Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities

Land Use Policy 95 (2020) 104576

Contents lists available at ScienceDirect

Land Use Policy journal homepage: www.elsevier.com/locate/landusepol

Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities

T

Sanwei Hea, Shan Yub, Guangdong Lic,*, Junfeng Zhanga,d a

School of Public Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China School of Urban and Regional Science, East China Normal University, Shanghai, 200062, China c Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China d Key Laboratory of Urban Land Resources Monitoring and Simulation，Ministry of Natural Resources，Shenzhen, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Urban form Land-use efficiency Spatiotemporal dynamics China

Effective land-use is a prerequisite for sustainable urbanization. Land-use efficiency is intimately related to factors such as economic growth and industrial policies; however, limited studies focus on the spatial effects of urban form on land-use efficiency. Our empirical analysis includes 336 Chinese prefecture-level cities located in 31 provinces and four regions. We use five landscape metrics (patch density, mean patch size, edge density, mean shape index and patch cohesion index) to characterize urban form. Overall, China’s urban form metrics demonstrate significant regional differences from 2000 to 2015. Furthermore, land-use efficiency also demonstrates significant regional disparities. We prove the importance of the spatial effects of urban form on land-use efficiency using spatial regression models. Moreover, the impact of urban form metrics on land-use efficiency is sensitive to regional heterogeneity and city sizes. An urban form characterized by high patch density and large urban patch size is not conducive to increases in land-use efficiency in small cities although it is conducive to such increases in large cities. This research facilitates policymaking in the areas of spatial regulation and spatial planning in connection with national land-use.

1. Introduction Rapid urbanization boosts economic development; however, an irrational and inefficient mode of land development has brought about adverse consequences including the loss of high-quality cultivated land, an increase in ghost cities, the shrinking of ecological green space, and the urban heat island effect (Chen et al., 2016a; Liu et al., 2014). How the needs of a growing urban population with limited urban land can be satisfied is seen as a key global challenge (UN-Habitat, 2016). A key principle of sustainable urban development is to promote efficient land development (Liu et al., 2014; Wei and Ewing, 2018). The efficient use of land resources can help alleviate the contradiction between population growth and limited land supply, which will be important in guiding sustainable land-use in the future (Kuang et al., 2016; Liu, 2018). Land-use efficiency refers to an increase in the output of a unit land area related to regional social and economic activities (Cao et al., 2019). Many scholars have studied the spatial patterns and regional differences of land-use efficiency (Liu et al., 2018), the measurement and evaluation systems of land-use efficiency (Lu et al., 2018), and the

⁎

driving mechanisms of land-use efficiency (Xie et al., 2018). In the context of economic globalization, land-use efficiency is significantly affected by economic restructuring (Wu et al., 2017), industrial relocation (Chen et al., 2018), land-use and urban planning policies (Chen et al., 2016b), and natural factors such as NDVI and DEM (Cao et al., 2019). Globalization, marketization, decentralization and urbanization are also key factors affecting urban land use efficiency in Yangtze River Delta (Wu et al., 2017). It is clearly of great practical importance to comprehensively examine the driving forces of land-use efficiency. Urban form refers to the spatial patterns of human activities at certain points in time. It includes three aspects: specifically, density, diversity and spatial-structure pattern (Tsai, 2005). In a broader sense, urban form may involve design categories such as block or site design (Cervero and Kockelman, 1997). Urban form is closely linked with economic productivity as well as economic performance. Economic productivity is closely correlated with urban spatial structure such that a fragmented urban landscape may lead to relatively low urban productivity (Li and Liu, 2018). A polycentric urban structure can increase economic performance in two ways: a center separation will diminish the negative externalities of agglomeration, and “borrowed size” will

Corresponding author. E-mail addresses: [email protected] (S. He), [email protected] (S. Yu), [email protected] (G. Li), [email protected] (J. Zhang).

https://doi.org/10.1016/j.landusepol.2020.104576 Received 22 October 2019; Received in revised form 13 February 2020; Accepted 5 March 2020 0264-8377/ © 2020 Published by Elsevier Ltd.

Land Use Policy 95 (2020) 104576

S. He, et al.

Fig. 1. The theoretical framework.

sensing can provide useful data for monitoring land-use and cover change. Hence, although many attempts have been made on the macro scale relying on statistical data, more reliable data is required to conduct a nationwide study on the finer scale across prefecture-level cities to investigate their relation between urban form and land-use efficiency. Overall, this paper contributes to the existing studies as below. First, this paper employs landscape metrics to represent the area, edge, shape and aggregation metrics of urban form and makes a thorough study about the effect of various urban form metrics on land-use efficiency. Second, a spatiotemporal heterogeneity perspective is useful to characterize the heterogeneous influence of urban form metrics on land-use efficiency in four regions of China and in cities with various sizes. Third, land-use efficiency is highly influenced by the multimechanism process of globalization, marketization, decentralization and urbanization in our theoretical framework. Four, spatial autocorrelation is recognized in the study of geographic space and spatial regression models have proven to be useful in examining the influence of urban form on land-use efficiency. In this study, using 336 cities of China, we systematically and comprehensively investigate the characteristics and the spatiotemporal dynamics of urban form and land-use efficiency from 2000 to 2015. Then, we conduct a comparative analysis of urban form and land-use efficiency to uncover the magnitude of the differences in these dynamics among regions and cities. Third, using spatial regression models, we examine the impact of urban form on land-use efficiency by considering spatial autocorrelation effects and other influential factors. Finally, this paper assesses the major findings and provides policy implications.

cut losses due to positive externalities (Berrigan and Troiano, 2002; Hathway and Sharples, 2012; Næss, 2012; Zhang et al., 2017). However, land-use efficiency is a dynamic process interwoven by multiple factors, and the effect of urban form on land-use efficiency has not received adequate attention (Ding and Zhao, 2014). Moreover, a spatiotemporal heterogeneity perspective is required to examine the heterogeneous effect of urban form on land-use efficiency in China with vastness in geography and socioeconomic contexts. Landscape metrics use remote sensing image data to analyze urban landscape structure, urban spatial configuration and urban dynamic changes through urban patch size, urban patch shape and spatial layout, which are effective in characterizing the spatial metrics of urban form. Urban landscape variables may carry different meanings at different levels (e.g., metropolitan areas, cities and neighborhoods), and they may affect human activities in different ways. By comparing changes in the urban landscape over multiple scales and multiple time periods, it has been found that urbanization is not simply a binary process switching between urban expansion and urban dispersion, but it is also a spiral process of transferring the dominant position among multiple growth modes (aggregated, linear, leapfrogging, and nodal) (Aguilera et al., 2011; Li et al., 2013). Aguilera et al. (2011) employed landscape metrics to analyze urban land use patterns in a Spanish metropolitan area. Therefore, landscape metrics allow us to characterize urban form from a multidimensional perspective, to uncover the dynamics of urban spatiotemporal evolution and to reflect the corresponding urban development mode, thus providing the scientific basis for formulating spatial planning strategies. The previous studies on China focus only on a single city or urban agglomeration (Sun and Zhao, 2018; Yu and Zhou, 2017), the conclusions from which cannot be applied to the regional or national scales. Due to the internal differences among resource endowments and socioeconomic contexts in China, it is necessary to explore how spatial patterns and the driving mechanisms of land-use efficiency demonstrate regional differences among the eastern, central, western and northeastern parts of China (Wu et al., 2017). Furthermore, the studies on land-use efficiency in China are shifting from the macroscopic scale such as the provincial or regional scales to the mesoscale such as the city level or industrial development zones (Huang et al., 2017). However, the current studies focusing on the city level mainly rely on statistical publications to obtain data about construction land (Chen et al., 2016b). Without exception, the land data from statistical yearbooks suffer from substantial margins of error (Li et al., 2015). Remote

2. Theoretical framework In our analytic framework as seen in Fig. 1, economic transition can be conceptualized by the multimechanism process of globalization, marketization, decentralization and urbanization, all of which significantly influence land-use efficiency (Wu et al., 2017). Globalization is measured by the actual foreign investment per capita (FOREIGN) at the nominal price in 2000, 2005, 2010 and 2015. Globalization promotes an increase in foreign investment and foreign trade in urban areas, especially in the coastal region and its metropolitan areas. Because foreign investment brings an influx of foreign capital to inject more advanced technology and better managerial skills, the built-up 2

Land Use Policy 95 (2020) 104576

S. He, et al.

Fig. 2. Spatiotemporal dynamics of urban land expansion from 2000 to 2015 in China and three metropolitan regions (BTH, YRD, and PRD).

more accessible land-use patterns and thus increasing land-use efficiency (Kii and Doi, 2005). TRAFFIC is measured by the area of urban paved road at year end to uncover the situation of the transportation infrastructure. The development of transportation infrastructure is conducive to urban and regional development as well as regional integration as it improves accessibility. Improving accessibility brings firms closer to the market, makes knowledge spillovers effective in neighboring areas, and promotes regional innovation across the urban landscape (Lopez et al., 2009).

areas can be used more effectively, especially in the development zones (Liu and Wu, 2011). Marketization is quantified by two variables: employment structure (EMP) and retail sales of consumer goods per capita (RETAIL). EMP is measured by the proportion of persons employed in private enterprises and self-employed individuals, reflecting the progress in labor market development. A high value of EMP represents the expansion of nonpublic-owned enterprises and their creation of a competitive environment. Through market competition, land resources are allocated to high-tech industries and advanced services, which can promote efficient land-use (Wu et al., 2017). RETAIL strongly reflects the market demand for consumer goods and consumers’ purchasing power, which is crucial in upgrading the consumption structure and promoting innovation in modes of consumption. Decentralization is denoted by the ratio of fiscal revenue to fiscal expenditure (FISCAL). The tax-sharing system of China in 1994 achieved remarkable success by decentralizing the administrative, fiscal and political functions of the central government to lower-level governments (Azfar et al., 1999). The negotiation of land-use rights between the central and local governments can affect the efficient use of urban land (Wu et al., 2017). Although these decentralization efforts are typically politically motivated, they have profound impacts on efficiency gain and economic growth. Urbanization includes three representative variables: industrial structure (INDU), population density (POPDEN) and traffic infrastructure (TRAFFIC). INDU is quantified by the ratio of the secondary industry output to the tertiary industry output. Reform-induced urbanization and industrialization tend to increase urban land prices and stimulate competition among different types of industries. Industrial transformation and upgrading can effectively stimulate regional economic growth and promote the intensive use of industrial land, thus increasing land-use efficiency (Wu et al., 2017). Increasing population density helps to reduce the demand of urban expansion and promote the compact development of cities. Land-use strategies such as city compaction are conducive to the creation of livable communities with

3. Materials and methods 3.1. Regional- and city-level differences analysis To detect regional differences, 336 Chinese cities are divided into four regions: eastern, central, western and northeastern, as indicated in Fig. 1. The eastern region refers to the coastal region of China, which has more geographic advantages and policy priorities to promote economic and urban development. The non-eastern region refers to the inland region of China, which has generally been less-developed in economic development compared to the coastal region. The coast-inland divide is the prominent feature of China’s economy. Because of regional differences in geography, socioeconomic context and development policies, cities have various differences related to land-use efficiency and urban landscape pattern. Therefore, this paper uses citylevel data to examine these differences at the administrative level. To investigate the effect of city size, we divide our sample cities into three categories: (1) small cities (with less than 0.5 million people in 2016), (2) medium-sized cities (0.5–1 million people in 2016), and (3) large cities (more than 1 million people in 2016). 3.2. Urban land-use datasets Urban land-use datasets are derived for four periods (2000, 2005, 2010 and 2015) from Landsat TM/ETM/OLI remote sensing images 3

Land Use Policy 95 (2020) 104576

S. He, et al.

urban patches are gradually enlarged and then merged as a larger urban patch, the value of PD may decrease. MPS is an effective indicator characterizing urban morphological changes such that a higher MPS corresponds to a more concentrated and continuous urban pattern. Edge metrics are measured by edge density (ED), which is the ratio of the total length of urban patch edges to total landscape area. Both MPS and ED can reflect urban fragmentation. Shape metrics include mean shape index (MSI), which is used to characterize the complexity of the urban landscape. High values of MSI reflect irregular and complicated urban morphology. Patch cohesion index (COHESION) as a measure of physical connectedness (Schumaker, 1996), describes a spatially aggregated urban form. Urban patches gradually coalesce as the proportion of urban cells increases, forming a large, highly connected patch. The value of COHESION is bounded between 0 and 100. Approaching 0 denotes that the urban landscape becomes increasingly subdivided and less physically connected. All the above landscape metrics were calculated using the software FRAGSTATAS 4.2.

with the spatial resolution of 30 × 30 m. To obtain the national landuse data, the satellite images must go through a series of processes including geometric correction, image enhancement and image fusion. Both supervised classification and traditional manual visual interpretation are used to classify the land into six categories (e.g., cultivated land, forestland, grassland, water body, urban/rural/industrial land and unused land). According to field validation, the average classification accuracy of cultivated land and urban/rural/industrial land reaches more than 85 %. Fig. 1 demonstrates the spatiotemporal dynamics of urban land expansion from 2000 to 2015 at the national level, especially in three metropolitan areas, namely, the BeijingTianjin-Hebei region (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD). According to Fig. 2, 2000, 2005, 2010 and 2015, the urban area in is, respectively, 45,265 km2, 58,795 km2, 91,357 km2 and 114,778 km2. The urban expansion rate reaches 29.89 % during 2000–2005, 55.38 % during 2005–2010 and 25.64 % during 2010-2015. The findings show that China’s process of urbanization and industrialization peaks during the time period of 2010–2015, which results in rapid urban land expansion. The newly built-up areas are mainly located in coastal provinces such as Shandong and Jiangsu and urban agglomerations such as the BTH, YRD, PRD, and Chengdu-Chongqing regions. It should be noted that some inland provinces such as Inner Mongolia and Xinjiang witnessed a fast-developing economy and rapid urbanization from 2000 to 2015 as a result of the rich natural resources and the influx of foreign capital within the western region.

3.5. Spatial regression model for driving forces analysis Land-use efficiency is affected by the endogenous features of landuse patterns but also by the exogenous spatial spillover effect of landuse efficiency on neighboring areas (Voss et al., 2006). The relative advantages of a location are enhanced by spatial spillover effects, and the land-use efficiency is also thereby increased. To detect the effect of spatial spillovers, a spatial regression model is employed to calculate the relative contribution of each driving force and spatial effects toward land-use efficiency. The advantage of spatial regression models over ordinary least squares (OLS) is that both the spatial heteroscedasticity and spatial dependence of error terms are considered. The estimation accuracy of spatial regression models can be ensured by effectively controlling spatial dependence in the form of lag and error dependence. The steps of choosing the appropriate spatial model are as follows. First, Moran’s I test is utilized to investigate whether spatial autocorrelation occurs in the residual of the independent variable. Second, if no spatial autocorrelation occurs, then the OLS is preferred; otherwise, a spatial lag or error model is preferred. Third, both Lagrange multiplier and robust Lagrange multiplier tests are employed to examine whether spatial autocorrelation occurs in the lag or error terms. Fourth, spatial autocorrelation must explicitly be considered to specify the model, after which the model is estimated using the maximum likelihood. Through the above steps, a spatial lag model (SLM) is selected according to the results of robust Lagrange multiplier tests. Since urban form is quantified in terms of area metrics, edge metrics and shape metrics, the following equations are used to investigate the effect of five urban form metrics on land-use efficiency:

3.3. Land-use efficiency A number of the previous studies have defined land-use efficiency as economic output of per land unit (Kuo and Tsou, 2015; Meng et al., 2008; Wu et al., 2017). Specifically, we used the added value of the secondary and tertiary industries per square kilometer as the indicator of land-use efficiency and the dependent variable. The data source is mainly from the Chinese city statistical yearbook (2001–2016).

LUEi = STIi UAi Where LUEi denotes land-use efficiency for the ith city; STIi is the added value of the secondary and tertiary industries for the ith city; UAi is the total urban area for the ith city. 3.4. Landscape metrics Many landscape metrics have been developed and widely applied to characterize various landscape patterns in the past few decades (Luck and Wu, 2002). According to the previous literature, four landscape metrics are selected to describe the spatial features of urban landscape: area metrics, edge metrics, shape metrics and aggregation metrics, as shown in Table 1. Area metrics are measured by patch density (PD) and mean patch size (MPS). In the process of urbanization, the number of patches tends to increase during the process of rapid urban growth, representing the discrete urban pattern. However, when the scattered

n

LUEit = α 0 + ρ∑ Wij LUEit + α1 PDit + α2 MPSit + α3 EDit + α 4 MSIit i=1

+ α5 COHESIONit + α 6 Controlit + εit where LUEit refers to land-use efficiency for the unit i and the year t ;

Table 1 Description of six landscape metrics. Category

Landscape metric

Formula

Description

Area metric

Patch density (PD) Mean patch size (MPS)

PD= n/ A

Edge metrics

Edge density (ED)

ED= (10000) ∑ j = 1 ej / A

Shape metrics

Mean shape index (MSI)

Aggregation metrics

Patch cohesion index (COHESION)

n =number of urban patches; A = total landscape area (ha); aj =area (ha) of urban patch j ; n =number of urban patches;

n

MPS= ∑ j = 1 aj / n n

n

MSI= ∑ j = 1

pj 2 πaj

ej =total length (m) of edge in landscape for urban patch j ; A = total landscape area (ha); pj = perimeter (m) of urban patch j ; aj =area (ha) of urban patch j ;

/n

COHESION = ⎡1 − ⎣

∑ pi

⎤ ⎡1 − ∑ (pi √ ai) ⎦ ⎣

4

−1 1 ⎤ √N⎦

n =number of urban patches; pi =the perimeter of patch in pixels; ai =the area of patch i in pixels (ha); N =the total number of pixels in a landscape.

Land Use Policy 95 (2020) 104576

S. He, et al.

Table 2 Temporal dynamics of urban form metrics from 2000 to 2015 in four regions of China. Variables

PD MPS ED MSI COHESION

National average

Eastern Region

Central Region

Western Region

Northeastern Region

2000

2015

2000

2015

2000

2015

2000

2015

2000

2015

0.56 285.20 0.39 1.25 76.74

1.45 248.23 0.93 1.19 80.35

1.12 281.24 0.77 1.21 78.46

2.43 308.63 1.75 1.19 82.52

0.64 200.21 0.39 1.20 75.64

1.96 173.71 1.03 1.16 80.19

0.17 271.17 0.13 1.27 73.91

0.59 223.95 0.38 1.20 77.29

0.24 553.73 0.23 1.37 84.91

0.59 346.57 0.45 1.24 86.01

landscapes in these regions. However, high-value MPS areas are mainly clustered in northeastern China, some coastal provinces such as BTH, Shandong and Jiangsu, and some western provinces such as Xinjiang, Inner Mongolia and Qinghai, indicating a more concentrated and continuous urban pattern in those locations. With regard to edge metrics, the ED hot spots are mainly located in metropolitan areas within the coastal and central regions such as BTH, YRD, PRD, and urban agglomerations in the middle reaches of the Yangtze River. Compared to the PD spatial pattern, the fragmented urban development is more evident in coastal and central regions with flat terrains where rapid urban development has caused the loss of cultivated land, environmental pollution and the inefficient provision of infrastructure and public services (Ewing, 2008). With regard to shape metrics, MSI showed a slight upward trend from 2000 to 2015 at the national scale. In 2000, the MSI in hot spots were mainly distributed in the northeastern region and some western provinces such as Xinjiang, Qinghai and Tibet, where the urban landscape becomes more complicated and irregular during the process of urbanization. It should be noted that, in 2015, the urban shape becomes less complicated, and the number of cities with high MSI decrease significantly. However, in addition to the complex urban shape of the northeastern and western regions, some coastal and central provinces such as Jiangsu and Anhui demonstrate an increasingly complex process of urbanization. With regard to aggregation metrics, the COHESION hot spots are mainly distributed in direct-controlled municipalities (Beijing, Shanghai, Tianjin and Chongqing), provincial capital cities such as Guangzhou, Wuhan and Changsha, and some coastal/northeastern provinces such as Jiangsu, Shandong, and Liaoning, demonstrating an aggregated urban form in those locations.

PDit , MPSit , EDit , MSIit , COHESIONit refer to patch density, mean patch size, edge density, mean shape index and patch cohesion index as described in Table 1. Controlit represents the other independent variables as depicted in section 2.5. Wij is an (N × N) block diagonal matrix based on queen contiguity; ρ (− 1 ≤ ρ≤ 1) is the coefficient of the spatially lagged dependent variable, which reflects the impact of neighboring units’ land-use efficiency on the unit i . A positive value of ρ is expected to represent the positive influence from neighbors. αi is a constant and estimative coefficient; εit is the random error term. 4. Result analyses 4.1. Spatiotemporal dynamics of urban form metrics From 2000–2015, China experienced an accelerated process of urbanization, leading to a series of significant changes in urban morphology. Table 2 demonstrates the temporal dynamics of urban form metrics from 2000 to 2015 at the national and regional levels. Overall, metrics such as PD and ED increased dramatically from 2000 to 2015 with the change rates of 158.93 %, and 138.46 %, respectively. It is therefore shown that patch density and edge density are more sensitive to the dynamics of urbanization. Furthermore, China’s urban form metrics demonstrate significant regional differences from 2000 to 2015. As shown in Table 2, the eastern region has the highest PD value in both years, indicating a high density of urban patches concentrated in China’s coastal region. However, the lowest growth rate of PD of 116.96 % in the eastern region indicates that the inland regions (central region, western region and northeastern region) experienced accelerating urbanization from 2000 to 2015 whereas the urbanization speed of the eastern region decelerated. Among the inland regions, the patch density (PD) of the western region demonstrates the most significant increase from 2000 to 2015 with a growth rate of 247.06 %. Meanwhile, the northeastern region has the largest MPS in both years although it decreased significantly from 553.73 ha in 2000 to 346.57 ha in 2015. It should be noted that only the eastern region experienced an increase in MPS, indicating a concentrated and continuous urban pattern in coastal China. Both the eastern region and the central region have higher EDs than the national average, denoting a fragmented urban edge and complicated development of the urban fringe. With regard to MSI, both the northeastern region and the western region have higher values than the national average, implying a more complicated urban landscape with irregular urban patches. The negative growth rate of MSI in all regions illustrates that the shape complexity of urban patches decreased from 2000 to 2010 under the rapid development of urbanization. Both the eastern region and the northeastern region have higher COHESION than the national average, indicating a more aggregated urban form. The increase of COHESION from 2000 to 2015 in all regions implies the tendency for urban patches to gradually become spatially connected. Fig. 3 demonstrates the spatial evolution of urban form metrics from 2000 to 2015. These urban area metrics indicate that the high-value PD areas are mainly distributed in central China in areas such as Hubei, Hunan and Jiangxi as well as in coastal provinces such as southeastern Shandong, Jiangsu, Zhejiang and Fujian, which implies discrete urban

4.2. Spatiotemporal dynamics of land-use efficiency Fig. 4 demonstrates the temporal trend of land-use efficiency from 2000 to 2015 at the national and regional levels. Overall, land-use efficiency demonstrates an upward trend from 2000 to 2015 with the average value of economic output per unit area increasing from 186.16 million yuan/km2 in 2000 to 554.82 million yuan/km2 in 2015 at an average annual growth rate of 7.55 %. Although the average level of land-use efficiency is improved against the background of rapid urbanization and industrialization, the range within 1.5 IQR of land-use efficiency among all cities becomes larger from 2000–2015, illustrating the widening internal differences among cities. Furthermore, land-use efficiency demonstrates significant regional differences. Although four regions illustrate an upward trend, the average level of land-use efficiency in the eastern region is the highest among four regions above the national average in 2000 and 2015, increasing from 230.31 million yuan/km2 in 2000 to 665.49 million yuan/km2 in 2015. However, the western region catches up with and surpasses the eastern region in 2005 with 309.89 million yuan/km2 and in 2010 with 448.57 million yuan/km2. As a result of “develop the west” strategy in 1999 and China’s entry into the World Trade Organization (WTO) in 2001, a large influx of domestic capital and foreign direct investment flows into the western China with rich natural 5

Land Use Policy 95 (2020) 104576

S. He, et al.

Fig. 3. Spatial pattern changes of urban form metrics from 2000 to 2015 in China.

resources and cheap land cost. This investment-led and policy-oriented growth drives western China to quickly develop in terms of industrialization and urbanization and thus brings rapid urban land

expansion during the period from 2005 to 2010. Constrained by inefficient marketization, insufficient investment in technological innovation and the gradual destruction of the ecological environment, the 6

Land Use Policy 95 (2020) 104576

S. He, et al.

cities in that region declined from 2010 to 2015. 4.3. The heterogeneous influence of urban form metrics on land-use efficiency in China 4.3.1. The national-level analysis Before running the regression model, Moran’s I index is calculated in ArcGIS 10.5 to verify whether spatial dependence is relevant in landuse efficiency. The z-score values of Moran’s I index in 2000, 2005, 2010 and 2015 are 13.84, 8.01, 11.77 and 21.17, respectively, all greater than 1.96, indicating the statistically significance of spatial dependence at the 5% level. Moreover, the Hausman test denotes that the fixed effect is superior to the random effect. Thus, this paper adopts a spatial lag model with fixed effects. Using the panel dataset of all cities from 2000–2015, SLM and OLS are compared to demonstrate the effect of urban form metrics on landuse efficiency. The model fitness (adjusted R2) of SLM is superior to OLS (Table 3), denoting the important role of spatial dependence in analyzing land-related issues. The positive spatial dependence of landuse efficiency can be found according to the value of a spatially lagged variable ρ at the significant level of 1%. This positive spatial autocorrelation is beneficial to improve land-use efficiency through the spatial spillover effect because the well-developed transport network helps to deliver the spatial externalities effects of land-use efficiency (Wu et al., 2017). Furthermore, control variables such as FOREIGN, EMP, RETAIL, INDU and POPDEN are positive and significant in both OLS and SLM whereas FISCAL is negative and significant in OLS but insignificant in SLM. This indicates that the dynamic processes of globalization, marketization and urbanization have strong effects on improving land-use efficiency. However, fiscal decentralization becomes insignificant after considering spatial autocorrelation in the model, indicating that OLS tends to overestimate the importance of fiscal decentralization. The previous studies have argued that land finance is the critical driver of urban land expansion in well-developed cities (Li et al., 2015), likely leading to the expansion of urban land-use. This paper proves that this negative effect of land finance can be counteracted by the spatial externalities of land-use efficiency. The variable of TRAFFIC has an insignificant effect on improving land-use efficiency in both models. Transport infrastructure influences land-use by improving accessibility, which is not evident at the national level. PD is positive and significant at the 5% level in both models, indicating that urban patch density improves land-use efficiency during the time period from 2000 to 2015. Although the previous studies have argued that PD as a measure of discrete urban patches tends to increase in the process of urban expansion (Fang et al., 2016), the variable of PD is positively significant in stimulating land-use efficiency, suggesting that the appropriate increase of urban patches from 2000 to 2015 conforms to the pace of urbanization and economic growth in China. However, the Chinese government gradually underlines the spatial planning of land-use and the intensive land-use of industrial land, leading to a rational increase of PD. Moreover, the increase of PD may correspond to the shifting of natural cities or megacities from monocentric cities to polycentric cities. The polycentric urban spatial structure is conducive to land-use efficiency because polycentric planning strategies address traffic congestion and other agglomeration diseconomies (Zhang et al., 2017). MPS is negative and significant at the 10 % level in SLM. Each urban patch is categorized by urban morphology as cities, towns, conurbations or suburbs. The increase in mean urban patch size may indicate the outlying urban growth on the fringe of existing urban patches, which is associated with low-density and dispersed build-ups and single urban functions that are highly dependent on automobile traffic (He et al., 2018). Thus, the increase in mean urban patch size is not beneficial to the enhancement of land-use efficiency. ED is negative and significant at the 1% level in both models. Different from the patch type-based metrics such as PD and MSP, the

Fig. 4. Boxplot of land-use efficiency (economic output per unit area) in different regions from 2000 to 2015 in China.

economic growth of the western region begins to decelerate, resulting in the inefficient use of industrial land after 2010. Moreover, the range within 1.5 IQR in the western region is the highest, indicating an internal imbalance of development efficiency among cities. The land-use efficiency of the central region gradually increases from 192.48 million yuan/km2 in 2000 to 556.97 million yuan/km2 in 2015, and its land-use efficiency is lower only than that of the eastern region in 2000 and 2015. As the major supplier of grain in China, basic farmland and ecological land are strictly prohibited from being developed, thus driving the local government to make maximum use of existing urban land. The land-use efficiency of the northeastern region increases slowly from 2000 with 165.07 million yuan/km2 to 445.59 million yuan/km2 in 2015, which is below the national average. As the largest old industrial base in China, the northeastern region endured the persistent influence of the past planned economy system and became an area in relative decline in economy and urbanization (Long et al., 2011; Zhang, 2008). Fig. 5 demonstrates the spatiotemporal dynamics of land-use efficiency in city-level China. The intraregional differences of land-use efficiency within the four regions cannot be neglected. First, the northsouth differentiation within the eastern region becomes more obvious from 2000 to 2015. The cities in the southeastern provinces such as Southern Jiangsu, Zhejiang, Fujian and Guangdong provinces, have maintained a high level of land-use efficiency whereas other cities in BTH, Shandong and Northern Jiangsu obviously have lower land-use efficiency. Notably intraprovincial differences of land-use efficiency are evident in Jiangsu, a coastal province experiencing dramatic economic and spatial restructuring (Wei and Fan, 2000). Southern Jiangsu’s market-oriented economy is more developed in favoring rural industrial enterprises whereas northern Jiangsu has a slower growth of old cities and state-owned enterprises, resulting in widening intraprovincial inequality in land-use efficiency and economic growth. Second, the spatial distribution of land-use efficiency in central China shifted from spatial dispersion pattern in 2000 to spatial clustering pattern in 2015. More cities with high land-use efficiency are clustered only in the metropolitan regions of central China such as the urban agglomeration in the middle reaches of the Yangtze River Delta and Wanjiang’s city belt. As growth poles, these metropolitan regions are significant in stimulating local economic growth by accelerating urbanization and industrialization. Third, the internal difference of land-use efficiency is significant within the western region. More cities in the western region, mainly clustered in Inner Mongolia, Shaanxi, Chongqing, eastern Sichuan and western Guizhou, show higher landuse efficiency from 2000 to 2015. The land-use efficiency of the northeastern region sustained a relatively low level and that of most 7

Land Use Policy 95 (2020) 104576

S. He, et al.

Fig. 5. Spatial pattern of land-use efficiency from 2000 to 2015 in Chinese cities (Red color denotes high land-use efficiency whereas blue color depicts low land-use efficiency) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

of urban fragmentation. Although urban land fragmentation has been influenced by transportation networks and the existence of rivers and hills in cities, decentralized decisions from hierarchical local governance regimes have greatly exacerbated this situation (Wei and Zhang, 2012). When an urban landscape becomes more fragmented, the landuse efficiency will decrease because the key to urban fragmentation is the reduction of service link costs with weak economies of scale and increased restrictions in the formulation of industrial agglomeration and international production/distribution networks (Kimura and Ando, 2003). MSI is negative and significant in OLS; however, it is statistically insignificant in SLM. This illustrates that OLS tends to overestimate the impact of MSI on land-use efficiency as the shape complexity is highly associated with the negative spatial dependence of land-use efficiency. The continuous and regular shape of urban patches is conducive to a reduction in the construction costs of transportation networks and thus facilitates the spatial externalities effects of land-use efficiency. Comparing to the results of SLM, the insignificant role of MSI illustrates that patch shape complexity does not directly influence land-use efficiency at the national level. COHESION is positive and significant at the 5% level in SLM. Increasing patch cohesion facilitates the improvement of land-use efficiency by connecting urban patches. Landscape cohesion reflects the spatially aggregated urban form, illustrating the convenient flow of labor, capital, advanced technology and innovation between urban patches.

Table 3 The national-level analysis by comparing results of OLS and SLM. Explanatory variable

Models OLS

ρ PD MPS ED MSI COHESION FOREIGN EMP RETAIL FISCAL INDU POPDEN TRAFFIC Constant Adjusted R-Squared

SLM

Coefficient

Std. error

Coefficient

Std. error

　 3805.39*** −0.13 −57.84*** −309.84*** 3.24* 0.29*** 5.48*** 24.47*** −181.20*** 66.76*** 19.03*** −0.13 173.21 0.36

　 1350.90 0.05 19.57 107.11 1.25 0.05 0.44 3.39 36.45 9.52 2.76 0.47 154.82 　

0.72*** 2419.26** −0.08* −77.10*** −120.09 2.40** 0.15*** 2.46*** 23.74*** −33.37 51.26*** 17.27*** −0.14 −156.72 0.53

0.03 1160.84 0.04 16.88 93.22 1.11 0.04 0.40 2.93 32.30 8.27 2.39 0.39 135.82 　

Note: *** denotes a significance level of 1%,** denotes a significance level of 5%,* denotes a significance level of 10 %.

edge type-based metric ED provides more comprehensive quantifications of urban fragmentation (Zeng and Wu, 2005). China’s fast-paced urban growth and the major paved road-ways are likely to result in landscape fragmentation and environmental disruption (Li et al., 2010). Almost all coastal provinces and biogeographic regions have high levels 8

Land Use Policy 95 (2020) 104576

S. He, et al.

Table 4 The region-level analysis by comparing results of OLS and SLM. Explanatory variable

ρ PD MPS ED MSI COHESION FOREIGN EMP RETAIL FISCAL INDU POPDEN TRAFFIC Constant Adjusted R-Squared

Eastern region

Central region

Western region

Northeastern region

OLS

SLM

OLS

SLM

OLS

SLM

OLS

SLM

7289.70*** 0.06 −52.02** −576.20*** 4.27** 0.20*** 6.00*** 15.32*** 102.27 −43.15** 67.91*** −0.67 175.00 0.45

0.70*** 7666.04*** 0.11 −80.64*** −537.94*** 5.94*** 0.17*** 2.82*** 13.22*** 76.23 −34.08* 67.01*** −0.80* −116.95 0.61

−2214.61 −0.14 −9.90 −4.89 −1.75 0.61*** 2.70*** 57.82*** −175.47*** 85.08*** 10.82** 9.38*** 133.32 0.66

0.49*** −5198.42** −0.03 56.84 −44.94 −2.30 0.35*** 1.72*** 56.36*** −75.95** 70.50*** 21.11*** 6.26*** 32.48 0.75

30210.24** −0.15 −483.80** −764.80** 11.43** 0.24 5.25*** 30.03*** −249.33*** 123.11*** 24.39*** −3.18 48.94 0.73

0.65*** −3447.42 −0.13 −19.11 −237.43 2.27 0.11 2.80*** 45.18*** −136.59* 80.95** 16.82** −2.10 19.69 0.80

22781.48* −0.02 −341.31* −137.18 0.19 0.10 2.58*** 69.74*** −149.68** 52.77*** 25.72 11.18*** 23.46 0.74

0.60*** 20745.98* 0.02 −407.60*** −49.13 −3.71 0.13** 0.14 64.88*** −39.37 49.91*** 26.20** 7.33*** 162.32 0.81

Note: *** denotes a significance level of 1%,** denotes a significance level of 5%,* denotes a significance level of 10 %.

The effect of PD is heterogeneous in different regions of China according to the results of SLM. PD is significant and positive in eastern and northeastern China. Driven by rapid urbanization and industrialization in coastal China, despite the rapid increase of PD, the higher economic output has effectively promoted the land intensive use in the eastern region. Due to the large loss of population, the urbanization process in northeastern China is relatively slow. However, the preferential policies under the strategy of reviving the northeastern China by the state government have stimulated the efficient operation of those state-owned enterprises, thereby promoting economic output and intensive land use. As the main supplier of grain in China, the negative impact of PD in the central region can be attributed to the extensive use of agricultural land influenced by complicated water system and low mechanization rate, as well as the large amount of unused urban land. More land management strategies like levying vacant land tax and encouraging transfers of rural land rights are required to improve the land-use intensity (Liu et al., 2014). Although the negative impact of MPS is significant in the nationallevel analysis, the influence of MPS is not evident in these four regions in both models. This proves that the outlying urban growth on the fringe of existing urban patches is no longer the major urban expansion way. With the limited supply of urban land, the infilling urban growth such as urban redevelopment or the leapfrogging growth represented by building new towns become dominant. According to the nation-level analysis, ED reflecting urban fragmentation is not conducive to intensive land use. However, this negative influence is only statistically evident in eastern and northeastern regions in SLM. The eastern region is highly fragmented by the densedistributed transport network and the complicated urbanization process, leading to heterogeneous urban landscape. The northeastern China has a large number of ecological transition zones and mountainous areas with 34 % of forest coverage and 22 % of fertile plain field for food production. The resource-based and investment-driven economy in the northeastern region puts urbanization’s pressure on the ecological environment, resulting in deteriorating environment such as rapid forest reduction, severe soil erosion, mineral resources exhaustion, etc. The fragmented urban landscape is unsustainable and cannot coordinate both natural and social processes, thus damaging economic efficiency. MSI and COHESION are only significant in SLM in eastern region. The impact of MSI is negative in coastal China, demonstrating that complicated urban morphology can bring down the land-use efficiency by improving construction costs and transport congestions. COHESION is significant and positive in eastern region, indicating spatially-aggregated urban form can facilitate the formation of agglomeration

4.3.2. The regional-level analysis To detect the regional differentiation of their influence, Table 4 demonstrates the results of OLS and spatial regression models at the regional level. According to the adjusted R-squared values, the performances of SLM in four regions are superior to those of OLS indicating the strong spatial spillover effect when modeling the influence of landuse efficiency. Positive spatial autocorrelation is present in four regions of China although this spatial clustering effect of efficient land use is the strongest in the coastal region. This proves that the essential productive factors such as labor, capital and technology are spatially interactive among neighboring cities along the coast, forming the strong collaboration network to achieve the coordinated development in the local region. Regarding to the influence of control variables, RETAIL and POPDEN are all statistically significant and positive in four regions indicating their effectiveness of marketization and urbanization to improve land-use efficiency in four regions of China. The impact of FOREIGN, EMP, FISCAL, INDU and TRAFFIC on land-use efficiency is spatially heterogeneous in four regions. FOREIGN demonstrates the positive effect on land-use efficiency in all regions but this impact becomes insignificant in the western region, reflecting the constrained effect of globalization on promoting the efficient land use in western China. The effect of EMP is significant and positive in all regions except the northeastern region. As the heavy-industry base in China, the excessive proportion of state-owned enterprises restricts the operational efficiency of enterprises, which is not conducive to revitalization of the northeastern China. FISCAL is significant and negative in both central and western regions of China but insignificant in other regions. Fiscal decentralization provides incentives for local governments to increase fiscal revenues through levying land conveyance fees and land-related taxes. The real-estate-driven issue of land finance is prominent in lessdeveloped regions like central and western China, leading to rapid urban expansion and inefficient land use. Although the effect of INDU is significant and positive in the national-level analysis, it only demonstrates a negative impact on land-use efficiency in coastal China, indicating the necessity of upgrading the industrial structure in the coastal region. The impact of TRAFFIC is significant and negative in eastern China, significant and positive in central and northeastern China, and insignificant in western China. As the most-developed region in China, the multi-mode transport network including railways and high-speed rails has been densely created, making the land space more segmented and fragmented. However, the improvement of transportation infrastructure in central and northeastern China is conducive to coordinated regional development through enhancing the spatial spillover effect. 9

Land Use Policy 95 (2020) 104576

S. He, et al.

Table 5 Results for OLS and spatial regression models for cities of varying sizes. Explanatory variable

ρ PD MPS ED MSI COHESION FOREIGN EMP RETAIL FISCAL INDU POPDEN TRAFFIC Constant Adjusted R-Squared

Large cities

Medium cities

Small cities

OLS

SLM

OLS

SLM

OLS

SLM

5549.09* −0.06 −73.23*** −476.68** 4.40* 0.22*** 5.83*** 20.77** −209.32*** 66.12*** 17.51*** 3.02 290.04 0.37

0.81*** 3253.01* −0.01 −76.75*** −448.45*** 5.11*** 0.14*** 2.94*** 18.90*** −27.22 44.83*** 15.09*** −1.95 −0.56 0.58

3030.47 −0.08 −132.34** −130.34 −0.90 0.57*** 3.53*** 63.36*** −293.27*** 81.75*** 26.75*** 5.86*** 204.29 0.41

0.58*** 5747.67** −0.09 −187.73*** 133.48 0.36 0.30*** 1.38** 52.09*** −131.34 66.22*** 26.80*** 4.42** −331.60 0.53

−45.61 −0.34** 11.27 −123.14 −1.83 0.13 5.33*** 44.59* −164.46* 74.67*** 6.76 0.05 374.33 0.31

0.85*** −11751.11** −0.32** 65.96 −107.94 0.79 −0.10 2.24** 32.73* −0.11 57.10*** 4.31 0.24 41.00 0.44

Note: *** denotes a significance level of 1%,** denotes a significance level of 5%,* denotes a significance level of 10 %.

insignificant in large and medium cities. Industry-oriented growth contributes significantly to urban land expansion in Chinese cities. Due to the weak urban competitiveness of small cities, the local governments lease the land resources at a low market price to attract investment from manufacturing firms, thereby increasing tax revenue and employment opportunities. Moreover, due to the industrial upgrading of large or medium cities, some polluting enterprises are forced to relocate to small cities, which can aggravate the environmental pollution of small cities while promoting economic growth. This phenomenon is especially prominent in small cities and results in inefficient industrial land-use. ED is negative and significant in large and medium cities whereas it is insignificant in small cities. In large and medium cities, the infilling and edge growth mostly occurs in street-towns that are close to the city center and constrained by geographic factors such as mountains and rivers (Yue et al., 2013), which largely mitigates urban fragmentation. In small cities, the dominant urban expansion type is that of leapfrog growth as indicated by economic and technological development zones (ETDZ) that appear in areas distant from urban centers. Therefore, fragmented urban growth is not beneficial for the land-use efficiency in large or medium cities. Thus, more strategies of spatial land-use planning should be directed at new urban development around urban fringes. MSI is negative and significant in large cities and insignificant in medium and small cities. Urban land expansion in large cities is more complicated and uncertain, which to some extent impedes the improvement of land-use efficiency. Although some new urban development follows land-use planning spatially, most urban land conversion occurs in urban fringes not targeted by plans for urban land development. Some large cities such as the capital city of Zhejiang province, Hangzhou, loosened development controls in their new master plans to accommodate projects that are inconsistent with earlier land-use plans (Yue et al., 2013). COHESION is positive and significant in large cities and insignificant in medium and small cities. The spatial connectivity of urban patches is important for large cities to improve their land-use efficiency. Due to top-down constraints and local interactions, large cities, especially megacities, demonstrate a more complex, dynamic, multidimensional configuration of urban sprawl that is a diffusion-coalescence process with a multinucleated urban pattern (Yu and Ng, 2007). The variation of COHESION detects the conversion from the diffusion of new urban center to coalescence toward a saturated urban landscape (Dietzel et al., 2005).

economies by efficient transport networks. However, the insignificant role of MSI and COHESION in inland regions indicates that physical urban shape complexity and patch connectedness are more important for intensive land use in developed areas than the less-developed areas. 4.3.3. The effect of city sizes According to the results in Table 5, the adjusted R-Squared value of SLM is much higher than that of OLS, implying the importance of considering the spatial effects of land-use efficiency in the model. The explanatory power of SLM decreases gradually from 58 % for big-city samples, to 53 % for medium-sized samples, and then to 44 % for small cities, revealing that the land-use efficiency of small cities is complicated and more sensitive to uncertain external factors. Thus, a spatial model is required to avoid biased estimated parameters. According to the value of ρ, positive spatial autocorrelation is evident in big cities, medium-sized cities and small cities. However, this spatial externality is the strongest in small cities. The crowding effect in small cities stimulates economic activities transferring to the periphery and finally drives some economic activities to break through the city boundary and then form a strong spatial dependence among geographically adjacent cities. Regarding to the effect of control variables, FOREIGN and POPDEN are positive and significant in big cities and medium-sized cities, indicating that globalization and urbanization can improve land-use efficiency greatly in big cities and medium-sized cities. EMP, RETAIL and INDU are positive and significant in all cities, illustrating the importance of employment and industrialization in increasing land-use efficiency. FISCAL is negative but insignificant in all cities. Although the land financial system is not beneficial to the intensive use of urban land in cities, its effect is not strong enough to reduce land-use efficiency. TRAFFIC is positive and significant in medium-sized cities. Improving transportation accessibility in medium-sized cities will facilitate the optimization of the structure of urban land-use and increase land-use efficiency. PD is positive and significant in large and medium cities whereas it is negative and significant in small cities. There are distinct differences in degree of urban sprawl among cities of different sizes in China. The previous studies have found that small cities have undergone greater sprawl than large cities during the last two decades (Gao et al., 2016). “Big city disease” such as environmental pollution and traffic congestion began to spread in small cities. Because of inefficient land regulation and weak industrial agglomeration, the urban landscape becomes more discrete and fragmented in small cities. Thus, more efforts should be targeted at small cities to stimulate compact urban development and efficient urban land-use. MPS is negative and significant only in small cities; however, it is 10

Land Use Policy 95 (2020) 104576

S. He, et al.

industries should be encouraged to provide technological support for environmental issues.

4.4. Policy implications 4.4.1. Strengthening spatial planning and spatial regulation for land-use The physical urban form is closely linked with land-use efficiency. Spatially aggregated urban form and polycentric urban spatial structure are conducive to efficient land-use. Therefore, the shift from the traditional regulation of urban land-use to the spatial regulation of urban land, nonconstructive land and ecological land becomes necessary in guiding efficient land-use and effective protection of important ecological space. Spatial planning and spatial regulation for land-use should be given top priority by urban planners and policy-makers. Designing a rational urban form will help to address some issues relating to rapid urban sprawl and thus promote compact urban growth and efficient land-use.

5. Conclusions China is in the process of transition and reformation, and it is fast becoming an example of the inevitable trend toward increasing urbanization. To detect the relationship between urbanization and land-use efficiency, this paper investigated the spatiotemporal dynamics of urban form metrics as well as land-use efficiency and then employed spatial regression models to discover the effect of urban form metrics on land-use efficiency at both the national level and city level. The following conclusions can be drawn. China’s accelerating urbanization has introduced a series of significant changes in urban morphology. Therein, two urban form metrics such as urban patch density and edge density showed dramatic changes from 2000 to 2015 and became more sensitive to the dynamics of urbanization. Moreover, China’s urban form metrics demonstrate significant regional differences during the period from 2000 to 2015. The eastern region displays an increase in MPS, indicating a concentrated and continuous urban pattern in coastal China. However, the central region, which shows high ED, shows a fragmented urban edge and a complicated development of its urban fringe. Although the average level of land-use efficiency improved from 2000–2015, the internal differences among cities are widening. Furthermore, land-use efficiency demonstrates significant regional differences. Although the eastern region shows the highest land-use efficiency in 2000 and 2015, the western region catches up and surpasses the eastern region in 2005 and 2010. The northeastern region displays a relative decline in land-use efficiency during the period from 2000 to 2015 as it endured the persistent influence of the past planned economy system. This paper adopts a spatial regression model and proves the significant impact of the spatial spillover effect of physical urban form on land-use efficiency. Patch density and patch cohesion are found to be positively associated with land-use efficiency whereas mean patch size and edge density representing urban fragmentation are negatively related to land-use efficiency. The mean shape index demonstrates insignificant impact on land-use efficiency. The impact of urban form on land-use efficiency is sensitive to regional heterogeneity and city sizes. The driving mechanisms of land-use efficiency are more complicated in small cities. It seems that globalization, fiscal decentralization and urbanization cannot effectively stimulate the land-use efficiency of small cities. Physical urban form with high patch density and large urban patch size is not conducive to enhancing land-use efficiency in small cities. However, large cities can effectively promote land-use efficiency by increasing urban patches and patch cohesion as well as reducing edge fragmentation and shape complexity. For medium cities, an urban form with high patch density and low edge density will improve land-use efficiency. The above-noted conclusions provide some significant insights into policy implications for China’s urban planning and land-use management. From a spatiotemporal perspective, spatial externalities effect is important to improve land-use efficiency. Moreover, this spatial spillover effect is the strongest in small cities. More strategies should be targeted at small cities to promote the efficient land-use by improving transportation systems and optimizing transportation networks among cities. The impact how urban form imposes influence on urban land-use efficiency is complicated in Chinese cities. The institutional context at the local level cannot be neglected. This study is limited in exploring the impact of local institutions such as planning policies and urbanization strategies. More efforts are required in this direction to make a comprehensive understanding about the mechanisms of urban land-use efficiency.

4.4.2. Enhancing geographical targeting Geographical unevenness is always one major issue facing the Chinese government. However, the impact of urban form on land use efficiency is spatially heterogeneous as aforementioned. Regarding to the eastern region, driven by continuing urbanization, how to formulate a spatially-aggregated urban form is significant. It is urgent for the state and local governments to break the administrative economy and promote regional cooperation among cities, thus stimulating efficient land use in the future. Regarding to the central region, urban expansion brings negative impacts such as the loss of agricultural land and vacant industrial land. As the main supplier of grain in China, it is urgent to strictly protect cultivated land and promote intensive use of industrial land, thus ensuring food security and rural sustainability in China. Regarding to the western and northeastern China, urban form metrics can stimulate spatial externalities of land use efficiency. The fragmented urban landscape with complicated urban patches is not conducive to the efficient land use. The urbanization’s threat to fragile natural environment and ecological diversity should be recognized and more efforts should be made to adjust the industrial structure and restore an agro-ecological environment conducive to the sustainable development. 4.4.3. Emphasizing the heterogeneous effect of urban sizes Urban form metrics demonstrate differing impact on land-use efficiency among cities with varying city sizes. For large cities, local governments should make use of spatial planning tools to improve the connectivity between urban patches and to reduce the complexity of the urban shape. For medium cities, the fragmented urban edge does not benefit land-use efficiency. More strategies of spatial land-use planning should be directed at new urban development around the urban fringe. Furthermore, the negative importance of patch density and mean patch size in small cities implies that small cities experience greater sprawl than other cities. Greater effort should be made in areas such as enhancing land-use regulations, assessing urban growth management and protecting the farmland and ecological land, especially in small cities, to develop compact urban patterns. 4.4.4. Maintaining the sustainable development strategy The rapid pace of urbanization and industrialization in China not only makes the urban landscape complicated but also brings fragmented natural landscape. Although China has achieved great success in economic development, it also encounters many challenging issues, such as land use, air quality, water conservation, energy resource, ecological conservation, etc. These environmental issues inflicted great damage on the economy and quality of life. In light of the worrying environmental situation and the weak environmental policies, China should take measures and actions to meet these challenges. The sustainable development strategy should be emphasized equally with economic development and more efficient environmental policies should be proposed by the central and local governments. Furthermore, economic restructuring should be promoted and environmental 11

Land Use Policy 95 (2020) 104576

S. He, et al.

CRediT authorship contribution statement

industrial land expansions in China between 1990 and 2010: a CLUD-based analysis of their trajectories, patterns, and drivers. Landsc. Urban Plan. 145, 21–33. Kuo, H.-F., Tsou, K.-W., 2015. Application of environmental change efficiency to the sustainability of urban development at the neighborhood level. Sustainability 7, 10479–10498. Li, Y., Liu, X., 2018. How did urban polycentricity and dispersion affect economic productivity? A case study of 306 Chinese cities. Landsc. Urban Plan. 173, 51–59. Li, T., Shilling, F., Thorne, J., Li, F., Schott, H., Boynton, R., Berry, A.M., 2010. Fragmentation of China’s landscape by roads and urban areas. Landsc. Ecol. 25, 839–853. Li, C., Li, J., Wu, J., 2013. Quantifying the speed, growth modes, and landscape pattern changes of urbanization: a hierarchical patch dynamics approach. Landsc. Ecol. 28, 1875–1888. Li, H., Wei, Y.D., Liao, F.H., Huang, Z., 2015. Administrative hierarchy and urban land expansion in transitional China. Appl. Geogr. 56, 177–186. Liu, Y., 2018. Introduction to land use and rural sustainability in China. Land Use Policy 74, 1–4. Liu, B.J., Wu, Y.-Y., 2011. Development zones in China: are STIPs a substitute for or a complement to ETDZs. Taipei Eco. Inq. 47, 97–145. Liu, Y., Fang, F., Li, Y., 2014. Key issues of land use in China and implications for policy making. Land Use Policy 40, 6–12. Liu, Y., Zhang, Z., Zhou, Y., 2018. Efficiency of construction land allocation in China: an econometric analysis of panel data. Land Use Policy 74, 261–272. Long, H., Zou, J., Pykett, J., Li, Y., 2011. Analysis of rural transformation development in China since the turn of the new millennium. Appl. Geogr. 31, 1094–1105. Lopez, E., Monzón, A., Ortega, E., Mancebo Quintana, S., 2009. Assessment of cross‐border spillover effects of national transport infrastructure plans: an accessibility approach. Transp. Rev. 29, 515–536. Lu, X., Kuang, B., Li, J., 2018. Regional difference decomposition and policy implications of China’s urban land use efficiency under the environmental restriction. Habitat Int. 77, 32–39. Luck, M., Wu, J., 2002. A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landsc. Ecol. 17, 327–339. Meng, Y., Zhang, F.-R., An, P.-L., Dong, M.-L., Wang, Z.-Y., Zhao, T., 2008. Industrial land-use efficiency and planning in Shunyi, Beijing. Landsc. Urban Plan. 85, 40–48. Næss, P., 2012. Urban form and travel behavior: experience from a Nordic context. J. Transp. Land Use 5, 21–45. Schumaker, N.H., 1996. Using landscape indices to predict habitat connectivity. Ecology 77, 1210–1225. Sun, Y., Zhao, S., 2018. Spatiotemporal dynamics of urban expansion in 13 cities across the Jing-Jin-Ji Urban Agglomeration from 1978 to 2015. Ecol. Indic. 87, 302–313. Tsai, Y.-H., 2005. Quantifying urban form: compactness versus’ Sprawl’. Urban Stud. 42, 141–161. UN-Habitat, 2016. World Cities Report 2016. Urbanization and Development: Emerging Futures. Voss, P.R., Long, D.D., Hammer, R.B., Friedman, S., 2006. County child poverty rates in the US: a spatial regression approach. Popul. Res. Policy Rev. 25, 369–391. Wei, Y.D., Ewing, R., 2018. Urban expansion, sprawl and inequality. Landsc. Urban Plan. 177, 259–265. Wei, Y.D., Fan, C.C., 2000. Regional inequality in China: a case study of Jiangsu province. Prof. Geogr. 52, 455–469. Wei, Y., Zhang, Z., 2012. Assessing the fragmentation of construction land in urban areas: An index method and case study in Shunde, China. Land Use Policy 29, 417–428. Wu, C., Wei, Y.D., Huang, X., Chen, B., 2017. Economic transition, spatial development and urban land use efficiency in the Yangtze River Delta, China. Habitat Int. 63, 67–78. Xie, H., Chen, Q., Lu, F., Wu, Q., Wang, W., 2018. Spatial-temporal disparities, saving potential and influential factors of industrial land use efficiency: a case study in urban agglomeration in the middle reaches of the Yangtze River. Land Use Policy 75, 518–529. Yu, X.J., Ng, C.N., 2007. Spatial and temporal dynamics of urban sprawl along two urban–rural transects: A case study of Guangzhou, China. Landsc. Urban Plan. 79, 96–109. Yu, W., Zhou, W., 2017. The spatiotemporal pattern of urban expansion in China: a comparison study of three urban megaregions. Remote Sens. (Basel) 9. Yue, W., Liu, Y., Fan, P., 2013. Measuring urban sprawl and its drivers in large Chinese cities: the case of Hangzhou. Land Use Policy 31, 358–370. Zeng, H., Wu, X.B., 2005. Utilities of edge-based metrics for studying landscape fragmentation. Comput. Environ. Urban Syst. 29, 159–178. Zhang, P., 2008. Revitalizing old industrial base of Northeast China: process, policy and challenge. Chin. Geogr. Sci. 18, 109–118. Zhang, T., Sun, B., Li, W., 2017. The economic performance of urban structure: from the perspective of Polycentricity and Monocentricity. Cities 68, 18–24.

Sanwei He: Writing - original draft. Shan Yu: Methodology, Data curation. Guangdong Li: Project administration, Conceptualization. Junfeng Zhang: Formal analysis. Acknowledgement This research is funded by the Major Program of the National Natural Science Foundation of China (41590840, 41590842), the National Natural Science Foundation of China (41601162), and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.landusepol.2020. 104576. References Aguilera, F., Valenzuela, L.M., Botequilha-Leitao, A., 2011. Landscape metrics in the analysis of urban land use patterns: a case study in a Spanish metropolitan area. Landsc. Urban Plan. 99, 226–238. Azfar, O., Kahkonen, S., Lanyi, A., Meagher, P., Rutherford, D., 1999. Decentralization, Governance and Public Services: the Impact of Institutional Arrangements. Centre for Institutional Reform and the Informal Sector, pp. 1–37. Berrigan, D., Troiano, R.P., 2002. The association between urban form and physical activity in US adults. Am. J. Prev. Med. 23, 74–79. Cao, X., Liu, Y., Li, T., Liao, W., 2019. Analysis of spatial pattern evolution and influencing factors of regional land use efficiency in China based on ESDA-GWR. Sci. Rep. 9, 520. Cervero, R., Kockelman, K., 1997. Travel demand and the 3Ds: density, diversity, and design. Transp. Res. D Transp. Environ. 2, 199–219. Chen, M., Liu, W., Lu, D., 2016a. Challenges and the way forward in China’s new-type urbanization. Land Use Policy 55, 334–339. Chen, Y., Chen, Z., Xu, G., Tian, Z., 2016b. Built-up land efficiency in urban China: insights from the general land use plan (2006-2020). Habitat Int. 51, 31–38. Chen, W., Shen, Y., Wang, Y., Wu, Q., 2018. The effect of industrial relocation on industrial land use efficiency in China: a spatial econometrics approach. J. Clean. Prod. 205, 525–535. Dietzel, C., Herold, M., Hemphill, J.J., Clarke, K.C., 2005. Spatio‐temporal dynamics in California’s central Valley: empirical links to urban theory. Int. J. Geogr. Inf. Sci. 19, 175–195. Ding, C., Zhao, X., 2014. Land market, land development and urban spatial structure in Beijing. Land Use Policy 40, 83–90. Ewing, R.H., 2008. Characteristics, Causes, and Effects of Sprawl: a Literature Review, Urban Ecology. Springer, pp. 519–535. Fang, C., Li, G., Wang, S., 2016. Changing and differentiated urban landscape in China: spatiotemporal patterns and driving forces. Environ. Sci. Technol. 50, 2217–2227. Gao, B., Huang, Q., He, C., Sun, Z., Zhang, D., 2016. How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data. Landsc. Urban Plan. 148, 89–98. Hathway, E., Sharples, S., 2012. The interaction of rivers and urban form in mitigating the Urban Heat Island effect: a UK case study. Build. Environ. 58, 14–22. He, Q., He, W., Song, Y., Wu, J., Yin, C., Mou, Y., 2018. The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’. Land Use Policy 78, 726–738. Huang, Z., He, C., Zhu, S., 2017. Do China’s economic development zones improve land use efficiency? The effects of selection, factor accumulation and agglomeration. Landsc. Urban Plan. 162, 145–156. Kii, M., Doi, K., 2005. Multiagent land-use and transport model for the policy evaluation of a compact city. Environ. Plann. B Plann. Des. 32, 485–504. Kimura, F., Ando, M., 2003. Fragmentation and agglomeration matter: japanese multinationals in Latin America and East Asia. North Am. J. Econ. Financ. 14, 287–317. Kuang, W., Liu, J., Dong, J., Chi, W., Zhang, C., 2016. The rapid and massive urban and

12