1. Introduction
During the past four decades, urbanization in China has increased rapidly from 17.92% in 1978 to 58.52% in 2017. This rapid urbanization is being accompanied by the agglomeration of the urban population, the utilization of urban land, and severe industrial emissions leading to high ambient air pollution [
1,
2]. The standardized daily value of PM
2.5 in China is 75 μg/m
3, meaning when the daily average concentration of PM2.5 higher than 75 μg/m
3, the air quality reaches the level of pollution, which is three times that of the World Health Organization (WHO) standard (25 μg/m
3) [
3]. According to the air quality monitoring results of 388 Chinese cities in 2016, only 84 cities reached the air quality standard, accounting for 24.9% of the cities tested. Of the tested cities, 254 cities, that is, approximately 75.1%, did not reach the standard.
The relationship between urbanization and air pollution has become a crucial issue for governments, residents, and academics [
4]. Regarding this, many extant studies refer to the Environment Kuznets Curve (EKC) hypothesis analysis framework. In their classic work, Grossman and Krueger (1991) tested the EKC hypothesis and found that per capita income exhibits an inverted U-shaped curve relationship on waste emissions [
5]. That is, the effect on the environment may worsen before it gets better as per capita income grows.
The EKC hypothesis was also utilized to detect the relationship between urbanization and the environment [
6,
7,
8]. However, despite the plethora of studies that has examined the influence of rapid urbanization using various empirical methods and datasets on different spatial scales, the findings have been inconsistent. Ji et al. examined the impacts of urbanization on PM
2.5 based on 79 developing countries from 2001 to 2010 and found an inverted U-shaped curve, in which low urbanization level countries have an increase tendency while high urbanization level countries have a decrease tendency [
9]. Contrary to these findings, Xu et al. [
10] used an extended STIRPAT model from 2005 to 2015 to explore the heterogeneity effect between urbanization and air pollutants based on provincial panel data in China. They pointed out there were different relationship curves of PM
2.5 and population urbanization in different regions. There was a U-shaped curve in the eastern region, a linear relationship in the central region, and a U-shaped or inversed N-shaped curve in the western region of China [
10]. Other scholars such as Lee and Oh obtained findings pointing to a similar U-shaped curve relationship between the environment and urbanization [
11]. Additionally, other types of relationships between urbanization and pollution were proposed, such as inverse N-shape curve and N-shaped curve [
12,
13,
14].
Based on the above, this study focuses on the following questions.
First, the heterogeneity impact of urbanization on PM
2.5 is one of the research questions of this study. Heterogeneity includes many aspects. Ji and Chen studied the heterogeneity of urbanization impact on energy consumption analyzed in different stages of urbanization and the heterogeneity of the energy-saving effect of urbanization at different income levels [
15]. Sun et al. considered urban traffic infrastructure investment in air pollution due to regional heterogeneity and city-scale heterogeneity [
16]. In our study, we take regional heterogeneity and urbanization heterogeneity into consideration. The regional heterogeneity of PM
2.5 may be correlated to different geographical conditions and urbanization levels. Considering China’s large geographical area and complex terrain, the influence of urbanization on PM
2.5 in regions with dissimilar urbanization levels may be significantly different. Most of the urban areas in East and Central China exhibited population density increases along with PM
2.5 decreases, while other areas in China show opposite tendencies [
17]. Due to the significant differences in socioeconomic development, complex terrain and landforms, a regional heterogeneity effect of urbanization on air pollution should be investigated and compared [
18]. Although many previous studies have focused on the heterogeneous influence of geographical characteristics [
8,
19,
20], the difference in the urbanization level has been neglected. However, the associations between urbanization and PM
2.5 may show heterogeneous patterns in the regions with different geographical characteristics and urbanization levels.
Second, the lack of consideration of dynamic effects and spatial dependence may also lead to a problematic outcome, causing estimation bias and leading to unreliable results. Wu et al. utilized a dynamic panel model to confirm the EKC hypothesis in China as a whole, as well as in East, Central, and West China separately [
21]. The fixed effect results show that a significantly negative effect of population urbanization on PM
2.5 appears in West China, and insignificant effcts in East and Central. However, when the model takes the time-lag effect into consideration, the result of the dynamic system generalized method of moments (GMM) indicated that population urbanization has a positive impact on PM
2.5 in West China and a significantly negative relationship in Central China. The spatial spillover effect of urbanization on PM
2.5 means urbanization in one area influence PM
2.5 in another adjacent area. In Du et al., the spillover effect was explained as the influence of neighboring urbanization in terms of the spatial dependence of PM
2.5 concentrations [
20]. They explored the spillover effect of urbanization on PM
2.5 in the Beijing–Tianjin–Hebei region, the Pearl River Delta, and the Yangtze River Delta from 2000 to 2010 with a spatial lag model, and claimed that urbanization had different spatial direct and indirect effects on PM
2.5. To our knowledge, few studies have considered these two problems simultaneously.
Third, each city has its own urbanization process, and therefore using city level data is superior to provincial data. Existing studies have focused mainly on rural areas or provinces, and few have investigated prefecture-level cities [
21,
22,
23]. The research of prefecture-level city could provide more detailed information, which help to stimulate a more accurate estimation, whereas national or provincial data may show bias [
24]. Furthermore, previous literature includes mainly cross-sectional data, ignoring the characteristic of dynamic changes concerning the effect of urbanization on air pollution [
25,
26].
Fourth, many studies addressing PM
2.5 concerntration in China are based on data from air quality monitoring stations, which have time limitations. As in China, full coverage of monitoring stations in urban area were built to detect PM
2.5 and other atmospheric pollutants after 2013. As a result, the existing studies in China usually have a tendency to focus on PM
2.5 concentrations for short-time series, such as daily, monthly, and single yearly. Wang and Fang used PM
2.5 data obtained from 241 new observation points in 54 cities of the Bohai Rim Urban Agglomeration in 2014 to determine the spatial–temporal distribution of PM
2.5 and its socioeconomic determinants [
27]. Guo et al. used the daily PM
2.5 of 35 monitoring sites from April 2013 to March 2015 that were calculated from daily Air Quality Index data and were collected from the official website of the Beijing Municipal Environmental Protection Bureau [
28]. Wang et al. employed the data from monitoring stations to analyze the spatial distribution of PM
2.5 in 190 cities in China in 2014 [
29]. However, short-time data cannot reflect stage heterogeneity. Additionaly, the monitoring stations are point measurements and it is not clear to represente the air quality of a given area. Fortunately, remote sensing data we used in the study helps to overcome the two drawbacks above.
Consequently, this study focuses on the following research highlights. (1) We use prefecture-level data to discuss the heterogeneity impact of population urbanization on PM2.5 from two perspectives: regional heterogeneity and urbanization heterogeneity. To address regional heterogeneity, we divide the Yangtze River Economic Belt (YREB) into three parts: upstream, midstream, and downstream. For the urbanization heterogeneity, the data sample is divided into three categories based on the annual growth rate of urbanization from 2006 to 2016: low, medium, and high urbanization levels. (2) To avoid possible estimation bias caused by spatial interaction effects and the dynamic effect, we address these two issues using a combination of a dynamic model and a spatial econometric specification. We believe that this integrated modeling framework provides new insights on the relationship between population urbanization and PM2.5 in China. (3) Panel data from prefecture-level cities are used for our study. The data concerning PM2.5 are from a combination of Aerosol Optical Depth retrievals from multiple satellite instruments.
This study is organized as follows. The next section describes the study area, dataset, and the empirical methods, while
Section 3 presents the empirical results. The final section discusses the dynamic, spillover, and heterogeneity effects, respectively, and then presents the conclusions.