Spatiotemporal characteristics and driving forces of urban sprawl in China during 2003–2017

Journal of Cleaner Production 241 (2019) 118061

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Spatiotemporal characteristics and driving forces of urban sprawl in China during 2003e2017 Yanchao Feng, Xiaohong Wang*, Wenchao Du, Jun Liu, Yuxuan Li School of Economics and Management, Harbin Institute of Technology, Harbin, 150001, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 January 2019 Received in revised form 5 August 2019 Accepted 15 August 2019 Available online 17 August 2019

As for the academics and policymakers, more attention has been given to the issue on urban sprawl in China, while in-depth study at the national scale was relatively limited. Based on the 285 prefecture-level and above cities in China from 2003 to 2017, this paper has adopted the remote sensing derived data from the LandScan Global Population Database to examine the spatiotemporal characteristics of urban sprawl, and to investigate its driving forces among different sizes of cities at national and regional levels. The results revealed that small and medium-sized cities sprawled most significantly, followed by large cities, while mega cities sprawled the least. In addition, some new findings have been unearthed compared to the existing literature. For example, the siphon effect was greater than the spillover effect among different sizes of cities at the national scale, indicating that the competition of development resources has to some extent hindered the deterioration of urban sprawl in China; when the spatial heterogeneity was considered, the driving forces of urban sprawl exhibited differentiation among different regions and urban sizes. Furthermore, the spatial spillover effect of urban sprawl has exacerbated its intensity, which highlighted the importance and necessity of joint governance in space. The research concludes with future policy implications necessary for a more sustainable, compact, and coordinated urban development. © 2019 Published by Elsevier Ltd.

Handling editor: Mingzhou Jin Keywords: Spatiotemporal characteristics Driving forces Urban sprawl

1. Introduction Although the definition of urban sprawl is debatable until now, it is commonly acknowledged that if the growth rate of land is greater than population growth, then a city can be acknowledged as sprawling (Davis and Schaub, 2005). Characterized by inefficient land resource utilization, poor planned, unlimited and sporadic physical expansion occurring in the fringes of cities and metropolitan areas, urban sprawl has received widespread concerns from researchers, academics, and policymakers over the last several decades (Wei and Ewing, 2018). For instance, it has been widely noted that urban sprawl has been evolved in many developed countries, such as, America (Ewing and Hamidi, 2015), Canada (Nazarnia et al., 2016), and large parts of Europe (Henning et al., 2015), etc. In addition, some developing countries are also affected by urban sprawl, such as, South America (Lovera, 2015),

* Corresponding author. E-mail addresses: [email protected] (Y. Feng), [email protected] (X. Wang), [email protected] (W. Du), [email protected] (J. Liu), [email protected] (Y. Li). https://doi.org/10.1016/j.jclepro.2019.118061 0959-6526/© 2019 Published by Elsevier Ltd.

Central Ethiopia (Dadi et al., 2016), and India (Dinda et al., 2019), etc. Similar to the West, the negative consequences of urban sprawl on environmental pressure and public health have been acknowledged in China, such as, the dislocation between home and work (Li and Li, 2019), the fragmentation of landscape (Chen et al., 2016), farmland occupation (Liu et al., 2018), urban heat island effects (Chen et al., 2018), and carbon footprint (Li et al., 2019), etc. However, in contrast to the extensive literature on international cases, the study of urban sprawl in China still deserves in-depth further studies. Since the initiation of the “reform and opening-up” policy in 1978, urbanization and economic development have taken place at an unprecedented rate in China (Chen et al., 2018). As a common phenomenon associated with urbanization and economic development, urban sprawl in China has also attracted widespread concern from researchers (Shu et al., 2018). Due to the contradiction between the growing demand for construction land and the ineffective exploitation of land resources (Lin et al., 2018), urban sprawl has been investigated in many Chinese cities and regions, such as, Beijing (Zhao, 2013; Gu et al., 2015), Shanghai (Yue et al., 2014; Tian et al., 2017), Hangzhou (Yue et al., 2013; Jim and Shan,

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Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

2013), Yangtze River Economic Belt (Wu et al., 2017; Tian and Sun, 2018), Chinese port cities (Li et al., 2016), West China (Schneider et al., 2015), Northern China (He et al., 2017), and Northeast China (Chen et al., 2018). However, due to the difficulty of obtaining sufficient data, the studies about urban sprawl of all cities in China were relatively limited. Furthermore, most of the existing literature about urban sprawl usually adopted the administrative census data, which could not effectively reflect the extent of urban sprawl due to the existence of illegal or unplanned developments (Tong et al., 2017). Fortunately, the utilization of the remote sensing derived data from nighttime stable light data (NSL) has been widely used in estimate urban sprawl in recent years, which makes it possible to calculate the extent of urban sprawl accurately (Bhatta et al., 2010). Interestingly and a little surprisingly, urban sprawl of all cities in China using the remote sensing derived data has not been fully investigated, thus our study would help to identify research opportunities for researchers interested in such subject (Liu et al., 2018). As a result, based on the remote sensing derived data from the LandScan Global Population Database,1 this paper has quantified urban sprawl in China using two metrics. According to the existing literature, there is no general agreement about the measurement of urban sprawl. In general, we can roughly divide the existing measure into two types: unidimensional index and multidimensional index (Li and Li, 2019). Since it is easier to calculate than multidimensional index, many early attempts at measuring urban sprawl are based on unidimensional index, such as, the growth rate between the annual growth of an urban area and the urban population (Li and Li, 2019), the reciprocal of population density (Wang et al., 2019), and the growth of urban built-up area (Liu et al., 2018), etc. However, the above-mentioned unidimensional indices have limited capacity to provide a relatively complete description of urban sprawl (Tian et al., 2017). Meanwhile, the two types measurement of urban sprawl are always highly correlated (Li and Li, 2019). Therefore, when multidimensional index is unavailable, it is appropriate to adopt unidimensional index (Li and Li, 2019). According to the existing measurements, most studies about the spatial characteristics of urban sprawl have been investigated. Generally, the spatial characteristics are easily to be observed, such as, low-density development, leapfrog or scattered development, and poor accessibility, etc. (Liu et al., 2018). Moreover, some studies have investigated the spatiotemporal characteristics of urban sprawl by comparing the changes about the spatial characteristics over time, which has to some extent shed light on our research (Xu and Min, 2013; Liu et al., 2018; Li and Li, 2019). Furthermore, the reality of economic development and urbanization among different regions are in large differences (Ren et al., 2018). Hence, for testing whether spatial heterogeneity is supported in China, this paper has divided the sample cities into three regions proposed by the National Bureau of Statistics (NBS). Recently, some empirical studies have confirmed that the sprawl extent of small and medium-sized cities were more critical than large and mega cities (Li and Li, 2019). However, there is little evidence of the sprawl difference among different sizes of

1 At approximately 1 km resolution (3000  3000 ), the Landscan Global Population Database is the finest resolution global population distribution data available and represents an ambient population (average over 24 h), which can depict urban outline and area accurately and meticulously, and identify the population density of its subdivided areas. In addition, the distribution of population in Landscan Global Population Database includes urban and rural areas simultaneously, while our analysis is mainly depending on urban population, so we should identify the urban area before investigation. Therefore, by referring to the study of Wang et al. (2019), we have extracted the area with a population density greater than 1000 person per squared kilometers as our research sample.

cities using the remote sensing derived data. Therefore, our study is of great importance and necessary to make up for this research gap and promote the development of related research. Identifying the driving forces of urban sprawl is very important for promoting urban development policy in the long run. According to the existing literature, the driving forces of urban sprawl can be captured from the following aspects: political, economic, sociological, environmental, etc. Different from the West, urban sprawl in China is not only driven by market forces, but also attributable to government reaction (Tian et al., 2017). In addition, industry development, settlement expansion, and infrastructure construction are the direct factors driving urban sprawl (Chen et al., 2018). Furthermore, with the increasing awareness of environmental protection, the pursuit of green level has to some extent affected the quality of urban development (de Oliveira et al., 2018). Hence, in order to bridge the gap between theory and practice, a comprehensive consideration of driving forces is essential in this paper. Meanwhile, given the spatial correlation exists in geographical data, traditional non-spatial regression methods may lead to errors (Cheng et al., 2018). In order to avoid such errors, this paper has adopted spatial econometric models to estimate the direct and spatial lag effects of driving forces on urban sprawl in China (Lin et al., 2018). Based on the above research streams, this paper has three objectives: (1) to illustrate the spatiotemporal characteristics of urban sprawl among different sizes of cities at national and regional levels, (2) to estimate the effects of driving forces on urban sprawl in China using the spatial econometric model, (3) to provide targeted suggestions for sustainable and coordinated urban development and to put forward valuable insights for further research. This paper is organized into five sections: Section 1 presents the introduction and research objectives; Section 2, the research method; Section 3, data source and variables calculation; Section 4, analysis and discussion, and Section 5, conclusions, policy implications, and research prospects, followed by References. In order to exhibit the studying steps of our research intuitively, a flowchart diagram was adopted in Fig. 1. 2. Method 2.1. Spatial autocorrelation test As one commonly used index of the exploratory spatial data analysis (ESDA), Moran's I had been utilized to test spatiotemporal characteristics by identifying spatial correlation and spatial heterogeneity. Generally, we can divide Moran's I into two types: global Moran's I (GMI) and local Moran's I (LMI), which are utilized to investigate spatial correlation and spatial distribution pattern respectively (Zhao et al., 2017). The equation for calculating global Moran's I (GMI) was set as follows:

n GMI ¼ Pn Pn i¼1

isj Wij

Pn Pn 

i¼1



* isj Wij ðxi  x Þ Pn * 2 i¼1 ðxi  x Þ

xj  x*

 (1)

where xi denotes the value of urban sprawl in city i, x* denotes the mean value of x, n stands for the number of cities, and Wij denotes the spatial weight matrix. In order to demonstrate the spatial distribution pattern of urban sprawl visually, this paper has utilized local Moran's I (LMI) for indepth investigation. Corresponding, the equation for calculating the global Moran's I (LMI) was set as follows:

  xj  x* ðxi  x* Þ Xn Xn LMI ¼ 1Pn W P i¼1 isj ij 1 n ðx  x* Þ * n i¼1 ðxi  x Þ n i¼1 i

(2)

Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

Data Source, the Calculation of Urban Sprawl and the Introduction of Driving Forces

Use Unit Root Test and Cointegration Test to Avoid Spurious Regressions

Introduction: Background and Literature Review

Analyze the Results based on Spatial Durbin Model under the Space-and-time Fixed Effect among Different Cities at National and Regional Levels

A Brief Introduction of Method: Spatial Correlation Test, Spatial Durbin Model, and Spatial Weight Matrix

Use Dynamic Change, Global MoranĆs I , and Local MoranĆs I to Display the Spatiotemporal Characteristics of Urban Sprawl

3

Provide Conclusions, Policy Implications, and Research Prospects

Fig. 1. Flow chart of the work progress.

The values of local Moran's I (LMI) also range from 1 to 1. Combining a Moran scatter plot with local Moran's I (LMI) values, we can generate the LISA cluster map from ArcGIS 10.6. If the value of local Moran's I (LMI) greater than zero, the High-High clustering or Low-Low clustering is established, implying that the value of a particular city resembles the values of the surrounding cities. If the value of local Moran's I (LMI) less than zero, the High-Low outlier or Low-High outlier is established, implying that the value of a particular city surrounds by the different surrounding values. 2.2. Spatial Durbin model After testing the significance of LM test, Robust LM test, and the likelihood ratio (LR) test of the joint significance, we investigated spatial Durbin model under the space-and-time-fixed effect was more suitable than other spatial econometric models (Elhorst, 2014). Hence, the selected model was set as follow:

2.3. Spatial weight matrix Spatial weight matrix is the core element of spatial econometric models, which can capture the spatial correlation between a certain city and its neighboring cities (Liu et al., 2017). Compared with spatial adjacent matrix, spatial weight matrix with the squared inverse distance has taken both the bordering cities and the nonbordering cities into consideration, which can accurately reflect the spatial correlation among different cities (Elhorst, 2014). Therefore, drawing on the law of gravitation, this paper has adopted the spatial weight matrix with the squared inverse distance as follows:

8 > > 0; i ¼ j > < 1 Wij ¼ >   ; isj > > : dij 2

(4)

ln USi;t ¼ b1 ln LFi;t1 þ b2 ln FDIi;t1 þ b3 ln HCi;t1 þ b4 ln IUi;t1 þ b5 ln ESi;t1 N N X X þb6 ln INFi;t1 þ b7 ln GLi;t1 þ q1 Wij LFi;t1 þ q2 Wij ln FDIi;t1 j¼1

þq3

N X

Wij ln HCi;t1 þ q4

j¼1

þq7

N X j¼1

N X j¼1

Wij ln GLi;t1 þ r

N X

Wij ln IUi;t1 þ q5

j¼1 N X

Wij ln ESi;t1 þ q6

j¼1

N X

Wij ln INFi;t1

(3)

j¼1

Wij ln USj;t þ mi þ lt þ εit

j¼1

where b denotes the direct coefficient of driving forces, q denotes the spatial lag coefficient of driving forces, r denotes the spatial autocorrelation coefficient of urban sprawl, lt denotes the time fixed effect, mi denotes the space fixed effect, εit denotes the random error vector, and the explanation of other symbols are reported in section 3.

where dij is the Euclidean distance between city i and city j, and Wij is standardized after every element value being divided by the sum of its row to ensure the sum of every row is 1.

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Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

3. Data source and variables calculation

UPSit ¼ 0:5*ðLPit  HPit Þ þ 0:5

3.1. Data source A panel data set covering 285 prefecture-level and above cities in China during 2003e2017 was utilized in this paper. According to China's current administrative division criteria, the samples were divided into three parts: the eastern cities, the central cities, and the western cities. Based on the principle of timeliness and comparability, we set the demarcation line of urban sizes at one million and two million, these cities were divided into three types by the resident population of urban area in 2016: small and medium-sized cities, large cities, and mega cities (Wang et al., 2019). Specifically, the distribution of sample cities grouped by regions and urban sizes in this paper can be seen in Fig. 2. Three types data are used in this paper: LandScan Global Population Database, Administrative Boundary Database, and Administrative Statistical Database. LandScan Global Population Database was collected from Oak Ridge National Laboratory's website (available at https://landscan.ornl.gov/landscan-datasets). Administrative Boundary Database was collected from the National Geomatics Center of China (available at https://www.ngcc.cn/). All the driving forces used in this paper are selected from Administrative Statistical Database, including China Land and Resources Almanac, China Urban Construction Statistical Yearbook, and China City Statistical Yearbook. 3.2. Dependent variables Based on the remote sensing data extracted from the LandScan Global Population Database, this paper has investigated urban sprawl from two aspects: urban land sprawl (ULS) and urban population sprawl (UPS).

ULSit ¼ 0:5*ðLLit  HLit Þ þ 0:5

(5)

(6)

where LLit (or HLit) denotes the proportion of the land area whose population density is lower (or higher) than the national average value accounts for the total land areas in city i at year t respectively; LPit (or HPit) denotes the proportion of the population whose population density is lower (or higher) than the national average value accounts for the total population in city i at year t. The values of ULSit and UPSit are both ranging from 0 to 1, and the greater value means the higher sprawl (Fallah et al., 2011; Wang et al., 2019). Specifically, the data processing was operated by the ArcGIS 10.6 software, and the major steps contained Extract by Mask, Project Raster, and Zonal Statistics as Table, etc. 3.3. Driving forces According to the up-to-data literature and the data availability, this paper has investigated the driving forces of urban sprawl, including the following variables: (1) land finance (LF), i.e. the shares of land leasing revenue in GDP; (2) foreign direct investment (FDI), i.e. the shares of foreign direct investment in GDP, and the annual exchange rate of RMB against the US dollars is used to convert FDI in US dollars to RMB; (3) human capital (HC), i.e. the number of college students per 10,000 people; (4) industrial upgrading (IU), i.e. the shares of the value of the tertiary industries in the value of the secondary industries; (5) electric intensity (EI), i.e. per capita electricity consumption for the whole society; (6) infrastructure (INF), i.e. per capita urban road area; (7) green degree (GD), i.e. urban construction land accounting for the proportion of urban areas. In order to eliminate the influence brought by heteroscedasticity, all variables have received the treatment of logarithmic. Considering the time lag of the effects, all driving forces were dealt with one lag period. In line with the classical econometric literature, SPSS 22.0 was utilized to carry out the descriptive statistics of all logarithmic variables used in Table 1. In order to avoid spurious regressions, the unit root test is performed to check whether the variables used in this article are stationary or integrated of the same order. With an intercept and linear trend, each of these tests was carried out to include an intercept. Consistent with previous relevant studies, this paper chooses the three most commonly-used tests: ADF-Fisher test (Maddala and Wu, 1999), LLC test (Levin et al., 2002), and IPS test (Im et al., 2003), and the results in levels and the first difference are reported in Table 2. The results of unit root test for all the time series in the first difference are statistically significant at the 1% significance level, while some have not passed the significance test in levels. Furthermore, panel cointegration tests have been used to test the long-term equilibrium between variables, and the results imply the rejection of null hypothesis of no cointegration (Zhang and Zhou, 2016). Therefore, the regression estimations conducted

Table 1 Descriptive statistics for all logarithmic variables.

Fig. 2. The distribution of sample cities grouped by regions and sizes in this paper.

Variables

Obs

Mean

Std.Dev

Min

Max

Kurtosis

Skewness

lnULSi,t lnUPSi,t lnLFi,t-1 lnFDIi,t-1 LnHCi,t-1 lnIUi,t-1 lnESi,t-1 lnINFi,t-1 lnGLi,t-1

3990 3990 3990 3990 3990 3990 3990 3990 3990

0.294 0.875 3.648 2.922 4.066 0.172 6.881 2.151 3.536

0.158 0.413 0.680 2.954 3.612 0.517 1.246 0.652 0.425

1.081 2.617 3.881 12.512 0.000 2.361 3.292 3.997 1.022

0.030 0.093 4.597 4.605 23.982 1.621 11.560 5.636 5.957

2.571 0.710 9.790 0.311 3.717 1.377 0.021 3.055 31.791

1.317 0.876 2.237 0.879 1.750 0.073 0.065 0.427 4.181

Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

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Table 2 The results of unit root test for all variables at the first difference. Variables

lnULSi,t lnUPSi,t lnLFi,t-1 lnFDIi,t-1 LnHCi,t-1 lnIUi,t-1 lnESi,t-1 lnINFi,t-1 lnGLi,t-1

Levels

First-difference

ADF-Fisher

IPS

LLC

ADF-Fisher

IPS

LLC

630.915** 380.205 842.670*** 844.102*** 641.723** 672.209*** 698.065*** 818.379 906.834***

3.405*** 2.187** 5.219*** 8.938*** 0.179 0.971 3.334*** 8.153*** 9.477***

17.294*** 15.536*** 8.796*** 40.280*** 6.609*** 11.750*** 34.401*** 68.309*** 40.515***

677.199*** 791.642*** 1480.390*** 3159.440*** 1220.790*** 1310.790*** 1220.020*** 1448.960*** 1383.910***

4.052*** 6.749*** 20.818*** 59.860*** 14.616*** 17.406*** 15.937*** 21.397*** 20.771***

22.267*** 27.198*** 27.707*** 99.721*** 18.002*** 25.091*** 31.607*** 45.666*** 37.762***

Note: *** denotes significance at 1% level; ** denotes significance at 5% level.

later would not suffer from spurious regressions. 4. Analysis and discussion 4.1. Spatiotemporal characteristics analysis 4.1.1. Comparison amongst regions and urban sizes In order to illustrate the dynamic change of urban land sprawl in an intuitive way, we have investigated the distribution of urban land sprawl grouped by urban sizes at national and regional levels during the period of 2003e2017 in Fig. 3. The results showed that the dynamic change of urban land sprawl among different scales were relatively similar to each other. Specifically, it increased obviously during the periods of 2003e2004 and 2010e2011, decreased obviously during the period of 2007e2008, and kept relatively stable during the periods of 2004e2007, 2008e2010, and 2011e2017. Moreover, we have investigated that small and medium-sized cities sprawled most significantly, followed by large cities, while mega cities sprawled the least, which was consistent with the study of Liu et al. (2018). Correspondingly, we have investigated the dynamic change of urban population sprawl grouped by urban sizes at national and regional levels during the period of 2003e2017 in Fig. 4. Compared with Fig. 3, we have two main observations. On one hand, the

dynamic changes of urban population sprawl were highly similar to urban land sprawl. On the other hand, we have investigated all the values of urban land sprawl were greater than urban population sprawl during the same period, implying the inefficient urban sprawl was ubiquity in China. 4.1.2. Global spatial correlation analysis Global Moran's I (GMI) for urban land sprawl and urban population sprawl during 2003e2017 were shown in Fig. 5. All the values of global Moran's I for urban land sprawl and urban population sprawl were positive and significant at the 1% significance level, which provided a strong proof for the establishment of obviously spatial dependence at the national scale. The results showed that the dynamic change of Global Moran's I (GMI) for urban land sprawl and urban population sprawl were relatively similar to each other. Specifically, they all increased obviously during the period of 2003e2004, decreased obviously during the periods of 2006e2007 and 2010e2012, and kept relatively stable during the periods of 2004e2006, 2007e2010, and 2012e2017. A trend of decreasing spatial correlation is revealed in Chinese cities during the period of 2004e2012, implying urban sprawl has gradually expanded at the national scale, which caused a disappearance of some clusters. Moreover, for the period of 2003e2010, the values of global Moran's I (GMI) for urban land sprawl were

Fig. 3. The average value of urban land sprawl grouped by urban sizes. at national and regional levels during the period of 2003e2017.

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Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

Fig. 4. The average value of urban population sprawl grouped by urban sizes at national and regional levels during the period of 2003e2017.

Fig. 6. LISA cluster map of Urban land sprawl in Chinese cities in 2003 (left) and 2017 (right). Fig. 5. Global Moran's I (GMI) for urban land sprawl and urban population sprawl in Chinese cities during the period of 2003e2017.

obviously greater than urban population; however, for the period of 2011e2017, the gap between them was relatively narrow, implying the intensity of spatial correlation between urban land sprawl and urban population sprawl gradually tend to be consistent. 4.1.3. Local spatial correlation analysis The LISA cluster map of urban land sprawl in Chinese cities in 2003 and 2017 was shown in Fig. 6. As shown in Fig. 6, urban land sprawl has formed three categories of spatial distribution pattern in 2003. For instance, High-High clusters of urban land sprawl exist in the northeast region, the middle of Shaanxi, the south of Anhui, and Jiangxi; Low-Low clusters of urban land sprawl exist in the Sichuan Basin region and Henan; High-Low outlier of urban land sprawl exist in the surrounding cities of Low-Low clusters. However, urban land sprawl has formed four categories of spatial distribution pattern in 2017. For instance, High-High clusters of urban land

sprawl exist in the east of Heilongjiang, Shannxi, Shanxi, the North China Plain, the middle of the Yangtze River Delta, and the west of Guangxi; Low-Low clusters of urban land sprawl exist in the southeast coastal regions, including Zhejiang, Fujian, and Guangdong; High-Low outlier of urban land sprawl exist in Urumqi, Tieling, Lijiang, and some southeast coastal cities; Low-High outlier of urban land sprawl exist in Nanning and Huangshan. Therefore, as time goes on, the High-High cluster in the northeast region and the Low-Low cluster in the Sichuan Basin region have disappeared, some new clusters and outliers have emerged, and the final intensities of spatial correlation of urban land sprawl have decreased, which in line with the results of global Moran's I. Furthermore, we investigated that most of the High-High clusters were the small and medium-sized cities, while most of the Low-Low clusters were large and mega cities, implying that small and medium-sized cities have experienced a critical urban land sprawl, which deserved more attention in the following urban management. The LISA cluster map of urban population sprawl in Chinese cities in 2003 and 2017 were shown in Fig. 7. Apart from some

Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

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Fig. 7. LISA cluster map of urban population sprawl in Chinese cities in 2003 (left) and 2017 (right).

cities, the spatial distribution pattern of urban population sprawl was basically consistent with urban land sprawl no matter in 2003 or 2017, implying that those two indices for quantifying urban sprawl were highly correlated. Moreover, the dynamic change of spatial distribution pattern of urban population sprawl between 2003 and 2017 could to some extent reflect the migrant trend of population, that is, from north to south and from west to east. However, due to the restrictions on land supply in the eastern coastal region, the extent of urban sprawl in the central and western regions have increased remarkably. 4.2. Estimation results at the national scale The estimated results at the national scale were reported in Table 3. In a summary, no matter the dependent variable was urban land sprawl or urban population sprawl, the gaps of most coefficients were very weak, indicating that the results were statistically robust. Specifically, several important conclusions can be drawn from the estimation results. First, unlike previous studies, we investigated that land finance has not promoted urban sprawl at the national scale, because the direct coefficients of land finance were not significantly positive. It is commonly acknowledged that in order to maximize revenue gains and attract investment, local governments have loosened the control and management for urban growth, which eventually results in serious urban sprawl (Tian et al., 2017). However, our study investigated that the increase of urban sprawl not mainly depended on the additional supply of urban construction land, which revealed the existence of illegal or unplanned developments (Liu et al., 2018). On the other hand, the scarcity of land resources and the rigid housing purchase demand leaded to the rapid increase in land price, which eventually pushed up the housing prices (Lin et al., 2018). However, the dilemma of urban sprawl and high housing prices has become the main contradiction to constrain the sustainable urban development in the future. Second, the direct coefficient of FDI was significantly positive only in mega cites but very weak in other samples, which to some extent implies the uneven distributing of FDI in China. One possible reason is that mega cities have a better economic foundation and profit margin to attract FDI, and the employment opportunities and construction demands created by the inflow of FDI in turn

promoted urban sprawl (Wu et al., 2017). Therefore, promoting the even distribution of FDI is an effective way to reduce the pressure of urban sprawl and to realize compact development patterns. Third, the direct coefficient of human capital was significantly negative only in mega cites but very weak in small and mediumsized cities and large cities, implying that the agglomeration of human capital is a powerful way to restrain urban sprawl. Most of China's universities and research institutes are located in mega cities, which was different from the West (Li and Wang, 2019). Hence, it is urgent for guiding the balanced spatial distribution of human capital to achieve compact and sustainable urban development. Fourth, the direct coefficient of industrial upgrading was significantly negative in large and mega cites but very weak in small and medium-sized cities, implying that the development of a modern service industry will contribute greatly to restrain urban sprawl. After all, the separation between work and residence caused by industrial manufacturing is more likely to result in urban sprawl, while optimizing the structure of industry is conducive to the intensive utilization of urban construction land (Chen et al., 2018). Fifth, the direct coefficients of electric intensity were significantly positive in all cities and mega cities but negative and not significant in small and medium-sized cities, implying that electric intensity has effectively promoted urban sprawl at the national scale and especially in mega cities. It's common to see that the utilization of electricity is inevitable in modern cities. However, the opposite results on urban sizes indicated that the urban sprawl in small and medium-sized cities was relatively inefficient due to the lacking of the coordinative electric facilities (Liu et al., 2018). Sixth, the direct coefficients of infrastructure were significantly positive in all cities, large cities, and mega cities but negative and not significant in small and medium-sized cities, implying that infrastructure has become an effective mean to promote urban sprawl at the national scale and especially in large and mega cities. The construction of infrastructure has promoted the miracle of China's economic development. However, the uneven development in urban sizes also indicated that the urban sprawl in small and medium-sized cities was relatively inefficient due to the relatively poor construction of infrastructure (Liu et al., 2018). Seventh, the direct coefficients of green level were not

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Table 3 The results for the national cities grouped by urban sizes. Variables

lnLFi,t-1 lnFDIi,t-1 LnHCi,t-1 lnIUi,t-1 lnEIi,t-1 lnINFi,t-1 lnGLi,t-1 W*lnLFi,t-1 W*lnFDIi,t-1 W*LnHCi,t-1 W*lnIUi,t-1 W*lnEIi,t-1 W*lnINFi,t-1 W*lnGLi,t-1

r Space-fixed Time-fixed R-squared Log-likeli -hood LM_ spatial_lag Robust_LM_ spatial_lag LM_ spatial_err Robust_LM_ spatial_err LR_joint_ space fixed LR_joint_ time fixed Obs

Urban Land Sprawl

Urban Population Sprawl

All Cities

Small and Medium -sized cities

Large cites

Mega cities

All Cities

Small and Medium -sized cities

Large cites

Mega cities

0.000 (-0.032) 0.000 (0.457) 0.001* (-1.731) 0.020*** (-6.564) 0.010*** (3.811) 0.010*** (3.680) 0.001 (-0.330) 0.011*** (-3.916) 0.001 (-1.392) 0.001 (-1.015) 0.020*** (-4.578) 0.007* (-1.744) 0.010** (-2.255) 0.000 (-0.043) 0.425*** (29.547) Yes Yes 0.888 5953.100

0.003 (-1.420) 0.000 (0.079) 0.000 (-0.054) 0.005 (-1.411) 0.002 (-0.792) 0.004 (-1.198) 0.000 (0.032) 0.002 (0.630) 0.002*** (-4.015) 0.000 (0.514) 0.006 (1.259) 0.001 (0.174) 0.003 (-0.667) 0.001 (-0.236) 0.383*** (18.543) Yes Yes 0.897 3183.011

0.005 (-1.155) 0.000 (0.285) 0.000 (-0.284) 0.034*** (-4.887) 0.012** (2.132) 0.028*** (4.425) 0.000 (0.036) 0.011* (-1.815) 0.002 (1.375) 0.000 (-0.269) 0.063*** (-7.057) 0.006 (0.603) 0.007 (0.717) 0.010 (1.006) 0.373*** (15.215) Yes Yes 0.853 1850.811

0.004 (0.722) 0.004** (2.348) 0.003*** (-3.346) 0.043*** (-4.579) 0.064*** (7.007) 0.025*** (3.784) 0.001 (-0.101) 0.033*** (-4.288) 0.003 (-1.079) 0.000 (-0.158) 0.004 (0.271) 0.019 (-1.599) 0.006 (-0.632) 0.006 (-0.482) 0.337*** (10.891) Yes Yes 0.886 1165.537

0.001 (0.244) 0.001 (1.172) 0.000 (0.140) 0.044*** (-6.835) 0.017*** (3.246) 0.019*** (3.352) 0.005 (0.819) 0.022*** (-3.891) 0.004*** (-3.382) 0.003** (-2.440) 0.038*** (-4.041) 0.021*** (-2.586) 0.020** (-2.215) 0.005 (-0.544) 0.477*** (35.228) Yes Yes 0.928 2953.587

0.010** (-2.137) 0.001 (-0.694) 0.001 (-0.683) 0.011 (-1.501) 0.007 (-1.243) 0.006 (-0.849) 0.004 (0.582) 0.005 (0.728) 0.006*** (-4.170) 0.001 (0.758) 0.021* (1.909) 0.007 (0.871) 0.004 (-0.397) 0.006 (-0.712) 0.408*** (20.244) Yes Yes 0.920 1662.023

0.004 (-0.460) 0.002 (1.170) 0.001 (0.543) 0.059*** (-4.244) 0.017 (1.446) 0.045*** (3.612) 0.000 (0.007) 0.015 (-1.168) 0.001 (-0.318) 0.000 (-0.032) 0.109*** (-6.092) 0.023 (-1.119) 0.019 (1.019) 0.002 (-0.103) 0.524*** (25.397) Yes Yes 0.903 874.890

0.017 (1.583) 0.010*** (2.911) 0.005*** (-2.832) 0.093*** (-5.017) 0.121*** (6.704) 0.030** (2.293) 0.021 (1.101) 0.039*** (-2.590) 0.011** (-2.387) 0.003 (-0.980) 0.087*** (2.720) 0.041* (-1.751) 0.030 (-1.607) 0.033 (1.286) 0.318*** (10.118) Yes Yes 0.935 598.575

930.802*** 74.970*** 876.737*** 20.205*** 6614.920*** 1143.957*** 3990

341.623*** 19.932*** 332.540*** 10.848*** 3429.306*** 616.899*** 1820

265.202*** 116.074*** 196.575*** 47.447*** 1911.785*** 427.917*** 1330

67.345*** 0.278 74.378*** 7.312*** 1246.281*** 271.207*** 840

1236.752*** 83.484*** 1174.249*** 20.981*** 7649.257*** 986.967*** 3990

373.864*** 19.857*** 362.589*** 8.582*** 3776.410*** 579.750*** 1820

427.802*** 128.771*** 340.166*** 41.134*** 2169.488*** 378.526*** 1330

71.988*** 5.561** 86.394*** 19.967*** 1551.752*** 194.132*** 840

Notes: The t-statistics are given in the parentheses; *** denotes significance at 1% level; ** denotes significance at 5% level; and * denotes significance at 10% level.

significant at the national scale, implying that the impacts of green level were relatively poor. Traditional views indicated that the requirements for green level was an effective tool to restrain urban sprawl. However, we investigated that the effect of green degree on urban sprawl was not significant. One possible reason is that even though the need for high-quality environment has increased in recent years, the impact of green level on urban sprawl is still relatively limited when compared with other socioeconomic factors (Yue et al., 2019). Furthermore, many spatial lag coefficients of driving forces (except industrial upgrading) were significantly negative at the national scale, implying that the siphon effect was relatively greater than the spillover effect among different sizes of cities at the national scale. Hence, the competition of socioeconomic resources among different sizes of cities has significantly restrained urban sprawl. Another notable finding was that the spatial lag coefficients of some variables were significantly negative for all cities at the nation scale but not significant among different size of cities, which implied the establishment of spatial heterogeneity, thus the unified policies may not always be suitable in control urban sprawl (Li and Li, 2019). Last but not least, the spatial autocorrelation coefficients of

urban sprawl were significantly positive at the national scale, which provided a strong proof for the spatial correlation of urban sprawl in China. Therefore, to reverse the deterioration trend of urban sprawl in China should not merely depend on restricting land supply, the establishment of benign competition mechanism among local governments is of great importance to reduce pressure of urban sprawl (Wang et al., 2019). 4.3. Estimation results for the sub-regional sample The estimation results for the eastern cities grouped by urban sizes were reported in Table 4. On one hand, the impacts of some direct coefficients of the driving forces in the eastern cities were in line with the national results. For example, human capital and industrial upgrading have restrained urban sprawl in the eastern cities, especially in small and medium-sized cities; electric intensity and infrastructure have promoted urban sprawl in the eastern cities, especially in mega cities; the spatial autocorrelation coefficients of urban sprawl were significantly positive in the eastern cities. On the other hand, some different findings can be drawn here. First, the direct impacts of land finance on urban sprawl faced

Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

9

Table 4 The results for the eastern cities grouped by urban sizes. Variables

lnLFi,t-1 lnFDIi,t-1 LnHCi,t-1 lnIUi,t-1 lnEIi,t-1 lnINFi,t-1 lnGLi,t-1 W*lnLFi,t-1 W*lnFDIi,t-1 W*LnHCi,t-1 W*lnIUi,t-1 W*lnEIi,t-1 W*lnINFi,t-1 W*lnGLi,t-1

r Space-fixed Time-fixed R-squared Log-likeli -hood LM_ spatial_lag Robust_LM_ spatial_lag LM_ spatial_err Robust_LM_ spatial_err LR_joint_ space fixed LR_joint_ time fixed Obs

Urban Land Sprawl

Urban Population Sprawl

All Cities

Small and Medium -sized cities

Large cites

Mega cities

All Cities

Small and Medium -sized cities

Large cites

Mega cities

0.003 (0.817) 0.002* (-1.846) 0.002** (-2.034) 0.022*** (-3.614) 0.027*** (5.490) 0.012** (2.563) 0.002 (0.360) 0.020*** (-4.509) 0.001 (0.830) 0.002 (-1.505) 0.002 (-0.258) 0.006 (-0.741) 0.010 (-1.496) 0.004 (-0.279) 0.275*** (10.356) Yes Yes 0.897 2256.592

0.007 (1.237) 0.000 (0.138) 0.004*** (-3.500) 0.012 (-1.569) 0.008 (-1.272) 0.011 (-1.278) 0.000 (-0.028) 0.003 (-0.424) 0.005** (-2.497) 0.000 (-0.272) 0.014 (0.960) 0.010 (0.825) 0.034** (2.349) 0.068*** (3.313) 0.220*** (4.719) Yes Yes 0.931 773.550

0.015*** (-3.155) 0.000 (-0.263) 0.001 (-0.825) 0.010 (-1.173) 0.011 (1.293) 0.010 (-1.187) 0.006 (0.559) 0.015** (-2.203) 0.003 (-1.444) 0.002 (0.978) 0.014 (-1.231) 0.019 (1.454) 0.015 (-1.187) 0.010 (-0.574) 0.183*** (4.316) Yes Yes 0.909 856.143

0.011 (1.597) 0.002 (0.818) 0.001 (-0.819) 0.024 (-1.650) 0.062*** (5.517) 0.024*** (2.922) 0.008 (0.655) 0.045*** (-5.184) 0.000 (-0.005) 0.002 (-0.948) 0.061*** (2.662) 0.005 (-0.333) 0.004 (0.323) 0.022 (1.450) 0.222*** (5.510) Yes Yes 0.868 755.583

0.018*** (2.603) 0.001 (-0.407) 0.004** (-2.512) 0.046*** (-3.798) 0.041*** (4.102) 0.018** (1.967) 0.030** (2.234) 0.035*** (-3.887) 0.004 (-1.241) 0.004* (-1.765) 0.006 (-0.312) 0.028* (-1.865) 0.041*** (-3.019) 0.034 (1.304) 0.269*** (10.105) Yes Yes 0.945 1296.309

0.024* (1.853) 0.006* (1.693) 0.009*** (-3.346) 0.055*** (-2.983) 0.027* (-1.880) 0.024 (-1.186) 0.032 (1.143) 0.006 (-0.421) 0.018*** (-4.166) 0.001 (0.321) 0.024 (0.719) 0.029 (1.061) 0.051 (1.526) 0.193*** (4.012) 0.124** (2.547) Yes Yes 0.944 434.723

0.014 (-1.447) 0.000 (0.002) 0.004 (-1.536) 0.018 (-1.040) 0.030* (1.803) 0.012 (0.712) 0.002 (-0.109) 0.039*** (-2.832) 0.013*** (-2.870) 0.001 (-0.213) 0.037 (-1.595) 0.043 (1.584) 0.004 (0.167) 0.051 (-1.459) 0.116*** (2.664) Yes Yes 0.948 516.886

0.040*** (3.240) 0.005 (0.991) 0.003 (-1.032) 0.018 (-0.670) 0.102*** (4.811) 0.020 (1.317) 0.045** (1.993) 0.049*** (-3.037) 0.003 (-0.477) 0.003 (-0.799) 0.208*** (4.834) 0.003 (0.115) 0.014 (-0.607) 0.077*** (2.748) 0.210*** (5.187) Yes Yes 0.935 420.371

89.860*** 1.411 88.501*** 0.051 2359.491*** 434.315*** 1414

20.563*** 1.179 19.518*** 0.134 873.639*** 163.461*** 406

25.567*** 17.462*** 19.355*** 11.250*** 916.037*** 208.487*** 476

22.300*** 0.132 23.028*** 0.860 715.259*** 162.072*** 532

96.838*** 0.050 99.235*** 2.447 3073.509*** 354.687*** 1414

7.737*** 2.449 6.509** 1.220 967.858*** 137.654*** 406

10.375*** 21.486*** 6.623** 17.735*** 1170.918*** 178.555*** 476

21.653*** 0.008 23.149*** 1.504 963.853*** 114.873*** 532

Notes: The t-statistics are given in the parentheses; *** denotes significance at 1% level; ** denotes significance at 5% level; and * denotes significance at 10% level.

significantly differentiation, because the direct coefficient of it on urban land sprawl in large cities was significantly negative, while the direct coefficients of it on urban population sprawl in all cities, small and medium-sized cities, and mega cities were significantly positive. One possible reason is that the massive inflow of migrants from the inland to coastal regions has provided ample momentum for urban population sprawl, while the supply of urban construction land was relatively limited in the eastern cities, thus leading to the rapid growth of urban population sprawl (Liu et al., 2018). Second, we have investigated that the direct coefficients of FDI on urban land sprawl and urban population sprawl were both weak and not significant. After all, due to the rising production costs and shrinking profit space in the eastern cities, some FDI has transferred into the inland or abroad (Wu et al., 2017). Third, we have investigated that the direct coefficients of green level on urban land sprawl were not significant, however, the direct coefficients of it on urban population sprawl in all cities and mega cities were significantly positive, implying that the pursuit of green level has to some extent increased the pressure of urban population sprawl in the eastern cities. Fourth, the spatial lag coefficients of driving forces were inconsistent, implying that the siphon effect and the spillover effect coexist in the eastern cities. Different from the drastically

competition at the national scale, the spatial lag coefficient of infrastructure and green level on urban land sprawl in small and medium-sized cities, and the spatial lag coefficients of green level on urban population sprawl in small and medium-sized cities, and mega cities were significantly positive, which indicated a radiation role of the corresponding driving forces in the process of urban sprawl. It is commonly acknowledged that the eastern cities have better living standards and more job opportunities, while those advantages have inevitably accelerated the spread of urban sprawl (Liu et al., 2018). The estimation results for the central cities grouped by urban sizes were reported in Table 5. On one hand, the impacts of some direct coefficients of the driving forces in the central cities were in line with the national results. For example, industrial upgrading has restrained urban sprawl in the central cities, especially in large cities and mega cities; electric intensity has promoted urban sprawl in the central region; the impacts of green level on urban sprawl were still very weak and not significant; all the spatial autocorrelation coefficients of urban sprawl were significantly positive, which provided a strong evidence for the spatial spillover effect of urban sprawl in the central cities. On the other hand, some different findings can be drawn from

10

Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

Table 5 The results for the central cities grouped by urban sizes. Variables

lnLFi,t-1 lnFDIi,t-1 LnHCi,t-1 lnIUi,t-1 lnEIi,t-1 lnINFi,t-1 lnGLi,t-1 W*lnLFi,t-1 W*lnFDIi,t-1 W*LnHCi,t-1 W*lnIUi,t-1 W*lnEIi,t-1 W*lnINFi,t-1 W*lnGLi,t-1

r Space-fixed Time-fixed R-squared Log-likeli -hood LM_ spatial_lag Robust_LM_ spatial_lag LM_ spatial_err Robust_LM_ spatial_err LR_joint_ space fixed LR_joint_ time fixed Obs

Urban Land Sprawl

Urban Population Sprawl

All Cities

Small and Medium -sized cities

Large cites

Mega cities

All Cities

Small and Medium -sized cities

Large cites

Mega cities

0.002 (-0.684) 0.000 (-0.038) 0.001** (2.065) 0.006 (-1.447) 0.020*** (4.555) 0.001 (-0.313) 0.002 (-0.382) 0.009* (-1.882) 0.003*** (-2.976) 0.001 (0.928) 0.000 (0.045) 0.001 (-0.117) 0.034*** (-4.940) 0.013 (-1.485) 0.487*** (22.499) Yes Yes 0.895 2364.509

0.010*** (-2.650) 0.000 (-0.262) 0.003*** (3.174) 0.004 (0.872) 0.006 (1.080) 0.003 (-0.601) 0.005 (-0.927) 0.005 (-1.117) 0.003*** (-2.868) 0.002 (1.616) 0.003 (-0.495) 0.015** (1.994) 0.053*** (-6.145) 0.007 (-0.990) 0.485*** (18.648) Yes Yes 0.909 1373.382

0.006 (0.874) 0.000 (0.220) 0.008*** (4.301) 0.025*** (-2.463) 0.033*** (3.566) 0.011 (1.018) 0.022 (-1.497) 0.016 (-1.503) 0.003 (0.955) 0.003 (0.964) 0.022* (-1.767) 0.013 (0.811) 0.001 (-0.063) 0.019 (-0.935) 0.268*** (5.994) Yes Yes 0.823 740.548

0.007 (0.664) 0.000 (-0.026) 0.001 (-0.887) 0.029 (-1.484) 0.108*** (5.732) 0.025** (-1.981) 0.045 (2.271) 0.052*** (-3.117) 0.001 (-0.186) 0.003 (1.525) 0.033 (-1.223) 0.006 (0.263) 0.032* (-1.805) 0.080** (2.546) 0.324*** (5.375) Yes Yes 0.933 304.911

0.004 (0.469) 0.000 (0.071) 0.006*** (4.070) 0.004 (-0.404) 0.050*** (4.586) 0.003 (0.315) 0.000 (-0.010) 0.030*** (-2.660) 0.009*** (-3.537) 0.002 (-0.750) 0.005 (-0.314) 0.002 (0.129) 0.081*** (-4.917) 0.037* (-1.798) 0.460*** (20.455) Yes Yes 0.909 1137.343

0.016** (-2.042) 0.001 (-0.554) 0.007*** (3.725) 0.016 (1.547) 0.019* (1.670) 0.002 (-0.175) 0.002 (-0.136) 0.017 (-1.537) 0.007*** (-2.954) 0.005** (2.020) 0.002 (0.100) 0.032* (1.866) 0.133*** (-6.897) 0.021 (-1.242) 0.507*** (20.164) Yes Yes 0.925 750.129

0.033* (1.864) 0.002 (0.309) 0.021*** (4.927) 0.040* (-1.701) 0.062*** (2.808) 0.018 (0.701) 0.038 (-1.080) 0.023 (-0.937) 0.001 (0.169) 0.008 (1.214) 0.038 (-1.301) 0.025 (-0.689) 0.018 (0.439) 0.053 (-1.107) 0.311*** (7.146) Yes Yes 0.835 336.499

0.009 (0.288) 0.003 (0.319) 0.000 (-0.054) 0.152*** (-2.767) 0.279*** (5.290) 0.047 (-1.320) 0.034 (0.599) 0.178*** (-3.785) 0.008 (-0.769) 0.008 (1.465) 0.123 (-1.603) 0.028 (0.425) 0.146*** (-2.937) 0.124 (1.406) 0.192*** (2.911) Yes Yes 0.929 135.163

405.100*** 38.667*** 381.055*** 14.621*** 2318.821*** 454.291*** 1400

250.682*** 44.691*** 226.599*** 20.608*** 1319.852*** 209.391*** 770

36.730*** 14.951*** 27.393*** 5.614** 628.265*** 182.598*** 462

24.036*** 1.892 22.158*** 0.014 291.325*** 97.851*** 168

367.575*** 24.999*** 348.920*** 6.344** 2419.464*** 343.385*** 1400

252.962*** 49.152*** 225.850*** 22.040*** 1434.520*** 187.839*** 770

40.871*** 10.500*** 33.249*** 2.878* 592.467*** 139.347*** 462

7.428*** 0.487 6.978*** 0.037 250.065*** 56.006*** 168

Notes: The t-statistics are given in the parentheses; *** denotes significance at 1% level; ** denotes significance at 5% level; and * denotes significance at 10% level.

the estimation results. First, the direct impacts of land finance on urban sprawl faced significantly differentiation, because the direct coefficient of it in small and medium-sized cities was significantly negative, but significantly positive in large cities and not significant in mega cities. Second, the direct coefficients of FDI were not significant in the central cities, which was related to the constraints of China's unbalanced development strategy and the disadvantage of the central region's geographical location (Liu et al., 2018). Third, the direct impacts of human capital on urban sprawl also faced significantly differentiation, because the direct coefficients of it in all cities, small and medium-sized cities, and large cities were significantly positive, but negative and not significant in mega cities. One possible reason is that, human capital is sensitive to the living cost in mega cities, while the relatively livable conditions of small and medium-sized cities and large cities have a strong appeal to human capital, and the rigid housing purchase demand of human capital in turn increased the pressure of urban sprawl (Li and Wang, 2019). Fourth, the direct coefficient of infrastructure in mega cities was significantly negative, while other direct coefficients of it were not significant, which highlighted that the land available for urban development is become increasingly scarce especially in mega cities (Liu et al., 2018). Last but not least, the spatial lag

coefficients of driving forces were inconsistent, implying that the siphon effect and the spillover effect also coexist in the central cities. Therefore, the “one size fits all” policies were not established, while spatial heterogeneity and spatial correlation should be reasonably considered in the policy-making process (Li and Li, 2019). The estimation results for the western cities grouped by urban sizes were reported in Table 6. Surprisingly, we have investigated that nearly all the direct coefficients of driving forces (except green level) were relatively in line with the national results. As for the spatial lag coefficients of driving forces, they were inconsistent in sign and significance, implying that the siphon effect and the spillover effect also coexist in the western cities. Moreover, the spatial autocorrelation coefficient's significance of urban sprawl in mega cities has decreased significantly, implying that the competitiveness of urban sprawl in mega cities is lower than the national level, which also in line with the former analysis of the dynamic change of urban sprawl in Figs. 3 and 4. Furthermore, the significance of the direct coefficients in small and medium-sized cities were relatively weaker than the corresponding spatial lag coefficients, which proved the establishment of spatial correlation once again (Wang et al., 2019).

Y. Feng et al. / Journal of Cleaner Production 241 (2019) 118061

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Table 6 The results for the western cities grouped by urban sizes. Variables

lnLFi,t-1 lnFDIi,t-1 LnHCi,t-1 lnIUi,t-1 lnEIi,t-1 lnINFi,t-1 lnGLi,t-1 W*lnLFi,t-1 W*lnFDIi,t-1 W*LnHCi,t-1 W*lnIUi,t-1 W*lnEIi,t-1 W*lnINFi,t-1 W*lnGLi,t-1

r Space-fixed Time-fixed R-squared Log-likeli -hood LM_ spatial_lag Robust_LM_ spatial_lag LM_ spatial_err Robust_LM_ spatial_err LR_joint_ space fixed LR_joint_ time fixed Obs

Urban Land Sprawl

Urban Population Sprawl

All Cities

Small and Medium -sized cities

Large cites

Mega cities

All Cities

Small and Medium -sized cities

Large cites

Mega cities

0.005 (-1.397) 0.001 (0.729) 0.003*** (-2.580) 0.027*** (-4.399) 0.000 (-0.072) 0.011** (2.052) 0.003 (-0.681) 0.013*** (-2.604) 0.001 (-0.839) 0.002 (-1.250) 0.025*** (-3.146) 0.006 (-1.019) 0.008 (-0.952) 0.005 (-0.734) 0.372*** (14.388) Yes Yes 0.878 1519.112

0.001 (0.275) 0.000 (0.099) 0.000 (-0.247) 0.009* (-1.715) 0.003 (-0.886) 0.007 (-1.512) 0.001 (0.423) 0.002 (0.564) 0.002* (-1.912) 0.000 (-0.200) 0.020** (2.539) 0.007 (-1.478) 0.011** (1.841) 0.002 (-0.436) 0.209*** (5.295) Yes Yes 0.877 1128.882

0.011 (-1.251) 0.000 (0.145) 0.005** (-2.082) 0.059*** (-3.503) 0.002 (-0.137) 0.055*** (4.326) 0.015 (-1.308) 0.014 (-1.369) 0.002 (0.813) 0.002 (-0.516) 0.113*** (-4.909) 0.015 (-0.711) 0.040** (2.072) 0.007 (-0.446) 0.192*** (3.786) Yes Yes 0.851 448.857

0.004 (-0.252) 0.007** (1.997) 0.006** (-2.313) 0.071*** (-3.822) 0.097*** (3.291) 0.076*** (2.661) 0.127*** (-3.821) 0.012 (0.401) 0.004 (-0.742) 0.004 (-0.812) 0.003 (0.071) 0.019 (0.377) 0.003 (-0.059) 0.132*** (-2.686) 0.081 (1.006) Yes Yes 0.899 178.319

0.018*** (-2.585) 0.001 (0.934) 0.004* (-1.835) 0.064*** (-5.381) 0.004 (-0.532) 0.018* (1.662) 0.002 (-0.213) 0.028*** (-2.776) 0.004** (-2.092) 0.006* (-1.860) 0.038** (-2.389) 0.017 (-1.472) 0.004 (-0.270) 0.023* (-1.798) 0.440*** (18.283) Yes Yes 0.924 700.796

0.010 (-1.572) 0.001 (-0.804) 0.004 (-1.372) 0.019 (-1.628) 0.010 (-1.447) 0.015 (-1.557) 0.002 (0.297) 0.005 (0.548) 0.004** (-1.962) 0.000 (0.003) 0.043** (2.443) 0.005 (-0.435) 0.029** (2.060) 0.012 (-1.235) 0.314*** (8.473) Yes Yes 0.918 592.769

0.026* (-1.701) 0.002 (0.874) 0.006 (-1.390) 0.113*** (-3.663) 0.027 (-1.292) 0.080*** (3.445) 0.020 (-0.957) 0.021 (-1.115) 0.002 (-0.405) 0.001 (0.189) 0.192*** (-4.521) 0.042 (-1.114) 0.062* (1.782) 0.029 (-0.948) 0.424*** (10.147) Yes Yes 0.909 202.393

0.019 (-0.579) 0.015** (2.232) 0.009* (-1.754) 0.151*** (-4.431) 0.219*** (4.084) 0.124** (2.377) 0.239*** (-3.959) 0.042 (0.794) 0.008 (-0.740) 0.014 (-1.636) 0.063 (0.861) 0.012 (-0.135) 0.023 (0.302) 0.146 (-1.639) 0.135* (1.711) Yes Yes 0.936 93.999

232.797*** 50.811*** 202.789*** 20.803*** 1857.858*** 424.758*** 1176

35.435*** 0.010 35.490*** 0.066 1124.673*** 275.100*** 644

33.289*** 45.051*** 14.722*** 26.484*** 545.216*** 192.206*** 392

0.252 2.222 0.004 1.973 185.604*** 69.555*** 140

377.559*** 65.428*** 330.434*** 18.303*** 2118.266*** 463.850*** 1176

75.262*** 0.064 75.508*** 0.309 1297.885*** 303.172*** 644

126.293*** 64.752*** 80.624*** 19.083*** 608.629*** 213.637*** 392

0.539 0.053 0.490 0.004 185.035*** 61.307*** 140

Notes: The t-statistics are given in the parentheses; *** denotes significance at 1% level; ** denotes significance at 5% level; and * denotes significance at 10% level.

5. Conclusions, policy implications, and research prospects Based on the above analysis and discussions, the final conclusions have drawn in this section. Moreover, in order to bridge the gap between theory and practice and shed light on further studies, the policy implications and research prospects have been conducted respectively. 5.1. Conclusions This paper has initially examined the spatiotemporal characteristics and driving forces of urban sprawl in China using the remote sensing derived data from the LandScan Global Population Database. Specifically, based on the spatial Durbin model under the space-and-time-fixed effect, a panel data set covering 285 prefecture-level and above cities during the period of 2003e2017 is utilized in this paper. The estimation results suggest: (1) Small and medium-sized cities sprawled most significantly, followed by large cities, while mega cities sprawled the least, which was consistent with the existing literature. In addition, urban sprawl has gradually expanded at the national scale, which caused a disappearance of some clusters. (2) The direct and spatial lag coefficients of driving

forces were not always consistent at the regional level, implying spatial heterogeneity was established in this paper. In addition, we have investigated that the siphon effect was greater than the spillover effect at the national scale, indicating that the competition of socioeconomic resources among different sizes of cities has to some extent hindered the process of urban sprawl. (3) Nearly all spatial autocorrelation coefficients of urban sprawl were significantly positive, which provided a strong evidence for the spatial spillover effect of urban sprawl in Chinese cities. Hence, it was of great importance and necessity to control urban sprawl through the implementation of joint governance at national, regional and urban size levels. 5.2. Policy implications Correspondingly, there are some important and straightforward policy implications for theory and practice, as follows: First, re-evaluate the land supply strategy depending on geographical location and urban size simultaneously, and utilize the internal potential of constructed urban areas. For instance, special laws or regulations should be formulated to control the disorder expansion of urban fringe in the central and western cities

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and especially in the small and medium-sized cities. Moreover, encouraging the reconstruction of old cities and land replacement are also valid to provide appropriate urban construction land. Second, differences in the critical socioeconomic factors driving urban sprawl should be reasonably considered in the process of policy-making, such as, deepening the reform of land finance, promoting the reasonable layout of FDI and human capital in space, optimizing the spatial distribution of industrial structure and green level, and strengthening the construction of electric faculties and infrastructure especially in small and medium-sized cities. Last but not least, the spatial spillover effect of urban sprawl should be seriously considered simultaneously. In order to control urban sprawl effectively, the establishment of a unified urban land use planning should be formulated, the urban construction land should be efficiently allocated, and the compact development strategies should be developed. Meanwhile, in order to attract population migrant to the newly developed urban areas, optimizing its production and living standards is of tremendous importance in alleviating urban sprawl. 5.3. Research prospects Although this paper has provided valuable insights for further research, it is undeniable that some limitations still need to be improved. For instance, due to the restriction of data availability, the measurement of urban sprawl was calculated by two unidimensional indices in this paper, while further study should be multidimensional rather than unidimensional (Li and Li, 2019). Moreover, the utilization of some other spatial analysis techniques such as standard deviation ellipse (Warntz and Neft, 2011), geographical and temporal weighted regression (Fotheringham et al., 2015), and Geodetector (Yang et al., 2019) may lead to more persuasive and explicit findings. Therefore, when a wider range of indicators and more analytical techniques are available, the spatiotemporal characteristics and driving forces of urban sprawl in China still deserve in-depth explored in future studies. Acknowledgments We would like to thank Dr. Jirí Jaromír Klemes (Editor) and other at least eight anonymous reviewers for their constructive and insightful comments. This work is supported by the National Natural Science Foundation of China (No.71473057 and No.71874042). Any remaining errors are the responsibility of the authors. References Bhatta, B., Saraswati, S., Bandyopadhyay, D., 2010. Urban sprawl measurement from remote sensing data. Appl. Geogr. 30 (4), 731e740. Chen, L., Ren, C.Y., Zhang, B., Wang, Z.M., Liu, M.Y., 2018. Quantifying urban land sprawl and its driving forces in Northeast China from 1990 to 2015. Sustainability 10 (1), 188. Chen, Y., Chen, Z.G., Xu, G.L., Tian, Z.Q., 2016. Built-up land efficiency in urban China: insights from the general land use plan (2006-2020). Habitat Int. 51, 31e38. Cheng, Z.H., Li, L.S., Liu, J., 2018. The spatial correlation and interaction between environmental regulation and foreign direct investment. J. Regul. Econ. 54 (2), 124e146. Dadi, D., Azadi, H., Senbeta, F., Abebe, K., Taheri, F., Stellmacher, T., 2016. Urban sprawl and its impacts on land use change in Central Ethiopia. Urban For. Urban Green. 16, 132e141. Davis, C., Schaub, T., 2005. A transboundary study of urban sprawl in the Pacific Coast region of North America: the benefits of multiple measurement methods. Int. J. Appl. Earth Obs. Geoinf. 7 (4), 268e283. de Oliveira, U.R., Espindola, L.S., da Silva, I.R., da Silva, L.N., Rocha, H.M., 2018. A systematic literature review on green supply chain management: research implications and future perspectives. J. Clean. Prod. 187, 537e561. Dinda, S., Das, K., Chatterjee, N.D., Ghosh, S., 2019. Integration of GIS and statistical

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