Temporal–spatial characteristics of urban land use efficiency of China’s 35mega cities based on DEA: Decomposing technology and scale efficiency

Land Use Policy 88 (2019) 104083

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Temporal–spatial characteristics of urban land use efficiency of China’s 35mega cities based on DEA: Decomposing technology and scale efficiency

T

⁎

Xinhua Zhua, Yan Lid, Peifeng Zhanga, Yigang Weib,c, , Xuyang Zhengb, Lingling Xiee a

School of Public Administration, Hohai University, Nanjing, China School of Economics and Management, Beihang University, Beijing, China c Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing, 100191, China d Business School, Shandong University at Weihai, Weihai, 264209, China e Guangxi University of Finance and Economics, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Urban land use Efficiency level DEA model Mega cities China

This study aims to measure the comprehensive, pure technical, and scale efficiencies of urban land use of China’s 35 mega cities from 2008 to 2015 and reveal their temporal and spatial characteristics of urban land use efficiency (ULUE) using a super efficiency slack-based measure (SBM) model. Results show that 1) the ULUE level of China’s 35 mega cities is relatively low, and the pure technical efficiency is a major influencing factor of the comprehensive efficiency. 2) From a temporal perspective, the comprehensive, pure technical, and scale efficiencies of the 35 mega cities all show a trend of slow growth; the average annual growth rates of these efficiencies are 1.07%, 0.24%, and 1.16%. 3) From a spatial perspective, the ULUE levels are quite different and show certain regional patterns. 4) The distribution of scale efficiency of urban land use in China is becoming increasingly dispersed from 2008 to 2015, whereas the distribution of pure technical efficiency is becoming increasingly concentrated in the same period. Estimations on pure technical and scale efficiencies are important to investigate the deep-rooted causes of land use inefficiency for filling an important knowledge gap in ULUE studies.

1. Introduction Urbanization, which is characterized by unplanned or uneven expansion of land and increase in population density, has become an important feature of urban development worldwide (Liu et al., 2018a; Liu, 2018; Shen et al., 2012). Researchers have determined the aftermath of land consumption and its low efficiency through decades of study (Bagheri and Tousi, 2018; Hasse and Lathrop, 2003; Liu et al., 2018b). Since China’s reform and opening up, urbanization has rapidly grown, and urban sprawl in mega cities has become a prominent problem (Zhao, 2010; Zhu et al., 2019a). China’s National Bureau of Statistics reported that the country’s urbanization rate increased from 17.92% to 58.52% in 1978–2017 and had an average annual growth rate of more than 3%. Between 1981 and 2016, the built-up area of Chinese cities increased from 7438 km2 to 54,331.5 km2 and had an average annual growth rate of 5.68%. The irrationality of urbanization in China is that the expansion rate of urban space is evidently higher than the growth rate of urban population (Fig. 1).

Urban sprawl and compactness are two forms of urban development. Each urban use form may associate key sustainability challenges to the urban economy, the society, and the environment. For example, urban sprawl will result in negative externalities, such as long commuting time and high energy consumption, which increase social and environmental costs (Hasse and Lathrop, 2003; Zhao, 2011; Wei et al., 2015; Liu et al., 2018c; Jia et al., 2018); urban compactness will also bring urban issues, including crucial questions of the environmental benefits accruing to compaction and its acceptability to local communities (Cervero and Duncan, 2003 Chen et al., 2016b; Wei et al., 2016). Therefore, scientific and reliable urban land use efficiency (ULUE) evaluation can serve as an important decision reference for promoting urban layout optimization and sustainable development. Although urban compactness is a countermeasure to control urban sprawl, it also leads to various “urban disease” issues, such as traffic congestion and increased cost of living caused by high density urban form. Different doctrines of ULUE research have investigated urban land use and growth in different aspects, including spatial patterns, dynamic

⁎ Corresponding author at: Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing, 100191, China. E-mail addresses: [email protected] (Y. Li), [email protected] (Y. Wei), [email protected] (L. Xie).

https://doi.org/10.1016/j.landusepol.2019.104083 Received 12 February 2019; Received in revised form 18 June 2019; Accepted 1 July 2019 0264-8377/ © 2019 Published by Elsevier Ltd.

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directions of technology and scale effectively, whereas comprehensive efficiency cannot be utilized to determine the direction of driving factors of ULUE accurately. Therefore, decomposing the comprehensive efficiency of urban land use into pure technical and scale efficiencies will provide in-depth explanations on the driving forces of ULUE from technical and scale effect perspectives to fill an important knowledge gap in ULUE studies. This research aims to estimate the comprehensive, pure technical, and scale efficiencies of urban land use and analyzes the spatial and temporal differences of ULUE. This research involves three specific tasks. First, a reasonable evaluation indicator system of ULUE is constructed on the basis of the definition of ULUE. Second, the comprehensive efficiency of urban land use in China’s 35 mega cities is calculated and decomposed into pure technical and scale efficiencies to reveal the micro-causes of ULUE changes by using DEA method. Third, this study investigates the spatial and temporal differences of ULUE, summarizes its laws, and provides policy suggestions for improving ULUE and promoting urban sustainable development in China. The innovation of this research lies in two aspects. First, this study constructs a complete and reliable model for the evaluation of ULUE with appropriate consideration of undesirable outputs (e.g., pollutions) of urban land use by using DEA analysis. Second, this study uses a super efficiency slack-based measure (SBM) model (DEA) to measure the comprehensive efficiency of urban land use and decompose it into pure technical and scale efficiencies in terms of the research perspective. The advantage is that pure technical and scale efficiencies can be used to analyze the deep-rooted driving forces of ULUE effectively. Specifically, the pure technical efficiency reflects the efficiency produced by the city in the existing space by improving the level of economic development, infrastructure construction, and urban planning (i.e., the efficiency of intensive urban development), whereas the scale efficiency reflects the efficiency produced by the new urban space (i.e., the efficiency of extensive urban development). The rest of the paper is organized as follows. The second section introduces the data and research methods. The third section discusses the estimation results. The last section summarizes the main findings and provides the policy recommendations on improving the ULUE.

Fig. 1. Changing trends of China’s urban built-up area and urban population in the period of 2006–2016.

mechanism, efficiency, and administrative policy. The research priority has been evolving from analytically summarizing urban growth patterns and changes from an ecological point of view to exploring the decision-making process and drivers of urban land development (Zitti et al., 2015) and from emphasizing market-orientated operation to focusing on smart management (Schiavone et al., 2019; Yeh and Fulong, 2010). With the negative externality of urban land use and the needs for public goods and services, an increasing number of state and city governments have implemented growth control or development management strategies to directly or indirectly control urban land growth and changes through collective intervention policies of urban planning (Kovács et al., 2019; Yeh and Fulong, 2010). In recent years, studies on ULUE have mostly focused on urban land use assessment (Xie and Wang, 2015a,b; Du et al., 2016), assessment methods (Kaur and Garg, 2019; Barbosa et al., 2015), affecting factors (Caputo et al., 2019; Yue et al., 2013; Paulsen, 2014; Zitti et al., 2015; Barbosa et al., 2015; Osman et al., 2016; Wu et al., 2017; Liu et al., 2018a), and improvement strategies of ULUE (Zitti et al., 2015; Wu et al., 2017). First, the indicators of assessment studies are gradually shifting from a single-indicator measurement system of urban land use to a multiple-indicator measurement system by integrating economic, social, and environmental factors (Kaur and Garg, 2019; Chen et al., 2016a; Tu et al., 2014; Xie and Wang, 2015a,b). This shift reveals the concept that regards the ULUE as a complex system composed of multiple factors of nature, economy, and society, which come and interact together. As a result, multiple representing indicators should be implemented in an optimal assessment study for ULUE. Moreover, deducting the internal unexpected outputs helps precisely measure the ULUE in consideration of the internal unexpected outputs, such as pollution, generated in the process of urban land use (Huang et al., 2019; Zhu et al., 2019b). Second, the research cases and scales are diversified and range from small-scale individual cities (Halleux et al., 2012; Tu et al., 2014) to medium-scale typical urban agglomerations (Wu et al., 2017). Third, the ULUE assessment methods have been evolving from descriptive- to qualitative-based models, such as multidimensional measurement (Liu et al., 2019a), regression analysis model (Ye et al., 2018), data envelopment analysis (Liu et al., 2019b), and panel data model (Du et al., 2016). Given the remarkable progress, the existing literature is limited in the following aspects. First, the mainstream of ULUE studies mainly focuses on a limited number of desirable outputs of land use (e.g., economic benefits and social well-beings) but neglects undesirable outputs (e.g., pollutions and industrial discharges during land use). The biased selection of measurable indicators may lead to containments of estimation results. Second, considerable literature has investigated ULUE and its measurement, but limited research has decomposed it into pure technical and scale efficiencies. Pure technical and scale efficiencies can be used to analyze the driving forces of ULUE from the two

2. Data and research method 2.1. Indicator On the basis of Cobb–Douglas production function, this study evaluates ULUE by using the relative value of input–output ratio. Indicators, which are summarized from literature reviews, are categorized into input and output groups. Therefore, through adhering to the principles of representativeness and availability of indicators and combining existing findings, this study selects indicators from the input and output groups to establish a DEA indicator system for evaluating ULUE. Descriptions on the rationales of indicator selections are shown in Table 1. 2.2. Data source This study selects China’s 35 mega cities (Fig. 2). The 35 large and medium-sized cities are selected as research objects mainly due to two considerations. First, these cities are of great socioeconomic importance. China’s cities are generally divided into four administrative levels: municipalities directly under the central government, provincial capitals, specific plan oriented cities, and general cities. The 35 cities belong to the first three categories. They have strong political, economic, and social influences on their region and are the core cities of the urban agglomeration. Second, they have a good representativeness of the rapid and widespread urban sprawl in China. In recent years, the 35 cities have shown a rapid trend of land expansion. From 2008–2015, the proportion of built-up area expansion, which is defined as the ratio 2

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Table 1 Indicators of input and output.

Inputs

Outputs

Composition of indicator

Description

References

Land Input Capital Input Workforce Input Energy Input Social Output

Area of Construction Land Gross Investment in Fixed Assets Number of Unit Job holders Total Electricity and Water Consumption Newly-Increased Urban Built-Up Area Newly-Constructed Housing Area Proportion of Urban Population Incremental Value of the Second and Third Industry Financial Income Incremental Value of GDP Emission Amount of Waste Water, Waste Gas, SO2, and CO2.

Lau et al., 2005; Tu et al., 2014; Chen et al., 2016a; Xie and Wang, 2015a,b; Lewis and Brabec, 2005; Barbosa et al., 2015; Pan et al., 2017; Zhu, 2018

Economic Output

Environmental Output

Navamuel et al., 2018;Wei et al., 2018; Li et al., 2018;Zhang et al., 2018 Yeh and Fulong, 2010; Wei et al., 2014; Chen et al., 2016a; Wei et al., 2019a; Tu et al., 2014; Halleux et al., 2012; Makowski et al., 2000; Barbosa et al., 2015; Liu et al., 2013

Hanif, 2018; Liu et al., 2015; Peng et al., 2018; Liang et al., 2019

determine the relative efficiency of decision-making units (DMUs) (Chen and Guan, 2012). DEA method is used to evaluate the relative effectiveness or benefit depending on the multiple input and output indicators (Wei et al., 2019b). Given that the parameters in DEA need not be estimated in advance, this method has advantages in avoiding the influence of subjective factors, simplifying operation process, and reducing errors. The traditional DEA models, such as Charnes, Cooper, and Rhodes and Banker, Charnes, and Cooper, ignore the slack variables of input and output. Furthermore, the efficiency values of traditional DEA models are between 0 and 1, which belong to the truncated data (Yang et al., 2015). When the DMUs are within the same effective range of DEA, their relative fit and unfit quality cannot be compared. Tone (2002) proposed a super efficiency SBM model, which adds the slack variables to the objective function. Contrary to the traditional DEA model, the super efficiency SBM model not only can deal with the

of the growth area of built-up area between 2008 and 2015 to that of built-up area in 2008, is more than 9% (Table 2). Among these cities, the proportion of built-up area expansion of Qingdao is the highest at 12%, whereas that of Beijing is the lowest at only 9%. Therefore, these cities can be used as representative cities of ULUE in China. Specifically, the data used by this study are obtained from the China City Statistical Yearbook (2008–2015), China Statistical Yearbook (2008–2015), and Statistical Yearbook of Land and Resources (2008–2015). 2.3. Research method 2.3.1. Super efficiency SBM model DEA method was first put forward by Charnes et al. (1979). They pointed out that DEA is a mathematical programming method used to

Fig. 2. The geographic locations of investigated cases. 3

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Table 2 Change of built-up area between.2008–2015. Data source: China Statistical Yearbook of Land and Resources (2008–2015). Cities

Change of built-up area (km2)

Change rate

Cities

Change of built-up area (km2)

Change rate

Beijing Changchun Changsha Chengdu Dalian Fuzhou Guangzhou Guiyang Harbin Haikou Hangzhou Hefei Hohehot Jinan Kunming Lanzhou Nanchang Nanjing

111.8 221.5 131.1 207 137.5 77.7 393.3 159.1 66.9 61 161.6 213.2 49.8 77.7 171.5 136.3 127.3 177.8

9% 78% 72% 51% 53% 43% 47% 114% 20% 67% 47% 95% 24% 25% 69% 81% 71% 31%

Nanning Ningbo Qingdao Shanghai Shenzhen Shenyang Shijiazhuang Taiyuan Tianjin Urumchi Wuhan Xian Xining Xiamen Yinchuan Zhengzhou Chongqing

108.3 100.5 315.7 113.1 136 118 91.4 142 313.9 194 182.3 232.1 12.5 120.1 19.2 116.6 662

60% 45% 126% 13% 18% 34% 49% 72% 55% 82% 47% 87% 17% 61% 13% 36% 99%

1+

1− = 1+

m 1 ∑ m t=1

s 1 (∑r1= 1 s1 + s2

s.t.

srg g yro

( )

TC=

io

srb

))

− ⎧ x o = Xλ + S ⎪ yog = Y gλ − s g

⎨ yog = Y bλ + s b ⎪ − g b ⎩ λ, s , s , s ≥ 0

srb b yro

))

(3)

(4)

ESt+ 1 (x t + 1, y t + 1) ESt (x t , y t )

ESt (x t + 1, t+1 t+1 ES (x ,

y t + 1) × ESt (x t , y t ) y t + 1) × ESt+ 1 (x t , y t )

(5)

(6)

3.1. Comprehensive efficiency of urban land use Comprehensive efficiency uses the input–output ratio to reflect the ULUE in general. Table 3 shows the comprehensive efficiency of China’s 35 mega cities from 2008 to 2015. Fig. 3 shows the radar chart of comprehensive efficiency in 2008, 2011, and 2015. 1) The overall level is still low. Rural decline (Yu, 2014; Liu and Li, 2017) and urban-rural dual land system (Liu et al., 2014) constitute the biggest challenges to utilization of urban land uses in China. On the one hand, alongside the process of rural decline, urban land use in China experienced rapid and widespread expansions, which leads to the key issue of inefficient urban land use. On the other hand, under the institutional arrangements of urban-rural dual land system, the free exchange and flow of urban land and rural land have still been greatly restricted in the unified land market. This institutional obstacle leads to the idleness of land and housing in suburban areas of cities, which undermines the efficiency of urban land use.2) From the view of time, the ULUE levels of the 35 mega cities maintain a stable state in general, slightly increases, and has an average annual growth rate of only 1.07%. Between 2008 and 2015, the average growth rates of urban area and disposable income in

m 1 x¯ ∑ ( ) m t = 1 xio 1 s y ∑r = 1 ( ¯rg ) s y

≥ Xλ ≤ Yλ ≥ x 0 , y¯ ≤ y0 ¯ 0 ≥ 0, y ≥

(

This study calculates the ULUE of China’s 35 mega cities from 2008 to 2015 and the average efficiency of each city on the basis of the above-mentioned methods and indicators, the DEA model, and the MaxDEA software.

(1)

ro

⎧ x¯ ⎪ y¯ s.t. ⎨ x¯ ⎪λ ⎩

s2 r=1

3. Results and discussion

where s−, s g , and sb refer to the slack variables of input, desirable output, and undesirable output, respectively. λ is the weight vector. When ρ* = 1, s− = 0 , s g = 0 , sb = 0 . Thus, the DMU is effective. The super efficiency SBM model is expressed as follows (Tone, 2002).

minρ* =

( )+∑

In Formulas (4–6), MI refers to the total factor productivity change index, that is, comprehensive efficiency. EC represents efficiency improvement index, that is, scale efficiency. TC is the technological progress index, that is, pure technological efficiency. (x t , y t ) and (x t + 1, y t + 1) refer to the input–output vectors for the t and t + 1 periods, respectively. ESt (x t , y t ) and ESt (x t + 1, y t + 1) represent the efficiency values of the t and t + 1periods and are obtained by the super efficiency SBM model and by setting the t period as reference sets. ESt+ 1 (x t , y t ) and ESt+ 1 (x t + 1, y t + 1) represent the efficiency values of the t and t + 1 periods and are obtained by super efficiency SBM model and by setting the t + 1 period as reference sets.

s−

b yro

io

MI= EC× TC

( xi ) s

srg g yro

2.3.2. Malmquist–Luenberger model When the evaluated DMU data are panel data with multiple observation time points, the method of Malmquist total factor productivity index can measure the changes in productivity and technological and scale efficiencies (Cheng, 2014; Li et al., 2017b). Malmquist–Luenberger model is the combination of super efficiency SBM model and Malmquist model, which is originally proposed by Chung et al. (1997). The formula of Malmquist–Luenberger model is shown as follows:

EC=

+ ∑r2= 1 (

s 1 (∑r1= 1 s1 + s2

x¯

(x )

⎧ x¯ ≥ Xλ ⎪ y¯g ≥ Y gλ s.t. ¯b ⎨ y ≥ Y bλ ⎪ x¯ ≥ x 0 , y¯g ≤ y0 , y¯b ≥ y0b , λ > 0 ⎩

undesirable outputs but also can compare the effective DMUs. In this research, the ULUE is evaluated using the super efficiency SBM model with undesirable outputs. This study supposes n DMUs with m input indicators, s1 desirable output indicators, and s2 undesirable output indicators. The inputs, desirable outputs, and undesirable outputs are expressed as x∈ Rm , y g ∈ Rs1 , and y b ∈ Rs2 . The input vector is X= (xij) ∈ Rm×n. The desirable output vector is Y g = (yrj) ∈ Rs1×n . The undesirable output vector is Y b = (yrj) ∈ Rs2 ×n . The SBM model with undesirable outputs is expressed as follows (Apergis et al., 2015; Tone, 2001):

minρ*

m 1 ∑ m t=1

minα * =

(2)

In accordance with Eqs. (1 and 2), the super efficiency SBM model with undesirable outputs is expressed as follows (Li et al., 2013).

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cities have a comprehensive efficiency of less than 1. In 2015, only 4 cities have a comprehensive efficiency of less than 1. Thus, the distribution of urban land use comprehensive efficiency tends to be reasonable. 3) Regionally speaking, from 2008 to 2015, the comprehensive efficiency of urban land use in the eastern region is the highest but with high fluctuation, followed by that in the western region, and that in the central region as the lowest. This finding shows that the comprehensive efficiency of urban land use is incompletely proportional to the level of economic development. China should pay close attention to the complex relationship between ULUE and different driving factors. Temporally, the comprehensive efficiency of urban land use across the country is quite different. Fig. 4 shows the average comprehensive efficiency of China’s 35 mega cities from 2008 to 2015. The comprehensive efficiency of the 35 mega cities is compared by dividing it into four sections according to its distribution. In 2008–2015, the average comprehensive efficiency of the 35 mega cities is distributed in four intervals: 0.9–1.0, 1.0–1.1, 1.1–1.2, and 1.2–1.3. The comprehensive efficiency of most cities is distributed in two intervals: 1.0–1.1 and 1.1–1.2. Moreover, the overall efficiency is supposed to be improved. The research also reveals that 1) the comprehensive efficiency of Yinchuan, Xi'an, Urumqi, Shanghai, Kunming, and Haikou ranges from 1.2 to 1.3, which is the highest among those in the 35 mega cities. Among the six cities, Shanghai is located in the eastern region of China. The reason for its high comprehensive efficiency is that Shanghai has made committed efforts to control the rapid expansion of urban scale in recent years, which is mainly driven by rapid economic growth (Li et al., 2017a). Several policies are enacted to effectively control urban sprawl, including specific urban functions, controls on the influx of migration, and planning-based land indicator management. These measures to some extent alleviate the tendency of land sprawl that is driven by economic growth. The five other cities are located in the central and western regions. The reason for their high comprehensive efficiency is that their economic development speeds are relatively slow, the demand for land is relatively weak, and the expansion of urban scale is generally comparable to their economic development speeds. The findings show that the land use governance should formulate scientific land use planning to ensure that urban land expansion synchronizes with economic development for promoting urban sustainable development. 2) The comprehensive efficiency of Tianjin, Shenzhen, Ningbo, Jinan, Hohhot, Hangzhou, Guiyang, Guangzhou, Dalian, and Beijing is between 1.1 and 1.2, which still needs to be improved. 3) The comprehensive efficiency of Chongqing, Zhengzhou, Xiamen, Wuhan, Taiyuan, Shijiazhuang, Shenyang, Qingdao, Nanjing, Lanzhou, Harbin, Fuzhou, Chengdu, Changsha, and Changchun range between 1.0 and 1.1, which is low. 4) The comprehensive efficiency of Xining, Nanning, Nanchang, and Hefei ranges from 0.9 to 1.0, which is the lowest among those in the 35 mega cities. Among the four cities, the comprehensive efficiency of urban land use in Nanning is the lowest, which indicates the most serious disorder and inefficient urban land use. The poor performance of urban land use in Nanning is due to two reasons. One is the rapid development of its industrial structure. In 2008 and 2015, expansions of the industrial sector maintain a rapid speed and have an average annual growth rate of 22.47%. Compared with the tertiary sector, the industrial sector consumes highly larger amount of land and has low ULUE. The other is the “siphon effect.” Core, central, and large cities are usually associated with good infrastructure, sound public services, rich employment opportunities, and great space for growth. Therefore, the “siphon effect” refers to the capability of cities to attract the resource of surrounding cities, small and medium-sized cities, and small towns (Zhao et al., 2017). Nanning City is nearby Guangdong, which is the most economically developed province in China. Given the “siphon effect” between cities, a large number of talents and capitals are attracted by Guangdong Province, which decreases the human resources and funds in Nanning City as measured by unit area of land.

Table 3 2008–2015 comprehensive efficiency of 35 mega cities in China.

Beijing Changchun Changsha Chengdu Dalian Fuzhou Guangzhou Guiyang Harbin Haikou Hangzhou Hefei Hohehot Jinan Kunming Lanzhou Nanchang Nanjing Nanning Ningbo Qingdao Shanghai Shenzhen Shenyang Shijiazhuang Taiyuan Tianjin Urumchi Wuhan Xian Xining Xiamen Yinchuan Zhengzhou Chongqing

2008

2009

2010

2011

2012

2013

2014

2015

1.037 1.060 1.352 1.006 1.037 1.582 1.059 2.026 0.961 0.745 0.893 1.036 1.383 1.219 0.877 0.964 1.128 1.060 0.820 0.946 1.115 0.947 1.011 1.091 1.451 1.022 1.050 1.057 1.084 0.972 1.043 1.428 1.229 1.151 0.981

0.766 0.840 0.997 0.557 0.939 0.442 0.822 0.798 0.908 0.753 0.816 0.976 0.971 1.047 1.084 0.884 0.627 0.918 0.693 0.858 0.711 2.822 0.805 1.067 0.980 1.115 0.855 1.313 0.952 1.548 1.122 0.844 1.046 0.769 0.714

2.437 1.528 1.158 1.484 2.470 1.205 2.040 1.374 1.026 0.977 2.368 1.026 0.990 1.717 1.545 1.202 1.249 1.384 0.934 2.785 2.089 0.708 1.838 1.563 1.690 1.535 2.892 1.034 1.328 1.723 1.590 1.043 2.379 1.503 2.042

0.440 0.875 0.609 0.791 0.459 0.865 0.641 0.412 1.242 0.831 0.467 0.508 0.657 0.638 0.720 0.627 0.781 0.615 0.689 0.411 0.530 0.457 0.659 0.629 0.840 0.663 0.348 2.837 0.648 0.612 0.517 0.601 0.553 0.662 0.510

1.412 1.066 1.304 1.156 1.134 1.092 1.124 1.353 1.059 1.566 1.037 0.924 1.763 1.071 1.467 1.230 1.068 1.098 0.990 1.018 1.017 1.070 1.432 1.147 0.853 1.070 1.071 0.219 1.156 1.184 0.798 0.874 0.990 0.989 0.919

0.978 1.062 0.838 0.730 0.850 0.998 0.953 0.731 0.946 0.665 0.895 0.909 0.850 1.045 1.107 0.986 0.930 0.786 0.822 0.949 1.241 1.030 0.716 0.722 0.931 0.874 0.975 0.995 1.124 0.687 1.045 1.038 0.922 0.950 0.927

0.981 0.981 1.230 1.788 0.753 1.034 1.041 1.478 0.964 3.321 1.055 0.929 0.853 1.044 1.631 1.475 1.097 1.024 1.039 0.970 0.783 1.179 1.352 0.932 0.920 1.112 0.894 1.141 0.942 1.264 0.847 0.939 1.866 0.954 0.956

1.315 1.043 1.077 1.059 1.437 1.026 1.416 1.018 1.221 1.072 1.381 1.196 1.399 1.530 1.335 1.104 1.069 1.398 1.300 1.244 1.227 1.447 1.119 0.957 0.943 1.192 1.394 1.150 1.215 1.638 0.967 1.356 0.630 1.218 1.192

Fig. 3. Radar chart of comprehensive efficiency of 35 mega cities in 2008, 2011 and 2015.

China are 2.07% and 8.89%, respectively. By contrast, the growth of ULUE is very slow. The average comprehensive efficiencies of urban land use in 2008 and 2015 are 1.109 and 1.208, which reach 69% and 95% of the optimal level (the maximum of ULUE). In 2008, only one city has a comprehensive efficiency value that is greater than 2, which accounts for 2.9% of the total number of the 35 mega cities. By 2015, the comprehensive efficiency of all cities is less than 2. In 2008, 10 5

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Fig. 4. Average comprehensive efficiency of China’s 35 mega cities in the period of 2008–2015.

The improvement in ULUE is mainly due to the policy reforms and consummation of China’s land market. Since 2000, the Chinese government has been committed to building a unified land market between urban and rural areas. The objective of the land market reform is to promote the free flow of land resources and capitals between urban and rural areas. Market players are expected to make rational and efficient use of land by eliminating institutional obstacles (Cheng et al., 2019). The perfection of land market is an important determining factor of land use technology level. This institutional reform has played an apparent role in promoting the pure technical efficiency of cities with relatively backward land use technology levels. Therefore, the number of cities with a pure technical efficiency of less than 1 shows a significant decrease. From 2008–2015, the pure technical efficiency of urban land use in the eastern region is highest, followed by that of western region, and that in the central region as the lowest. These findings can be explained by the different economic development patterns in different regions in China. Cities in the central region mainly rely on the expansion of urban scale to promote urban development, and this situation leads to a low level of pure technical efficiency of land use; by contrast, cities in the western region drive its economic growth mainly through undertaking industrial transfer of eastern cities, and this strategy improves the level of pure technical efficiency accompanying the inflow of high-end technology (Chen et al., 2018). Spatially, the pure technical efficiency of urban land use of these

3.2. Pure technical efficiency of urban land use Pure technical efficiency reflects the technical level of a DMU and can be measured from input and output perspectives. Pure technical efficiency is measured by the degree of output maximization under the given input and is measured by the degree of input minimization under the given output (Farrell, 1957). The pure technical efficiency of urban land use is measured by the efficiency of “intensive” urban land use. Table 4 shows the pure technical efficiency of China’s 35 mega cities from 2008 to 2015. Fig. 5 illustrates the radar chart of pure technical efficiency in 2008, 2011, and 2015. Temporally, the pure technical efficiency of the 35 mega cities in the period of 2008–2015 is at a low level on the whole. This efficiency has a relatively slow growth and has an average annual increase of 0.24%, which is considerably lower than those of the comprehensive and scale efficiencies. In general, these mega cities have not laid full emphasis to the development and utilization of urban stock space. The inefficiency of urban land use is due to highly considering the development of urban incremental space while ignoring the integration and optimization of urban stock space. The average values of pure technical efficiency of urban land use in 2008 and 2015 are 0.968 and 0.987, respectively. Meanwhile, the number of cities with a pure technical efficiency of less than 1 drops from 22 in 2008 to 14 in 2015. This situation indicates that the pure technical efficiency of urban land use is widespread and consistently improved.

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Kunming, and Haikou ranges from 1.15 to 1.25, which is the highest among those in the 35 mega cities. Among the three cities, the pure technical efficiency of urban land use in Kunming is the highest. The three cities are at a relatively low level of development. No large-scale “extension” sprawls in 2008–2015, and the three cities allocate financial capital and material resources into upgrading urban infrastructure. The Statistical Yearbook of Chinese Cities indicated that the total investment in fixed assets in Yinchuan, Kunming, and Haikou increased rapidly from 2008 to 2015. The annual average growth rates of the total investment in fixed assets in Yinchuan, Kunming, and Haikou reached 19.70%, 16.19%, and 21.45%, respectively. The investment in urban infrastructure construction increased their pure technical efficiency level. 2) The pure technical efficiency of urban land use in Xining, Xi'an, Tianjin, Shijiazhuang, Shanghai, Jinan, Hangzhou, Fuzhou, Dalian, and Changchun ranges between 1.05 and 1.15, which still needs to be improved. 3) The pure technical efficiency of urban land use in Chongqing, Zhengzhou, Xiamen, Wuhan, Urumqi, Taiyuan, Shenyang, Shenzhen, Qingdao, Ningbo, Nanjing, Lanzhou, Hohhot, Harbin, Guiyang, Guangzhou, Chengdu, Changsha, Beijing, and Nanchang ranges between 0.95 and 1.05, which is low. 4) The pure technical efficiency of urban land use in Nanning and Hefei ranges between 0.85 and 0.95, which is the lowest among those in the 35 mega cities. Between the two cities, the urban land use in Nanning has the lowest technical efficiency. Hefei and Nanning have certain similarity in geographical position. They are located in Anhui Province and Guangxi Zhuang Autonomous Region and bordered by Jiangsu and Guangdong Provinces, which are the two most economically developed provincial administrative regions in China. Given the strong “siphon effect” of Jiangsu and Guangdong Provinces (Zhao et al., 2017), the population and talents of Hefei and Nanning have been flowing out to the neighboring provinces. Meanwhile, the state’s financial resources are highly inclined to Jiangsu and Guangdong Provinces, and this situation has nearly deteriorated the development of Hefei and Nanning in recent years. The two cities also have limited capability in improving their pure technical efficiency.

Table 4 2008–2015 pure technical efficiency of 35 mega cities in China.

Beijing Changchun Changsha Chengdu Dalian Fuzhou Guangzhou Guiyang Harbin Haikou Hangzhou Hefei Hohehot Jinan Kunming Lanzhou Nanchang Nanjing Nanning Ningbo Qingdao Shanghai Shenzhen Shenyang Shijiazhuang Taiyuan Tianjin Urumchi Wuhan Xian Xining Xiamen Yinchuan Zhengzhou Chongqing

2008

2009

2010

2011

2012

2013

2014

2015

1.013 0.995 0.971 0.907 0.919 1.007 1.122 1.021 0.802 0.901 0.972 1.004 1.199 0.954 0.725 0.760 0.933 0.945 0.950 0.656 0.987 0.888 0.993 0.820 1.385 0.854 1.098 1.045 0.975 0.867 1.023 1.065 1.063 1.103 0.976

0.966 0.855 0.988 0.663 0.907 0.998 1.000 1.046 0.861 0.943 0.579 0.983 0.918 1.110 1.264 1.101 0.667 0.915 0.605 1.531 0.941 1.913 0.980 1.047 0.860 1.053 0.801 1.050 0.955 1.503 0.920 0.971 1.817 0.641 0.979

1.063 1.140 0.945 1.234 1.700 0.539 1.041 1.037 0.819 0.513 1.774 0.915 0.878 1.314 1.131 1.054 0.980 1.241 0.868 1.007 1.023 0.882 1.027 1.496 1.241 1.422 1.992 1.013 1.078 1.403 2.617 0.962 1.102 1.547 1.086

0.954 1.305 0.972 1.211 0.977 1.854 1.021 0.900 1.517 0.922 0.992 0.969 0.941 1.216 1.228 0.757 1.071 0.895 0.886 1.009 1.047 0.711 1.013 1.010 1.188 0.786 0.589 1.391 0.965 1.070 0.317 1.038 1.059 1.025 0.916

1.014 0.859 1.059 1.005 1.000 0.997 1.003 1.018 0.958 2.353 1.003 0.577 1.267 0.748 0.860 0.865 0.958 0.875 0.916 0.991 0.919 0.830 1.002 0.998 0.876 0.785 0.919 0.249 0.989 0.996 0.665 0.722 0.484 0.984 0.663

0.978 1.164 1.021 1.007 0.991 1.022 0.996 1.004 1.058 0.478 0.689 1.055 0.927 1.296 1.481 1.164 0.970 0.935 0.892 0.736 1.186 1.117 1.013 0.945 0.970 0.986 1.045 0.894 1.240 0.990 1.291 1.343 1.044 0.998 1.505

0.998 1.681 0.983 1.015 0.653 0.989 0.997 0.998 0.927 2.316 1.425 0.872 1.069 1.606 1.715 1.549 1.025 1.065 1.072 1.361 0.855 1.003 1.019 0.729 1.953 1.128 1.721 1.262 0.901 1.026 0.885 0.650 1.863 1.059 0.697

1.011 0.545 1.091 1.038 1.530 1.012 1.016 1.002 1.029 0.991 1.022 1.082 1.060 0.795 1.028 0.796 1.010 1.285 1.178 1.021 1.004 1.065 0.982 0.765 0.400 0.995 0.999 0.921 1.068 0.995 0.838 0.976 0.891 1.029 1.065

3.3. Scale efficiency of urban land use Scale efficiency reflects the benefit level of a DMU from its scale enlargement. The scale efficiency of urban land use is the benefit produced by unit input in the process of urban “extension” land use. Table 5 shows the scale efficiency of China’s 35 mega cities from 2008 to 2015. Fig. 7 shows the radar chart of scale efficiency in 2008, 2011, and 2015. Temporally, the scale efficiency of urban land use in the 35 mega cities slowly increases from 2008 to 2015 and has an average annual increase of 1.16%. Compared with the comprehensive and pure technical efficiencies, the scale efficiency shows the most evident increase. This situation indicates that China’s mega cities have made some achievements in controlling the aimless expansion between 2008 and 2015, such as Beijing taking the 2008 Beijing Olympic Games as an opportunity to change the urban development model and control the expansion of urban area (Ou, 2014). Since 2007, Shanghai has taken “innovation-driven and compact development” as its core to change the “big cake” development pattern (Gao and Guan, 2009; Guan and Chen, 2010; Dai, 2015). Guangzhou actively promotes the transformation of the urban model from “new city” to “old city.” The average scale efficiencies of urban land use in 2008 and 2015 are 1.151 and 1.263, respectively. Out of the 35 mega cities in China in 2008 and 2015, 5 and 3 cities have a scale efficiency of is less than 1. Moreover, only the scale efficiency of Shijiazhuang in 2015 is greater than 2. Compared with the data in 2008, those in 2015 are more balanced and reasonable and show a spindle structure. Therefore, China’s mega cities have achieved some success in controlling the scale of urban sprawl and improving the ULUE by using land use planning, urban planning, and other management tools. From 2008 to 2015, the scale efficiency of urban land use in the eastern region is

Fig. 5. Radar chart of pure technical efficiency of 35 mega cities in 2008, 2011, and 2015.

cities is also quite different. Similarly, this study divides the pure technical efficiency of China’s 35 mega cities into four intervals according to their distribution from 2008 to 2015 (Fig. 6). In 2008–2015, the average pure technical efficiency of urban land use in the 35 mega cities is distributed in four ranges: 0.85–0.95, 0.95–1.05, 1.05–1.15, and 1.15–1.25. 1) The pure technical efficiency of urban land use in Yinchuan, 7

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Fig. 6. Average pure technical efficiency of China’s 35 mega cities in the period of 2008–2015.

3.4. Relationship among comprehensive, pure technical, and scale efficiencies of urban land use

higher than that in the western region, and the scale efficiency of urban land use in the central region is the lowest. Spatially, the scale efficiency of urban land use of these cities is also quite different. This study divides the 35 mega cities into three intervals according to their size and efficiency from 2008 to 2015 (Fig. 8). In 2008–2015, the average scale efficiency of the 35 mega cities is distributed in three intervals: 0.95–-1.05, 1.05–1.15, and 1.15–1.25. 1) The scale efficiency of urban land use in Urumqi, Ningbo, and Beijing ranges from 1.15 to 1.25, which is the highest among those in the 35 mega cities. Moreover, the scale efficiency of Urumqi is the highest. 2) The scale efficiency of urban land use in Chongqing, Yinchuan, Xiamen, Xining, Xi'an, Taiyuan, Shijiazhuang, Shenyang, Shenzhen, Shanghai, Qingdao, Lanzhou, Kunming, Jinan, Hohhot, Hangzhou, Haikou, Harbin, Guiyang, Guizhou, Fuzhou, Chengdu, Changsha, and Changchun is between 1.05 and 1.15, which still needs to be improved. 3) The scale efficiency of urban land use in Zhengzhou, Wuhan, Tianjin, Nanning, Nanjing, Nanchang, Hefei, Dalian, and other cities ranges between 0.95 and 1.05, which is low. Among them, the scale efficiency of Zhengzhou and Nanning is the lowest. Zhengzhou, which is a key developing city in central China, has developed rapidly in recent years. The scale of the city has expanded at a high speed. However, the expansion speed of the city has exceeded the limit of economic development capacity, thereby resulting in the low scale efficiency of urban land use.

Using the basic principle of DEA model, comprehensive efficiency can be decomposed into pure technical and scale efficiencies. Pure technical efficiency is an effective indicator to reflect the production technology level, resource utilization and allocation level, and management ability of a city. Scale efficiency is an effective indicator to reflect the population scale effect, population density, industrial agglomeration, market capacity, and other factors. Although the values of pure technical and scale efficiencies are helpful in explaining the impact of urban land use, the relationship among comprehensive, pure technical, and scale efficiencies is still an issue to be explained and clarified. Fig. 7 attempts to explain this problem. Fig. 9 (a and b) depict the relationship among the comprehensive, pure technical, and scale efficiencies of urban land use in 2008 and 2015, respectively. In Fig. 9 (a and b), the X axis represents pure technical efficiency, and the Y axis represents scale efficiency. The scattered points below the 45° line indicate that the contribution of pure technical efficiency to comprehensive efficiency is greater than that of scale efficiency. On the contrary, scattered points above the 45° line indicate that scale efficiency contributes more to overall efficiency than pure technical efficiency. The scatter plots in Fig. 9 (a and b) are mainly located above the 45° line. Therefore, the scale efficiency of urban land use in China’s mega cities has a more significant impact on comprehensive efficiency than pure technical efficiency. This feature does not change over time. Although the main feature of significant impact of scale efficiency 8

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2015, whereas the distribution of pure technical efficiency is becoming increasingly concentrated in the same period. The increasingly concentrated distribution of pure technical efficiency indicates that the gap between cities is narrowing and reflects the progress of pure technical efficiency of urban land use in China to some extent.

Table 5 Scale efficiency of China’s 35 mega cities in the period of 2008–2015.

Beijing Changchun Changsha Chendu Dalian Fuzhou Guangzhou Guiyang Harbin Haikou Hangzhou Hefei Hohehot Jinan Kunming Lanzhou Nanchang Nanjing Nanning Ningbo Qingdao Shanghai Shenzhen Shenyang Shijiazhuang Taiyuan Tianjin Urumchi Wuhan Xian Xining Xiamen Yinchuan Zhengzhou Chongqing

2008

2009

2010

2011

2012

2013

2014

2015

1.024 1.066 1.393 1.109 1.129 1.571 0.944 1.984 1.198 0.827 0.919 1.032 1.154 1.277 1.209 1.269 1.208 1.121 0.863 1.443 1.130 1.067 1.018 1.331 1.048 1.196 0.956 1.012 1.113 1.121 1.020 1.340 1.157 1.044 1.006

0.793 0.982 1.010 0.840 1.036 0.443 0.822 0.762 1.055 0.799 1.409 0.993 1.058 0.943 0.857 0.803 0.939 1.004 1.147 0.561 0.756 1.475 0.821 1.019 1.140 1.059 1.067 1.250 0.997 1.030 1.220 0.869 0.576 1.199 0.730

2.293 1.341 1.226 1.203 1.453 2.233 1.959 1.325 1.253 1.905 1.335 1.121 1.127 1.306 1.366 1.140 1.275 1.115 1.076 2.765 2.043 0.803 1.790 1.045 1.362 1.080 1.452 1.021 1.231 1.228 0.607 1.084 2.160 0.971 1.880

0.461 0.671 0.626 0.653 0.470 0.467 0.627 0.458 0.819 0.901 0.471 0.524 0.699 0.524 0.587 0.829 0.729 0.687 0.777 0.407 0.506 0.642 0.650 0.623 0.707 0.843 0.591 2.039 0.671 0.572 1.633 0.579 0.522 0.646 0.557

1.392 1.241 1.231 1.150 1.134 1.095 1.121 1.329 1.105 0.665 1.034 1.603 1.391 1.432 1.705 1.422 1.114 1.255 1.082 1.028 1.106 1.289 1.429 1.149 0.974 1.363 1.165 0.878 1.169 1.188 1.200 1.211 2.044 1.005 1.386

1.000 0.912 0.821 0.725 0.858 0.976 0.957 0.727 0.893 1.389 1.300 0.862 0.917 0.806 0.747 0.847 0.959 0.840 0.921 1.288 1.046 0.922 0.707 0.765 0.960 0.886 0.933 1.113 0.907 0.693 0.809 0.773 0.883 0.952 0.616

0.983 0.584 1.252 1.761 1.153 1.045 1.044 1.481 1.040 1.434 0.740 1.065 0.798 0.650 0.951 0.952 1.070 0.962 0.969 0.713 0.916 1.175 1.327 1.278 0.471 0.985 0.519 0.905 1.046 1.231 0.957 1.444 1.002 0.901 1.372

1.301 1.913 0.987 1.020 0.939 1.014 1.393 1.016 1.186 1.081 1.351 1.106 1.320 1.925 1.298 1.386 1.058 1.088 1.104 1.218 1.221 1.359 1.141 1.250 2.358 1.198 1.395 1.249 1.138 1.646 1.154 1.389 0.708 1.184 1.119

3.5. Spatial and temporal changes in ULUE in 2008 and 2015 As shown in Fig. 4, the average comprehensive efficiency of urban land use in China in 2008 is 1.109 and that in 2015 is 1.208. Thus, the average comprehensive efficiency in 2015 is better than that in 2008. In fact, the efficiency in most cities shows a positive change in percentage between 2008 and 2015. Whether the ULUE of the 35 mega cities in 2015 is better than that in 2008 is unclear. The problem can be solved directly by converting the 35 mega cities into 70 entities. Each of these entities is given two representations for research: one for 2008 and the other for 2015. Table 6 summarizes the results of the above-mentioned framework. The first column represents the name of DMU, which corresponds to the name of each city. The DMUs numbered from 1 to 35 in column 2 refer to 35 mega cities in 2008, whereas the DMUs numbered 36 to 70 in column 4 refer to the same 35 mega cities in 2015. Columns 3 and 5 correspond to the efficiency of the 70 DMUs. The last column shows the percentage changes in each mega city from 2008 to 2015. This way provides answer to the question of whether efficiency in mega cities has generally improved between 2008 and 2015. The efficiency of China’s 35 mega cities increases by 13.71% on average in 2008 and 2015. However, the efficiency values of 10 cities, namely, Changchun, Changsha, Fuzhou, Guiyang, Nanchang, Shenyang, Shijiazhuang, Xining, Xiamen, and Yinchuan, show negative percentage changes. A total of 7 of these cities are located in central and western China. This discovery can be explained by the evident gradient of regional economic development between the eastern and central and western regions. The central and western regions of China have lagged behind the economically developed coastal areas of China due to their location and resource conditions.

3.6. Comparisons of the trends of comprehensive, pure technical, and scale efficiencies From comparing the changing trends of comprehensive, pure technical, and scale efficiencies (Fig. 10), this study obtains the following key findings. 1) The growth rates of the comprehensive, pure technical, and scale efficiencies of China’s 35 mega cities in the period of 2008–2015 are slow. The average annual growth rates of comprehensive, pure technical, and scale efficiencies are 1.07%, 0.24%, and 1.16%, respectively. By contrast, the growth rate of scale efficiency is the largest. 2) The fluctuation of comprehensive and pure technical efficiencies is more evident than that of scale efficiency. The standard deviations of comprehensive, pure technical, and scale efficiencies between 2008 and 2015 are 0.24, 0.22, and 0.08, respectively. The large fluctuation of comprehensive and pure technical efficiencies is due to the vast fluctuation of gross investment in fixed assets investment in recent years. Specifically, Fig. 10 shows that the fluctuations of comprehensive and pure technical efficiencies follow the similar pattern of the growth rate of gross investment in fixed assets. In addition, the reason for the weak fluctuation of scale efficiency is policy stability (Peng et al., 2017). In recent years, the Chinese government has adopted strict land use management and population control policies to control urban scales of large cities, especially for first tier and provincial capital cities. 3) The change trends of comprehensive and pure technical efficiencies have a similar pattern. This finding shows that the comprehensive efficiency of urban land use is mainly influenced by the pure technical efficiency.

Fig. 7. Radar chart of scale efficiency of 35 mega cities in 2008, 2011, and 2015.

does not change over time, micro changes in the data also reveal some clues. The Y value in Fig. 9 (a) is mainly distributed between 0.8 and 2.0, and the Y value in Fig. 9 (b) is mainly distributed between 0.6 and 2.0. The X value in Fig. 9 (a) is mainly distributed between 0.6 and 1.4, and the X value in Fig. 9 (b) is mainly distributed between 0.7 and 1.4. This finding indicates that the distribution of scale efficiency of urban land use in China is becoming increasingly dispersed from 2008 to 9

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Fig. 8. Average scale efficiency of China’s 35 mega cities in the period of 2008–2015. (a) The year 2008 (b) The year 2015.

Fig. 9. Scatter diagram of comprehensive, pure technical, and scale efficiencies of urban land use. 10

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Table 6 Comprehensive efficiency of 70 DMUs in 2008 and 2015. urban Beijing Changchun Changsha Chengdu Dalian Fuzhou Guangzhou Guiyang Harbin Haikou Hangzhou Hefei Huhehaote Jinan Kunming Lanzhou Nanchang Nanjing Nanning Ningbo Qingdao Shanghai Shenzhen Shenyang Shijiazhuang Taiyuan Tianjin Urumchi Wuhan Xian Xining Xiamen Yinchuan Zhengzhou Chongqing Average

DMU(2008)

Comprehensive efficiency

DMU(2015)

Comprehensive efficiency

Changes of DMU in 2008 vs 2015(percentage)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 —

1.037 1.060 1.352 1.006 1.037 1.582 1.059 2.026 0.961 0.745 0.893 1.036 1.383 1.219 0.877 0.964 1.128 1.060 0.820 0.946 1.115 0.947 1.011 1.091 1.451 1.022 1.050 1.057 1.084 0.972 1.043 1.428 1.229 1.151 0.981 1.1092826

36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 —

1.315 1.043 1.077 1.059 1.437 1.026 1.416 1.018 1.221 1.072 1.381 1.196 1.399 1.530 1.335 1.104 1.069 1.398 1.300 1.244 1.227 1.447 1.119 0.957 0.943 1.192 1.394 1.150 1.215 1.638 0.967 1.356 0.630 1.218 1.192 1.208146543

26.76% −1.60% −20.40% 5.27% 38.63% −35.13% 33.67% −49.76% 27.07% 43.78% 54.55% 15.40% 1.14% 25.58% 52.23% 14.50% −5.20% 31.94% 58.53% 31.45% 9.98% 52.79% 10.78% −12.27% −34.97% 16.62% 32.75% 8.79% 12.06% 68.51% −7.26% −5.07% −48.72% 5.83% 21.42% 13.71%

4. Conclusion

follows. First, the comprehensive, pure technical, and scale efficiencies of urban land use in China’s 35 mega cities are relatively low. The average comprehensive efficiency of the 35 mega cities from 2008 to 2015 is 1.094. Among the three efficiencies, the pure technical efficiency is the lowest and mainly affects the comprehensive efficiency. The average pure technical efficiency is 1.037. Second, from a temporal perspective, the comprehensive, pure technical, and scale efficiencies of China’s 35 mega cities show a slow growth trend and have average annual growth rates of 1.07%, 0.24%, and 1.16%, respectively. Third, from a

Urbanization and pursuits of sustainability are two salient trends of human society in the new century. However, various adverse effects of urban sprawl have made ULUE a serious challenge to the progress of sustainable development. The research of ULUE will help improve the quality of urban development. This study uses a super efficiency SBM model to calculate the comprehensive, pure technical, and scale efficiencies of urban land use in China by using the data of its 35 mega cities from 2008 to 2015. The results of this study are summarized as

Fig. 10. Comparisons of the trends of comprehensive, pure technical, and scale efficiencies and growth rate of gross investment in fixed assets. 11

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urban land (Jaeger et al., 2010), but excessive intensive use of urban land may lead to negative phenomena, such as urban congestion and reduced livability (Peng et al., 2017). Thus, some scholars believe that “compact cities” are the fundamental solution to the problem of inefficient urban land use. Compact cities cannot limit the reasonable demand of land for urban development but play a significant role in controlling the inefficient use of land by the uncontrolled spread of cities. Therefore, the future of urban land use and compact urban construction will be combined, and handling the relationship between the two aspects will lead to a strong practical prospect.

spatial perspective, the ULUE levels in China’s 35 mega cities are quite different. In terms of comprehensive efficiency, the ULUE in the eastern region is the highest at 1.087, followed by that in the western region at 1.075, and that in the central region as the lowest at 1.028. Pure technical and scale efficiencies also show the same spatial rule. China’s mega cities have achieved some success in controlling the scale and improving the ULUE by using land use planning, urban planning, and other management tools, and the ULUE is incompletely proportional to the level of economic development. China should pay close attention to the complex relationship between ULUE and the different driving factors. The spread efficiency of China’s mega cities should be promoted due to three reasons. 1) China is currently in the rapid development stage of industrialization and urbanization. In recent years, the problem of urban sprawl is becoming increasingly serious. Excessive pursuit of development scale and neglect of development efficiency result in the low level of comprehensive efficiency of urban land use. 2) The difference between the pure technical and scale efficiencies of urban land use in China indicates that the main means of promoting urban development in China is still the increase in urban area. However, the remarkable improvement in scale efficiency shows that China has begun to pay close attention to the efficiency of urban spatial sprawl and strives to end the “big cake” development in the past. The low pure technical efficiency also shows that Chinese cities do not pay sufficient attention to the optimization and integration of stock space. 3) The regional disparity of ULUE in China reflects that the ability of the Chinese government to coordinate regional development needs to be improved under its limited financial capacity. The eastern region is the leader in the development of the whole country, and the government has the greatest support for the development of eastern cities. The ULUE in the western region has also changed considerably due to major development strategies and projects, such as the development of the western region, the transmission of gas from the west to the east, and the transmission of electricity from the west to the east. The government in the central region should pay close attention to urban development. On the basis of the present situation and problems of ULUE in China, this study puts forward the following policy suggestions. 1) A scientific urban planning with the use of big-data analyses and simulations is an imperative means to monitor or improve the spatial distribution of urban and rural land use (Liu et al., 2018b). 2) We should strengthen the rational planning of new urban space and attempt to construct innovative urban areas in new urban space to transform the development of cities from factor input and capital into an innovation-driven one and improve the pure technical efficiency of urban land use (Guan and Gao, 2009). 3) In view of the low efficiency of urban land use in a few cities (e.g., Nanning and Hefei), the government should actively promote the construction of urban agglomerations and compile regional development plans to clarify the status and functions of cities, exchange needs with other cities, and improve the efficiency of these cities through regional coordinated development. 4) In view of the regional disparity of ULUE in China, the government should vigorously promote the economic development and scientific and technological innovation of the cities in the western region. Moreover, the government should promote the transformation of the development mode of the central region, especially the energy-based cities, to improve the quality of urban development and the ULUE in the central and western regions. This research has some gaps, which can be addressed to help promote this type of study in the future. First, given that urban land use is a dynamic process, whether different evaluation indicators should be used for urban land use effect in different stages of urban development is unclear. Therefore, the future research on ULUE should pay attention to building a highly dynamic and comprehensive indicator system for reflecting the characteristics and nature of urban land use problem objectively. Second, most policy proposals for improving the ULUE in the existing studies focus on improving the level of intensive use of

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