Measuring spatial spillover effects of industrial emissions: A method and case study in Anhui province, China

Wang, Chaoran; Du, Xinming; Liu, Yi

doi:10.1016/j.jclepro.2016.09.182

Cited by 32 publications

(11 citation statements)

References 40 publications

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“…All of the ρ values in the three tables are significantly positive, indicating positive spillovers through all three kinds of networks, although the results for the spatial network, shown in Table 5, are significantly higher than those for the other two channels. This is not surprising, given that pollutant emissions are mainly spread across geographical areas [3,69]. It can also be seen from Tables 4 and 5 that the ρ values for sulfur dioxide and soot are higher than that for wastewater, confirming that air pollution always spreads further than wastewater pollution.…”

Section: Findings From Integrated Regression Modelsmentioning

confidence: 68%

Effects of Agglomeration, Environmental Regulations, and Technology on Pollutant Emissions in China: Integrating Spatial, Social, and Economic Network Analyses

Wang

Wei

2019

Sustainability

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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.

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Section: Findings From Integrated Regression Modelsmentioning

confidence: 68%

Effects of Agglomeration, Environmental Regulations, and Technology on Pollutant Emissions in China: Integrating Spatial, Social, and Economic Network Analyses

Wang

Wei

2019

Sustainability

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“…Numerous studies in the disciplines of geography and economics have extensively explored different industrial pollution emissions from different perspectives. Mainstream research on industrial pollution emissions mainly focuses on the status of industrial pollution [6,7], industrial pollution emission treatment [8][9][10], the assessment of industrial pollution emissions [11,12], regional differences in industrial pollution emissions [13,14], and the driving mechanisms of industrial pollution emissions from different regional scales, basically forming an important analytical framework that contains assessments and formation mechanisms. Specifically, the literature on assessments of industrial pollution emissions is usually conducted to judge the pollution status of current regional development.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

2021

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Winning the battle against pollution and strengthening ecological protection in all respects are vital for promoting green development and building a moderately prosperous ecological civilization in China. Using the entropy weight method, this paper establishes and evaluates a comprehensive industrial pollution index that contains and synthesizes six major industrial pollutants (wastewater, COD, waste gas, SO2, NOx, and solid waste) in the 2006–2015 period. Subsequently, this paper studies the spatiotemporal characteristics and influencing factors of industrial pollution via the Moran index and spatial econometric analysis. The empirical results indicate that (1) the temporal evolution of the industrial pollution index is characterized by an overall trend of first decreasing and then increasing. (2) The industrial pollution index of each county has certain geographical disparities and significant spatially polarized characteristics in 2006, 2009, 2012, and 2015. (3) The Moran test shows that there is a relatively significant spatial autocorrelation of the industrial pollution index among counties and that the geographical distribution of the industrial pollution index tends to show clustering. (4) Spatial regression models that incorporate spatial factors better explain the influencing factors of industrial pollution. The economic development level, technological progress, and industrialization are negatively correlated with industrial pollution, while population density and industrial production capacity are positively correlated. (5) Consequently, as relevant policy recommendations, this paper proposes that environmental cooperation linkage mechanisms, environmental protection credit systems, and green technology innovation systems should be established in different geographical locations to achieve the goals of green county construction and sustainable development.

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“…Alternatively, one or several typical pollutants (e.g., sulfur dioxide emissions, smoke and dust emissions, nitrogen oxide emissions, etc.) can be selected to represent the industrial pollution emission level [ 35 ]. Additionally, when measuring and monitoring industrial emissions, environmental performance index is employed by some institutions and scholars [ 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Can Environmental Quality Improvement and Emission Reduction Targets Be Realized Simultaneously? Evidence from China and A Geographically and Temporally Weighted Regression Model

Dong

Wang

Zhang

2018

IJERPH

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The reductions of industrial pollution and greenhouse gas emissions are important actions to create an ecologically stable civilization. However, there are few reports on the interaction and variation between them. In this study, the vertical and horizontal scatter degree method is used to calculate a comprehensive index of industrial pollution emissions. Then based on carbon density, a geographically and temporally weighted regression (GTWR) model is developed to examine the interaction between industrial pollution emissions and carbon emissions. The results specify that there exists spatial autocorrelation for carbon density in China. Overall, the average effect of industrial pollution emissions on carbon density is positive. This indicates that industrial pollution emissions play a driving role in carbon density on the whole, while there are temporal and spatial differences in the interactions at the provincial level. According to the Herfindahl index, neither time nor space can be neglected. Moreover, according to the traditional division of eastern, central and western regions in China, the situation in 30 provinces is examined. Results show that there is little difference in the parameter-estimated results between neighboring provinces. In many provinces, the pull effect of industrial pollution emissions on carbon density is widespread. Thus, carbon emissions could be reduced by controlling industrial pollution emissions in more than 60% of regions. In a few other regions, such as Shanghai and Heilongjiang, the industrial pollution emissions do not have a pull effect on carbon density. But due to spatial and temporal heterogeneity, the effects are different in different regions at different times. It is necessary to consider the reasons for the changes combined with other factors. Finally, the empirical results support pertinent suggestions for controlling future emissions, such as optimizing energy mix and reinforcing government regulation.

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Measuring spatial spillover effects of industrial emissions: A method and case study in Anhui province, China

Cited by 32 publications

References 40 publications

Effects of Agglomeration, Environmental Regulations, and Technology on Pollutant Emissions in China: Integrating Spatial, Social, and Economic Network Analyses

Effects of Agglomeration, Environmental Regulations, and Technology on Pollutant Emissions in China: Integrating Spatial, Social, and Economic Network Analyses

Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

Can Environmental Quality Improvement and Emission Reduction Targets Be Realized Simultaneously? Evidence from China and A Geographically and Temporally Weighted Regression Model

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