Responses of PM2.5 pollution to urbanization in China

Energy Policy 123 (2018) 602–610

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Responses of PM2.5 pollution to urbanization in China a

b

Xiaomin Wang , Guanghui Tian , Dongyang Yang Zhongmei Liua

c,d,⁎

, Wenxin Zhang

a,⁎⁎

T c,e

, Debin Lu ,

a

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China College of Environment and Planning, Henan University, Kaifeng 475004, China School of Geographic Sciences, East China Normal University, Shanghai 200241, China d Henan Key Laboratory of Integrative Air Pollution Prevention and Ecological Security, Henan University, Kaifeng 475004, China e Department of Tourism and Geography, Tongren University, Tongren 554300, China b c

A R T I C LE I N FO

A B S T R A C T

Keywords: PM2.5 Urbanization Clean energy Environmental Kuznets Curve

Rapid urbanization and economic development caused serious environmental pollution burden in China. This study explored the spatiotemporal profile of PM2.5 concentrations in China from 1998 to 2016, examined its relationship with urbanization and other socioeconomic factors, including industry, abatement investment, and clean energy consumption by constructing the Environmental Kuznets Curve (EKC) model, and interpreted its responses to urbanization and these factors using the Generalized Additive Model (GAM). The results showed that PM2.5 pollution generally presented a worsening situation in most provinces during the study period. PM2.5urbanization relationship approved an inverted U-shape EKC pattern in whole China and the central and eastern region, but presented an N-shape EKC pattern in the developed eastern region. Industry and its interaction with urbanization drove increasing PM2.5 concentrations. The interactions of urbanization with abatement investment and clean energy consumption had negative effects on PM2.5 concentrations nationally, but showed different impacts across regions. The GAM's results further verified that PM2.5 concentrations increased along with urbanization and industry, but enhancing abatement investment and clean energy consumption can reverse the increased trend. The major findings and policy implications can contribute to successful policy-making aimed at successful PM2.5 pollution abatement.

1. Introduction Equilibrating socioeconomic development and environmental benefits has been one of the most serious policy challenges in China (Cao et al., 2014; Sueyoshi and Yuan, 2015). China has undergone a rapid urbanization and economic development, but was accompanied by severe air pollution and the related public health burden (Gong et al., 2012; Yang et al., 2018b; Yang, 2013). Urbanization and vast industry are further moving ahead in this country, which will rise industry and energy consumption, and inevitably generate new and sustained influence on air environment (Jiang and Lin, 2012; Zhang et al., 2018). Examining what the rapid urbanization and growing number of megacities means for air pollution has been considered as one of the urgent needs of new interdisciplinary research studies (Baklanov et al., 2016). In China, it is particularly important and pressing to examine the dynamic of air pollution and its relation to urbanization and the socioeconomic factors, including industry, environmental abatement, and

⁎

energy consumption. Urbanization is a transformation process of integrative socio-economic factors, including industrial production, energy consumption, building construction, and transport, which are directly related to the emission sources of PM2.5 (Chowdhury et al., 2007; Huang et al., 2014; Timmermans et al., 2017; Wang et al., 2015). Thus, urbanization would inevitably affect PM2.5 pollution. Especially in China, industrialization with high energy consumption always played an important role in supporting China's urbanization and economic development (Wang, 2014; Wang and Li, 2016; Wang et al., 2016; Ye et al., 2017). The development of industry contributed to massive pollution emissions and degenerated environment. The air pollutant emissions with direct effects on the ambient environment have led to serious environmental consequences and posed the biggest health risk (Kan, 2014). The World Health Organization has documented that China was one of the countries experienced the highest air pollution levels with the greatest death number attributed to ambient air pollution (World Health Organization,

Corresponding author at: School of Geographic Sciences, East China Normal University, Shanghai 200241, China. Corresponding author. E-mail addresses: [email protected] (D. Yang), [email protected] (W. Zhang).

⁎⁎

https://doi.org/10.1016/j.enpol.2018.09.001 Received 9 June 2018; Received in revised form 20 August 2018; Accepted 4 September 2018 0301-4215/ © 2018 Elsevier Ltd. All rights reserved.

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2016). Researchers have increasingly concerned the relationship between air pollution, including PM2.5 pollution, and urbanization in recent years. The Environment Kuznets Curve (EKC) hypothesis, which implied that environmental pollution would present an inverted U-shape relationship with economic growth, was often used to examine the relationship between PM2.5 concentrations and urbanization (Han et al., 2018; Ji et al., 2018; Yang et al., 2018b). However, characterized by vast territory, China's urbanization and PM2.5 concentrations had obvious spatial heterogeneity. The regional differences in the relationship between urbanization and PM2.5 are worthy of further study. Especially, serious air pollution has forced the Chinese government to increase abatement investment and promote clean energy consumption in recent years, which were supposed to improve environmental benefits (Yang et al., 2017). Abatement investment and clean energy consumption are two important variables, but were often omitted in examining the potential PM2.5-urbanization EKC relationship. Furthermore, the dynamic responses of PM2.5 concentrations to the interactive impacts of urbanization and abatement investment and clean energy consumption were also rarely studied and remained a main gap in our current knowledge. Thus, grasping the dynamic of PM2.5 pollution and its relationship to urbanization and other socioeconomic factors are important, which can contribute to effective policy-making to abate and control air pollution. This study aimed to explore the spatiotemporal evolvement of PM2.5 concentrations in China and its relationship to urbanization from 1998 to 2016, and further interpret its responses to urbanization and other socioeconomic factors, including industry, abatement investment, and clean energy consumption, and its responses to the interactions between urbanization and the socioeconomic factors. The detailed research perspective and contents were described in the following literature review. The study concluded with the major findings and policy implications.

concentrations were positively correlated with urbanization. While, the power from environmental regulation, energy innovation, and capital was also considered to have positive effects in improving air pollution (Alvarez-herranz et al., 2017; Liu et al., 2015; Lott et al., 2016; Rao et al., 2017; Walsh, 2014). Urbanization, as a representation of comprehensive socioeconomic, has the potential to improve air pollution, which the EKC hypothesis also implied. Thus, scholars also examined the potential PM2.5-urbanization EKC relationship. In this aspect, Ji et al. (2018) has found that PM2.5 have an inverted U-shape relationship with urbanization by analyzing the panel data of 79 developing countries from 2001 to 2010. Han et al. (2018) has investigated the relationship between PM2.5 and urbanization in global large cities from 1999 to 2011, and found that there was a significant inverted U-shape relationship between PM2.5 trends and population change rates in Asian large cities. Yang et al. (2018b) has documented similar findings in examining the relationship between urbanization and PM2.5 concentrations from a global perspective. In China, Han et al. (2016) has indicated that PM2.5 concentrations exhibited an inverted U-shape relationship with urban population in large cities in China based the data for population and annual average PM2.5 concentrations from 2001 to 2006. And Xu et al. (2016) has found that PM2.5 emissions had an inverted U-shape relationship with urbanization by analyzing provincial panel data during 2001–2012. Besides, some scholars have indicated that the later decreases in environmental pollution may be a temporary phenomenon and environmental pollution would increase again along with economic growth with a result of an N-shape EKC, especially in developed countries or regions (Alvarez-herranz et al., 2017; Dinda, 2004). Different development strategies and locational conditions resulted that urbanization and other socioeconomic factors, such as industry and energy consumption, differed significantly across China. Given the differences in economic level and geographic location, China's Seventh Five-Year Plans has divided the mainland provinces into three major economic regions: the eastern region, the central region, and the western region. The regional differences in socioeconomic were often considered by scholars in researching related issues (Xu and Lin, 2016; Yao et al., 2015). The regional differences and temporal variations of PM2.5 concentrations may be related to different urbanization and socioeconomic factors. Thus, the potential PM2.5-urbanization EKC relationship may be different in different regions in China, but was not examined. Further, the Chinese government has increased investment to abate air pollution and advocated clean energy consumption in recent years. Abatement investment and clean energy consumption may transform air environment toward better (Yang et al., 2017), but the two factors were not included in examining the potential PM2.5-urbanization EKC relationship in previous studies. Besides, abatement investment and clean energy consumption may change the temporal variations of PM2.5 concentrations along with urbanization. However, to the best of our knowledge, few studies have examined the dynamic responses of PM2.5 concentrations to the interactive impact of urbanization and abatement investment and clean energy consumption. Thus, the current study intended to explore the time variation of PM2.5 concentrations in China and its three major economic regions, and the spatial pattern and dynamic of provincial PM2.5 concentrations during the process of rapid urbanization from 1998 to 2016. On this basis, we examined the potential PM2.5-urbanization EKC relationship in China and its three major economic regions. Further, we investigated the variations of PM2.5 concentrations under the impacts of urbanization and other socioeconomic factors, including industry, abatement investment, and clean energy consumption, and the variations under the interactive impacts of urbanization and industry, abatement investment, and clean energy consumption.

2. Literature review Researches have verified that industrial activities, traffic emissions, and coal consumption were the main anthropogenic sources of PM2.5 pollution in many cities in China (Huang et al., 2014; Liu et al., 2016; Timmermans et al., 2017; Wang et al., 2013, 2015). Hence, the related socioeconomic factors, such as increasing population, industry, urban expansion, and traffic, were also documented as the main factors that influenced PM2.5 concentrations (Gummeneni et al., 2011; Hao and Liu, 2016; Heimann et al., 2015; Yang et al., 2018a). Urban is usual the concentrated space of the above socioeconomic activities. And, urban meteorological conditions, such the urban heat islands effect, were often favorable for the accumulation of air pollutants and the formation of secondary pollutants, and enhance PM2.5 pollution (Aslam et al., 2017; Bloomer et al., 2009; Liu et al., 2016; Zhang et al., 2009). While, urbanization is generally characterized by population growth, industrial restructuring, and urban land expansion (Wei and Ye, 2014; Ye et al., 2017). The development of urbanization can undoubtedly transform the socioeconomic factors and affect local meteorological conditions related to PM2.5. Thus, urbanization would further affect the local PM2.5 concentrations, and even the regional pattern of PM2.5 concentrations. Urbanization has a highly suspicious relationship with increasing PM2.5 concentrations. But, there were just a few studies that investigated the relationship between PM2.5 pollution and urbanization in recent years. Typically, Han et al. (2014) has found that there were significant positive correlations between urban PM2:5 and urban population in China. Li et al. (2016) has indicated that PM2.5 concentrations had a positive relationship with urbanization in China by including municipal data of urbanization, economic growth, and industrialization from 1999 to 2011 into a panel model. Yang et al. (2018b) has examined the evolvement of global urbanization and PM2.5 and indicated that China was one of the countries where PM2.5 603

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Fig. 1. China and the eastern, the central and the western economic regions.

3. Data and methods

software. The dataset has been documented with good accuracy on a global scale (the cross-validated R2 is 0.81) (Van Donkelaar et al., 2016), and has been widely used by many scholars (Lu et al., 2017; Pinault et al., 2016). The China Statistical Yearbook and China Statistical Yearbook on Environment provided the data for provincial investment completed in the treatment of waste gas. The two kinds of data were consistent, but there were missing values in some years. Thus, the two kinds of yearbook were gathered from the China National Knowledge Infrastructure (CNKI: http://www.cnki.net/) to obtain the complete data from 1998 to 2016. Data for the percentage of urban population in total from 2005 to 2016 was obtained from the China Statistical Yearbook, the percentage of the urban population or the nonagricultural population in total from 1998 to 2004 was obtained from provincial statistical yearbook on the CNKI. Data for the industrial added value during 1998–2016 was also obtained from the China Statistical Yearbook. Non-fossil energy was considered as clean energy in this study. Data for the total consumption of non-fossil energy (standard measure: 1 × 104 t of standard coal) in each year from 1998 to 2014 was gathered from the Integrated National Energy Modeling System (INEMS: http://www.inems.org/Home/Menu). There was no data in 2015 and 2016. While, the National Bureau of Statistics of China provided the total consumption of clean energy, including hydropower, nuclear, and wind power, which were generally consistent with the sum of that in 31 provinces in other years in mainland China on the INEMS. Thus, the clean energy consumption in the 31 provinces in 2015 and 2016 was estimated according to their proportions in total in 2014.

3.1. Study area The study area is mainland China, excluding Hong Kong, Macau, and Taiwan, where the air pollution criteria are different from mainland China and some socioeconomic data are unavailable. Mainland China and the division of the eastern region, the central region, and the western region are shown in Fig. 1. As mentioned above, different development strategies and locational conditions resulted in regional differences in urbanization and economic development across regions. The eastern region was encouraged to lead in development and maintained the most developed economy and industry, and the highest urbanization level in the past decades generally. While, the eastern region also faced more serious environmental pollution, including air pollution than the central and the western regions. Although there were abundant resources of energy and minerals, the central and the western region were underdeveloped in urban and economic development. However, there has been a trend of polluting industry transfer from the eastern region to the central and the western region because of the environmental regulation from the eastern developed region and the demand for urbanization and economic development in the central and the western region (Yin et al., 2015). Discrepant distribution of PM2.5 concentrations may be related to the regional differences in urbanization and economic development, the temporal variations of PM2.5 concentrations may also different under the impacts of different pattern of urbanization and economic development.

3.3. Methods

3.2. Data

3.3.1. Time trend (Slope) The unitary linear regression model is a common method used to examine the time variation trend of time series observations. This method fits the linear relation or slope of the observations in each location (province or region) and time to reflect the variation trend of observations. The formula is as follows:

PM2.5 data is gathered from the Atmospheric Composition Analysis Group at Dalhousie University (http://fizz.phys.dal.ca/~atmos/ martin/?page_id=140), which provides a global dataset of PM2.5 concentrations from 1998 to 2016. The dataset is estimated PM2.5 concentrations based on Aerosol Optical Depth (AOD) and further calibrated by the geographically weighted regression method based on monitoring data of PM2.5 (Van Donkelaar et al., 2016). The spatial resolution of the raw data is 0.1° × 0.1°. China is completely covered by the dataset, and the subset of the dataset for China was extracted. The average values of PM2.5 concentrations in each province in mainland China were calculated by using zonal statistics tool in the ArcGIS

n

Slope =

n n 1 ( ∑i = 1 i)( ∑i = 1 Yi ) n n n 1 2 ∑i = 1 i 2 − n ( ∑i = 1 i)

∑i = 1 i·Yi −

(1)

where, Y is the observations (PM2.5 concentrations) in each research location, n is the time span of the study period, and Yi represents the 604

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observation in time unit (year) i. Positive Slope means the observation presents a growth trend over time, while negative Slope means the observation presents a decreased trend over time.

poisson distribution with a link function g relating the expectation of the dependent variable to the independent variables via the following structure:

3.3.2. EKC model The quadratic curve is common empirical model to form the potential EKC relationship between environmental pollution and urbanization (or income level) in previous studies. While, there were seen increasing studies that used the cubic curve to examine the potential Nshape EKC relationship between environmental pollution and income level in recent years (Alvarez-herranz et al., 2017; Zhang and Zhang, 2018). To examine the potential PM2.5-urbanization EKC relationship, the common quadratic and cubic form can be modeled as the following Eq. (2) and Eq. (3) respectively.

⎛ ⎞ g E (Y ) = β0 + ⎜ ⎟ ⎝ ⎠

Yit = α + β1 x it + β2 x it2 +εit

(2)

Yit = α + β1 x it + β2 x it2 + β3 x it3 +εit

(3)

g (E (Y )) = β0 + f (Xi , Xj )

(6)

(7)

The degree of freedom (DF) derived by the model is used to determine the relationships between the dependent variable and independent variables. If the value of DF is greater than 1, the bivariate relationship is nonlinear; if the value of DF is equal to 1, the relationship is linear; the larger the value of DF is, the more complicated the nonlinear relationship is. Details regarding the fundamental theory of GAM can be found in (Wood, 2017), which also illustrated how to construct and estimate GAM, and visualize the results by using the R program package “mgcv”. 4. Results 4.1. The spatiotemporal profile of PM2.5 concentrations 4.1.1. The time variation of PM2.5 concentrations The mean PM2.5 concentrations in the whole country and in the three major economic regions from 1998 to 2016 were calculated to show their time variations. The results are shown in Fig. 2. There were no much difference in PM2.5 concentrations between the total, the eastern, the central, and the western region in 1998. The mean PM2.5 concentrations was respectively 26.00 μg/m3, 26.26 μg/m3, 25.95 μg/ m3, and 25.75 μg/m3. While, the variations of PM2.5 concentrations differed obviously among the three regions, especially during the period from 1998 to 2006. During this period, the mean PM2.5 concentrations in the eastern region had the fastest growth. The variation of PM2.5 concentrations in the central region was generally consistent with the total mean PM2.5 concentrations of the country, increased slower than that in the eastern region, but faster than that in the western region. The western PM2.5 concentrations also increased. During the period from 2007 to 2016, PM2.5 concentrations in the country and in the three regions presented a minor decreased trend. The Slopes of PM2.5 concentrations from 1998 to 2016 were calculated using the unitary linear regression model. The Slope was 0.60 in the country, 0.98 in the eastern region, 0.69 in the central region, and 0.07 in the western region respectively, indicating PM2.5 concentrations generally increased during the study period. PM2.5 concentrations in the eastern region was far higher than that in the central and the western region in recent years because its rapid growth in the first half period. In 2016, the mean PM2.5 concentrations was 34.46 μg/m3 in the country, 42.41 μg/m3 in the eastern region, 33.84 μg/m3 in the central region, and 25.48 μg/m3 in the western region respectively.

PMit = αi + βUrb1 Urbit + βUrb2 Urbit2 + βUrb3 Urbit3 + βInd Indit + βAba Abait + βCl Clit + βUrb

ηUrbit *ηIndit + βUrb Aba ηUrbit *ηAbait * Ind * Cl ηUrbit *ηClit + εit

(4) * where, PMit is the natural logarithm of PM2.5 concentrations in province i in year t; Urbit, Indit, Abait, and Clit respectively represent the natural logarithm of urbanization, industry, abatement investment, and clean energy consumption in country i in year t; η is the notation of deviation, ¯ . ηUrbit = Urbit − Urb However, social economy is generally less developed in China, especially the urbanization levels are low in most provinces in the central and western region, which may result in non-significant β coefficients for urbanization in the cubic polynomial form (Eq. (4)). Alternatively, we conducted the quadratic form modeled as the following Eq. (5):

PMit = αi + βUrb1 Urbit + βUrb2 Urbit2 + βInd Indit + βAba Abait + βCl Clit ηUrbit *ηIndit + βUrb Aba ηUrbit *ηAbait * Ind * + βUrb Cl ηUrbit * ηClit +εit *

j=1

where E (Y ) is the expected value of dependent variable Y (such as PM2.5 concentrations); f j (·) is a smooth functions for each independent variable Xj , the function may be parametric, such as polynomial or thin plate regression splines, or may be non-parametric, or semi-parametric, and can be estimated using a scatter smoother. A sum of smooth m m functions ∑ j = 1 f j (Xj ) is essentially a replacement of ∑ j = 1 βj (Xj ) in traditional linear regression model, m is the number of independent variables. The above Eq. (6) is a general multivariable additive model and can be used to capture the dynamic variations (linear or nonlinear) of PM2.5 pollution driven by multi-factor. Further, we constructed a bivariate smooth model as the following Eq. (7) to specially examine the responses of PM2.5 pollution to the interaction of pair of factors.

where, Yit is the natural logarithm of PM2.5 concentrations in province i in year t; xit is the natural logarithm of urbanization rates (%), εit is the error term. The inverted U-shape or N-shape pattern of the EKC can be verified using the positive or negative value of the β coefficients. Industrial level or economic growth was often considered to influence the pollution-urbanization relationship and was included as a control variable in the EKC model. While, other omitted variables, such as pollution abatement investment and clean energy consumption, and the interaction between urbanization and industry, abatement investment, and clean energy consumption may also generate important impacts on their relationship at the current development stage of China's urbanization. Thus, we constructed the following model (Eq. (4)) to examine the potential EKC relationship between PM2.5 and urbanization by controlling the above socioeconomic factors and the interaction between urbanization and these factors. Urbanization rate (%), industrial added value (1 × 108 Yuan), investment completed in the treatment of waste gas (1 × 104 Yuan), and non-fossil energy consumption (1 × 104 t of standard coal) were respectively included as the proxy variable of urbanization, industry, abatement investment, and clean energy consumption into the model.

+ βUrb

m

∑ f j (Xj )

+ βUrb

(5)

3.3.3. Generalized additive model (GAM) The GAM is a nonparametric regressive model, which uses a link function on the dependent variable to account for the relationship between linear predictor and the expected value of the dependent variable. The probability distribution of dependent variable is specified by one of exponential family distributions, such as normal, binomial, or

4.1.2. The spatiotemporal dynamic of PM2.5 concentrations The PM2.5 concentrations in 1998, 2005, 2010, and 2016, and the 605

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Total

Eastern region

Central region

Western region

60.00

PM2.5 (μg/m3)

50.00 40.00 30.00 20.00 10.00

Year Fig. 2. Time variations of PM2.5 concentrations in China and the three economic regions.

Fig. 3. Spatial pattern of PM2.5 concentrations and their time variations Note: PM2.5_SL donates the Slope of PM2.5 concentration.

4.2. Verification of PM2.5-urbanization EKC relationship

Slopes in each province in China were mapped to examine the spatiotemporal dynamic of PM2.5 pollution (Fig. 3). PM2.5 concentrations were high in the northern provinces in the eastern coastal region, especially in Tianjin, Shandong, and Jiangsu, and the growth trends of PM2.5 in the three provinces were the highest. PM2.5 concentrations were also high in most central provinces, and the growth trends were high in several provinces adjacent to the eastern provinces with high PM2.5 concentrations and rapid growth, such as Henan, Anhui, and Jilin. In the western region, PM2.5 concentrations in the northern provinces were higher than that in the southern provinces. PM2.5 concentrations in Xinjiang were the highest in the western region. While, the growth trends in the western provinces were generally lower than that in the eastern and the central provinces. Especially, PM2.5 concentrations presented a downtrend in Shaanxi, Ningxia, and Gansu. Comparing PM2.5 concentrations in the four years, it can be found that PM2.5 increased in most provinces from 1998 to 2010, but decreased from 2010 to 2016. However, the positive Slopes indicated that PM2.5 pollution generally increased in most provinces of China during the study period.

We now analyse and verify the PM2.5-urbanization EKC relationship in whole China and its eastern region, central region, and western region by estimating the results from Eq. (4) and if there are no significant β coefficients for variable urbanization, we further estimate the results from Eq. (5). The fixed-effects panel data model is suitable to examine the effects of variable with time variations on the dependent variable. We estimated the above two equations as an individual fixed-effects panel data model to control the influences of time-invariant characteristics. We conducted the estimation by adding control variable one by one in order to test the robustness of the models, and the key coefficients for urbanization were found to vary very little. The summarized results of the β coefficients and the robust standard errors clustered at provincial levels are presented in Table 1. The estimated β coefficients of urbanization from Eq. (4) were nonsignificant in the whole country, the central and western region. While, the estimated β coefficients of urbanization from Eq. (5) showed that βUrb was significantly positive (p < 0.05) and βUrb2 was significantly negative (p < 0.1) across China, indicating that the PM2.5-urbanization relationship followed an inverted U-shape EKC in the whole country. The significant coefficients from Eq. (5) in the central and western 606

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Table 1 Results of the estimated coefficient. Coefficient

Nationwide

βUrb

2.2243** (1.0842)

20.6299* (10.7452)

5.3825***(1.5700)

2.9368*(1.6622)

βUrb2

−0.3040* (0.1563)

−5.6365* (2.9820)

−0.8080***(0.1998)

−0.4708*(0.2632)

βUrb3 †

Eastern

Central

Western

0.5134* (0.2741)

βInd

0.1001*** (0.0265)

0.1671*** (0.0354)

0.1510** (0.0723)

0.1083** (0.0443)

βAba

0.0273*** (0.0104)

−0.0078 (0.0138)

0.0380*(0.0194)

0.0188 (0.0177)

−0.0096 (0.0159)

−0.0181 (0.0192)

0.0352 (0.0370)

−0.0151*(0.0115)

0.2274*** (0.0494)

0.0066 (0.0620)

−0.4317** (0.1818)

−0.0604 (0.0776)

−0.1099*** (0.0307)

−0.0295 (0.0401)

0.0519 (0.1048)

−0.0125 (0.0551)

−0.0549* (0.0314)

−0.0621* (0.0325)

0.0888 (0.0680)

0.0592*(0.0348)

0.37

0.57

0.54

0.22

βCl βUrb βUrb

* Ind * Aba

βUrb Cl * R2

Note: †there is no coefficient of βUrb3 in the quadratic form (Eq. (5)) of EKC model; Robust standard error clustered at provincial levels are given in () and significance level: ***0.01, ** 0.05, and * 0.1.

region also supported an inverted U-shape EKC relationship between PM2.5 and urbanization. In the developed eastern region, the estimated β coefficients of urbanization from Eq. (4) were all significant, indicating the PM2.5-urbanization relationship supported an N-shape EKC. Now we move on to the estimated coefficients of the control variables. Industry was found to have significantly positive impact on PM2.5 pollution in whole China and its three region. The positive impact in the eastern and the central region seemed to be stronger than that in the western region. Abatement investment was expected to have negative impact on PM2.5 pollution, but it was found to have significantly positive impact on PM2.5 pollution nationally and in the central region. In the eastern region, the impact was negative, but was not significant. The coefficients of clean energy consumption were negative nationally and in the eastern region and the western region, indicating that clean energy consumption reduced PM2.5 pollution in the country and the eastern region and the western region, but the negative impact was just significant in the western region. Following are the interaction terms. The coefficients of the interaction of urbanization and industry was significantly positive in the country, indicating the development of urbanization together with industrialization caused increasing PM2.5 pollution in China. While, the interaction of urbanization and industry was found to have significantly negative impact on PM2.5 pollution in the central region. As expected, the interactions of urbanization and abatement investment and clean energy consumption showed significantly negative impacts on PM2.5 pollution in whole China. But the interactions showed different impacts across regions and only the interaction urbanization and clean energy consumption was found to have significantly negative impacts on PM2.5 pollution in the eastern region.

Table 2 The estimated results of GAM. Model

Variable

Edf

F

p

R2

Multivariable additive model

s(Urbanization) s(Industry) s(Abatement) s(Energy) s(Urbanization, Industry) s(Urbanization, Abatement) s(Urbanization, Energy)

1 2.861 2.967 1 6.167

0.258 9.359 6.207 2.692 16.4

0.6224 0.0020 0.0101 0.1310 < 0.0001

0.894

0.876

4.51

7.914

0.0006

0.704

5.981

16.93

< 0.0001

0.874

Bivariate models

Note: s() donates the fitted smoother for each (pair) of independent variables, Edf is the estimated degree of freedom.

significant nonlinear responses to industry and abatement investment (Edf > 1, p < 0.05). In addition, the results of bivariate models showed PM2.5 concentrations have more complicated variation driven the interactions of urbanization and each of the other socioeconomic factors (Edf > 4). With the speciality of fitting local smooth, the models all showed high explanatory performance (R2 > 0.7). The responses of PM2.5 pollution to each of these influencing factors were plotted to show the specific variation (Fig. 4). It can be found that PM2.5 concentrations presented linear growth along with the development of urbanization (Fig. 4a). PM2.5 concentrations presented a nonlinear variation along with industry, it increased rapidly when the industrial added value was less than approximately 140,000 (100 million Yuan), but began to present a slow downtrend when the industrial added value was more than the turning point (Fig. 4b). As expected that increased investment of pollution abatement and consumption of clean energy can abate PM2.5 pollution, PM2.5 concentrations generally presented a decreased trend along with increases in abatement investment and clean energy consumption (Fig. 4c and d). While, PM2.5 concentrations presented a nonlinear fluctuate with increasing abatement investment and presented a linear decrease with increasing clean energy consumption. The specific variation of PM2.5 driven by the interactions of urbanization and each of the other factors were showed in Fig. 5. PM2.5 concentrations increased along with urbanization under the interactive influence of urbanization and each of the three factors. PM2.5 concentrations presented a fluctuation situation along with industry under the interactive influence of urbanization and industry (Fig. 5a), but generally presented an increased trend. It increased when the abatement investment was less, and presented a downtrend along with increasing abatement investment under the interactions of urbanization and abatement investment. Under the interactions of urbanization and clean energy consumption, PM2.5 concentrations increased slightly when the clean energy consumption was less and present an obvious

4.3. The responses of PM2.5 to urbanization and other socioeconomic factors The above EKC's results showed the long-term relationships between PM2.5 and urbanization and other socioeconomic factors, we now analyse the dynamic responses of PM2.5 concentrations to urbanization and other socioeconomic factors using the GAM's results. We first estimated the multivariable additive model (Eq. (6)) by integrating PM2.5 concentrations as dependent variable and the socioeconomic factors, including urbanization, industry, abatement investment, and clean energy consumption as independent variables to examine the linear or nonlinear responses of PM2.5 pollution to these factors. Then, we estimated the bivariate smooth model (Eq. (7)) to specially examine the dynamic variations of PM2.5 pollution driven by urbanization and each of the other socioeconomic factors. The statistical results (Table 2) showed that PM2.5 concentrations presented linear variations along with urbanization and clean energy consumption, but the linear variations was non-significant (Edf = 1, p > 0.1). However, it presented 607

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b

0 -10

-5

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2000 000 40000 00 6000000 Abatement investment

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Fig. 4. The responses of PM2.5 pollution to urbanization (a), industry (b), abatement investment (c), and clean energy consumption (d). Note: the solid line represents the smoother corresponding to independent variable, the dotted line represents the 95% confidence interval; the horizontal axis donates the observations of independent variable, and the vertical axis is the smooth function value.

across regions, the study integrated industry, abatement investment, and clean energy consumption as control variables and examined the PM2.5-urbanization relationship by constructing the EKC model in the country and the three major economic regions. Moreover, the study examined the responses of PM2.5 to urbanization and other socioeconomic factors, including industry, abatement investment, and clean energy consumption, and the responses of PM2.5 to the interactions of urbanization and the socioeconomic factors. The time variations of PM2.5 concentrations showed that PM2.5 concentrations increased obviously from 1998 to 2006, and presented a

downtrend along with an increase in clean energy consumption. 5. Discussion and Conclusion The time variations of PM2.5 concentrations in China and in the eastern, the central, and the western regions, the spatial pattern of PM2.5 concentrations in different years and the time trend in each province were analyzed to explore the spatiotemporal profile of PM2.5 pollution in China from 1998 to 2016. PM2.5 concentrations and socioeconomic development, including urbanization, differed obviously

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Fig. 5. The responses of PM2.5 pollution to the interactions of urbanization and industry (a), abatement investment (b), and clean energy consumption (c). 608

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driven by the winter monsoon. In conclusion, PM2.5 pollution remained a serious environmental challenge in China, especially in most eastern and central provinces. PM2.5 concentrations generally presented a growth trend along urbanization in China during the study period. PM2.5 concentrations showed an inverted U-shape EKC relationship with urbanization nationally and in the central and western region, but presented an N-shape EKC relationship with urbanization in the eastern region. The interactions of urbanization with abatement investment and clean energy consumption were found to have negative impacts on PM2.5 concentrations on the national scale. Especially, increasing clean energy consumption can reverse the current growth trend of PM2.5 concentration along urbanization. Comprehensive measures, such as transforming the current urbanization pattern, encouraging the clean energy consumption and technological innovation, and increasing abatement investment, should be jointly implemented to achieve successful PM2.5 pollution abatement and urban sustainability in China.

minor decreased trend from 2007 to 2016. Lin et al. (2018) has documented similar findings in investigating PM2.5 trends in China from 2001 to 2015. While, this study verified that PM2.5 concentrations generally presented an increased trend during the study period. The spatiotemporal dynamic of PM2.5 concentrations showed the detailed variations, and Tianjin, Shandong, Jiangsu, and their neighboring provinces were found to have severe PM2.5 pollution and the fastest growth. In reality, these provinces were the areas with rapid urbanization in China, especially these areas concentrated a lot of industrial enterprises which produced more industrial waste gas than other areas (He et al., 2014; Hu et al., 2014; Yang et al., 2018c). The EKC model's results verified that PM2.5 concentrations presented the inverted U-shape relationship with urbanization in the whole country and its central and western region. While, the evidence from the eastern region supported an N-shape EKC pattern. Having experienced decades of rapid urbanization and industrialization, the eastern developed region concentrates too many polluting enterprises. The growing population vehicles, energy consumption, and sprawling land along with urbanization may further increase anthropogenic emission sources of PM2.5 in the eastern region in recent years. However, the interaction between urbanization and abatement investment was found to have significantly negative effects on PM2.5 concentrations in the country, and the interaction between urbanization and clean energy consumption was found to have significantly negative effects on PM2.5 concentrations in the country and the eastern region. The GAM model's results further revealed that PM2.5 concentrations presented an increased trend along with urbanization and industry, and presented a decreased trend along with abatement investment and clean energy consumption. The concurvity may result in unstable estimations in the multivariable additive model (Wood, 2017). However, the significant results of bivariate models indicated that PM2.5 concentrations decreased along with increasing clean energy consumption in the interaction of urbanization and clean energy consumption (Table 2 and Fig. 3c). PM2.5 concentrations reached the turning point and presented a decreased trend along with increasing abatement investment in the interaction of urbanization and abatement investment. The spatiotemporal profile of PM2.5 concentrations also showed that the increased trend of PM2.5 pollution is changing in recent years (Fig. 2 and Fig. 3). This may be related to the increasing pollution abatement investment and clean energy consumption. The study would propose some policy implications from the following aspects. First, the current urbanization's pattern, characterized by rapid industrialization, should be transformed. Industrial emissions have been identified as the main source of PM2.5 pollution in China (Huang et al., 2014; Timmermans et al., 2017). The on-going industrial urbanization would inevitably induce increasing industrial pollution sources. Thus, it is urgent to inspect and transform the current urbanization's pattern. Second, the investment of pollution control and clean energy consumption should be further added, especially in the central and the western region. As presented in the results, abatement investment and clean energy consumption were found to have negative effects on PM2.5 concentrations, while the effects were not significant in the central and the western region. Hence, adding the investment of pollution control and clean energy consumption are expected to contribute significantly to abating PM2.5 pollution and avoid worsening PM2.5 pollution again. Besides, the transfer of polluting enterprises from the eastern developed regions to the central or the western region should be restricted. In the eastern region, increasing pollution abatement investment, technological innovation, and industrial restructuring may be the important pathways to reduce pollution emission, regional collaborative governance may effectively abate PM2.5 pollution. While, the transfer of polluting enterprises should be curbed and be restricted to not make the central and the western region become the new “Pollution Haven”(Zheng and Shi, 2017). Polluting enterprises in the central and western region can also affect the air pollution in the eastern region, because air pollutant can be transmitted to the eastern region

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