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The study of cross-regional environmental spillover effects can reveal the spatial information of pollution, and helps to promote regional environmental governance under inter-government cooperation. Based on the non-linear granger causality test and social network analysis, we constructed and analyzed the network of cross-regional environmental spillover effects and the driving factors of the network structure. The results showed that Beijing, Shanghai, Guangzhou, Jiangsu, and Shandong have the most significant spatial spillover effects of environmental pollution to other provinces. GDP, foreign direct investment (FDI), wage levels (WL) and infrastructure levels (IFL) could explain 61.7% of the variance in the cross-regional environmental effects. Among them, GDP was the most powerful explanation for the network, and FDI, WL, and IFL were the main factors that affect the network’s relationship. Finally, based on the above empirical results, this paper put forward the policy recommendations of cross-regional environmental governance. Therefore, in the development of regional economic growth targets, we should take full account of the constraints of regional ecological environment carrying capacity, to further improve the regional industrial production methods to promote the balanced development of cross-regional industries.
Implications: As a relationship, the environmental spillover effect links the environmental emissions from different regions into a system, which is represented by the Network of Environmental Spillover Effects (NESE). The change of its structure will have an impact on the status, function and role of each region in the network structure, thus affecting the environmental changes in the region. Therefore, the study of the NESE is helpful to promote cross-regional environmental collaborative governance and reduce environmental pollution.
The study of cross-regional environmental spillover effects can reveal the spatial information of pollution, and helps to promote regional environmental governance under inter-government cooperation. Based on the non-linear granger causality test and social network analysis, we constructed and analyzed the network of cross-regional environmental spillover effects and the driving factors of the network structure. The results showed that Beijing, Shanghai, Guangzhou, Jiangsu, and Shandong have the most significant spatial spillover effects of environmental pollution to other provinces. GDP, foreign direct investment (FDI), wage levels (WL) and infrastructure levels (IFL) could explain 61.7% of the variance in the cross-regional environmental effects. Among them, GDP was the most powerful explanation for the network, and FDI, WL, and IFL were the main factors that affect the network’s relationship. Finally, based on the above empirical results, this paper put forward the policy recommendations of cross-regional environmental governance. Therefore, in the development of regional economic growth targets, we should take full account of the constraints of regional ecological environment carrying capacity, to further improve the regional industrial production methods to promote the balanced development of cross-regional industries.
Implications: As a relationship, the environmental spillover effect links the environmental emissions from different regions into a system, which is represented by the Network of Environmental Spillover Effects (NESE). The change of its structure will have an impact on the status, function and role of each region in the network structure, thus affecting the environmental changes in the region. Therefore, the study of the NESE is helpful to promote cross-regional environmental collaborative governance and reduce environmental pollution.
Investigating network structure of cross-regional environmental spillover effects and driving factors
Wang, Bin (author)
Journal of the Air & Waste Management Association ; 70 ; 243-252
2020-03-03
10 pages
Article (Journal)
Electronic Resource
English
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