and social relationships among cities [3]. Previous studies [4,5,6] have examined the mechanisms that influence pollutant emissions from the perspectives of regulations, governance, technology, energy structure, and institutions, and have explored the relationship between industrial agglomeration and emissions [3,7,8]. The impact of social and economic networks on pollutant emissions has yet to be fully considered [15]. Social media platforms have accumulated a large number of geotagged messages reflecting social concerns about pollution-related issues [13,16,17], and these data can be used to construct spatial social networks [15]
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