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Journal of Geographical Sciences >

2023 , Vol. 33 >Issue 2: 289 - 310

DOI: https://doi.org/10.1007/s11442-023-2083-0

Special Issue: Land for High-quality Development

Understanding the change of land space utilization efficiency with different functions and its coupling coordination: A case study of Urban Agglomeration in the Middle Reaches of the Yangtze River, China

  • YANG Bin , 1, 2 ,
  • YANG Jun , 1, 2, * ,
  • TAN Li 3 ,
  • XIAO Jianying 1, 2
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  • 1. School of Public Policy & Management, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
  • 2. Research Center for Transition Development and Rural Revitalization of Resource-Based Cities, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
  • 3. School of Management, Wuhan Polytechnic University, Wuhan 430048, China
*Yang Jun (1987-), PhD and Associate Professor, E-mail:

Yang Bin (1992-), PhD, specialized in land space utilization and management. E-mail:

Received date: 2021-09-03

  Accepted date: 2021-12-24

  Online published: 2023-02-21

Supported by

National Natural Science Foundation of China(42171249)

National Natural Science Foundation of China(42201270)

National Social Science Foundation of China(20BJY119)

Jiangsu Social Science Foundation(18GLC016)

Copyright

© 2023
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Abstract

Land spaces function in capacities of urban development, agricultural production, and ecological conservation, among many others. Research of land space utilization efficiency (LSUE) and coupling coordination relationships among its subsystems are significant for sustainable land space development. In this study, taking the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) as the study area, we establish a measurement index system to evaluate the LSUE (2000-2018) and analyze its coupling coordination degree by utilizing an improved coupling coordination model. The main results include the following. (1) The average efficiency levels of urban space and agricultural space in the UAMRYR increased 2000-2018, while the average efficiency of ecological space declined. (2) The spatial pattern of the LSUE values varied greatly, with the distributions of high-efficiency and low-efficiency levels significantly different. (3) The coupling degree of LSUE includes three types, i.e., high-level coupling, break-in, and antagonism. Each coupling degree type was characterized by change over time. (4) The proportion of areas with high coupling coordination and moderate coupling coordination increased from 2000 to 2018, while the proportion of areas with basic coupling coordination, moderate imbalance, and serious imbalance declined during this period. Given that the spatial differentiation of the LSUE and its coupling coordination, it is necessary to implement a differential land space development strategy in the UAMRYR. This study is helpful to promote the efficient utilization and coordinated development of land space utilization systems.

Key words： land use; utilization efficiency; coupling coordination; sustainable development; Urban Agglomeration in the Middle Reaches of the Yangtze River

Cite this article

YANG Bin , YANG Jun , TAN Li , XIAO Jianying . Understanding the change of land space utilization efficiency with different functions and its coupling coordination: A case study of Urban Agglomeration in the Middle Reaches of the Yangtze River, China[J]. Journal of Geographical Sciences, 2023 , 33(2) : 289 -310 . DOI: 10.1007/s11442-023-2083-0

1 Introduction

Since the reform and opening-up of China in recent years, the economy has developed rapidly (Han et al., 2020). GDP increased from 362.41 billion yuan to 9030.95 billion yuan from 1978 to 2018 with an annual average increase of 31.99% and the average urbanization level rose from 17.9% to 59.58% in the forty years (Wang et al., 2020; Lai and Zhu, 2022). This rapid development has caused a variety of issues. Urban space has been expanding in a disorderly fashion, with demand for construction land rapidly increasing (Xu et al., 2019; Li et al., 2020b). At present, about 15%-30% of the stock construction land in China is idle or being inefficiently utilized (Jiao et al., 2020). Furthermore, environmental impacts are often ignored when utilizing land space resources (Hanaček and Rodríguez-Labajos, 2018; Yang et al., 2020a). Large amounts of industrial wastewater, nitrogen oxides, dust, sulfur dioxide, and other pollutants are discharged, causing serious environmental problems (Nin et al., 2016; Zou et al., 2020; Oliveira-Andreoli et al., 2021). With the rapid development of urbanization and industrialization in China, some agricultural production spaces and ecological conservation spaces are co-opted for development, which poses threats to the food security and ecological security of the country (Su et al., 2016; Zhou and Li, 2017; Wang et al., 2021b). In addition, the excessive utilization of production materials, such as pesticides and chemical fertilizers, have led to the deterioration of water quality, soil compaction, heavy metal pollution, and reduction of biodiversity, resulting in the structure and function of regional ecosystems being seriously degraded (He et al., 2019; Liang et al., 2019; Zou et al., 2020). These issues indicate that the traditional mode of driving economic growth through the consumption and extensive utilization of land space resources is unsustainable. Therefore, improving land space utilization efficiency (LSUE) has become an inevitable choice to ensure sustainable development.
LSUE is a significant indicator for measuring regional land space development and utilization quality (Liu et al., 2019a; Yang et al., 2020a). Various studies to date have primarily focused on the measurement methods (Lewis and Brabec, 2005; Blancard and Martin, 2014; Yang et al., 2020a), spatial differences (Lu et al., 2018; Luo et al., 2020; Tan et al., 2021) and their influencing factors (Xie et al., 2018a; He et al., 2020; Yang et al., 2021). Others have explored the optimal utilization path and development strategies of land space resources from regional and national perspectives (Liu et al., 2019a; Dong et al., 2020; Chilombo and Van Der Horst, 2021). Overall, these studies only targeted the LSUE in a single land use type, such as urban construction land (Lu et al., 2018; Tan et al., 2021; Zhao et al., 2021a), industrial land (Xie et al., 2018a; Jiang, 2021), cultivated land (Wang et al., 2015; Kuang et al., 2020), development zones (Huang et al., 2017; Sun et al., 2020), or urban agglomeration areas (Yu et al., 2019; Ding et al., 2021). With new urbanization and regional coordinated development strategies, these studies are insufficient to promote optimal utilization and sustainable development of land space. In particular, the interactions and coupling coordination between urban efficiency, agriculture efficiency, and ecological efficiency is neglected.
The Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) is an important part of the Yangtze River Economic Belt of China. The total GDP of the UAMRYR reached 7027.547 billion yuan in 20181(Data come from the China Statistical Yearbook.), a core role in China’s economic development. The UAMRYR is a key area for promoting the economic rise of the central China region, a pioneering area for new urbanization, and a demonstration area for inland opening and cooperation (Sun et al., 2018; Zheng and He, 2021). Because of the prominent strategic position and rapid urbanization development, land use in this region has become increasingly intense (Yang et al., 2020a). Therefore, it is of great importance to coordinate land use among urban development, agricultural production, and ecological conservation in the UAMRYR.
In this study, we establish a measurement index system for the LSUE covering urban space, agriculture space, and ecological space with the undesirable externalities explicitly considered. An improved coupling coordination model is applied to investigate the interaction between the three land uses. The main objectives in this study include (1) evaluating the LSUE values using an SBM-Undesirable model; (2) revealing spatio-temporal characteristics of the LSUE 2000-2018 in the UAMRYR; (3) exploring the coupling coordination of the three subsystems. This research will be of significance for promoting the coordinated development of land resources, thereby ensuring the healthy and stable development of the economy and society.

2 Theoretical framework

2.1 Analysis of the land space utilization system

2.1.1 Structure and function of the land space utilization system

Land space is a complex territorial system composed of three subsystems, i.e., urban space utilization, agricultural space utilization, and ecological space utilization (Wang et al., 2018; Jin et al., 2020). The urban space utilization system has several functions, including supporting economic development, industrial agglomeration, social stability, and living services, and providing land, assets, and capital (Liu et al., 2019a; Kurowska et al., 2021). The land types of the urban space utilization system include urban construction land, industrial and mining land, and other construction lands. The agricultural space utilization system plays an important role in ensuring national food security and maintaining social stability (Tudor, 2014; Zhao et al., 2021b). The agricultural space utilization system provides grain, cotton, oil, fishing, animal husbandry, and a variety of raw materials for human survival and development. The system also has a strong social security function by accommodating labor force demand and providing a living space for rural residents (Yang et al., 2020b). These areas include cultivated land (paddy fields, drylands) and rural residential land. An ecological space utilization system can provide some indispensable substances for human survival, e.g., organic matter, air, and water (Wu et al., 2018; Fu et al., 2021). The system performs functions such as soil conservation, climate regulation, and biodiversity protection (Liu et al., 2019b; Grêt-Regamey and Weibel, 2020; Li et al., 2020a). These areas include forests, grasslands, water bodies, and other non-developed lands.

2.1.2 Interaction mechanisms of land space utilization system

The three subsystems interact to provide a basis for sustainable development (Figure 1). The urban space utilization system provides economic benefits including a variety of social security services (Liu et al., 2020a). It provides means of production (e.g., fertilizer, pesticide, seeds, plastic film), funds, and related technical support for agricultural space. A series of ecological space capacities, including restoration, protection, and governance, can be carried out by financial and technical support provided by the urban space subsystem. The agricultural space utilization system provides basic material resources, such as grain, cotton, linen, and oil for human survival and production, which plays a basic role in guaranteeing the high-quality development of urban space. The agricultural space utilization system also has some ecological service functions and can promote ecological space-efficient utilization. The ecological space utilization system supports human survival and production by providing natural products and services, which support the urban and agricultural subsystems. The three subsystems interact with each other and promote the stable and sustainable development of the overall land space utilization system.
Figure 1 The interaction of land space utilization subsystems

2.2 Workflow of this study

We designed a workflow of this study (Figure 2) for the objectives which were proposed in the Introduction. First, we established the measurement index system of LSUE from the perspective of input and output. Second, the SBM-Undesirable model was used to quantitatively measure the level of LSUE and examine the spatio-temporal characteristics of land space subsystem efficiencies. Third, we modeled the coupling coordination relationship among land space subsystem efficiencies. Fourth, land space development strategies based on the spatial differences are proposed to promote the efficient utilization and coordinated development of the overall land space utilization system. Additionally, this paper concentrates on the administrative division of the Urban Agglomeration in the Middle Reaches of the Yangtze River at the county level from 2000 to 2018, including 165 counties (or districts) in total.
Figure 2 The workflow of this study

3 Materials and methods

3.1 Study area

The UAMRYR is situated in central China, covering three provinces: Hubei, Hunan, and Jiangxi (Figure 3). The UAMRYR’s total area is about 326,900 km2 and the region is composed of three metropolitan areas, i.e., Wuhan Metropolis, Poyang Lake City Group, and Changsha-Zhuzhou-Xiangtan Metropolis. UAMRYR is in a basin, with the surrounding areas higher elevation and the center flat terrain. The land space types in the UAMRYR include urban, agricultural, and ecological space, accounting for 4.86%, 36.77%, and 58.37% of the total area, respectively. In recent years, the UAMRYR has developed rapidly and has become an important agricultural and industrial production base of China. The GDP of the region was 694.21 billion yuan in 2000 and it reached 7.03 trillion yuan in 2018, with an average growth rate of 13.90%1( Data come from China Statistical Yearbook.). Moreover, the urbanization level in this region increased from 19.23% in 2000 to 54.71% in 2018.
Figure 3 Location and elevation of the Middle Reaches of the Yangtze River (UAMRYR)

3.2 Measurement index system of land space utilization efficiency

There are not only interactions between the three subsystems, but also significant differences in utilization objectives. Therefore, the measurement indicators of different space utilization efficiency should be designed according to their functional characteristics. The measurement index of urban space utilization efficiency was mainly used to measure the quality of urban space utilization, which was divided into two parts: urban space inputs and outputs of social, economic, and environmental aspects, which included eight variables. We selected urban construction land areas to characterize land input (AI-1). Fixed asset investment reflects the scale, structure, and cost of the government’s investment in urban space, so we used the total fixed-asset investment to characterize the capital input (AI-2). The number of employees in the secondary and tertiary industries was used to represent the input of labor (AI-3). As GDP is an important indicator that characterizes regional economic development, we selected the GDP of the secondary and tertiary industries to represent the economic output (AO-1). The employment of urban residents maintains social stability and promotes social development. Therefore, the average salary of urban employees was used to represent the social output of urban space utilization (AO-2). Urban space not only results in the production of industrial products and economic benefits but also causes pollution (Tan et al., 2021; Zhang et al., 2021). Based on existing research (Yue and Li, 2017; Lu et al., 2018; Sun et al., 2018; Yu et al., 2019), we chose industrial wastewater discharge (AO-3), industrial sulfur dioxide discharge (AO-4), and the amount of solid waste (AO-5) to represent the negative environmental outputs.
The measurement indicators of agricultural space utilization efficiency also include two parts, i.e., agricultural inputs and outputs of social, economic, and environmental aspects, with a total of nine variables. Inputs variables comprise three parts: land, labor, and means of production. The sown area of crops can better reflect the production and utilization of agricultural land than the area of cultivated land at the end of the year (Yang et al., 2021), so the sown area of crops is used to characterize the land input for agricultural space utilization (BI-1). We selected the number of employees in the primary industry to characterize the input of labors for agricultural space utilization (BI-2). The means of production used in agricultural space primarily refer to various machinery, fertilizers, pesticides, and seeds needed for agricultural production (Kuang et al., 2020; Xie et al., 2018b). We selected the total power of agricultural machinery (BI-3), the amount of agricultural film used (BI-4), and the amount of chemical fertilizer (BI-5) to characterize the input of agricultural production materials.
Among the outputs, the total agricultural output value reflects the economic benefits brought by various crops (Han and Zhang, 2020), therefore, we choose the annual agricultural output value to represent the economic output (BO-1). The per capita annual income of farmers reflects the well-being of rural residents, which plays an important role in maintaining the stability and development of rural society (Liu et al., 2020b). Based on this, we select the per capita annual income as the social output for agricultural space (BO-2). However, because of agricultural machinery, plowing activities, and the use of agricultural films, fertilizers, and pesticides, pollution is inevitable. Referring to existing literature (Feng et al., 2015; Xie et al., 2018b; Kuang et al., 2020; Yang et al., 2022), we choose agricultural non-point source pollution and carbon emissions as the undesirable outputs in this study. The main sources of agricultural non-point pollution are chemical fertilizers, organic fertilizers, and farmland wastes. The calculation method was as follows. First, we calculated the total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD) resulting from agricultural production (Table 1). Then, we converted to equal standard emissions based on the formula: equal standard pollutant emissions = initial pollutant emissions/pollutant emission assessment criteria. According to Feng et al. (2015), the emission assessment standards of COD, TN, and TP were set as 20 mg/L, 1 mg/L, and 0.2 mg/L, respectively.
Table 1 Composition and measurement of agricultural non-point source pollutions
Pollution source Measurement Reference
Chemical fertilizers Nitrogen fertilizer, phosphorus fertilizer, compound fertilizer × TN/TP pollution coefficient × loss rate Li et al. (2011)
Organic fertilizers Rural population and the number of pigs, cattle, sheep, and poultry ×
(1- the rate of utilization) × Fecal and urine excretion coefficient × pollution coefficients of COD, TN, and TP		 Chen et al. (2006);
Pan and Ying (2013)
Farmland wastes Crop straw, fruit, and vegetable outputs × pollution coefficients of COD, TN, and TP Li et al. (2011)
Carbon emissions from agricultural production mainly come from the two aspects. One aspect is the use of means of agriculture production, such as chemical fertilizers and plastic film for agriculture. Another aspect is carbon emissions from the use of electricity and fossil fuels for agricultural machinery and irrigation. The estimation formula is:
$C=\mathop{\sum }^{}{{E}_{i}}\times {{\varepsilon }_{i}}$
where C is the total carbon emission from agricultural production, Ei is the carbon emission from various carbon sources, and εi is the carbon emission coefficient corresponding to carbon sources. According to relevant research, the carbon emission coefficients are summarized in Table 2.
Table 2 Agricultural carbon emission coefficients
Carbon source Coefficients Units References
Chemical fertilizers 0.896 kg·kg−1 West and Marland (2002)
Plastic film 5.170 kg·kg−1 Li et al. (2011)
Agricultural machinery 0.190 kg·kw−1 Tian et al. (2012)
Agricultural plowing 312.580 kg·km−2 Tian and Zhang (2013)
Agricultural irrigation 20.476 kg·hm−2 Li et al. (2011)
The measure indicator of ecological space utilization efficiency is the ability to maintain regional water and soil conservation, climate regulation, environmental purification, and biodiversity. In this study, we take the proportion of ecological land area as the input variable (CI-1); forests, grasslands, garden lands, water bodies, and marshland are defined as ecological land (Wang et al., 2018; Chen et al., 2019). Socio-economic development inevitably consumes energy, which has some impacts on the ecological environment. Therefore, we chose energy consumption per unit GDP as the energy inputs (CI-2). We selected the ecosystem service value as the output variable (CO-1), which is referred to in the research results of Xie et al. (2015). The calculation is:
$ESV=\mathop{\sum }^{}{{S}_{k}}\cdot V{{C}_{k}}$
where ESV is the total value of ecosystem services, Sk represents the land area of the kth land type, and VCk represents the evaluation value coefficient of the kth land type.

3.3 SBM-Undesirable model

Data envelopment analysis (DEA) is widely used to measure efficiency, as proposed by Charnes et al. (1978), which is referred to as the Charnes-Cooper-Rhodes (CCR) model. In traditional DEA models, the efficiency measurements of homogeneous units are mainly based on the radial and angle levels to minimize inputs and maximize outputs. However, it ignores the undesirable outputs in the evaluation process. To end this, Tone (2001) overcame this problem by developing a slack-based measure (SBM) model based on non-radials and non-angles, which can measure the inefficiency incorporating the desirable and undesirable outputs at different rates. The specific computational formulas are:
$\text{min}\rho =\frac{\frac{1}{m}\mathop{\sum }_{i=1}^{n}\left( {{x}^{-}}/{{x}_{i0}} \right)}{\frac{1}{{{r}_{1}}+{{r}_{2}}}\left( \mathop{\sum }_{s=1}^{{{r}_{1}}}{{y}^{d}}^{-}/y_{s0}^{d}+\mathop{\sum }_{q=1}^{{{r}_{2}}}{{y}^{u}}^{-}/y_{q0}^{u} \right)}s.t.\ {{x}^{-}}\ge \underset{j=1,\ne 0}{\overset{n}{\mathop \sum }}\,{{x}_{ij}}{{\lambda }_{j}};\ {{y}^{d}}^{-}\le \underset{j=1,\ne 0}{\overset{n}{\mathop \sum }}\,y_{sj}^{d}{{\lambda }_{j}};{{y}^{d}}^{-}\ge \underset{j=1,\ne 0}{\overset{n}{\mathop \sum }}\,y_{qj}^{d}{{\lambda }_{j}};\ {{x}^{-}}\ge {{x}_{0}};{{y}^{d}}^{-}\le y_{0}^{d};{{y}^{u}}^{-}\ge y_{0}^{u};{{\lambda }_{j}}\ge 0,i=1,2,\cdots,m;j=1,2,\cdots,n,j\ne 0; s=1,2,\cdots,{{r}_{1}};q=1,2,\cdots,{{r}_{2}}$
where ρ is the value of LSUE, n is the number of evaluation units, the evaluation index consists of input index (m) and output index (desirable output index r1 and undesirable output index r2), x, yd, and yu are the elements of the input matrix, including desirable output matrix and undesirable output matrix, respectively. The vectors of x-, yd-, and yu- are the input relaxation vector, the desirable output relaxation vector, and the undesirable output relaxation vector, respectively; λ is the weight vector.
Table 3 The measurement index system of the land space utilization efficiency
Criteria Level indicators Units Attributes
Urban space
utilization
efficiency (A)		 Inputs Urban construction land area (AI-1) ha +
Total investment in fixed assets (AI-2) 100 million yuan +
People employed in secondary and tertiary industries (AI-3) persons +
Outputs GDP of secondary and tertiary industries (AO-1) 100 million yuan desirable output
Average wages of urban workers (AO-2) yuan desirable output
Discharge of industrial wastewater (AO-3) tons undesirable output
Emissions of industrial SO2 (AO-4) tons undesirable output
Emissions of solid waste discharge (AO-5) tons undesirable output
Agricultural
space utilization
efficiency (B)		 Inputs The sown area of crops (BI-1) ha +
People employed in primary industry (BI-2) person +
Total power of agricultural machinery (BI-3) 10,000 kilowatts +
Amount of agricultural film (BI-4) tons -
Amount of chemical fertilizer (BI-5) tons -
Outputs The annual output value of agriculture (BO-1) 100 million yuan desirable output
Per capita annual income of farmers (BO-2) yuan desirable output
Agricultural non-point source pollution (BO-3) ton undesirable output
Carbon emissions (BO-4) ton undesirable output
Ecological
space utilization
efficiency (C)		 Inputs The proportion of ecological land area (CI-1) % +
Energy consumption per unit of GDP (CI-2) tons -
Outputs Ecosystem service value (CO-1) yuan desirable output

3.4 Coupling coordination degree model

The concept of coupling originates from physics, which is widely used to describe the interaction between two or more subsystems (Zhang et al., 2008; Li et al., 2012; Tang, 2015). The classical formula of the coupling degree model is:
$\text{ }\!\!~\!\!\text{ }C=\sqrt[n]{\left\{ ({{u}_{1}},{{u}_{2}},\cdots,{{u}_{n}})/\left[ \mathop{\prod }^{}({{u}_{i}}+{{u}_{j}}) \right] \right\}}$
where u1, u2, …, and un represent the measure functions of each subsystem.
This model is simple and practical. However, the measurement of the coupling degree is zero when one subsystem’s value is zero. In addition, the numerical range of the measurement is usually narrow and lacks a strict hierarchy. Referring to existing research (Zhang et al., 2008; Liu et al., 2018; Tomal, 2021), we derived an improved coupling degree model as follows:
$C={{\left[ 2-\frac{3\times \left( U_{1}^{2}+U_{2}^{2}+U_{3}^{2} \right)}{{{\left( {{U}_{1}}+{{U}_{2}}+{{U}_{3}} \right)}^{2}}} \right]}^{K}}$
where K represents the adjustment coefficient and K≥3. The value range of C is [01]. The larger it is, the higher the coupling degree is. When the values of U1, U2 and U3 are equal, the value of C is 1, indicating that the coupling degree is the highest. When the value of C is 0, the subsystems are completely uncoupled and are in an independent state.
Based on the coupling degree measurement model, we expanded the connotation of the model and constructed the coupling coordination degree model. The calculation formulas are:
$U=\text{ }\!\!~\!\!\text{ }\alpha {{U}_{1}}+\beta {{U}_{2}}+\gamma {{U}_{3}}$
$D=\sqrt{C\times U}$
where C is the coupling degree, D represents the coupling coordination degree, U is the comprehensive value of the LSUE, and α, β and γ are the weight coefficients of U1, U2, and U3, respectively.

3.5 Data sources

The data in this study include land use, Digital Elevation Model (DEM) data, and socioeconomic data. Land use data were derived from the Resources and Environmental Data Center of The Chinese Academy of Sciences (http://www.resdc.cn) in 2000, 2005, 2010, 2015, and 2018, with a resolution of 30 m × 30 m. We reclassified the land use types into six categories (i.e., arable land, woodland, grassland, water, construction land, and unutilized land). The DEM data at a 30 m spatial resolution were derived from the website of the Geospatial Data Cloud, Chinese Academy of Sciences (http://www.gscloud.cn). Moreover, with the help of surface analysis in ArcGIS, topographic factors, such as elevation and slope, were obtained based on DEM data. Socioeconomic data include Gross Domestic Product (GDP), population, urban development, agricultural production, medical and health, and environment. These data were collected from the Statistical Yearbooks for Hubei, Hunan, and Jiangxi provinces in 2001, 2006, 2011, 2016, and 2019. Energy consumption data, such as coal, petroleum, and natural gas, were derived from the Chinese Energy Statistical Yearbook and City Statistical Yearbook in the study area.

4 Results

4.1 The evolution of land space utilization efficiency in the UAMRYR

Based on Formula (3), we measured urban space utilization efficiency in the UAMRYR from 2000 to 2018. Then we calculated the regional and integral average efficiency of the study area, as shown in Figure 4. It was found that the urban efficiency continuously improved from 2000 to 2018, increasing from 0.556 in 2000 to 0.703 in 2018, with an average increase of 6.06%. From the perspective of regions, the efficiency of three sub-metropolitan areas all improved 2000-2018. The efficiency of the Wuhan metropolitan area increased from 0.528 in 2000 to 0.682 in 2018, with an average increase of 6.68%. The efficiency of Changsha-Zhuzhou-Xiangtan urban agglomeration increased from 0.582 in 2000 to 0.745 in 2018, with an average increase of 6.36%. The efficiency of Poyang Lake urban agglomeration increased from 0.572 in 2000 to 0.695 in 2018, with an average increase of 5.01%. The average value of urban space use efficiency was highest in the Changzhou-Zhuzhou- Xiangtan urban agglomeration, the second-highest in the Poyang Lake urban agglomeration, and the lowest in the Wuhan Metropolitan area.
Figure 4 The results of urban space use efficiency in the UAMRYR from 2000 to 2018
Based on Formula (3), we measured agriculture space utilization efficiency in the UAMRYR 2000-2018. Then we calculated the regional and integral average efficiency of the study area, as shown in Figure 5. It is found that agriculture efficiency continuously improved from 2000 to 2018. The efficiency value increased from 0.495 in 2000 to 0.686 in 2018, with an average increase of 8.53%. From the perspective of regions, the agricultural space utilization efficiency of the three sub-metropolitan areas all improved 2000-2018. The efficiency of the Wuhan metropolitan area increased from 0.550 in 2000 to 0.746 in 2018, with an average increase of 7.91%. The efficiency of Changsha-Zhuzhou-Xiangtan urban agglomeration increased from 0.460 in 2000 to 0.672 in 2018, with an average increase of 9.96%. The efficiency of Poyang Lake urban agglomeration increased from 0.445 in 2000 to 0.613 in 2018, with an average increase of 8.33%. The average value of agriculture efficiency was the highest in the Wuhan Metropolitan area, the second-highest in the Changzhou-Zhuzhou-Xiangtan urban agglomeration, and the lowest in Poyang Lake urban agglomeration.
Figure 5 The results of agricultural space use efficiency in the UAMRYR from 2000 to 2018
We also measured ecological space use efficiency in the UAMRYR 2000-2018 based on Formula (3) and calculated the regional and integral average efficiency, as shown in Figure 6. It is found that the average efficiency in the UAMRYR declined from 0.661 in 2000 to 0.612 in 2018. From the perspective of regions, the ecological efficiency of the three sub-metropolitan areas all declined from 2000 to 2018. The efficiency of the Wuhan metropolitan area declined from 0.720 in 2000 to 0.678 in 2018. The efficiency of Changsha-Zhuzhou-Xiangtan urban agglomeration declined from 0.639 in 2000 to 0.570 in 2018.
Figure 6 The results of ecological space use efficiency in the UAMRYR from 2000 to 2018
The efficiency of Poyang Lake urban agglomeration declined from 0.598 in 2000 to 0.553 in 2018. The average value of ecological efficiency was highest in the Wuhan metropolitan area, the second-highest in the Changsha-Zhuzhou-Xiangtan urban agglomeration, and the lowest in Poyang Lake urban agglomeration.

4.2 Spatio-temporal characteristics of land space utilization efficiency in the UAMRYR

4.2.1 Urban space utilization efficiency

Figure 7 depicts the spatio-temporal variations of urban space utilization efficiency in the study area for the selected five years. In addition, to compare the efficiency level of different areas, we divided the efficiency values into four categories: high efficiency (0.75-1.00], medium-high efficiency (0.50-0.75], medium-low efficiency (0.35-0.50], and low efficiency (0.00-0.35]. Overall, the number of counties (districts) with high and medium-high efficiency levels increased, while the number of counties (districts) with medium-low and low-efficiency levels decreased 2000-2018. This indicates that urban space use efficiency in the UAMRYR showed a significant improving trend during the study period. Areas with high-efficiency levels were mainly distributed in Wuhan, Changsha, Nanchang, and their surrounding counties. It is worth noting that the efficiency in some counties (districts) of Yichang and Xiangyang increased after 2015. The main reasons may be the increasing input of production factors, optimization of industrial structure, and rapid improvement of economic development level in these areas. Areas of low efficiency were primarily distributed around the UAMRYR and some counties (districts) bordering Hubei and Jiangxi. These areas are mostly mountainous with a high elevation and large slope gradient, which is not conducive to the development and utilization of urban space. Being far away from the central cities, some favorable factors, such as industrial agglomeration, factor inputs, and preferential policies are limited, resulting in the urban efficiency remaining at a lower level.
Figure 7 Spatio-temporal variations of urban space use efficiency in the UAMRYR from 2000 to 2018

4.2.2 Agricultural space utilization efficiency

Figure 8 shows the spatio-temporal variations of agricultural space utilization efficiency in the study area for the selected five years. Moreover, in order to compare the efficiency level of different areas, we divided the efficiency values into four categories: high efficiency (0.75-1.00], medium-high efficiency (0.50-0.75], medium-low efficiency (0.35-0.50], and low efficiency (0.00-0.35]. Overall, the number of counties (districts) with high and medium-high efficiency levels increased, while the number of counties (districts) with medium-low and low-efficiency levels decreased 2000-2018. At the end of 2000, the number of counties (districts) with high efficiency, medium-high efficiency, medium-low efficiency, and low efficiency was 9, 54, 62, and 40, respectively. At the end of 2018, the number of counties (districts) with high efficiency and medium-high efficiency increased to 22 and 94, the amount of medium-low efficiency declined to 48, while only one county was at low efficiency. Areas with high efficiency were distributed in the Jianghan Plain (e.g., Jianli County, Zhongxiang City, and Chibi City), Dongting Lake Plain (e.g., Yueyang County and Huarong County), and Poyang Lake Plain (e.g., Yugan County and Boyang County). These areas have flat terrain and fertile soil, as well as abundant light, heat, and water, resulting in high agricultural outputs and high agricultural production efficiency. Areas with low efficiency are concentrated in the marginal areas of the region and mountainous areas bordering Hubei and Jiangxi Provinces. Only two counties (Zigui and Xingshan) had low efficiency at the end of 2015, and only one county (Zigui) had low efficiency at the end of 2018, indicating that agricultural space utilization efficiency in the UAMRYR primarily was at high and medium-high levels after 2015.
Figure 8 Spatio-temporal variations of agricultural space use efficiency in the UAMRYR from 2000 to 2018

4.2.3 Ecological space utilization efficiency

Figure 9 depicts the spatio-temporal variations of ecological space utilization efficiency in the study area for the selected five years. Overall, the number of counties (districts) with high and medium-high efficiency levels decreased first and then increased, the number of counties (districts) with medium-low efficiency levels was stable, and the number of counties (districts) with low-efficiency levels increased first and then decreased. Areas with high-efficiency levels were mainly distributed in the surrounding areas of the region and border areas of Hubei and Jiangxi. The main reason may be that the terrain in these areas is mountainous with rich ecological resources, and some functions such as water and soil conservation, air purification, water conservation, and biodiversity are robust. Also, urbanization and industrialization in these areas are relatively low and the ecological environment is relatively preserved. Areas with low efficiency are primarily located in the central area of the UAMRYR, especially in the surrounding areas of Wuhan, Changsha, and Nanchang.
Figure 9 Spatio-temporal variations of ecological space use efficiency in the UAMRYR from 2000 to 2018

4.3 Coupling coordination of land space utilization efficiency

The coupling degrees of the LSUE were calculated based on formula (5) and were visualized with ArcGIS 10.2 software as shown in Figure 10. To analyze the intensity of interaction among urban efficiency, agricultural efficiency, and ecological efficiency, we divided the coupling degrees of LSUE into three categories: high-level coupling state (>0.80), break-in state (0.50-0.80], and antagonism state (0.00-0.50]. The coupling degrees of the LSUE were generally at the break-in stage and high coupling stage 2000-2018 (Figure 11). This suggests that land space sub-systems have obvious interactive effects with one another. The counties (or districts) with high-level coupling stages were mainly distributed in a strip from the northwest to southeast of the study area, and these areas gradually expanded over time. Areas with break-in stages were distributed in a wide range, covering the entire UAMRYR, whereas areas with antagonism stage were few, mainly distributed around the study area and at the border of Hubei and Jiangxi provinces. The proportion of the counties (or districts) at the high-level coupling stage of the three efficiencies increased during the study period, from 50.91% in 2000 to 58.79% in 2018 (Figure 11). The proportion at the break-in stage fluctuated, whereas the proportion of the counties (or districts) at the antagonism stage declined over time from 14.55% in 2000 to 6.06% in 2018. In sum, the charge in proportions for coupling stages indicated that the interactive effects of the three subsystems were increasing over time.
Figure 10 Spatio-temporal variations of coupling degree in the UAMRYR from 2000 to 2018
Figure 11 The proportion of areas at different coupling stages 2000-2018 in the UAMRYR
Based on the results of coupling degree in the UAMRYR, we calculated the coupling coordination of the three efficiencies by formulas (6) and (7) at the county level in the study area 2000- 2018. Referring to existing research (Cheng et al., 2019; Yang et al., 2020b), we divided the coupling coordination degree into five states: high coupling coordination (0.800-1.000], moderate coupling coordination (0.600-0.799], basic coupling coordination (0.400- 0.599], moderate imbalance (0.200- 0.399], and serious imbalance (0.000-0.199]. Spatio-temporal distribution was visualized with ArcGIS 10.2 software as shown in Figure 12. Spatially, the spatial pattern characteristics were similar to that of the coupling degree in the UAMRYR. The areas in various coupling coordination states showed a relatively random distribution in the study area. During the study period, the total number of counties (or districts) with moderate coupling coordination and basic coupling coordination types accounted for more than 90% of areas (Figure 13). From the perspective of temporal scale, the proportion of counties (or districts) with high coupling coordination and moderate coupling coordination increased, while the proportion of counties (or districts) with basic coupling coordination, moderate imbalance, and serious imbalance declined 2000-2018. For instance, the change of moderate imbalance was 4.85%→2.42%→1.21%→0.00%→0.00% over time, the change of basic coupling coordination was 49.09%→42.42%→32.73%→33.33%→24.24%, and that of moderate coupling coordination was 45.45%→55.15%→63.64%→62.42%→69.09%. This indicated that the coupling coordination degree of the LSUE in the UAMRYR has shown a good trend during the study period.
Figure 12 Spatio-temporal variations of coupling coordination degree in the UAMRYR from 2000 to 2018
Figure 13 The proportion of areas at different coupling coordination stages 2000-2018 in the UAMRYR

5 Discussion

5.1 Factors influencing land space utilization efficiency

The LSUE levels are influenced by multiple factors, such as natural geographic factors, socioeconomic development level, and regional policies (Wang et al., 2015; Yue and Li, 2017; Liu et al., 2020a; Yang et al., 2021). Natural geographic factors, including topography, climate, hydrology, and location conditions are the basis for land space utilization, which fundamentally affect the LSUE (Liu et al., 2021a; Zhou et al., 2017; Zhang et al., 2020). Specifically, elevation and topographic variation directly restrict the development and utilization of urban space (Birhane et al., 2019). Likewise, for agricultural space, the differences in natural conditions such as slope, elevation, temperature, and precipitation are the basis to determine agricultural production activities (Pan and Ying, 2013; Liu et al., 2020b). From the perspective of location condition, the better is it, the greater the land space development intensity (Wang et al., 2021a). The distance from the central city and main traffic arteries has a significant influence on the development and utilization of urban space through factors such as land prices and agglomeration effects (Lu and Ke, 2018; Gao et al., 2020; Qu et al., 2020). The distance to rivers, main roads, and the nearest rural settlements can affect agricultural space development and affects drive its spatio-temporal trends (Liu et al., 2020b; Ma et al., 2021). For the ecological space, the farther away from the main roads and residential areas, the less disturbance from human activities, and the higher of ecosystem service values (Chen et al., 2020; Ouyang et al., 2021).
The socio-economic development level directly affects the degree of land space development and utilization, which has a direct impact on the investment capacity of urban space, determines its development and utilization intensity, and thus plays an important role in mediating efficiency (Lu et al., 2018; Gao et al., 2020; Zhao et al., 2021a). For agricultural space, the impact of socioeconomic development on agricultural efficiency includes two aspects. First, the socio-economic development level affects the input of means of production, such as labor force, machinery, seeds, and plastic films for agricultural production, and directly affects the utilization efficiency (Yang et al., 2021). Second, the level of agricultural science and technology advances in rapid socioeconomic developing regions is generally high, which will further promote the optimization of agricultural space (Xie et al., 2018b; Kuang et al., 2020). For ecological space, the socio-economic development and urban land expansion will inevitably co-opt large amounts of ecological space, which results in environmental pollution and ecological deterioration, thus affecting ecological space utilization efficiency (Wang et al., 2018; Zheng and He, 2021).
In addition, some policy systems have an important influence on LSUE. Regional integration can upgrade and optimize the urban industrial structure, reduce carbon emissions, and improve urban space utilization efficiency by promoting factor flow and regional cooperation (Gao et al., 2020; Wen et al., 2021). With industrialization and urbanization, the area of cultivated land is decreasing and its quality is degraded, which poses a great threat to the country’s food security (Deng et al., 2015; Yang et al., 2021).To this end, the government has formulated a series of measures and policies to protect cultivated land, which plays an important role in improving farmland utilization efficiency and ensuring national food security. Faced with the reality of environmental deterioration in recent years, the government introduced a series of ecological compensation policies, which improved ecological efficiency and promoted sustainable development (Hu et al., 2019; Lu et al., 2021). Also, some environmental systems, such as carbon emissions permit trading systems and environmental pollution control investments, have impacts on LSUE (Xian et al., 2020; Fan et al., 2021).

5.2 Policy implications

Based on this study’s findings, several policy implications can be drawn. First, because of the different patterns of coupling and coordination relationships among land space sub-system efficiencies, a differential land space development strategy should be implemented that varies by region. Areas surrounding the UAMRYR with high ecological efficiency should actively explore an eco-priority growth path to promote sustainable socio-economic growth. For the rapidly developing economic areas, such as Wuhan, Changsha, and Nanchang, it is necessary to optimize the industrial structure and further improve urban space utilization efficiency (Liu et al., 2021b). Investments should be increased for ecological space protection and promote the coordinated development of urban construction and ecological conservation in these areas. For agricultural production bases in the central UAMRYR, the government should strengthen the cultivated land protection to further improve agricultural production and food security. Further, scientific and technological innovation strategies should be advanced. This will be helpful to drive the transformation of secondary and tertiary industries to gradually eliminate enterprises with high energy consumption and low output. It will also promote the large-scale modernization of agricultural production to reduce production costs and increase outputs. Finally, the government should strengthen regional cooperation and integrated development to improve the LSUE in the whole region.

5.3 Limitations

This paper has some limitations. First, the coupling and coordination relationships of LSUE were based on limited available data, leaving some important influencing mechanisms remain unclear. Future studies should select specific factors and models to reveal the key mechanisms driving the coupling coordination of the LSUE. Second, the conclusions in this paper are only based on land space optimal utilization. How to guide land space optimal utilization based on the utilization efficiency and to solve specific issues in practice, such as through the improvement of policy regulations, warrants more study.

6 Conclusions

In this paper, taking the UAMRYR as the study area, we establish a measurement index system to quantify the LSUE by using the SBM-Undesirable model 2000-2018. An improved coupling coordination model was applied to investigate the coupling coordination degree of the LSUE. The main conclusions included the following. (1) The average efficiencies of urban space and agricultural space in the UAMRYR both increased from 2000 to 2018, while the average efficiency of ecological space declined. (2) The spatial pattern of the LSUE varies greatly. Areas with a high level of urban space utilization efficiency were mainly distributed in Wuhan, Changsha, Nanchang, and their surrounding areas, while areas with low-efficiency levels were primarily distributed around the UAMRYR and some areas bordering Hubei and Jiangxi. Areas with a high level of agricultural space utilization efficiency were mainly distributed in the central UAMRYR, while areas with low efficiency concentrated in the marginal areas of the region. Areas with a high level of ecological space utilization efficiency were mainly distributed in the surrounding areas of the UAMRYR and the borders of Hubei and Jiangxi, while areas with low efficiency are mainly located in the central areas. (3) The coupling degree of LSUE in the UAMRYR includes three types, i.e., high-level coupling, break-in degree, and antagonism. The proportion of areas at the high-level coupling stage of the three efficiencies increased during the study period, from 50.91% in 2000 to 58.79% in 2018. The proportion at break-in degree fluctuated through the period. The proportion of areas at the antagonism degree level declined over time, from 14.55% in 2000 to 6.06% in 2018. (4) The proportion of areas with high coupling coordination and moderate coupling coordination continuously increased, while the proportion of areas with basic coupling coordination, moderate imbalance, and serious imbalance during 2000-2018. These findings can promote the efficient utilization and coordinated development of land space systems. Given the spatial differentiation of the LSUE and its coupling coordination degree, it is necessary to implement a land space development strategy that differs by region in the UAMRYR.

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