Journal of Geographical Sciences >
Air pollution effects of industrial transformation in the Yangtze River Delta from the perspective of spatial spillover
Chen Yufan (1994), PhD Candidate, specialized in environmental economy and sustainable development. Email: chenyf.16s@igsnrr.ac.cn 
Received date: 20210716
Accepted date: 20211020
Online published: 20220325
Supported by
The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23020101)
National Natural Science Foundation of China(41901181)
The Yangtze River Delta (YRD) is a region in China with a serious contradiction between economic growth and environmental pollution. Exploring the spatiotemporal effects and influencing factors of air pollution in the region is highly important for formulating policies to promote the highquality development of urban industries. This study uses the spatial Durbin model (SDM) to analyze the local direct and spatial spillover effects of industrial transformation on air pollution and quantifies the contribution of each factor. From 2008 to 2018, there was a significant spatial agglomeration of industrial sulfur dioxide emissions (ISDE) in the YRD, and every 1% increase in ISDE led to a synchronous increase of 0.603% in the ISDE in adjacent cities. The industrial scale index (ISCI) and industrial structure index (ISTI), as the core factors of industrial transformation, significantly affect the emissions of sulfur dioxide in the YRD, and the elastic coefficients are 0.677 and 0.368, respectively. The order of the direct effect of the explanatory variables on local ISDE is ISCI>ISTI>foreign direct investment (FDI)>enterprise technological innovation (ETI)>environmental regulation (ER)> per capita GDP (PGDP). Similarly, the order of the spatial spillover effect of all variables on ISDE in adjacent cities is ISCI>PGDP>FDI>ETI>ISTI>ER, and the coefficients of the ISCI and ISTI are 1.531 and 0.113, respectively. This study contributes to the existing research that verifies the environmental Kuznets curve in the YRD, denies the pollution heaven hypothesis, indicates the Porter hypothesis, and provides empirical evidence for the formation mechanism of regional environmental pollution from a spatial spillover perspective.
CHEN Yufan , XU Yong , WANG Fuyuan . Air pollution effects of industrial transformation in the Yangtze River Delta from the perspective of spatial spillover[J]. Journal of Geographical Sciences, 2022 , 32(1) : 156 176 . DOI: 10.1007/s1144202119296
Figure 1 Location of the Yangtze River Delta 
Table 1 Descriptive statistics 
Variable  Obs  Mean  Std. Dev.  Min  Max  VIF  

ln ISDE  Industrial Sulfur Dioxide Emissions  451  10.519  0.913  7.563  13.115   
ln ISCI  Industrial Scale Index  451  0.029  0.220  1.024  0.479  3.81 
ln ISTI  Industrial Structure Index  451  0.769  0.241  1.895  0.202  2.56 
ln PGDP  Per Capita GDP  451  10.563  0.719  8.408  11.906  5.39 
ln ETI  Enterprise Technological Innovation  451  7.905  1.771  2.996  11.496  3.63 
ln FDI  Foreign Direct Investment  451  2.996  0.855  5.369  1.115  1.85 
ln ER  Environmental Regulation  451  1.352  0.448  2.527  0.236  1.10 
Table 2 The results of the HT unit root test 
ln ISDE  ln ISCI  ln ISTI  ln PGDP  ln ETI  ln FDI  ln ER  

Horizontal sequence  0.660**  0.792  0.711  0.798  0.722  0.774  0.231*** 
First order difference  0.355***  0.101***  0.099***  0.048***  0.045***  0.007***  0.247*** 
Notes: ***, **, and * represent significance at 1%, 5%, and 10%, respectively. 
Table 3 The results of the Granger test 
Granger causality test of hypothesis a  FStatistic  Granger causality test of hypothesis b  FStatistic 

ISDEISCI  10.796***  ISCIISDE  5.946*** 
ISDEISTI  12.818***  ISTIISDE  15.742*** 
ISDEPGDP  2.152**  PGDPISDE  2.839*** 
ISDEETI  7.911***  ETIISDE  4.615*** 
ISDEFDI  9.038***  FDIISDE  9.721*** 
ISDEER  4.594***  ERISDE  5.359*** 
Notes: a—the explanatory variable does not Grangercause the explained variable. b—the explained variable does not Grangercause the explanatory variable. ***, **, and * represent significance at 1%, 5%, and 10%, respectively. 
Figure 2 Spatial distributions of the variables  ISDE, ISCI, ISTI, PGDP, ETI, FDI and ER 
Table 4 Global Moran’s I of the ISDE in the Yangtze River Delta from 2008 to 2018 
Year  2008  2009  2010  2011  2012  2013 

Moran’s I  0.201**  0.191**  0.223***  0.257***  0.258***  0.254*** 
Year  2014  2015  2016  2017  2018  
Moran’s I  0.206***  0.207***  0.231***  0.236**  0.227*** 
Notes: ***, **, and * represent significance at 1%, 5%, and 10%, respectively. 
Figure 3 Cluster maps of the ISDE in the Yangtze River Delta 
Table 5 Results of different spatial regression estimations in the Yangtze River Delta 
Variables  SEM  SLM  SDM 

ln ISCI  0.574***  0.560***  0.677*** 
ln ISTI  0.505***  0.262*  0.368** 
ln PGDP  0.027  0.230*  0.045 
ln ETI  0.136***  0.090**  0.148*** 
ln FDI  0.154**  0.233***  0.170** 
ln ER  0.088*  0.059  0.027 
W*ln ISCI  0.296  
W*ln ISTI  0.261  
W*ln PGDP  0.573*  
W*ln ETI  0.284***  
W*ln FDI  0.458***  
W*ln ER  0.009  
$\rho$  1.102*  0.603***  
R^{2}  0.249  0.028  0.418 
sigma2_e  0.062***  0.058***  0.061*** 
Wald test  28.73***  
Lratio test  172.89***  
Hausman test  51.45*** 
Notes: ***, **, and * represent significance at 1%, 5%, and 10%, respectively. 
Table 6 Direct, indirect and total effects of the SDM in the Yangtze River Delta 
Variables  Direct effect  Indirect effect  Total effect 

ln ISCI  0.745***  1.531*  2.530* 
ln ISTI  0.367**  0.113  0.254 
ln PGDP  0.010  1.306*  1.296** 
ln ETI  0.134***  0.461**  0.328* 
ln FDI  0.139**  0.886***  0.746** 
ln ER  0.022  0.019  0.041 
Notes: ***, **, and * represent significance at 1%, 5%, and 10%, respectively. 
Figure 4 Four impact patterns of the explanatory variables 
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