1. Introduction
Since the proposition of the neoclassical growth theory in the mid-1960s by Solow [
1], the disparities in regional growth have become a hot topic attracting the attention of many scholars. There is a consensus that imbalanced regional development will lead to serious social problems, and correspondingly reduce the welfare generated by high growth [
2,
3,
4]. Therefore, reducing regional growth differences became an important issue for regional development in many countries. Urban sprawl is a byproduct of regional growth phenomena, and scholars have different views on it. Some scholars have highlighted the negative effects of urban sprawl, such as energy consumption and air pollution [
5,
6], environmental degradation and wildlife loss [
7], economic inefficiency and agricultural decline [
8,
9], and unequal public services as well as social segregation [
10,
11]. In contrast, others have emphasized the benefits of urban sprawl, such as affordable housing, free parking, free movement, sufficient space, and yards and neighborhoods with green areas [
12,
13]. Meanwhile, sprawl in the suburbs provides space for the renewal of inner cities, leading to the update of existing infrastructure, which provides a good opportunity for the economic recovery of the core areas [
14]. Actually, the pros and cons of urban sprawl depend largely on the extent of the sprawl. Excessive urban sprawl may lead to “urban disease”, but it is difficult to generate an agglomeration economy if the urban scale is too small [
15]. Since 1978, China has experienced rapid economic reforms. At the same time, the Chinese government began to reflect on the failure of the balanced regional economic development strategy of the previous 30 years, and proposed a regional unbalanced development strategy, which was first implemented in the eastern areas, such as Zhengjiang and Guangdong provinces [
16,
17]. This strategy not only significantly increased the urbanization rate of Chinese cities from 17.92% in 1978 to 60.6% in 2019, but also increased the regional differences among the east, middle, and west. This process has led to the rapid expansion of cities, which inevitably leads to different levels of urban sprawl in various regions. Meanwhile, the country also faces serious challenges arising from unbalanced regional development and intensifying social injustice, which may threaten national unity and social stability [
18]. The Chinese government began to consciously reduce regional differences. For example, China began to implement the western development strategy in 2000 to narrow the regional disparities. In 2014, China liberalized the restrictions on the settlement of small cities which further promoted the development of the central and western regions. These policies have made some achievements. Scholars have confirmed that the inequality between regions has decreased since 2004 [
18]. However, the central and western regions of China have limited capacity to undertake industries, and the supply of infrastructure and public services is insufficient, which is not conducive to urban sprawl. Therefore, it is necessary to further evaluate the regional differences and changes in urban sprawl in China, which will provide a basis for solving the social and economic problems brought about by it.
Previous studies have highlighted the spatial pattern and driving factors of urban sprawl in China. There is still a lack of convergence analysis of urban sprawl among regions. Therefore, it is still unclear whether the inequality of urban sprawl in the east, middle, and west has been alleviated under the efforts of a series of policies of the Chinese government. Clarifying the above issues is of great significance for formulating regional development policies in the future. This paper aims to address this issue, including analyzing the spatiotemporal distribution, regional differences, and convergence of urban sprawl in China.
The remainder of this paper is structured as follows.
Section 2 is the literature review including the prevailing definition and calculation of urban sprawl, and related convergence research.
Section 3 briefly introduces the study area and explains the data as well as methodology.
Section 4 presents the regional differences in urban sprawl in China.
Section 5 further analyzes the convergence of urban sprawl in China. Conclusions are given in
Section 6.
4. The Spatiotemporal Distribution and Regional Differences of Urban Sprawl in China
Using the Jenks natural breaks method to classify the USI in 2006, and based on this classification standard, cities were divided into three categories: high-sprawl cities (USI = 0.48–1), medium-sprawl cities (USI = 0.42–0.48) and low-sprawl cities (USI = 0–0.42). Visualizations through Arcgis 10.4 are shown in
Figure 3. It can be found that, in eastern China, the cities with high values of USI are mainly located in Heilongjiang, Jilin, and Shandong Provinces. The USI value of southeastern coastal cities is generally low. From the perspective of temporal–spatial evolution, the urban sprawl in the northeast and southeast regions of eastern China has gradually declined, and increased in central regions. In central China, the spatial distribution of urban sprawl has no obvious geographical pattern. In term of temporal–spatial evolution, the sprawl of cities around Wuhan shows a trend of increasing first and then decreasing, such as Xianning and Huanggang. Moreover, the USI in some cities in the southwest of central China has shown a continuous downward trend. In western China, the urban sprawl is generally high in the northern and southern regions, and low in the middle parts. As for temporal–spatial changes, the urban sprawl in northern cities of western China such as Hulun Buir and Chifeng continues to decline. Cities in the central west, such as Jiuquan, Yulin, and Qingyang, showed a trend of decline first and then growth. Meanwhile, it is relatively stable in the southwest with little change.
In order to further understand the differences in urban sprawl among regions, we analyzed the changes in regional average USI (
Figure 4) and the proportion of three types of cities in their respective regions (
Table 2).
Figure 4 shows that the average USI during the research period showed a downward trend; that is, the USI values of all cities and regions in 2019 were lower than those in 2006, which indicates that Chinese cities have become relatively compact. In detail, the average values for eastern cities in 2006 and 2019 were 0.46028 and 0.44541, respectively, a decrease of 3.34%. As for central cities, the values were 0.44368 and 0.43819 in 2006 and 2019, respectively, a 1.25% reduction. When it comes to western cities, the USI declined by 2.27% in research period, with values of 0.44998 and 0.44001 in 2006 and 2019, respectively. This illustrates that the urban sprawl in the east decreased the fastest, followed by that in the west, and the central cities was the lowest.
This process is also rolling, that is, the USI showed a unilateral downward trend from 2006 to 2010, followed by an increase first and then a decreasing trend from 2010 to 2013, and a stable trend from 2013 to 2016, as well as an upward-fluctuating trend from 2016 to 2019. In detail, during 2006–2010, the USI of eastern cities decreased by 4.07% on average, and that of cities in the central and west shrunk by 2.11% and 2.44%, respectively. It can be seen that the eastern cities shrunk faster during this period, while those in the west and the middle were similar. However, the USI fluctuated sharply between 2010 and 2013, showing a trend of increasing first and then decreasing, more obvious in the eastern cities. Among them, the USI of eastern cities rose by 5.45% at first, but it dropped rapidly to the level of 2010 in only one year. This increasing tendency was 2.25% and 1.38% in central and western cities, respectively, and all cities rapidly dropped to the level of 2010. From 2013–2016, the USI was stable at 0.42–0.43. After 2016, USI showed a rising but volatile trend. In this stage, the eastern and western regions spread rapidly, while the central regions were relatively stable.
According to the classification results of the Jenks natural breaks method of USI, cities were divided into three categories: high-sprawl cities (USI = 0.48–1), medium-sprawl cities (USI = 0.42–0.48), and low-sprawl cities (USI = 0–0.42). On this basis, we further compared the regional differences in urban sprawl in China.
Table 2 reflects the proportion of the three types of cities in their respective regions.
According to
Table 2, there was no significant difference among the three regions in the proportion of high-sprawl cities in 2006. However, in 2012, the proportion in the east and west was significantly lower than that in the middle. By 2019, the proportion of high-sprawl cities in the eastern region exceeded that of the central region again. In terms of medium-sprawl cities, their proportion in the western region was significantly higher than that in the other two regions, while it showed a downward trend in the eastern cities and a decline first and then growth in the central cities. As for low-sprawl cities, the central region enjoys the highest proportion, followed by the east, and finally the west. The above results prove the regional differences in urban sprawl. However, whether these differences are reduced is still nebulous. Therefore, convergence analysis is needed.
5. The Convergence Analysis of Urban Sprawl in China
In this section, the convergence analysis model is employed to further investigate whether urban sprawl in China has marginal decline and finally tends to be stable. In
Table 3, Model I show the analysis results of the absolute β convergence. Models Ⅱ to Ⅳ are outcomes of the conditional β convergence analysis after gradually adding natural, economic, and social factor variables. The β coefficients of all models are negative, which indicates that there is a convergence effect on the sprawl of Chinese cities. That is to say, cities with a lower degree of sprawl show a latecomer advantage and catch-up effect, and the difference in expansion narrows among regions. Apart from that, the convergence rate and the half-life in absolute β convergence are 0.0259 and 26.76, respectively, which are similar to that of condition β convergence. It indicates that it will take 26–27 years for cities with low sprawl to catch up to cities with high sprawl following the current trend.
Due to spatial spillover effects, regional differences and geographical location play an indispensable role in the mechanism of urban sprawl. In view of this, a spatial econometric model was established with an inverse distance as the spatialized weighted matrix. The premise of the spatial econometric models is that variables are spatially autocorrelated. Therefore, the Moran’s I of the dependent variable was calculated, as presented in
Table 4. The analysis results showed that the convergence of urban sprawl had spatial autocorrelation.
To increase the robustness of the results, SLM, SEM, and SDM models were used for statistical testing based on the inverse distance space weight matrix. The results in
Table 5 show that the convergence coefficients of the three models are negative, which indicates that the convergence trend of Chinese cities is still obvious while controlling for the spatial spillover effect. Meanwhile, the coefficients of β in
Table 2 and
Table 4 are similar, indicating that the analysis results are clearly robust. In addition, after adding the spatial econometric model, the convergence rate and half-life had no significant changes compared with the results in
Table 2. Therefore, it can be concluded that the urban sprawl in China has obvious convergence, and the cities with slower sprawl have a catch-up effect. The catch-up time is about 26 years, if the current trend is maintained.
After verifying the convergence of urban sprawl in China, we further checked the club convergence effect in each region (as presented in
Table 6). The results of OLS in different regions showed that the coefficients of β were significant and negative at the level of 1%, which was still the case after adding the spatial econometric model, indicating that there is a club convergence effect of urban sprawl in eastern, central, and western China. However, in terms of convergence rate, there is a certain heterogeneity among the three regions. The half-life of the central and western regions decreased significantly in contrast to their OLS analysis result after adding the spatial econometric model. This means that convergence in the central and western regions has remarkable spatial spillover effects. That is, the western region has the fastest convergence speed, followed by the central region, while the eastern region has the slowest convergence speed.
6. Conclusions
The unbalanced regional development strategy of the Chinese government has led to regional differences in urban construction, although a great deal of research and policy is trying to narrow this gap. However, for urban sprawl, it is far from enough. This is because there is a prejudice against urban sprawl and most people believe that all cities should control their sprawl. However, as we mentioned above, this position lacks sufficient evidence. Although excessive urban sprawl may lead to “urban disease”, it is difficult to generate an agglomeration economy if the urban scale is too small. Meanwhile, unlike urban expansion, which is limited to a single land perspective, urban sprawl provides us with a richer dimension to understand cities, such as the perspective of human–land relationship. Therefore, it is meaningful to analyze the regional differences and convergence tendency of urban sprawl in China, which will help us to have a more comprehensive understanding of China’s urban and regional development, and provide planners and managers with a decision-making basis. In this context, we measured the regional differences and convergence of urban sprawl in China.
In terms of spatial distribution and spatiotemporal evolution of urban sprawl, we found they are different in eastern, central, and western China. The cities with high, medium, and low sprawl in the east and west are relatively concentrated and have obvious geographical characteristics. However, the distribution of these three types of cities in the central region is chaotic, with no obvious geographical pattern. In addition, in the central and southern areas of the eastern region, the evolution of urban sprawl is in the opposite direction. The cities with high urban sprawl in the former are gradually increasing, while the cities with low urban sprawl in the latter are gradually increasing. The spatiotemporal evolution of urban sprawl in central China is mainly reflected in the reduction in urban sprawl around the core city (Wuhan). Moreover, we found the high-sprawl cities in the western region are mainly concentrated in the resource-based cities in the north and the economically backward cities in the south, with a decreasing trend. For example, Hulunbeier, Chifeng, Yulin, and Jiuquan in the northern region of western China are dominated by coal, steel, gold, and other resource mining, while Hechi, Baise, Guigang, and other ethnic areas in the south are particularly backward in economic development. Planners and managers should pay more attention to such cities.
The urban sprawl index (USI) showed a downward trend during the research period. This process has obvious stage characteristics. It can be roughly divided into four stages: 2006–2010, 2010–2013, 2013–2016, and 2016–2019, which respectively correspond to a unilateral downward trend, growth first and then a downward trend, a stable trend, and a wavelike rising trend. We found the USI in 2010–2013 fluctuated significantly. This phenomenon is reminiscent of the financial crisis that swept the world in 2008. The Chinese government issued a CNY4 trillion investment plan to expand domestic demand. In particular, CNY900 billion of this plan was used for affordable housing projects to increase support for housing construction. This policy has accelerated the expansion of urban land. At the same time, the population of China did not grow rapidly, which eventually led to significant sprawl. In addition, because of the lag of urban construction and policy effects, this exogenous impact was reflected after 2010 and began to weaken in 2012. Moreover, the proportion of cities with high, medium and low sprawl in each region is also significantly different.
We also found that there is a convergence effect in China’s urban sprawl; that is, the urban sprawl growth rate that was originally high slows down, while the urban sprawl speed that was originally slow becomes relatively fast. According to the current trend, it will take about 26 years for the latter to catch up with the former. In addition, the eastern, central, and western regions have club convergence effects. Meanwhile, convergence in the central and western regions has remarkable spatial spillover effects. As for convergence rate, the western region is the fastest, followed by the central region, and the eastern region is the slowest. However, the trend of urban development is not static. Under the influence of such natural, economic, and social factors, urban sprawl will tend to shrink or become polycentric, which is a prospect that needs to be studied in the future. How to promote regional coordinated development is a long-term problem faced by managers and planners. As far as urban sprawl is concerned, it has obvious advantages and disadvantages. Due to the existence of spatial spillover effects and regional linkage effects, the level of urban sprawl in different regions should not differ too much to ensure reasonable resource carrying capacity and an agglomeration economic effect. Our model shows that adjusting the industrial structure, improving the public service and scientific and technological level, strengthening infrastructure construction, and improving the level of urbanization are conducive to narrowing the differences in urban sprawl between regions. Meanwhile, the coefficient of government intervention is negative, indicating that the current policy still strengthens the differences among regions, so it is necessary to adjust the policy attitude in time. We therefore advise managers and planners to propose more specific control and encourage strategies to accelerate the convergence of urban sprawl in China, based on the different impacts of natural, social, and economic factors.
Undoubtedly, there are still some deficiencies in our research, and we look forward to filling them in future research. First of all, our research stays in the macro perspective, although the sprawl shape of each city is included in the step of calculating USI and can be visualized. However, it is still difficult for us to refine it to such an extent in this article. Apart from that, as mentioned in the literature, a polycentric urban structure and urban sprawl may exist at the same time. Their difference is that the former is planned and organized. Therefore, it is necessary to incorporate them into the same analytical framework in future research.