The relationship between industrial restructuring and China's regional haze pollution: A spatial spillover perspective

Journal of Cleaner Production 239 (2019) 115808

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The relationship between industrial restructuring and China's regional haze pollution: A spatial spillover perspective Shengming Chen a, Yang Zhang b, *, Yabin Zhang a, Zhenxi Liu a a b

Hunan University, School of Economy and Trade, Changsha, 410079, China Hunan University of Commerce, School of Economy and Trade, Changsha, 410205, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 June 2018 Received in revised form 17 January 2019 Accepted 7 February 2019 Available online 7 August 2019

The haze phenomenon in China is becoming increasingly severe. Although some meteorological factors contribute to this phenomenon, it is ultimately linked to economic factors such as the extensive development and the unbalanced industrial structure. By using a semi-parametric global vector autoregressive model (SGVAR) and the environmental Kuznets curve, this study takes a spatial correlation perspective to investigate the influence of industrial restructuring on haze pollution and its spatial spillover effects. The empirical analysis is based on China's provincial quarterly panel data between 2001 and 2010. The results show that (1) spatial spillover characterizes China's haze pollution. That is, haze pollution in one area will aggravate haze pollution in surrounding areas; the industrial structure, dominated by heavy industry, will exacerbate haze pollution and further aggravate its spatial spillover. (2) At the regional level, the air pollution spillover effect in eastern China is stronger than that in the central and western (backward) areas. (3) There is an approximate inverse-U-shaped relationship between haze pollution and economic growth in most provinces, except Beijing and Shanghai, where haze pollution is still expected to intensify with economic growth. Therefore, in the formulation of haze pollution control policies, full consideration must be given to the impact of spatial factors, not only to promote the transfer of industrial structures in the eastern region but also to strengthen the promotion and coordination between provinces and regions and, ultimately, control pollution. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Yutao Wang Keywords: Haze pollution control Industrial structure Economic growth China Semi-parametric global vector autoregressive model

1. Introduction The haze phenomenon is extremely widespread in China, and its impact not only translates into a serious threat to people's health but also causes the deterioration of China's global position. For example, the weather factor has become a significant obstacle in attracting foreign investment, foreign talents, and tourists. The “Report on the State of the Environment in China (2015)” pointed out that the inhalable particulate pollution in cities is still severe: 78.4% of the 338 prefecture-level towns have poor air quality, exceeding the limits for most pollutants. The frequent occurrence of smoggy weather reflects a series of problems accumulated during the past 40 years in China, especially linked to the country's industrial structure, which overemphasizes heavy industries, leading to today's overcapacity and haze pollution. In the context of regional economic integration, all regions interact in the economic

* Corresponding author. E-mail address: [email protected] (Y. Zhang). https://doi.org/10.1016/j.jclepro.2019.02.078 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

system; thus, spatial factors play a key role in the study of environmental economic problems (Anselin, 2001). This study will conduct spatial analysis on the pollution caused by emitted inhalable particulates (haze pollution), analyze the spatial spillover effects of haze pollution, and verify the influence of the industrial structure on haze pollution as well as its spillover effects in different geographical environments. Our results provide references for the formulation of relevant policies aimed at lowering the pollution level in China. The link between the haze phenomenon and industrial structure has received academic attention only recently, and as such, has not been extensively researched. Most studies targeted specific pollutants, such as SO2, soot, NOX and their association with the industrial structure. Grossman and Krueger (1993) found that in different economic development stages, the evolution of the industrial structure had an inverted U-shaped relationship with SO2 and soot-based environmental pollution. However, Brajer et al. (2011) suggested that different pollution forms would lead to different results, and the inverted U-shaped relationship might not be established in all cases. Oosterhaven and Broersma (2007)

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S. Chen et al. / Journal of Cleaner Production 239 (2019) 115808

argued that upgrading the industrial structure is the most important factor for reducing environmental pollution. Van Donkelaar et al. (2010) made the first global map of haze pollution based on satellite data. After that, the International Geoscience Information Network Center of Columbia University used satellite-mounted equipment to measure the aerosol optical thickness (AOD) and produced data on the annual global average values of haze pollution between 2001 and 2010. Academic researchers began investigating the influence of different factors on haze pollution. Austin et al. (2012) used spatial aggregation research to address inhalable particulates in the United States and found a significant aggregation effect in the selected 109 air pollution monitoring sites, corresponding to their different geographical location and economic factors. In a spatial research conducted on Israel's air pollution and cancer incidence, Eitan et al. (2010) showed that due to the spatial spillover effect of air pollution, the level of contamination of inhalable particulates has a highly positive correlation with the risk of lung cancer. In a research on inhalable particulate pollution, Klejnowski et al. (2009) found that, due to different geographical environments, economic levels, and vehicle ownership rates, the concentration and composition of inhalable particulates, haze pollution, and PM10, may present significant differences across regions. Ma and Zhang (2014) used the spatial econometric model to analyze the PM2.5 emissions in key regions of the eastern, central and western regions of China and the data of key emission industries. They concluded that there are differences in the dominant factors of PM2.5 emissions in different regions. The interaction effect of PM2.5 between regions has a “negative effect” and it is unfeasible to control atmospheric pollution through long-term industrial transfer. Cao Huifeng et al. (2015) constructed vector autoregression (VAR) models, generalized impulse response functions, and variance decomposition functions using time series data to analyze the relationship between industrial structure and atmospheric pollution. The results show that the upgrading of the industrial structure in the short term will cause serious air pollution, and the upgrading of the industrial structure in the long term will be conducive to improving the air pollution. Haze pollution is not just a local environmental problem but can spread or transfer to neighboring areas through natural factors such as atmospheric circulation and atmospheric chemical actions, as well as economic mechanisms such as industrial transfer, pollution leakage, industrial agglomeration, and traffic flows. This requires local governments to abide by a fundamental principle: combining territorial management with regional coordination in the governance of haze pollution and introduce joint policies across regions. The investigation and control of the inherent spatial correlation of haze pollution can significantly improve the robustness of the analysis results and thus provide more accurate references for decision-making. Recent research used spatial metrological analysis for empirical study in different settings. Most scholars used the spatial error model and space lag model to analyze the spatial effects of air pollution and heavy haze pollution. Rupasingha et al. (2004) were the first to use this method to discuss the relationship between per capita income and air pollution of 3,029 counties in the United States. Their analysis showed a highly improved accuracy in the metrological approach after introducing the spatial variables. Maddison (2007) analyzed European countries using environmental quality indicators, such as SO2 and NOx , and found that significant spillover effects exist between countries. Poon et al. (2006) used spatial metrology to study the influence of energy, transportation, and foreign trade on China's atmospheric conditions, mainly focusing on SO2 and soot, and confirmed that spillover effects exist between China's provinces. Chen et al. (2017) used the spatial metrology to show the existence of a positive spatial

correlation between the air pollution caused by carbon emissions and energy consumption in China. Hosseini and Rahbar (2011) used the same methodology to analyze two major air pollutants in Asian countries, CO2 and PM10, and showed that these two pollutants have substantial spillover effects across Asian countries, a clear sign of spatial factors working behind this phenomenon. Hosseini and Kaneko (2013) used six categories of weight matrices to establish six spatial models and verified that pollution and environmental policies present a spatial spillover effect across countries. Some scholars also use the spatial Durbin model (SDM) for empirical research. For instance, Ma and Zhang (2014) built a spatial regression model based on the economic, social, and natural factors influencing PM10 to analyze the spatial effects of regional air pollution as well as the impact of the industrial structure. Wang et al. (2016) also applied SDM to analyze the spatial dependence of the emissions intensity of industrial pollutants. Liu et al. (2017) applied SDM to estimate the impact and spatial spillovers of different natural and anthropogenic factors on air quality. The above-mentioned scholars generally used spatial metrological analysis based on space lag to measure the spatial spillover effect. In contrast, the global vector autoregressive model (GVAR) was first introduced by Pesaran et al. (2004) and later developed by Dees et al. (2007). Compared to the traditional VAR model, GVAR constructs a global system based on the vector autoregression with exogenous variables (VARX), which corresponds to the VAR model applied to different countries. By considering the intrinsic relationship between different economies, this approach can analyze how a given shock to a global variable impacts endogenous variables and their spillover effects in different economies. The traditional environmental Kuznets Curve (EKC) hypothesis postulates that environmental quality tends to a U-shaped curve, which worsens before improving again under the influence of economic growth. However, existing studies suggest that there could be U-shaped, N-shaped, or inverted-N-shaped curve relationships between environmental variables and economic growth (Grossman and Krueger, 1995). PM10 and PM2.5 are rarely used in the empirical analysis of the EKC hypothesis due to data limitations; therefore, considerable differences across hypotheses exist. Chang and Chang (2012) suggest that economic growth has a linear impact on PM10. Shao et al. (2016) argue that haze pollution in China has an evident spatial spillover effect and a high emission of “CLUB” concentration. They also argue in favor of a U-shaped relationship between haze pollution and economic growth. Ai and Chen (2003) suggested that, by using non-parametric methods, this problem can be solved, in particular without presetting the model's structural forms and using appropriate methods to estimate its structural relationship from observed sample data. This approach can be used to empirically test non-linear effects between factors. Based on this background, this study identifies haze pollution as the research object. In line with the EKC framework, we conduct a systematic empirical analysis on the spatial spillover effect of provincial haze pollution and the influence of industrial structure on it. The main contribution of this study is twofold. In terms of practical significance, we show that the spatial aspect is of great importance to the research on environmental economic issues. In particular, haze pollution is not a simple local environmental problem. We argue that the degree of haze pollution has a close relationship with the haze pollution level of surrounding areas. This is essential to policymakers. This requires all local governments of China to pay attention and adopt policies of regional joint prevention and control in the process of controlling haze pollution. Besides, natural factors such as the current, wind direction as well as some anthropogenic factors, such as the industrial shift, are very likely to deepen on the haze pollution spatial

S. Chen et al. / Journal of Cleaner Production 239 (2019) 115808

spillover effect. We also highlight the influence of the industrial structure on haze pollution as well as its spatial differentiation since the main sources of PM2.5 are fossils fuels and the construction fugitive dust. Finally, the EKC curve reveals that the environmental quality deteriorates as income increases. However, after a certain income threshold, the environmental quality begins improving again, which means that the relationship between the environmental quality and income appears to have an inverted-U shape. On this basis, we will look for the optimal path of economic development to control haze pollution. In terms of measurement innovation, compared to the traditional spatial panel metrology method, the present analysis is not confined to the use of spatial lags to measure the space spillover effect. In particular, by using the GVAR model, we can fully identify the impact of the industrial structure on haze pollution in a region and its surrounding areas, which allows a more comprehensive identification of the spillover effect. Finally, existing studies suggested that haze pollution and economic growth might have linear, U, N, or inverted-N-shaped relationships. These pieces of research are often based on the EKC hypothesis and preset specific parameter forms for the relationship between the haze pollution and economic growth. However, the non-parametric approach does not need to make a priori assumptions regarding the parameters of interest, which reduces the likelihood of errors and allows a better fit for the non-linear relationship between industrial restructuring and haze pollution. According to above ideas, this paper will first map satellite data to the population-weighted PM2.5 concentration values in China's provinces from 2001 to 2010, then will adopt the geographic distance weight matrix and the economic distance weight matrix in the semiparametric space vector autoregressive (SGVAR) method on the environmental Kuznets Curve framework, and will discuss the impact of industrial structure on haze pollution and its spatial spillover effects from a spatial correlation perspective.

2. Model settings and data 2.1. The SGVAR model settings

3

Yit ¼ ai þ Fi1 Yi;t1 þ / þ Fi;pl Yi;tpl þ Li0 Y *it þ / þ Liql Y *i;tqi þ ji0 dit þ /jiri di;tri þ εit i ¼ 1; /; N; t ¼ 1; /; T (1) Yit ¼ ðYi1t ; /; Yi;k;t Þ0 represents the ki  1 regional endogenous variables of region i, and Y*it is a vector of exogenous variables of P region i. In Y*it ¼ N j¼0 wij Yji , the weight wij is calculated from the weight associated with trade or distance of zone j in region i, reflecting the influence between regions. Fij , Lij , and L*ij are ki  ki coefficient matrices, and εit is the ki  1 vector of random error terms for regional spontaneous shocks. Assume that these shocks are not serially correlated, and their mean is zero, that is, Eðεit Þ ¼ 0; P varðεit Þ ¼ Si . Besides, assume that i ði ¼ 0; 1; /; NÞ does not change over time and dit is a d  1 vector of exogenous variables of region i, which can also include global variables such as the international oil price or monetary policy variables. We also assume that these global variables are weak exogenous variables in the global economy. Next, we introduce the non-parametric factors and set up the SGVAR model. The modeling process is as follows. We first set up a semi-parametric vector autoregressive model for region i:

Yit ¼ ai þ Fi1 Yi;t1 þ / þ Fi;pl Yi;tpl þ Li0 Y *it þ /   þ Liql Y *i;tqi þ ji0 dit þ /jiri di;tri þ gi Pit ; /; Pi;tsi

(2)

þ εit i ¼ 1; /; N; t ¼ 1; /; T Pit is another exogenous variable for region i, or another global variable. git ð ,Þ is a ki  1 unknown non-parametric function, which reflects the non-linear relationship between Pit and each endogenous variable of region i. Let us assume that Egi ðPit ; /; Pi;tsi Þ ¼ 0, and merge it to ai otherwise. 0 Let Zit ¼ ðYit ; Y *it Þ . Assuming li ¼ maxðpi ; qi Þ; Equation (2) can be rewritten as

Ai Zit ¼ ai þ Bi1 Zi;t1 þ / þ Bi;li Zi;tli þ ji0 dit þ / þ jiri di;tri   þgi Pit ; /; Pi;tsi þ εit (3)

Spatial factors are of great importance to the study of environmental economy issues. The spillover effects of air pollution, as well as its governance, have already been addressed in the literature. The regional spatial effect of the local haze pollution has been accentuated by some natural factors such as the current and wind direction as well as some anthropogenic elements (e.g., the industrial shift). Most analyses employ spatial econometrics to study haze pollution and its determinants, mainly using the spatial lag to measure the spatial spillover effects. The GVAR model can help shed new light on the impact of haze space spillover. Compared to the traditional VAR model, GVAR constructs a global system based on the VARX that represents the VAR model in different countries. By considering the intrinsic relationship between different economies, this approach can clarify how a given shock to a global variable impacts endogenous variables and their spillover effects across different economies. Moreover, the existence of a U-shaped relationship between economic growth and haze pollution is still worth exploring, and non-parametric measurement methods seem to be particularly useful to address this issue. The semi-parametric global vector autoregressive model (SGVAR) combines the nonparametric model and global vector autoregressive model features, efficiently describes variables in different economies and their spatial spillover effects, and also fit non-linear relationships among relevant variables. First, we set up the general GVAR model:

where Ai ¼ ðIki ;  Li0 Þ;Bij ¼ ðFij ;Lij Þ.Аi and Вij are matrices of order ki  2ki , and Аi is a non-singular matrix, with rank (Аi ) ¼ ki . Combining all regional models, we obtain a k  1 vector, P 0 Yt ¼ ðY0t ; /; YNt Þ , where k ¼ N i¼0 ki is the number of endogenous variables in the global model system. Zit ¼ Wi Xt , and Wi is a ð2ki Þ  k matrix; each of its components is already known and corresponds to the coefficient on the weight of trade or weight of distance. Wi can be described as the connection matrix that links the regional SVARX* models. Combining the above equations, we obtain:

Ai Wi Yt ¼ ai þ Bi1 Wi Yt1 þ / þ Bi;li Wi Ytli þ jl0 dit þ /   þ jiri di;tii þ gi Pit ; /; Pi;tsi þ εit

(4)

where Аi Wi and Вij Wi are ki  k matrices. Since l ¼ maxðl0 ; /; lN Þ; r ¼ maxðr0 ; /; rN Þ; s ¼ maxðs0 ; /sN Þ, we transform the above equations into upper and lower stacks and obtain the semi-parametric global vector autoregressive model (SGVAR) as follows:

GYt ¼ a þ H1 Yt1 þ / þ Hl Ytl þ j0 dt þ / þ jr dtr þ gðPt ; /; Pts Þ þ εt

(5)

The SGVAR estimation involves two steps. First, we estimate the unknown parameters and non-parametric functions in each

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S. Chen et al. / Journal of Cleaner Production 239 (2019) 115808

regional SVARX* model. Second, on the basis of the first estimation, we use the trade weight matrix to calculate the coefficient matrices in the SGVAR model, G and H1 ;/;Hl ;. Therefore, there is no need to re-estimate the SGVAR model. In addition, we can use the derived components, s2ik and sijk , to calculate impulse response functions. The impulse response function is an application of the VAR model, developed in the process of VAR evolving to GVAR, and the impulse calculation method of the SGVAR model is similar to that of the GVAR model. Since the orthogonal impulse response function suggested by Sims (1980) is very sensitive to the order of variables, due to its usage of Cholesky decomposition, different orders of impulses will result in different analysis results. To avoid this shortcoming, the GVAR uses a generalized impulse response function (GIRF), and the SGVAR also uses the GIRF to analyze how a one standard deviation shock to an endogenous variable or a global variable can impact the endogenous variables, that is, the spatial spillover between variables. The spillover effect implies that the haze pollution level is closely related to the level of economically or geographically similar regions. In recent years, researchers have compared the positive and negative values of the coefficient on the spatial lag variable, W*pm, by setting different weight matrixes to estimate whether the spatial spillover effect is positive. However, the differences in the economic condition, industrial structure, and geography of the surrounding area affect the spillover effect in different ways. In this respect, the SGVAR can provide a comprehensive perspective of the spillover effect of haze pollution as well as the impact of industrial restructuring on the spillover effect of haze pollution. 2.2. Variables and data selection This study uses GVAR Toolbox, R, and Matlab as metrological analysis tools. The analysis is based on quarterly panel data of 30 Chinese provinces from 2001 to 2010 and uses the SGVAR model to assess the regional effect of economic variables at the provincial level in China. We first set N provincial-level regions; Yit is a vector of ki endogenous variables in region i. Yit consists of four variables: regional haze pollution, energy structure, industrial structure, and technology level. Y *it represents k*t weak exogenous variables obtained from endogenous variables in other regions (apart from region i), and Y *it1 is the lagged value of Y *it . The non-parametric variable Pit indicates the GDP per capita. The regional SVARX* regression model (SVARX (1,1)) is

Yit ¼ ai0 þ ai1 t þ Fi Y1;t1 þ Li0 Y *i;t þ Li1 Y *i;t1 þ gi ðPit Þ þ εi;t X i ¼ 1; 2; /; 30; t ¼ 1; 2; /; 56; εit  i; i; d:ð0; Þ i

(14) In Equation (14), ai0 and ai1 are the coefficients of the intercept and trend, respectively; Fi is the ki  ki endogenous variable coefficient matrix; Li0 and Li1 are ki  ki weak exogenous variable coefficient matrices; εi;t is the residual term, which follows a standard normal distribution of zero mean and normal variance, assuming that the spontaneous shocks from different regions are P non-serially correlated. Y *i;t ¼ N j¼0 wij Yjt refers to the other regions, where wij ¼ 0. Wij indicates the influence of region j on region i, that is, the spatial spillover effect between these regions. The metrological indicators for the relevant variables are chosen as follows: Haze pollution degree (PM). Due to the lack of data regarding the haze pollution before 2012, we use the famous Greenhouse GaseAir Pollution Interactions and Synergies (GAINS) China model data to describe the haze pollution emission in the sampled areas. These data are global annual averages of haze pollution between

2001 and 2010 (Battelle Memorial Institute, Center for International Earth Science Information Network [CIESIN], 2013), obtained by measuring the aerosol optical thickness (AOD) from satellitemounted equipment, in line with Van Donkelaar et al. (2010). These measures are consistent with the haze situation judgment provided by the Ministry of Environmental Protection of China in February 2012, and it is a general opinion that such information is highly reliable. Energy structure (ES). Burning fossil fuels, especially coal, is one of the major sources of haze pollution. China is one of the few countries with a coal-dominated energy structure. We refer to Shao et al. (2011) and use the proportion of coal consumption in the total energy consumption to describe the energy consumption structure. Industrial structure (SEC). Fossil fuel combustion and construction dust in the secondary industry are also major sources of haze pollution. Since China is still in an accelerated stage of industrialization, the scale of industrial energy consumption is higher than that of other sectors. In addition, the recent prosperity of China's real estate industry has led to the continuous development of the construction sector and propelled the development of heavy industries such as steel and cement. The construction industry assumed an exacerbating role in both direct and indirect emissions of haze pollution (Ma and Zhang, 2014). Therefore, we use the GDP proportion of secondary industry's added value, contributed by both industrial and construction industries, to describe the influence of the industrial structure on haze pollution, and we expect the coefficient on this variable to be positive. In this study, industrial restructuring means decreasing the high energy consumption in the secondary industry, that is, using more efficient energy sources. Technology level (RD). Technological innovation for energy saving and emission reduction is a meaningful way to tackle environmental pollution. Unlike most existing studies, which use a single indicator to measure the technology level, we use the GDP per unit of energy consumption (at constant 1997 price levels) to measure it. This variable is an external reflection of the energy saving and emission reduction technology improvement and its R&D performance. The higher the value, the less energy consumed by the same level of production, and the less haze pollution. Economic growth (GDP). The classic EKC hypothesis suggests that the economic growth and environmental quality are linked by an inverted-U-shaped trend, that is, the initial deterioration is followed by later improvement. However, recent research showed that U, N, or inverted-N shapes relationships might exist between environmental variables and economic growth (Grossman and Krueger, 1995). Therefore, we introduce GDP per capita (at constant 1997 price levels) in the model. The data used in this study consist of panel data from 30 provincial administrative districts in Mainland China (Tibet is excluded due to the lack of data) observed from 2001 to 2010. The sample size is relatively small, especially because the SGVAR needs more than 30-periods for the analysis. We employed the Eviews 6.0 data interpolation tool to convert the annual data into quarterly data. All information is sourced from the “China Statistical Yearbook,” “China Energy Statistical Yearbook,” “China Statistical Yearbook on Science and Technology,” “China Compendium of Statistics 1949e2008,” and statistical yearbooks of each province. For using the SGVAR model, we need to set a regional weight matrix to reflect the closeness of linkages among regions. The traditional spatial weight based on geographic relation is not comprehensive enough to represent the spatial correlation of haze pollution. Anselin (2001) showed that the degree of spatial interaction between regions is negatively correlated with their spatial distance. We use Vansteenkiste and Hiebert's (2011) estimation method to build a matrix from the geographic distances between

S. Chen et al. / Journal of Cleaner Production 239 (2019) 115808

regional capitals, and use its reciprocal form as weighting. Then, we convert the distance matrix into a weight matrix reflecting the degree of association between regions. Table 1 reports the weight matrix for ten of these provinces (or cities), each of which indicates the influence of the row's region on the column's region. It can be found from Table 1 that as the distance increases, the economic weight between provinces will slightly reduce. For example, the geographical distance between provincial capitals of Beijing, Anhui, and Fujian is 270 km, 734 km and 1558 km respectively, the corresponding weight results to be 0.343, 0.004, and 0.002.

Table 2 Unit root test result for each variable.

2.3. Test of model settings 2.3.1. Unit root test To address the long-term relationship between regional endogenous variables, Yit, exogenous variables, Y*it, and global variables Pit , in the SGVAR model, we adopt the Augmented DickeyeFuller (ADF) unit root test to verify the integration order of each variable considering their level, their first-order difference, and their second-order difference. The results in Table 2 show that each variable is an I (1) process containing a unit root (10% statistical significance) and need to enter the model after a first-order difference transformation. 2.3.2. Lag order selection and cointegration test Before estimating the SGVAR model, we need to determine the domestic endogenous variable, pi, exogenous variable, r1i, and the dominant unit's lag term, r0i. We use the Akaike information criterion (AIC) criterion to select the lag order of the regional SVAR* model. Due to the limited number of observations, we assume that the maximum lag order is pmax ¼ r1max ¼ r0max ¼ 2. Then, we need to clarify the number of cointegration relationships for each variable in the model. To test the number of cointegration relationships, we use the Johansen maximum eigenvalue test and trace test on weak exogenous I (1) variables suggested by Pesaran et al. (2004). We determine the number of cointegration relationships by the asymptotic thresholds of trace test statistic with 95% significance level. Our results shown in Table 3 indicate that the dominant unit has a lag order r0i ¼ 2 in each regional model, and the number of cointegration relationships is 1. This means that the variable will have a long-term influence on other variables in the model. 3. Empirical results and discussion Haze pollution is not only an environmental problem in some areas, but it will spread or transfer to the neighborhood to a great extent through natural factors such as atmospheric circulation, atmospheric chemistry, and so on, or economic mechanisms such as industry transfer, pollution leakage, industrial agglomeration, traffic flow, and the like. The spillover effect implies that the haze pollution level is closely related to the level of economically or

5

Variable

Statistical value

¼ Value of P

Stability

PM ES SEC RD GDP D (PM) D (ES) D (SEC) D (RD) D (GDP)

15.6265 36.6454 32.4354 12.0546 34.2646 5.5792*** 2.7461*** 15.3219*** 14.6053*** 16.5778***

1.0000 1.0000 1.0000 0.1654 0.7856 0.0000 0.0029 0.0000 0.0000 0.0000

Unstable Unstable Unstable Unstable Unstable Stable Stable Stable Stable Stable

geographically similar regions. In the presence of spillover effects, the haze pollution will be aggravated. Under the mutual effect of some natural factors such as wind direction, temperature differences, precipitations, and socio-economic factors (e.g., industrial shift, productive factors, and commodity trade), the haze pollution of the surrounding areas will also be aggravated (Ma and Zhang, 2014). Next, we will make a special effort to investigate and study the empirical analysis about how relative economic factors, including key variables-industrial structure, control variableenergy structure, technological level, etc., affect haze pollution and its spillover effects. In the SGVAR model framework, we choose the general impulse response function to analyze the dynamic impact of the industrial restructuring on the haze pollution in each province (or district or city). The general impulse response function was first proposed by Koop et al. (1996) and then used in the VAR framework by Pesaran and Shin (1996). This approach can effectively avoid the interference of variable orders on the result. Figs. 1e4 show the response of the industrial restructuring and haze pollution to a given standard deviation positive shock to the economic growth rate of each province (or district or city) or the east, central, or western areas. All dynamic responses are generated by the standard Bootstrap method with 500 simulations. The horizontal axis shows a time span of 40 quarters, and the vertical axis measures the response of each variable to the shock (in percentage points). We will first compare the influence of industrial restructuring on haze governance, without considering the spatial spillover. Then, we will focus on the study of spatial spillovers. According to a “more adjacent areas” principle, we select the highly polluted areas of Tianjin and Henan to represent eastern China and central and western China, respectively. We generate a one standard deviation positive shock to the haze pollution or industrial restructuring in these two areas, and, then, we directly observe its impact on the haze pollution in the local area and the surrounding areas. Finally, we analyze the difference in the spillover effects. The eastern area comprises 11 provinces including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and

Table 1 Distance weight matrix for the different regions. Province

Anhui

Beijing

Chongqing

Fujian

Gansu

Guangdong

Guangxi

Guizhou

Hainan

Hebei

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei

0 0.003 0.017 0.048 0.012 0.024 0.014 0.012 0.017 0.004

0.011 0 0.006 0.006 0.01 0.005 0.004 0.004 0.005 0.343

0.013 0.002 0 0.016 0.037 0.031 0.054 0.162 0.034 0.002

0.043 0.002 0.018 0 0.011 0.1 0.029 0.018 0.043 0.002

0.006 0.002 0.026 0.007 0 0.011 0.014 0.02 0.016 0.002

0.019 0.001 0.031 0.087 0.015 0 0.132 0.046 0.179 0.001

0.01 0.001 0.049 0.023 0.017 0.121 0 0.164 0.202 0.001

0.009 0.001 0.151 0.015 0.026 0.043 0.168 0 0.061 0.001

0.012 0.001 0.032 0.036 0.021 0.172 0.213 0.063 0 0.001

0.012 0.337 0.007 0.006 0.01 0.005 0.004 0.005 0.005 0

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S. Chen et al. / Journal of Cleaner Production 239 (2019) 115808

Table 3 Lag orders and cointegration relationships in each regional model. Region

Anhui Beijing Chongqing Fujian Gansu Guangdong Guangxi Guizhou Hainan Hebei Heilongjiang Henan Hubei Hunan Jiangsu

Model Lag Order

Cointegration Relationship

pi

r1i

2 2 2 1 2 1 2 2 2 2 2 2 2 2 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Fig. 1. Haze pollution response to a shock to industrial restructuring in the eastern area.

Region

Jiangxi Jilin Liaoning Ningxia Inner Mongolia Qinghai Shandong Shanghai Shaanxi Shanxi Sichuan Tianjin Xinjiang Yunnan Zhejiang

Model Lag Order

Cointegration Relationship

pi

r1i

1 2 2 2 2 2 2 1 2 2 2 2 2 2 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Fig. 3. Haze pollution response to a shock to industrial restructuring in the western area.

Fig. 4. Haze pollution response to a shock to industrial restructuring in eastern, central, and western China. Fig. 2. Haze pollution response to a shock to industrial restructuring in the central area.

Hainan; the middle area covers 9 provinces including Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; the western part comprises 11 provinces including Chongqing, Sichuan, Guangxi, Guizhou, Yunnan, Xizang, Shan Xi, Gansu, Ningxia, Qinghai, and Xinjiang. 3.1. Industrial restructuring and haze governance without consideration of the spatial spillover Following the one standard derivation positive shock to

industrial restructuring, the haze pollution in eastern, central, and western China show similar dynamic characteristics. As shown in Fig. 1, in the eastern area, the haze pollution response in economically developed areas such as Guangdong, Jiangsu, and Tianjin is mostly a positive process, with cumulative response values at 3.12%, 2.25%, and 2.13%, respectively, and then gradually converges to 0. In Liaoning, Fujian, and Hainan, haze pollution responses are positive after stabilization, but the cumulative response values are below 0.8%. This indicates that the large number of secondary industries, including industrial and construction industries, in the eastern area exacerbates China's haze pollution. Such an effect is particularly

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evident in developed areas. Fig. 2 shows that the haze pollution of each province (or city) in the central region presents a small positive process before gradually stabilizing. Except for Shanxi province, where haze pollution cumulative responses remain strong, up to 1.88%, the other regions' responses are far below at 0.8%. This shows that the acceleration of industrialization is also a major reason for the haze pollution in the central area, but its influence is much lower than that in the eastern area. Fig. 3 shows that the haze pollution in western provinces also presents small positive responses and, then, gradually stabilizes; Sichuan maintains a strong cumulative haze pollution response, up to 1.2%, while the other areas' responses are far below at 0.6%. This shows that the optimization of the industrial structure in the western area is associated with a relatively weak influence on haze pollution governance. Furthermore, we analyzed the impact of a one standard deviation positive shock to industrial restructuring in the eastern, central, and western areas, respectively, and observed their responses, as shown in Fig. 4. We found that the carbon emissions in the eastern, central, and western areas have positive responses at the early stage, with maximum values reaching 0.50%, 0.29%, and 0.28%, respectively, but the cumulative response amplitudes in the 40 industrial restructuring periods are 3.45%, 1.48%, and 0.32%, respectively. The empirical results indicate that industrialization exacerbates haze pollution in all provinces (or cities), but its influence varies across areas. The proportion of secondary industry affects haze more significantly in the eastern region than in the central and western areas. While the industrial division of labor is significantly adjusting both globally and domestically, many industries are rapidly moving from the coastal regions of eastern China to the central and western areas. The central and western areas are actively accepting the industrial transfer from foreign and domestic areas, and this phenomenon will not only stimulate their new industrialization and urbanization process but also have a large positive effect on their haze governance. In other words, the upgrade of the service sector in the eastern area, along with the industrial development and industrial transfer in the central and western areas, will bring significant benefits to both the economy and environment. The influence of industrial restructuring on environmental problems is a hot topic, which generates increasing public concerns, but is easily overlooked by academic studies. 3.2. Industrial restructuring and haze governance with consideration of spatial spillover From the above analysis, we can infer that the influence of industrial restructuring on haze pollution largely varies across different areas. Invisible endogenous linkages lie behind the industry transfers among regions. According to the SGVAR model estimation results, industrial development has a significant influence on the spillover effect of regional haze pollution. 3.2.1. Spatial spillover effects of haze pollution in the eastern area (Tianjin) and the industrial restructuring influence In the spatial dimensional view shown in Fig. 5, with a one standard deviation positive shock to haze pollution in the eastern area (Tianjin), haze pollution in Tianjin and its surrounding areas respond with similar positive dynamic characteristics, which gradually weaken with time. We found that, except for Beijing, the response values in the surrounding areas are less than the value of Tianjin itself. The response to Tianjin's haze pollution in coastal provinces (cities) such as Beijing, Hebei, and Liaoning is much stronger than that in central areas such as Shanxi, Henan, and Inner Mongolia. Under the shock on Tianjin's haze pollution, the cumulative response values of haze pollution in Beijing, Tianjin, Hebei, and Liaoning reached 11.26%, 8.94%, 7.07%, and 7.15%, respectively,

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Fig. 5. Haze pollution in Tianjin and its surrounding areas in response to a shock to haze pollution in Tianjin.

while the shock experienced by central and western areas is relatively mild, with the haze pollution changes of Inner Mongolia, Shanxi, Henan, and Anhui reaching 4.37%, 6.66%, 6.02%, and 3.39%, respectively. This indicates that the degree of haze pollution in Tianjin is closely related to the haze pollution level of its geographical vicinity, showing the characteristics of “bounded together for good or ill,” and once again confirms the apparent spatial spillover features in China's haze pollution phenomenon. However, it also indicates that Tianjin's spillover effects vary significantly in different areas: the spillover to the eastern surrounding areas is much stronger than that of the central areas. The reason behind this difference may be the relatively high level of economic growth in the eastern area, which has optimized its industrial structure and a large proportion of the tertiary industry. Such economic structure is more vulnerable to influences from neighboring provinces. Therefore, when the “pollution's spillover effect” is larger than the regional “industrial structure optimization effect,” the eastern area can hardly achieve any improvement of its atmospheric quality. This also means that isolated policy guidance for provinces will have limited effects on the environment, especially in the developed areas. To tackle the air pollution issue, China needs a “coalition government” to set up a global arrangement, which takes spatial spillovers into account. Furthermore, faced with a one standard deviation positive shock to Tianjin's industrial restructuring, the haze pollution in Tianjin and its surrounding areas responded with similar positive dynamic characteristics (Fig. 6). Following the shock to Tianjin's industrial restructuring, the cumulative response of haze pollution in Tianjin, Shanxi, Inner Mongolia, and Liaoning reached 2.13%, 2.14%, 1.43%,

Fig. 6. Haze pollution in Tianjin and its surrounding areas in response to a shock to industrial restructuring in Tianjin.

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and 1.64%, respectively, while the rest of the eastern area suffered a relatively weak impact, with haze pollution responses in Beijing, Hebei, and Shandong all below 0.75%. This means that the change in Tianjin's industrial structure has less effect on the atmospheric environment of the surrounding developed areas than that of the nearby central areas. The example of Tianjin indicates that, while industrialization exacerbates haze pollution in the local region, it also increases this type of pollution in the surrounding areas. Industrial restructuring increases the spatial spillover effect of haze pollution. The spillover effects in the surrounding central areas are larger than those in the eastern areas. In other words, the optimization of the industrial structure in developed areas, such as the increase in the weight of the tertiary industry and reduction of the secondary industry, will have spillover effects on the alleviation of air pollution. This effect is significant in the central area and mild in the eastern developed area. To summarize, the air pollution in the eastern area has a weak spillover effect on the central area and strong spillover effect on the eastern developed area, while the optimization of the industrial structure in the eastern area has stronger alleviation effect on air pollution in the central area, but it has a weak alleviation effect in the developed area. 3.2.2. Spatial spillover effects on haze pollution in the central area (Henan) and the industrial restructuring influence After the shock to Henan's haze pollution, haze pollution in the local and surrounding areas also follows a dynamic response process (Fig. 7). Henan's haze pollution has a positive response, 0.8%, in the first period, which gradually tends to 0. The shock's effect is slightly weaker in surrounding areas than in Henan. The cumulative response values of haze pollution in Hebei, Shanxi, and Shandong following the shock to Henan's haze pollution are only 1.03%, 1.43%, and 1.34%, respectively, and even smaller in other areas. This suggests that the spillover effect of Henan's haze pollution is relatively mild. One possible reason is that the economic development level in the central region is not high enough, with a middle-early stage of industrial development and little systematization. Due to the lack of ubiquitously connected industrial clusters, the impact of a shock remains local, as shown by the dotted scatters in Figs. 8 and 9. The primary source of air pollution is from local industrial and economic activities, with insignificant spillover effects. Moreover, as shown in Fig. 8, with a one standard deviation shock to Henan's industrial restructuring, the haze pollution responses in Henan and its surrounding areas show similar positive dynamic characteristics. The cumulative response values of haze pollution in Henan, Shanxi, and Shandong following the shock to Henan's industrial restructuring are 0.51%, 0.30%, and 0.23%, respectively, and other

Fig. 7. Haze pollution in Henan and its surrounding areas in response to a shock to haze pollution in Henan.

Fig. 8. Haze pollution in Henan and its surrounding areas in response to a shock to industrial restructuring in Henan.

Fig. 9. Haze pollution in Gansu and its surrounding areas in response to a shock to haze pollution in Henan.

region's cumulative response values are as low as 0.1%. This suggests that the change in the industrial structure can also increase the spatial spillover effect on Henan's haze pollution, but compared to the eastern area, the effect is very mild. 3.2.3. Spatial spillover effect on haze pollution in the western area (Gansu) and the industrial restructuring influence Taking Gansu as an example, the dynamic response process in this region and neighboring areas under the impact of haze pollution is also weak (Fig. 9). Considering the cumulative response to haze pollution in Gansu, haze pollution in Sichuan, Shaanxi, and Inner Mongolia report the top three values, 1.52%, 1.50%, and 1.04%, respectively. This shows that the spillover effect of haze pollution in Gansu is not significantly different from that observed in the central region. In contrast, under the impact of a one standard deviation shock to the industrial structure in Gansu, haze pollution in the neighboring areas showed similar positive dynamic characteristics (Fig. 10). The top three cumulative response values were in Sichuan, Chongqing, and Xinjiang, at 0.95% 0.64%, and 0.63%, respectively. These results show that, in line with the eastern and central regions, haze pollution and industrial structure changes will increase the spillover effect of haze pollution in Gansu province, but the impact is very weak. The possible causes are also similar to those in the central region and may depend on the low level of industrial clustering. From the above empirical analysis of the shock response and its spatial spillover effects, we can derive the following conclusions. In the developed area, air pollution has relatively stronger spillover

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Fig. 10. Haze pollution in Gansu and its surrounding areas in response to a shock to industrial restructuring in Henan. Fig. 11. Non-parametric derivative map of the GDP per capita to haze pollution.

effects, and its influence on the surrounding developed area is stronger; industrial restructuring in the developed areas also has a significant influence on the neighboring areas, but the alleviation effect on haze pollution is more evident in the central area. However, both air pollution and industrial restructuring in the central area have very limited spillover effects. This conclusion suggests two recommendations. First, the Chinese government should break the existing “divide and rule” model of environmental governance and build up an effective joint mechanism across provinces, especially between the developed area and the underdeveloped central and western areas, to carry on a comprehensive analysis and joint actions on issues related to air pollution and industrial arrangement. Second, developed areas should be encouraged to accelerate their industrial structure optimization, that is, to vigorously promote the tertiary industry and transfer industries westwards, as this will help improve the overall situation of China's air pollution. 3.3. Re-explore the environmental Kuznets curve of haze pollution The EKC emphasizes that, during the early stage of a country's economic development, the increase in income per capita is often accompanied by high pollution levels. When the economy and income levels reach a certain threshold, the further increase in revenue will improve the environmental quality or reduce the pollution level. The trend of most pollutants presents an invertedU-shaped relationship with the trend of income per capita. In this study, we use the non-parametric term in the local linear method to derive the relationship between economic development and haze pollution. In Fig. 11, the horizontal axis represents the GDP per capita measured at 1997 prices; the vertical axis represents the derivative of GDP per capita with respect to haze pollution, that is, the effect of one unit growth in the GDP per capita on the haze pollution. By comparison, we find that the derivative graph presents some fluctuation, instead of a simple linear shape; this verifies the rationality of our non-parametric setting. We can also notice that, as the GDP per capita gradually rises from zero, the slope of haze pollution with respect to GDP per capita is not constant but gradually increases, which means that, at this stage, the increase in the GDP per capita has a strong effect on haze pollution. Taking 1997 as the base year, when the GDP per capita rises to a value close to 17,000 Yuan, its slope drops sharply from four to zero, and the haze pollution reaches its maximum point. With the continuous increase in the GDP per capita, haze pollution declines at an increasing speed. An intuitive explanation for this phenomenon is that, in the early stage of economic development, productivity is relatively low; thus, there is less output and less pollution. With the increase in production, pollution also increases (the

extensive development stage). When the GDP per capita reaches a certain threshold, people pay more attention to pollution problems brought by the economic development, and their raising living standards lead to higher demands regarding their living environment. In addition, the higher level of productivity empowers people with more capability to tackle the environmental problem. Consequently, the pollution situation is rapidly alleviated by continuous economic development. In summary, there is a nonlinear correlation between the GDP per capita and haze pollution; the environmental variable has a U-shaped relationship with the economic progress, which shows an initial deterioration and later improvement. It can also be easily inferred from the figures that U, N, or N-shaped relationship obtained by previous studies are likely derived from research targeting different development stages, characterized by different GDP per capita in the map. This means that economic growth has a very close relationship with environmental pollution. A high level of economic development is always associated with a high level of environmental pollution control. Therefore, when pursuing rapid economic growth, environmental governance is of fundamental importance. Severe pollution may slow down the pace of economic development. An underdeveloped area that is expected to improve its environment level can import high-tech solutions from developed areas, vigorously develop its economy, optimize its industrial structure, and adopt intensive production styles, which avoid delays in the development process. To further verify the robustness of the test results, we change the setting of the time-varying distance matrix in the reference model and use a spatial connection matrix based on the law of universal gravitation to reflect the spatial influence of geographical distance and economic linkages in a more realistic way. The result of the impulse response is still robust, that is, the dynamic response direction of each key variable to the shock remains unchanged. Moreover, from the comparison between the two settings, we can infer that the time-varying spatial adjacency matrix can not only be used to measure the rapid evolution of economic relationships but also to improve the stability of the model's parameters, enhancing the reliability of the empirical results. Therefore, the above results ensure the rationality of the empirical method used in this study as well as the robustness of the empirical evidence.

3.4. Other control variables This study focuses not only on the effects of the industrial structure (SEC) on haze pollution and its spillover effects but also on the non-linear impact of economic growth (GDP) on haze

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pollution. In addition, we have also introduced technological progress (RD) and energy structure (ES) as control variables. Our results are as follows: (1) The energy structure optimization can reduce haze pollution in the area, but its role in reducing the haze pollution in the surrounding area is relatively limited. One possible cause is that the energy structure adjustment in China is relatively slow, making it hard to exert a positive spillover effect on haze pollution. Moreover, the regional energy structure is related to the resource endowment in the region. For example, the coal production in Shanxi is relatively widespread, and the utilization rate of coal is very high. In other areas, the energy structure is hard to change in the short term because of transportation costs and cultural inertia; hence, the spillover effect of the energy structure on haze pollution is relatively limited. (2) The regional RD not only helps reduce haze pollution in a region but also has positive spillovers on the surrounding area through technology, resulting in different degrees of haze pollution reduction in the surrounding zone. Technological progress will not only weaken local haze pollution but can also play a decisive role in the reduction of haze pollution by decreasing the haze pollution in surrounding areas. It is worth mentioning that, if the surrounding area is the economically developed eastern part of the country, the effect of the weakened haze pollution is even more evident. The likely explanation is that the developed eastern regions have a favorable institutional environment and strong technological absorptive capacity. 4. Conclusions Spatial factors have been shown to play a significant role in the study of environmental economic problems. Economic factors such as the industrial transfer further strengthen the spatial linkages between regional environmental quality and economic development. Therefore, spatial factors may not be overlooked in academic research. Traditionally, academic studies assumed that the environment of each region is independent of each other, which is apparently inconsistent with the reality in China. Compared with traditional space panel measurement methods, we no longer use space lag terms to measure spatial spillover effects. Instead, we use the SGVAR model to conduct a more comprehensive identification of such spillover effects. The more serious pollution areas in Tianjin, Henan, and Gansu haze respectively represent the eastern and central western regions of China, which specifically express the impact of industrial structure on haze pollution in each region and haze pollution in the surrounding areas, and we comprehensively identify the spillover effects of haze pollution. In this study, we adopt a spatial correlation perspective, use the SGVAR and the EKC to discuss and analyze the relationship between industrial restructuring and haze pollution in 30 selected provincial administrative regions in China, as well as their differences across regions. The result shows that (1) there are obvious spatial spillover characteristics in China's haze pollution; the industrial structure, dominated by the heavy industry, will exacerbate haze pollution and further aggravate its spatial spillover. This means that the worsening of haze pollution in one area will deepen the haze pollution in the surrounding areas and show the characteristics of “be bound together for good or ill.” China's heavy industry-based industrial structure is an essential factor in this phenomenon. (2) At the regional level, the air pollution spillover effect in eastern China is stronger than that of the central and western (backward) areas. This shows that the eastern region is in the urgent need of adjusting the industrial structure and reduce haze pollution both in the region and its surrounding areas. (3) There is an approximately inverse-U-shaped relationship between the haze pollution and economic growth, with most provinces, except for Beijing and

Shanghai, still in the stage where haze pollution will intensify with the rising level of economic growth. The result also indicates that industrial restructuring has a close relationship with haze pollution in China. While economic development in most parts of China aggravates haze pollution, after a certain threshold, such as in Beijing and Shanghai, economic advantages will drive technological advancement and eventually prevent haze pollution. (4) Energy structure optimization may reduce the regional haze pollution, but it has limited impact on the pollution in surrounding areas. A possible reason might be that China has a slow pace in adjusting its energy structure, that makes it difficult to impact neighbor areas and exert positive spillover effects on their haze pollution. (5) The technological advancement (RD) in a region can not only help to reduce haze pollution locally, but also reduce the haze pollution in surrounding areas to various extends through technology spillovers. If the surrounding areas are eastern areas with more developed economies, such effect will be more significant. Some specific policy recommendations are as follows. First, policymakers should focus on the establishment of regional joint prevention and control of haze pollution as well as the formation of effective local governance for haze pollution. The spillover effects and regional agglomeration characteristics of haze pollution imply that unilateral haze management efforts may become futile due to the leakage effect of haze pollution between regions. Therefore, the effective implementation of haze reduction policies must be based on regional joint prevention and control. Second, the implementation of scientific industrial restructuring is very important to develop and implement a policy for reducing pollution from the source. To realize an effective governance of haze pollution, the government needs to transform the mode of economic development, force the green upgrade of the industrial structure through market-based environmental regulation, and establish a long-term mechanism to assure consistent policy implementation. Finally, according to the regional differences in the haze pollution degree and level of economic development, China needs to implement a focused regional haze control strategy. This study shows that haze pollution tends to present an inverted-U-shape relationship with economic growth. Most areas are confronted with haze pollution, and haze programs should be implemented according to local conditions. The limitations of this study are as follows. First, due to the constraint of non-parametric estimation methods used in the SGVAR model, we need to summarize all observations and transform such information into non-parametric figures. Hence, we cannot investigate how economic growth influences haze pollution in non-linear ways. Second, due to the many empirical conclusions, this study only focuses on the impact of industrial structure on haze pollution and its spatial spillover effects based on a framework of environmental Kuznets curve. Our future research will cover more focus points, such as holding back the methodological issues and including more relevant background information. Third, since PM2.5 data are only available between 2001 and 2010, and the SGVAR model requires a large sample, in line with Zhang and Wang (2015), we use Eviews 6.0 data interpolation tools to smoothly convert annual values to quarterly values. Acknowledgments This research was financially supported by the National Natural Science Foundation of China: Model and empirical study on economic growth of large country based on scale advantages (71373075), the National Natural Science Foundation of China: Model and Empirical Study for Large Countries to Realize their Factor's Supply and Demand Equilibrium (71573083), the National

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