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Management Studies, May-June 2021, Vol. 9, No. 3, 190-202 doi: 10.17265/2328-2185/2021.03.003 D D AV I D PUBLISHING

A Comparative Study on the Land Use Efficiency of Three Urban Agglomerations—Based on the Three-Stage DEA Model

Yang Lu University of Finance and Economics, Nanchang,

This paper uses a three-stage DEA model to measure the land use efficiency of the three major urban agglomerations in the River Delta, --, and the from 2007 to 2018. The following conclusions are drawn through research: first, the urban land use efficiency of the three major urban agglomerations in the Yangtze River Delta, Beijing-Tianjin-Hebei, and the Pearl River Delta all showed a downward trend, with the rates of decline being 6.06%, 2.86%, and 24.34% respectively. In particular, the Pearl River Delta urban agglomeration had the largest decline. Second, the overall urban land use efficiency of the Beijing-Tianjin-Hebei urban agglomeration is relatively high, and the amount of redundancy is relatively small. The rate of decline is significantly lower than the urban land use efficiency of the two major urban agglomerations in the Yangtze River Delta and the Pearl River Delta. The land use efficiency of the Yangtze River Delta and Pearl River Delta urban agglomerations is in a state of continuous decline. Third, the proportion of cities with the effectiveness of returns to scale of urban land use efficiency in the three major urban agglomerations has decreased by 10.53%, 10%, and 33.34%, respectively. The Pearl River Delta has the largest decline. Fourth, the land use efficiency of the Yangtze River Delta urban agglomeration is quite different. The central-peripheral phenomenon is evident for the Beijing-Tianjin-Hebei urban agglomerations and the Pearl River Delta urban agglomerations.

Keywords: three-stage DEA model; three city groups; land use efficiency

Introduction Since the reform and opening up, Chinese economy has achieved rapid development and achieved the “Chinese miracle” that has attracted worldwide attention. Along with the rapid economic development, major breakthroughs have been made in urbanization, but what followed is that a large amount of land used to be agricultural land, but now gradually have been changed to industrial land and urban land. With the rapid advancement of the urbanization process and the steady increase in the urbanization rate, the scale of urban land use in China is also expanding. In 2007, China’s urban built-up area was approximately 35,500 square kilometers. In 2018, China’s urban built-up area reached approximately 58,400 square kilometers1, an increase of 64.5% over 2007. The continuous expansion of the scale of the city requires a large amount of land resources. In the case of a sharp increase in land demand, the irrational structure of land use and the scattered distribution of land have caused the problems of extensive and inefficient use of urban land and continue to aggravate.

Yang Lu, Ph.D., lecturer, Jiangxi University of Finance and Economics, Nanchang, China. Correspondence concerning this article should be addressed to Yang Lu, Jiangxi University of Finance and Economics, No. 169, Shuanggang East Street, Changbei National Economic and Technological Development Zone, Nanchang, China. 1 The data come from China City Statistical Yearbook in 2007 and 2018.

A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY 191

Improving the efficiency of urban land use has become an inherent requirement for the construction of a conservation-oriented society and an important measure to achieve sustainable economic and social development. The report of the 19th National Congress of the Communist Party of China (CPC) clearly proposed that the delineation of the three control lines of ecological protection red line, permanent basic farmland, and urban development boundary should be completed, and an urban pattern with large, medium, and small cities and small towns developing in coordination should be built with urban agglomerations as the main body. Therefore, on the basis of the delineation work, how to obtain the largest economic output and social benefits with the smallest land input, that is, to improve the efficiency of urban land use, has become an important issue that must be addressed in the urbanization process with the theme of urban agglomerations in China. This paper intends to compare the land use efficiency of Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta city clusters, and then provide good experience for the development of other urban clusters.

Literature Review Regarding the study of urban land use efficiency, the earlier ones were the concentric circle model proposed by Burgess in 1925 and the polycentric theory proposed by Hals and Ullman in 1954 . In recent years, on the basis of early theoretical models and combined with the reality of modern urban development, international academic circles have conducted empirical analysis on how to avoid failures in the land market, the operating mechanism of the land market itself, and the factors affecting the relationship between supply and demand. Bajocco, De Angelis, Perini, Ferrara, and Salvati (2012) used different expansion waves (1960-2010) to conduct a multi-dimensional analysis of the urban land use efficiency index (building area per capita, LUE) of Attica in the Mediterranean city. Studies have shown that low efficiency of urban land use concentrated in coastal areas and marginal areas of Athens in 1960, but the most remote rural areas of the least efficient in 2010. Fetzel, Niedertscheider, Haberl, Krausmann, and Erb (2016) used the human occupancy of net primary productivity (HANPP) framework to analyze the land system and its dynamics in Africa from 1980 to 2005. Studies have shown that there may be potential to improve biomass production efficiency in terms of existing land use, rather than increasing production through further land expansion. Masini, Tomao, Barbati, Corona, Serra, and Salvati (2019) conducted a multi-dimensional analysis of land use efficiency on the per capita construction area of 417 metropolitan areas from 27 European countries. Studies have shown that rich cities are characterized by high land use efficiency, and under the background of diversified urban landscapes, land use efficiency has increased. Chinese scholars have carried out extensive and in-depth research on the efficiency of urban land use in China based on relevant foreign theories and models. Huang, Zhang, Lu, and Yang (2018) used a spatial measurement model to study the spatial effect of land supply structure on economic growth, and the results showed that land supply structure has a certain spatial spillover to economic growth and there are differences in land use types. Other scholars use data envelopment analysis to measure and evaluate the efficiency of urban land supply in China. Xiong, Han, and Bao (2017) used panel data to calculate the land use efficiency of nine cities in the Pearl River Delta urban agglomeration. The results show that the growth of pure technical efficiency and scale efficiency of land use in urban agglomerations in the Pearl River Delta is gradually slowing down. The main way to improve land use efficiency is to improve land use technology in the future. Yang, Wen, and Zhong (2018) took 108 cities at prefecture level and above in the Yangtze River Economic

192 A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY

Zone as the research object, and used DEA model and Malmquist index to conduct static and dynamic analysis of urban land use efficiency. The results show that the overall land use efficiency is at a low level, and the spatial pattern shows that the downstream area is significantly higher than the middle and upper reaches, and the area south of the Yangtze River is significantly higher than the north area. Li and Hu (2020) took the urban land use of nine prefecture-level cities in Province as the research object, and used the DEA method to calculate urban land use efficiency from two dimensions of time and space from 2007 to 2016. From the above literature, whether it is theoretical model or empirical analysis, the research objects are basically individual cities or urban agglomerations, and there is a lack of comparative research on them. In addition, the empirical analysis method of land use efficiency is mainly (DEA), but Fried (2002) pointed out that the traditional DEA model did not consider the impact of environmental factors and random noise on the efficiency evaluation of decision-making units. Therefore, this paper adopts the three-stage DEA model based on Fried (2002) to compare the land use efficiency of the three major urban agglomerations in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta, hoping to provide valuable experience for the reform of “crossing the river by feeling the stones” in land use in developed areas.

Model and Indicator System Construction Study Area The regions studied in this paper are the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration. There are 10 cities in the Beijing-Tianjin- Hebei urban agglomeration: Beijing, Tianjin, , , , , , , , . There are 16 cities in the Yangtze River Delta City Group2: , , , , , , Taizhou, , , , , , , , , Taizhou. There are 9 cities in the Pearl River Delta City Group3: , , , , , , , , . According to China City Statistical Yearbooks in 2008 and 2018, the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration, the land use area (square kilometers) of built-up areas increased from 2,389, 3,836, and 2,013 to 3,602, 5,952, and 4,529 respectively, with an average annual growth rate of 2.97%, 3.19%, and 5.96%, and they are still growing. With the rapid advancement of urbanization, the demand for land is still growing rapidly, but there are not many studies focusing on the efficiency of land use. Research Model The establishment of the research model mainly includes the following steps: (1) The first stage: the traditional DEA model analyzes the initial efficiency. We use the original input-output data for initial efficiency evaluation. The DEA model is divided into input-oriented and output-oriented. According to the specific analysis purpose, different orientations can be selected. Since the variable return to scale (BCC) model decomposes technical efficiency into scale efficiency and pure technical efficiency on the basis of the constant return to scale (CCR) model, it is possible to judge whether each

2 At present, there are roughly three kinds of definitions of the spatial scope of the Yangtze River Delta: the small Yangtze River Delta, the large Yangtze River Delta, and the Pan-Yangtze River Delta. The Yangtze River Delta studied in this article is the small Yangtze River Delta, that is, 16 cities in the traditional sense. 3 Since Kong and data are difficult to obtain, and Macau are not included in this article.

A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY 193 decision-making unit is in the optimal state of scale, but also accurately examine whether internal management is effective. Therefore, in most of the relevant literatures, the investment-oriented BCC model is selected. For any decision-making unit, the dual form of BCC model under input orientation can be expressed as:

(1)

Among them, j = 1, 2,…, n, represents the decision-making unit, X, Y are input and output vectors respectively. The DEA model is essentially a linear programming problem. If θ =1, S+ = S- = 0, the DEA of the decision-making unit is valid; If θ =1, S+ ≠ 0, or S- ≠ 0, the weak DEA of the decision-making unit is effective; If θ < 1, the decision-making unit is not DEA valid. The efficiency value calculated by the BCC model is the comprehensive Technical Efficiency (TE), which can be further decomposed into Scale Efficiency (SE) and Pure Technology Efficiency (PTE), namely TE = SE*PTE. Fried (2002) believes that the performance of decision-making units by inefficient management (Managerial Inefficiencies), environmental factors (Environmental Effects), and statistical noise (Statistical Noise) effects, it is necessary to separate the three affected. (2) The second stage: SFA regression eliminates environmental factors and statistical noise. This stage mainly concerns slack variable [x﹣Xλ], and that such slack variables may reflect the early beginning of inefficiency, by environmental factors, management inefficiency and statistical noise constituted. The main goal of the second stage is to decompose the slack variable in the first stage into the above three effects. To achieve this goal, only with the aid of SFA regression, in the SFA regression, the slack variable in the first stage regresses the environmental variables and the mixed error term. Therefore, according to Fried (2002), we can construct the following similar SFA regression function (taking input-oriented as an example):

(2)

Among them, Sni is the slack value of the nth input of the ith decision-making unit; Zi is the environmental variable, βn is the coefficient of the environmental variable; νni + μni is the mixed error term, νni represents 2 random interference and μni represents management inefficiency. Among them v ~ N(0, ) is the random error term, which represents the impact of random interference factors on the input slack variable; μ is σν management inefficiency, which represents the impact of management factors on the input slack variable, assuming that it obeys a normal distribution truncated at zero, namely ~ N+(0, 2). The purpose of SFA regression is to eliminate the influence of environmental factors and random factors μ σμ on the efficiency measurement, so that all decision-making units can be adjusted to the same external environment. The adjustment formula is as follows:

194 A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY

(3)

A Among them, Xni is the input after adjustment; Xni is the input before adjustment; [max(f(Zi; n)) − f(Z ; )] is the adjustment of external environmental factors; [max(ν ) − ν ] is the adjustment of the errors of i n ni n i β� all decision-making units. β� (3) The third stage: the adjusted input-output variable DEA efficiency analysis. The adjusted input-output variables are used to measure the efficiency of each decision-making unit again. At this time, the efficiency has been excluded from the influence of environmental factors and random factors, which is relatively true and accurate. Description of Index Selection and Data Description The evaluation index system of land use efficiency mainly includes two aspects: input and output. At the same time, in terms of the stability of the administrative division of the three urban agglomerations and the availability of data, and referring to relevant literature such as Yang, Hu, and Wang (2015) and Xiong et al. (2017), this paper selects land, capital, and labor as input indicators, and uses urban construction land area (square kilometers), total fixed asset investment (100 million ), and employment in the secondary and tertiary industries and (10,000 people) to represent the above investment indicators respectively. In terms of output, considering the economy, society, and environment, this paper uses the regional gross product (100 million yuan), fiscal budget revenue (100 million yuan), and green coverage (percentage) to represent the above output indicators respectively. Urbanization rate (percentage), industrial structure upgrade level (percentage) and economic openness (100 million yuan) are used to express the external environment. The urbanization rate is calculated by dividing the urban population by the total population, and the level of industrial structure upgrading is calculated by dividing the added value of the tertiary industry by GDP, and the degree of economic openness is expressed by the amount of foreign contractual investment. Since the “Law of the People’s Republic of China on Urban and Rural Planning” was implemented in January, 2008, and the law stipulates that the planning period for construction planning is five years, this paper selects the data of 2008, 2013, and 2018. The data come from the “China City Statistical Yearbook” in 2008, 2013, and 2018. At the same time, in order to ensure the consistency of the statistical caliber, the consumer price index (CPI) was used to smooth the total fixed asset investment, regional GDP and fiscal budget income, eliminating the influence of price factors, and making the data comparable. In addition, interpolation is used to complete the missing individual data.

Table 1 Statistics Table of Indicator Variables Index Variable Calculation formula Land area of urban construction area Input Total investment in fixed assets indicators The number of employees in the secondary and tertiary industries GDP Output Fiscal budget revenue indicators Green coverage Urbanization rate Urban population/total population Environmental Industrial structure upgrade level Added value of tertiary industry /GDP indicators Economic openness Foreign contract investment amount

A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY 195

Analysis of Empirical Results Empirical Analysis of the First Stage of Traditional DEA Substituting the selected input-output variables into the DEA-BCC model, using DEAP 2.1 software, the technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) of each city in 2008, 2013, and 2018 are obtained. The calculation results are shown in Table 2. It can be seen from the calculation results that, without considering external environmental factors and random errors, the trends in the average change of the technical efficiency (TE) of the three major urban agglomerations from 2007 to 2018 are as follows: the Yangtze River Delta decreases first and then increases, and the Beijing-Tianjin-Hebei increase first and then decrease, the Pearl River Delta has been decreasing. This shows that the land use level trends of three major urban agglomerations are very different. During this period, the state has issued different urban agglomeration development strategies for the three major cities, resulting in different development trends. In addition, both the pure efficiency level and the scale efficiency level are at a relatively high level, and the two are comparable.

Table 2 Results of the First Stage of the Yangtze River Delta Cities 2008 2013 2018

TE PTE SE SCALE TE PTE SE SCALE TE PTE SE SCALE Shanghai 1.000 1.000 1.000 - 1.000 1.000 1.000 - 1.000 1.000 1.000 - Nanjing 0.51 0.523 0.976 irs 0.429 0.47 0.912 drs 0.603 0.603 1 - Wuxi 0.757 0.814 0.93 drs 0.711 0.807 0.88 drs 1 1 1 - Changzhou 0.471 0.559 0.843 irs 0.65 0.661 0.984 drs 1 1 1 - Suzhou 1 1 1 - 0.835 1 0.835 drs 1 1 1 - Nantong 1 1 1 - 0.624 0.65 0.959 drs 1 1 1 - Yangzhou 0.537 0.643 0.836 irs 0.571 0.623 0.916 irs 0.698 0.724 0.963 irs Zhengjiang 0.594 0.718 0.828 irs 0.592 0.644 0.919 irs 0.788 0.832 0.946 drs Taizhou 0.601 0.788 0.763 irs 0.681 0.755 0.902 irs 0.76 0.772 0.984 irs Hangzhou 0.818 0.818 0.999 irs 0.708 0.797 0.889 drs 0.933 1 0.934 drs Ningbo 0.772 0.774 0.998 - 0.73 0.783 0.933 drs 0.986 0.987 0.999 irs Jiaxing 0.589 0.739 0.797 irs 0.873 0.933 0.936 irs 0.934 0.969 0.964 irs Huzhou 0.659 0.825 0.798 irs 0.787 0.873 0.901 drs 0.878 0.881 0.996 drs Shaoxing 0.638 0.749 0.851 irs 0.767 0.831 0.923 irs 0.775 0.806 0.962 irs Zhoushan 0.63 1 0.63 irs 1 1 1 - 1 1 1 - Taizhou 0.604 0.694 0.871 irs 0.652 0.701 0.929 irs 0.695 0.716 0.971 drs Average 0.699 0.790 0.883 irs 0.726 0.783 0.926 0.878 0.893 0.982

Table 3 Results of the First Stage of the Beijing-Tianjin-Hebei Cities From 2007 to 2018 2008 2013 2018

TE PTE SE SCALE TE PTE SE SCALE TE PTE SE SCALE Beijing 1 1 1 - 1.000 1.000 1.000 - 1 1 1.000 - Tianjin 0.717 0.72 0.996 irs 0.732 0.979 0.748 drs 1 1 1 Shijiazhuang 0.439 0.466 0.941 drs 0.451 0.452 0.998 irs 0.609 0.653 0.933 Tangshan 0.661 0.69 0.957 irs 0.652 0.671 0.972 drs 0.553 0.555 0.998

196 A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY

(Table 3 to be continued) Qinhuangdao 0.851 1 0.851 - 1 1 1 - 1 1 1 Baoding 0.569 0.685 0.83 - 0.469 0.546 0.86 irs 0.568 0.573 0.991 Zhangjiakou 0.63 0.911 0.691 irs 0.637 0.642 0.991 drs 0.691 0.744 0.929 Chengde 0.568 0.88 0.646 irs 0.883 0.886 0.996 irs 0.798 0.991 0.805 Cangzhou 0.758 1 0.758 irs 1 1 1 - 1 1 1 Langfang 0.672 0.908 0.74 - 1 1 1 - 1 1 1 Average 0.687 0.826 0.841 irs 0.782 0.818 0.957 0.821

Table 4 Results of the First Stage of the Pearl River Delta Cities From 2007 to 2018 2008 2013 2018

TE PTE SE SCALE TE PTE SE SCALE TE PTE SE SCALE Guangzhou 0.801 0.961 0.834 drs 0.775 1 0.775 drs 1.000 1.000 1.000 1.000 Shenzhen 1 1 1 - 1 1 1 - Zhuhai 0.715 0.907 0.788 irs 1 1 1 - Foshan 0.823 0.825 0.998 irs 0.954 1 0.954 drs Jiangmen 0.889 1 0.889 irs 0.828 0.847 0.977 irs Zhaoqing 1 1 1 - 0.678 0.773 0.877 irs Huizhou 0.628 0.719 0.836 irs 0.555 0.653 0.85 irs Dongguan 1 1 0.873 - 1 1 1 - Zhongshan 0.777 1 0.777 irs 0.776 0.898 0.864 irs Average 0.848 0.790 0.883 0.841 0.908 0.922 Analysis of the Second Stage SFA Regression Results The slack of the input variables of each decision-making unit obtained in the first stage is used as the explained variable, and the environmental variables selected above are used as the explanatory variable. The SFA model is used to disassemble the impact of environmental variables, random errors and internal management inefficiency. Then second formula is used to remove these external influence factors to obtain new input variables. Frontier 4.1 software is used to perform regression analysis, and the results are shown in Table 5. It can be seen from Table 5 that the proportion of the secondary industry’s GDP to the number of employees in the secondary and tertiary industries and the total fixed asset investment slack variables have all passed the significance level test of 1%. The proportion of the tertiary industry’s GDP has a significant impact on the second and third industries. The number of industrial employees, urban construction land area and total fixed asset investment slack variables have passed the tests of significance levels of 5%, 10%, and 1% respectively, which indicates that external environmental factors will have a significant impact on the slack variables of various input factors. Therefore, it is very necessary to use this regression result to adjust the input variables of each city so that each city faces the same external environmental characteristics and objective luck. According to the SFA results, when the regression coefficient is positive, it means that adding external environmental variables will increase the input slack variable, resulting in input waste or output reduction, which is not conducive to the improvement of urban land utilization; otherwise, it means that it will increase land utilization rate. It can be seen from Table 2 that: firstly, the regression coefficient of the proportion of the secondary industry in GDP to the total fixed asset investment is positive, and it has passed the 1% significance

A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY 197 level test, indicating that the increase in the proportion of the secondary industry in GDP is not conducive to the improvement of the economic efficiency of urban land use. The actual situation is that the secondary industry is mostly an extensive processing industry. The number of resources invested in the early stage is large, and the environmental pollution caused in the later stage is large, and its proportion should not be too high. Secondly, the regression coefficient of the tertiary industry’s share of GDP to the total fixed asset investment is negative, and it has passed the 1% significance level test, indicating that the increase of the tertiary industry’s share of GDP is conducive to the improvement of urban land utilization. The actual situation is that the tertiary industry is mostly a service industry, with less reliance on resource input in the early stage, and higher output value in the later stage. Most cities in , , and Shanghai have taken the development of the tertiary industry as an important means of transforming economy and increasing land utilization. Therefore, rationally adjusting the industrial structure, appropriately controlling the secondary industry, and striving to develop the tertiary industry are good ways to improve the economic efficiency of urban land use.

Table 5 SFA Regression Results Slack of the number of employees Slack of land area of urban Slack of total investment in fixed in the secondary and tertiary construction area assets industries Coefficient T-test Coefficient T-test Coefficient T-test Constant -4.77E + 01 -4.78E + 01(***) 5.80E + 01 5.80E + 01(***) -2.32E + 06 -2.32E + 06 (***) Ratio of secondary 6.27E - 01 4.12E + 00(***) -5.17E - 01 -8.10E - 01 7.25E + 04 7.25E + 04(***) industry to GDP Ratio of tertiary 3.12E - 01 2.08E + 00 (**) -8.79E - 01 -1.20E + 00 (*) -6.27E + 04 -6.27E + 04(***) industry to GDP Sigma-squared 2.82E + 02 2.82E + 02(***) 9.19E + 02 9.19E + 02(***) 1.04E + 13 1.04E + 13(***) Gamma 1.00E + 00 7.51E + 04(***) 1.00E + 00 8.91E + 04(***) 1.00E + 00 5.18E + 00 (***) Log likelihood -8.40E + 01 -9.91E + 01 -3.93E + 02 Notes. t value is an indicator to test whether the explanatory variable has a significant impact on the explained variable; (***) means passing the test with a significance level of 1%; (**) means passing the test with a significance level of 5%; (*) means passing the test with a significance level of 10%.

Table 6 Results of the Third Stage of the Yangtze River Delta Urban Cities 2008 2013 2018

TE PTE SE SCALE TE PTE SE SCALE TE PTE SE SCALE Shanghai 1.000 1.000 1.000 - 1.000 1.000 1.000 - 1 1 1 - Nanjing 0.399 0.427 0.934 irs 0.346 0.35 0.989 drs 0.504 0.505 0.999 - Wuxi 0.813 0.948 0.857 irs 0.695 0.702 0.99 drs 1 1 1 - Changzhou 0.542 0.91 0.596 irs 0.561 0.581 0.965 irs 1 1 1 - Suzhou 1 1 1 - 1 1 1 - 1 1 1 - Nantong 1 1 1 - 0.528 0.53 0.997 drs 1 1 1 - Yangzhou 0.504 0.88 0.573 irs 0.482 0.574 0.839 irs 0.693 0.808 0.857 irs Zhengjiang 0.5 0.902 0.554 irs 0.518 0.608 0.852 irs 0.861 0.862 0.999 irs Taizhou 0.535 0.917 0.584 irs 0.58 0.685 0.847 irs 0.76 0.845 0.9 irs Hangzhou 0.714 0.765 0.933 irs 0.687 0.69 0.996 irs 0.989 1 0.989 irs Ningbo 0.626 0.815 0.769 irs 0.673 0.676 0.994 irs 0.963 0.982 0.981 irs

198 A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY

(Table 6 to be continued) Jiaxing 0.563 0.828 0.681 irs 0.808 0.903 0.896 irs 0.873 0.977 0.893 irs Huzhou 0.549 0.894 0.615 irs 0.853 0.854 0.999 drs 0.904 0.92 0.983 irs Shaoxing 0.558 0.828 0.674 irs 0.673 0.765 0.88 irs 0.745 0.849 0.878 irs Zhoushan 0.426 1 0.426 irs 1 1 1 - 1 1 1 - Taizhou 0.47 0.823 0.571 irs 0.606 0.712 0.851 irs 0.747 0.756 0.987 irs Average 0.637 0.871 0.735 irs 0.688 0.727 0.943 0.877 0.907 0.967

Table 7 Results of the Third Phase of the Beijing-Tianjin-Hebei Urban Agglomeration From 2007 to 2018 2008 2013 2018

TE PTE SE SCALE TE PTE SE SCALE TE PTE SE SCALE Beijing 1 1 1 - 1.000 1.000 1.000 - 1.000 1.000 1.000 - Tianjin 0.64 0.721 0.887 irs 0.87 0.962 0.905 drs 1 1 1 irs Shijiazhuang 0.474 0.724 0.654 irs 0.337 0.347 0.971 irs 0.644 0.645 0.999 irs Tangshan 0.577 0.746 0.774 irs 0.552 0.553 0.998 irs 0.552 0.564 0.98 irs Qinhuangdao 0.792 1 0.792 irs 1 1 1 - 1 1 1 - Baoding 0.444 0.755 0.588 irs 0.374 0.489 0.765 irs 0.618 0.698 0.886 irs Zhangjiakou 0.54 0.924 0.584 irs 0.615 0.631 0.975 Irs 0.737 0.824 0.895 irs

Table 8 Results of the Third Stage of the Pearl River Delta Urban Agglomeration From 2007 to 2018 2008 2013 2018

TE PTE SE SCALE TE PTE SE SCALE TE PTE SE SCALE Guangzhou 0.939 0.942 0.997 irs 0.962 1 0.962 drs 1.000 1.000 1.000 - Shenzhen 1 1 1 - 1 1 1 - 1 1 1 irs Zhuhai 0.636 0.916 0.694 irs 1 1 1 - 0.815 0.821 0.993 Irs Foshan 0.78 0.927 0.842 irs 1 1 1 - 0.685 0.707 0.968 irs Jiangmen 0.835 1 0.835 irs 0.76 0.866 0.878 irs 0.707 0.729 0.969 irs Zhaoqing 1 1 1 - 0.618 0.806 0.766 irs 0.979 1 0.979 drs Huizhou 0.496 0.746 0.665 irs 0.49 0.677 0.724 irs 0.664 0.746 0.89 irs Dongguan 1 1 1 - 1 1 1 - 1 1 1 - Zhongshan 0.628 1 0.628 irs 0.674 0.908 0.742 irs 1 1 1 - Average 0.813 0.948 0.851 0.833 0.917 0.897 0.872 0.889 0.978 Analysis of DEA Empirical Results After Adjusting Investment in the Third Stage According to the third-stage DEA model, the results and average values of land use efficiency in the three major urban agglomerations from 2007 to 2018 were calculated. Judging from the average value of land use efficiency of the Yangtze River Delta urban agglomeration (Table 6), from 2007 to 2018, the overall land use efficiency showed a downward trend, and it increased slightly in 2018, indicating that the land input-output efficiency of the Yangtze River Delta urban agglomeration gradually decreased from 2007 to 2018. From the perspective of the types of land use efficiency of specific cities, the urban land use efficiency of the Yangtze River Delta urban agglomeration from 2007 to 2018 can be roughly divided into six categories: first, cities concentrated between 0.996 and 1.000: Shanghai, Wuxi, and Suzhou. This type of city is the three most

A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY 199 economically developed cities in the Yangtze River Delta, and its land use efficiency is extremely high. The main reason is that the land in Shanghai is becoming more and more tense. Many companies have set their sights on the surrounding areas of Shanghai when choosing plant sites. However, this transfer is not a “white transfer”. The preferred locations are the economically developed neighboring Suzhou and Wuxi. It satisfies the need to actively integrate with Shanghai and reduces costs. Second, concentrated in cities ranging from 0.956 to 0.975: Shaoxing, Nantong, and Taizhou. The economic development of this type of city in the Yangtze River Delta is relatively backward, but the land use efficiency is relatively high. The main reason is that the large-scale development and construction of and real estate in China from 2007 to 2018 coincide with the background. More backward areas are also engaged in development and construction, and the land use efficiency is higher. Third, focus on 0.902~0.926 Cities: Taizhou, Ningbo, Changzhou. This type of city has relatively good economic development in the Yangtze River Delta, and its land use efficiency is average. Fourth, cities concentrated between 0.831 and 0.852: Yangzhou, Hangzhou, Jiaxing, Zhenjiang. The development of such cities is uneven and the land use efficiency is low. Fifth, cities concentrated between 0.739 and 0.769: Huzhou and Nanjing. The land use efficiency of these cities is very low. From third to fifth types of cities have differences in land use efficiency due to multiple factors such as local environment, policies, and foundations. Sixth, there is only one city of this type in Zhoushan (0.655), and the land use efficiency is the lowest among the cities in the Yangtze River Delta, mainly due to its own location ( island on the sea). Judging from the average land use efficiency of the Beijing-Tianjin-Hebei urban agglomeration (Table 7), the overall development presents an “M”-shaped fluctuating development. 2007~2010 is an upward trend, 2010~2012 is a downward trend, 2012~2014 is an upward trend, and 2014~2018 is a downward trend. From the perspective of the types of land use efficiency in specific cities, it can be roughly divided into three types: first, the effectiveness coefficient is 1.000, and there are five cities: Beijing, Tianjin, Tangshan, Cangzhou, and Langfang. Most cities of this type are economically developed and are regional development centers (Beijing and Tianjin are the centers of the Beijing-Tianjin-Hebei urban agglomeration, and Tangshan is the sub-center), or enclaves captured by central cities (Langfang). Second, cities concentrated in 0.830~0.928: Shijiazhuang, Qinhuangdao, Baoding. This type of city has low land use efficiency. Third, concentrated in the 0.692-0.709 of the city, mainly in Zhangjiakou, Chengde two cities, two cities for the metropolitan area shadow of Beijing-Tianjin-Hebei, bringing together a number of state-level poverty-stricken counties, is lagging behind economic development, land-use efficiency relatively low. Overall, Beijing-Tianjin-Hebei land use efficiency is mainly characterized by polarization trend, the center-peripheral phenomenon is obvious. Judging from the average value of the land use efficiency of the Pearl River Delta urban agglomeration (Table 8), the overall trend is declining, but the magnitude is not large and basically the same, indicating that the land use efficiency of the Pearl River Delta urban agglomeration has not changed much from 2007 to 2018 . There is a slight drop. From the perspective of the types of land use efficiency in specific cities, it can be roughly divided into three types: first, cities with an effective coefficient of 1.000: Guangzhou, Shenzhen, Dongguan, Zhongshan, and Foshan, which are economically developed and have high land use efficiency. Second, cities concentrated at between 0.709 and 0.795: Jiangmen and Zhuhai; cities concentrated between 0.667 and 0.669: Zhaoqing and Huizhou. The situation is basically similar to the Beijing-Tianjin-Hebei urban agglomeration. Cities with better economic development occupy a central position, and their land use efficiency is higher. The surrounding areas of Zhaoqing and Huizhou, which are less developed, have lower land use efficiency. In general, the same central-peripheral phenomenon is obvious. In summary, from the general

200 A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY overview of the land use efficiency of the three major urban agglomerations, it is basically a law that the higher the economic development of the city, the higher the land use efficiency. However, the difference between the Yangtze River Delta urban agglomeration and the Beijing-Tianjin-Hebei urban agglomeration and the Pearl River Delta urban agglomeration is that the economically underdeveloped regions in the Yangtze River Delta urban agglomeration have higher land use efficiency and greater differences. The economically underdeveloped regions of the Beijing-Tianjin-Hebei and Pearl River Delta urban agglomerations are generally very low in land use efficiency, indicating that the Beijing-Tianjin-Hebei and Pearl River Delta urban agglomerations are more polarized than the Yangtze River Delta.

Comparison of Land Use Efficiency in the Three Major Urban Agglomerations As a key element of urban agglomeration development, land use efficiency has received more and more attention from various departments. After removing environmental factors and random errors through a three-stage DEA model, this paper provides a comprehensive analysis of the Yangtze River Delta, Beijing-Tianjin-Hebei and Pearl River Delta cities. A comprehensive measurement of the land use efficiency of the urban agglomerations shows that the average land use efficiencies were 0.886, 0.906, and 0.866 from 2007 to 2018. The urban land use efficiency of the Beijing-Tianjin-Hebei urban agglomeration was generally higher, and the amount of redundancy was relatively small. Moreover, it can be known from the urban land use efficiency value of each year that the urban land use efficiency value of the Beijing-Tianjin-Hebei urban agglomeration is significantly lower than the urban land use efficiency of the two major urban agglomerations in the Yangtze River Delta and the Pearl River Delta. According to the actual input and output level of land use efficiency, the Beijing-Tianjin-Hebei urban agglomeration can always be maintained at a relatively high level in the process of urban land use, and the input and output are relatively matched. Whether it is the built-up area or the total investment in fixed assets or the number of employees, it does not appear to be redundant in the amount of input, and the effectiveness of returns to scale is basically maintained. As for the Pearl River Delta urban agglomeration, the urban land use efficiency showed a significant decline in 2011, and the effective year of return to scale dropped from seven cities in 2010 to three cities in 2011, and the urban land use efficiency suddenly increased. The sudden decline has led to the emergence of an “M”-shaped development trend. So far, the land use efficiency of the Pearl River Delta urban agglomeration has continued to decrease, which is why the land use efficiency of the urban agglomeration is the lowest. So far, the land use efficiency of the Pearl River Delta urban agglomeration has continued to decline, which is why the land use efficiency of the urban agglomeration is the lowest. The inefficiency of returns to scale may be manifested as increasing returns to scale, and the number of input elements is relatively small. The future development trend is to increase the amount of input, such as Zhuhai and Jiangmen in 2018; or manifested as diminishing returns to scale, relatively excessive number of input elements, and redundancy. The future development trend is to reduce the amount of input and improve its utilization efficiency, such as Foshan in 2018. The reason for the ineffective development of scale returns is not only related to the urban agglomeration’s own concept of land use and the amount of input, but also involves deeper institutional issues, such as land rights and land income distribution issues. It is imperative to deepen the reform of the system and mechanism and improve the efficiency of urban land use. From 2007 to 2018, the urban land use efficiency of the three major urban agglomerations in the Yangtze River Delta, Beijing-Tianjin-Hebei and the Pearl River Delta showed a general downward trend, with a decline rate of 6.06%, 2.86%, and 24.34% respectively, especially in the Pearl River Delta urban agglomeration. The largest

A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY 201 decline was 4 times and 8.5 times that of the Yangtze River Delta and Beijing-Tianjin-Hebei, which is enough to confirm the rapidity of the decline in urban land use efficiency in the Pearl River Delta urban agglomeration. According to the initial factor input, it can be seen that from 2007 to 2018, the absolute amount of various input factors in the urban land use of the Pearl River Delta urban agglomeration is increasing. However, the utilization efficiency is not consistent with it, and presents the opposite development direction, indicating that the level of efficiency is not much related to the initial input amount. More importantly, the degree of utilization of the elements is whether it can be fully utilized to become the level of efficiency. Full utilization has become an important measure of the level of efficiency. It also shows that the pattern of the urban land use of the urban agglomeration tends to increase towards the extensive type. And from the comparison of the urban land use efficiency of the three major urban agglomerations, the Pearl River Delta ranks at the bottom, such as 0.892, 0.882, and 0.747 respectively in 2018. In the face of this development reality, what measures should be taken to narrow the gap with the other two urban agglomerations has become an important task for this urban agglomeration. According to the calculation results of returns to scale, the proportions of cities with effective returns to scale in land use efficiency of the three major urban agglomerations in the Yangtze River Delta, Beijing-Tianjin-Hebei and Pearl River Delta in 2007 were 47.37%, 60%, and 77.78%, respectively, and the Pearl River Delta was significantly higher than that, ranking first, which shows the high efficiency of land use in each city. By 2018 , the proportions are 36.84% , 50%, and 44.44% respectively. The return to scale of urban land use efficiency in the Yangtze River Delta urban agglomeration. The proportion of effective cities is significantly lower than that of the other two urban agglomerations, ranking last. From 2007 to 2018, the proportion of cities with the effectiveness of returns to scale of urban land use efficiency in the three major urban agglomerations decreased by 10.53%, 10%, and 33.34%, respectively. The Pearl River Delta had the largest decline, which was 3.17 times and 3.33 times that of the Yangtze River Delta and the Beijing-Tianjin-Hebei Region. This requires that in the future urban land use process, the proportion of cities with effective returns to scale should be increased to strengthen the efficiency of urban land use.

Conclusions This paper uses the three-stage DEA model to quantitatively analyze the land use efficiency of the three major urban agglomerations from 2007 to 2018, and draws the following main conclusions: First, after using the SFA model to eliminate the influence of external environmental factors and random errors, the technical efficiency, pure technical efficiency, and scale efficiency of each city all have different degrees of fluctuations, which shows that the second stage of SFA analysis is very necessary. The urban land use efficiency of the three major urban agglomerations in the Yangtze River Delta, Beijing-Tianjin-Hebei and the Pearl River Delta all showed a downward trend, with the rates of decline being 6.06%, 2.86%, and 24.34% respectively. In particular, the Pearl River Delta urban agglomeration had the largest decline, with 4 times and 8.5 times that of the Yangtze River Delta and the Beijing-Tianjin-Hebei region, which are sufficient to confirm the rapid decline in the urban land use efficiency of the Pearl River Delta urban agglomeration. Second, the overall urban land use efficiency of the Beijing-Tianjin-Hebei urban agglomeration is relatively high, and the amount of redundancy is relatively small. The rate of decline is significantly lower than the urban land use efficiency of the two major urban agglomerations in the Yangtze River Delta and the Pearl River Delta. The land use efficiency of the Yangtze River Delta and Pearl River Delta urban agglomerations is in a state of continuous decline. Third, the proportion of cities with the effectiveness of returns to scale of urban land use efficiency in the three major

202 A COMPARATIVE STUDY ON THE LAND USE EFFICIENCY urban agglomerations has decreased by 10.53%, 10%, and 33.34%, respectively. The Pearl River Delta has the largest decline, which is 3.17 times and 3.33 times that of the Yangtze River Delta and Beijing-Tianjin-Hebei. Fourth, the land use efficiency of the Yangtze River Delta urban agglomeration is quite different. The central-peripheral phenomenon is evident for the Beijing-Tianjin-Hebei urban agglomerations and the Pearl River Delta urban agglomerations.

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