This paper studies the environmental pollution and its impacts in China using prefecture-level cities and municipalities data. Moran’s
Since the adoption of reform and opening up policies in 1978, China’s rapid economic growth has been well documented and widely touted with an average annual growth rate of 9.8% in gross domestic product (GDP) from 1979 to 2013 (
Many theoretical and empirical studies have examined the relationship between fiscal decentralization and environmental pollution. The debate continues whether fiscal decentralization will improve or deteriorate environmental quality [
After the tax sharing reform in 1994, taxes were reassigned between the central and local governments; thus, the intergovernmental relationship in China showed such a framework with Chinese characteristics that local governments have greater financial autonomy and central government has the political decision-making power. Both political and economic incentives became the core of China’s governances [
The relationship of economic growth and environmental degradation also had been widely concerned [
Most studies in current literatures are based on provincial level data and few of the analysis are from the view point of spatial effects. This paper contributes to the literature in the following ways. Firstly, this paper represents the first study that uses detailed prefecture-level city data to investigate the relationship between fiscal decentralization and environmental pollution in China. As pollution sources are diverse, environmental pollution shows significantly territorial features. The governments of prefecture-level cities and municipalities play an important role in environmental pollution treatment, so analyses limited to provincial level are likely to ignore the discrepancies that exist among provinces [
The paper is organized as follows. Section
Evaluation of environmental pollution should be based on the objective facts and avoid interference caused by human subjective factors. Therefore, the method of dynamic comprehensive evaluation is used to analyze the regional environmental pollution index.
Yang [
Firstly, normalize the data. As different indictors have different dimensions and units, nondimensional treatment is a must to eliminate the incommensurability brought by different dimensions and units. Assuming
Secondly, calculate the weight
Thirdly, calculate the environmental pollution index
This study concerns a selection of prefecture-level cities and municipalities in China. In view of the serious loss of data in some cities, panel data of 272 cities from 2003 to 2012 are selected and the study area is shown in Figure
The study area.
The weight values corresponding to the maximum eigenvalue determined by real symmetric matrix are
The environmental pollution index of ten cities.
2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | |
---|---|---|---|---|---|---|---|---|---|---|
Beijing | −0.38 | −0.42 | −0.51 | −0.59 | −0.57 | −0.68 | −0.65 | −0.57 | −0.46 | −0.44 |
Tianjin | 0.26 | 0.05 | 0.13 | 0.01 | 0.00 | −0.08 | −0.02 | 0.05 | −0.01 | 0.03 |
Shijiazhuang | 0.23 | 0.15 | 0.02 | 0.07 | 0.05 | −0.29 | −0.26 | −0.21 | 0.01 | 0.10 |
Tangshan | 1.44 | 0.90 | 0.80 | 0.86 | 0.87 | 1.25 | 0.88 | 0.66 | 0.84 | 0.76 |
Qinhuangdao | −0.02 | −0.08 | −0.15 | −0.17 | −0.22 | −0.40 | −0.22 | −0.15 | 0.19 | 0.22 |
Handan | 0.08 | 0.02 | −0.05 | −0.05 | −0.02 | −0.16 | −0.26 | −0.23 | 0.00 | −0.05 |
Xingtai | −0.02 | −0.05 | −0.12 | −0.12 | −0.12 | 0.05 | −0.16 | −0.20 | −0.11 | −0.09 |
Baoding | −0.46 | −0.47 | −0.50 | −0.52 | −0.56 | −0.68 | −0.64 | −0.52 | −0.36 | −0.33 |
Zhangjiakou | 0.24 | 0.15 | 0.01 | 0.06 | 0.07 | −0.29 | −0.04 | 0.00 | −0.08 | −0.10 |
Chengde | −0.24 | −0.12 | −0.15 | −0.13 | 0.08 | 0.13 | 0.16 | −0.02 | −0.07 | −0.10 |
In order to test whether there exist spatial effects, global spatial autocorrelation test and local spatial autocorrelation test are used to reveal the spatial properties. There are two forms of Moran’s
Local Moran’s
The global Moran’s
The statistical significance of Moran’s
The results of global Moran’s
Global Moran’s
Years | Moran’s |
|
|
|
|
---|---|---|---|---|---|
2003 | 0.3850 | −0.0037 | 0.0016 | 9.8158 | 0.0000 |
2004 | 0.4228 | −0.0037 | 0.0015 | 10.9042 | 0.0000 |
2005 | 0.4547 | −0.0037 | 0.0015 | 11.7872 | 0.0000 |
2006 | 0.4503 | −0.0037 | 0.0016 | 11.4713 | 0.0000 |
2007 | 0.4236 | −0.0037 | 0.0016 | 10.6792 | 0.0000 |
2008 | 0.4156 | −0.0037 | 0.0017 | 10.2805 | 0.0000 |
2009 | 0.4338 | −0.0037 | 0.0016 | 10.8598 | 0.0000 |
2010 | 0.4804 | −0.0037 | 0.0015 | 12.3233 | 0.0000 |
2011 | 0.3228 | −0.0037 | 0.0016 | 8.1697 | 0.0000 |
2012 | 0.3415 | −0.0037 | 0.0015 | 8.9503 | 0.0000 |
As shown in Table
To visually explore spatial correlation, Moran’s
Figure
Moran’s
Local Moran’s
To detect the local variation in spatial association, space lags are included. The maps drawn according to local Moran’s
LISA maps of environmental pollution index.
When spatial effects exist, using OLS models to estimate the regression of the spatial data would lead to bias or invalid results [
The expression for the spatial lag model is
The expression for the spatial error model is
The expression for the spatial Durbin model is
The fixed effects model is generally more appropriate than the random effects model since spatial econometricians tend to work with space-time data of adjacent spatial units [
As interaction effect is an important part in spatial economic models, LeSage and Pace [
The indicators selected are environmental pollution indicators, fiscal decentralization, foreign direct investment, economic growth, industrial structure, and population density. The data sources are the same as Section 3.2.
In order to eliminate the heteroscedasticity and collinearity, all the variables are logarithmic, and the descriptive statistics of indicators are shown in Table
Descriptive statistics of variables.
Series |
|
|
Obs | Mean | Std | Max | Min |
---|---|---|---|---|---|---|---|
EPI | 272 | 10 | 2720 | 0 | 0.798 | 7.070 | −0.886 |
FD | 272 | 10 | 2720 | 3.569 | 0.078 | 4.514 | 2.629 |
PGDP | 272 | 10 | 2720 | 9.884 | 0.763 | 12.115 | 4.595 |
FDI | 272 | 10 | 2720 | 2.819 | 1.648 | 6.118 | −2.303 |
SEC | 272 | 10 | 2720 | 3.864 | 0.057 | 4.453 | 2.754 |
PD | 272 | 10 | 2720 | 5.769 | 0.764 | 7.887 | 1.548 |
In order to test whether there exist spatial effects, nonspatial panel model is needed as a comparison. In the meanwhile, we need to determine whether the spatial lag model or the spatial error model is more appropriate [
According to Grossman and Krueger [
Estimation results of panel data models without spatial interaction effects.
Pooled OLS | Spatial fixed effects | Time-period fixed effects | Spatial and time-period fixed effects | |
---|---|---|---|---|
FD | 0.6802 |
0.3660 |
0.4001 |
0.1393 |
FDI | −0.0314 |
−0.0070 (−0.7350) | −0.0566 |
−0.0073 (−0.7634) |
SEC | 1.0419 |
0.1027 (1.2399) | 0.9357 |
0.1116 (1.3222) |
PD | −0.2038 |
−0.1088 |
−0.2221 |
−0.1124 |
GDP | −1.2683 |
0.5764 |
−1.0800 |
0.6716 |
GDP2 | 0.0654 |
−0.0326 |
0.0692 |
−0.0401 |
intercept | 0.9134 (0.5919) | |||
|
0.4831 | 0.1033 | 0.4644 | 0.1032 |
|
0.2416 | 0.0144 | 0.2723 | 0.0152 |
|
−2867.7 | −768.6501 | −2813.4 | −767.4535 |
LM spatial lag | 604.0905 |
12.5084 |
527.3881 |
12.3951 |
LM spatial error | 558.9572 |
13.7019 |
462.9329 |
13.4994 |
robust LM spatial lag | 53.7319 |
1.9313 | 69.1703 |
1.5220 |
robust LM spatial error | 8.5987 |
3.1248 |
4.7151 |
2.6264 |
Notes:
Nonspatial panel parameter estimation and spatial autocorrelation test results are shown in Table
As can be seen from Table
Joint significance test of spatial and time-period fixed effects.
LR |
|
Prob | |
---|---|---|---|
Spatial fixed effects | 4091.8760 | 272 | 0.0000 |
Time-period fixed effects | 2.3934 | 10 | 0.9923 |
The joint significance tests show that spatial fixed effects are significantly, while spatial and time-period fixed effects are not. Therefore, spatial fixed effects model should be built. The results in Table
Table
Estimation results of spatial Durbin model with spatial specific effects.
Spatial fixed effects | Spatial fixed effects bias corrected | Random spatial effects bias corrected | |
---|---|---|---|
|
0.0810 |
0.0800 |
0.0800 |
FD | 0.3292 |
0.3292 |
0.3292 |
FDI | −0.0001 (−0.0042) | −0.0001 (−0.0050) | −0.0001 (−0.0053) |
SEC | 0.2374 |
0.2373 |
0.2373 |
PD | −0.1251 |
−0.1250 |
−0.1250 |
GDP | 0.6758 |
0.6759 |
0.6759 |
|
−0.0376 |
−0.0376 |
−0.0376 |
|
0.0857 (0.5070) | 0.0862 (0.5100) | 0.0862 (0.5374) |
|
−0.0195 (−1.1330) | −0.0195 (−1.1332) | −0.0195 (−1.1945) |
|
−0.2925 |
−0.2926 |
−0.2926 |
|
0.0903 (0.9492) | 0.0901 (0.9475) | 0.0901 (0.9988) |
|
−0.3892 (−1.1438) | −0.3883 (−1.1410) | −0.3883 (−1.2026) |
|
0.0202 (1.1467) | 0.0201 (1.1437) | 0.0201 (1.2054) |
|
0.1134 | 0.1134 | 0.1021 |
|
0.8397 | 0.8397 | 0.0234 |
|
0.0181 | 0.0181 | 0.0181 |
|
−765.2388 | −765.24067 | −31185.402 |
Wald test (SAR) | 11.4533 |
10.3209 |
11.4533 |
LR test (SAR) | 11.4563 |
11.4602 |
NA |
Wald (SEM) | 10.4061 |
9.3653 |
10.4061 |
LR test (SEM) | 10.4034 |
10.4075 |
NA |
Notes:
The first column in Table
To test the hypothesis whether the spatial Durbin model can be simplified to the spatial lag model, Wald or LR test may be performed. The values of Wald test and LR test are 10.3209 and 11.4602, respectively. Besides, both of statistics reject the null hypothesis at 10% or 5% significance level. Similarly, the hypothesis that the spatial Durbin model can be simplified to the spatial error model must be rejected for Wald test and LR test rejects the null hypothesis at 10% significance level. Therefore, the spatial Durbin model is chosen.
The results of spatial Durbin model with spatial fixed effects show that the environment pollution of one unit is not only influenced by the explanatory variables, such as fiscal decentralization, foreign direct investment, industrial structure, population density, and economic development, but also influenced by the environment pollution and explanatory variables of the adjacent units. The coefficient of spatially lagged dependent variable on the environment pollution is 0.0800 and significant at 1% significance level, which indicates that the cities adjacent to severe environmental pollution areas are more likely to be heavily environmentally polluted. That is, the environmental pollution in China shows spatial aggregation features and the cities with serious pollution play significant role in promoting the environmental pollution adjacent to them.
The coefficient of fiscal decentralization affecting the environmental pollution in local unit is 0.3292 and significant at 1% significance level. The coefficient of fiscal decentralization in adjacent areas influenced by the environmental pollution in the local unit is positive, but it is not significant. This indicates that fiscal decentralization of local unit plays a significant role in promoting the environmental pollution. Besides, fiscal decentralization in adjacent areas will exacerbate the environmental pollution in local unit to some extent.
Firstly, we analyze the relationship between economic growth and environmental pollution from the perspective of the local space unit. The coefficients of GDP per capita and its squared term affecting the environmental pollution in local unit are 0.6759 and −0.0376, respectively; those are both significant at 1% significance level, which confirms the inverted U-shaped relationship between GDP per capita and environmental pollution. Secondly, the relationship between economic growth and environmental pollution is taken from the perspective of the adjacent space units. The coefficients of GDP per capita and its squared term in adjacent areas are −0.3883 and 0.0201, respectively, but they are not significant.
The coefficients of foreign direct investment and foreign direct investment in adjacent areas affecting the environmental pollution are −0.0001 and −0.0195, respectively, but they are not significant at 10% significance level. This means that the effect of FDI on environmental pollution cannot be determined accurately through this model.
The coefficient of industrial structure affecting the environmental pollution in local unit is 0.2373 and significant at 5% significance level. The coefficient of industrial structure in adjacent areas influenced by environmental pollution in local unit is −0.2926 and significant at 5% significance level. It indicates that the secondary industry of local unit plays a significant role in accelerating environmental pollution, while the secondary industry in adjacent areas slows down the environmental pollution in local unit. This may be due to the scale effect of secondary industry. The higher the level of secondary industry development in a unit is, the easier it is to attract the secondary industry in adjacent units, which mitigates the environmental pollution in adjacent units.
The regression coefficient of population density in local unit is −0.1250 and significant at 5% significance level, meaning that the greater population density does not help aggravate environmental pollution. This may be due to the double-edged sword of population density affecting the environmental pollution [
As spatial Durbin model is more suitable to describe the characteristics of space data, which means that it is biased when using nonspatial panel model, thus, comparing the differences between the two is put forward. However, Elhorst [
Direct and indirect effects of spatial Durbin model with spatial fixed effects.
Direct effect | Indirect effect | Total effect | |
---|---|---|---|
FD | 0.3271 |
0.1211 (0.6782) | 0.4483 |
FDI | −0.0001 (−0.0060) | −0.0208 (−1.1465) | −0.0208 (−1.0786) |
SEC | 0.2319 |
−0.2951 |
−0.0632 (−0.4304) |
PD | −0.1255 |
0.0873 (0.8598) | −0.0382 (−0.3868) |
GDP | 0.6734 |
−0.3573 (−1.0149) | 0.3161 (0.8964) |
GDP2 | −0.0374 |
0.0184 (1.0121) | −0.0190 (−1.0835) |
Notes:
The estimate coefficients and significance level of direct effects are nearly the same with those in spatial Durbin model, and the slight difference in values is due to the feedback effects. Spatial lag terms in dependent variables and explained variables are the cause of feedback effects.
The direct effect coefficient of fiscal decentralization affecting the environmental pollution is 0.3271 and significant at 1% significance level, while the coefficient of fiscal decentralization affecting the environmental pollution in nonspatial panel data model is 0.3660 and 11.89% overvalued. The coefficient of fiscal decentralization affecting the environmental pollution in spatial Durbin model is 0.3239; thus, the feedback effect of fiscal decentralization is 0.0032, accounting for 0.98% of the direct effects. The direct effect coefficients of GDP per capita and its square term affecting the environmental pollution are 0.6734 and −0.037; both of them are significant at 1% significance level. While the coefficients in nonspatial panel data model are 0.5764 and −0.0326, both of them are underestimated in nonspatial panel data model. The coefficients of GDP per capita and its square term affecting the environmental pollution in spatial Durbin model are 0.6759 and −0.0376, respectively; both of them are significant at 1% significance level. The feedback effects of GDP per capita and its square term are negative. The direct effect coefficients of foreign direct investment, industrial structure, and population density affecting the environmental pollution are −0.0001, 0.2319, and −0.1255; the latter two of them are significant at 5% significance level, while the coefficients of the three affecting the environmental pollution in nonspatial panel data model are −0.0070, 0.1027, and −0.1088. The foreign direct investment and industrial structure are underestimated in nonspatial panel data model, while population density is overvalued. The coefficients of foreign direct investment, industrial structure, and population density affecting the environmental pollution in spatial Durbin model are −0.0001, 0.2373, and −0.1250, there exist negative feedback effects for the terms of industrial structure and population density, and the feedback effect of foreign direct investment is not obvious.
The direct effect of fiscal decentralization on environmental pollution is overestimated by a big margin in the nonspatial panel data model, while it is bias corrected in the spatial Durbin model. From the view of feedback effect, there are positive feedback effects of fiscal decentralization and the population density which influenced environmental pollution, while the effects of foreign direct investment and the industrial structure which influenced environmental pollution are negative. It is due to the independence of the fiscal decentralization and the competitiveness of foreign direct investment and industrial structure. The feedback effect of GDP per capita which influenced environmental pollution is negative, while the effect of its square term is positive. As both of them are not significant, it is not obvious that the economic development affects the environmental pollution of the adjacent areas through feedback effects. There exist spillover effects for parameter estimation in spatial Durbin model, while there are none in nonspatial panel data model. The spillover effects of explanatory variables play a role in the environmental pollution of different areas to some degree. The spillover effects of fiscal decentralization and population density are positive, while the spillover effects of foreign direct investment and industrial structure are negative. The total effect coefficient of fiscal decentralization on environmental pollution is 0.4483 and significant at 5% significant level, indicating that fiscal decentralization has a significant role in promoting the environmental pollution.
In this paper, we put forward an empirical evaluation on the relationship between fiscal decentralization, economic growth, and environmental pollution by the method of spatial economic analysis. This study provides strong evidence of spatial autocorrelation of environmental pollution. The significant values of both global Moran’
The further analysis confirms that spatial Durbin model is more suitable for this study. The environment pollution of one unit is not only influenced by the explanatory variables but also influenced by the environment pollution and explanatory variables of the adjacent units. That is, there exists feedback effect of environmental pollution. Environment pollution shows significant spatial aggregation features; the cities with serious pollution play significant role in aggravating the environmental pollution adjacent to them. Fiscal decentralization of local unit plays a significant role in promoting the environmental pollution. The feedback effect of fiscal decentralization on environmental pollution is also positive, though it is not significant. There exists the inverted U-shaped relationship between GDP per capita and environmental pollution. In the meanwhile, the feedback effect of economic growth is not significant. When considering the effects of local space unit, the effects of industrial structure and population density affecting the environmental pollution are significant at 5% significance level, while the foreign direct investment is not significant. When considering the effects of adjacent space areas, only the industrial structure is significant.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors are grateful to the support provided by the National Social Science Fund Project of China (no. 14BJY159).