Assessing Land Use Efficiency in the Tarim River Basin: A Coupling Coordination Degree and Gravity Model Approach

Abstract

The Tarim River Basin, a core region for economic development and ecological security in China’s inland arid areas, faces the pressing challenge of synergistically improving land use efficiency to resolve human-land conflicts under water resource constraints and achieve sustainable development. Based on the “economic-social-ecological” benefit coordination theory, this study constructs a land use efficiency evaluation system with 16 indicators and integrates the coupling coordination degree model and gravity model to quantitatively analyze the spatiotemporal differentiation patterns and coupling mechanisms of land use efficiency in the basin from 1990 to 2020. Results show that economic and social benefits of land use increased during this period, exhibiting a “high-north, low-south” spatial pattern, while ecological benefits remained relatively high but declined gradually. The coupling coordination degree of subsystem benefits displayed significant spatial heterogeneity, with an overall upward trend, where composite factors emerged as the primary constraint. Spatially, land use efficiency coupling coordination evolved from “core polarization” to “axial expansion” and finally “networked synergy,” with stronger linkages concentrated in oasis irrigation districts. These findings provide theoretical support for ecological conservation, water management, and policy-making in southern Xinjiang, offering pathways to synergize the “economic-social-ecological” system and promote sustainable development in arid regions.

1. Introduction

The arid regions in Northwestern China are among the most ecologically vulnerable areas in both China and globally [1]. Due to extreme climatic conditions and geographical factors, the region has long been plagued by issues such as water scarcity, low ecological carrying capacity, and conflicts over land use, which significantly constrain the region’s sustainable development. Studies indicate that the water shortage in the arid regions has become a major bottleneck for economic and social development, while the fragile ecosystems are exacerbated by desertification, soil salinization, and other environmental issues [2,3]. Overexploitation of water resources and unreasonable land use have led to the degradation of the environment, further intensifying the region’s ecological vulnerability. Therefore, how to effectively manage resources and reconcile economic, social, and ecological benefits has become a key issue for the sustainable development of arid regions.

The Tarim River Basin, as the largest inland river basin in China, is located at the heart of Central Asia’s arid desert region [4]. It serves as a crucial ecological and economic barrier for southern Xinjiang [5]. As the core area for both regional economic development and ecological security, the Tarim River Basin plays a pivotal role in ensuring water supply, rational land resource utilization, and ecological protection [6]. However, the basin is now facing unprecedented ecological crises due to the combined impacts of climate change and human activities [7]. Since the 1950s, industrialization, population growth, and large-scale agricultural development have led to excessive water extraction, inefficient irrigation systems, and pollution, which have significantly reduced forest and wetland resources, lowered groundwater levels, and increased land salinization and desertification [8]. These environmental challenges not only threaten the basin’s ecological systems but also directly affect human livelihoods and economic activities [9].

Currently, the inefficiency and imbalance in land use within the Tarim River Basin remain prominent [10]. Research shows that the continuous expansion of cropland and construction land has led to the encroachment of forests, grasslands, and water bodies, particularly in areas like Aksu and Kashgar, where intensifying human activities have disrupted land use structures, significantly decreasing the proportion of ecological land [11]. At the same time, issues such as water shortages, ecosystem degradation, and advancing desertification are severely affecting the quality of life and economic development of local residents [12]. Scientifically evaluating the land use efficiency levels and coordination characteristics in different regions of the basin and revealing the optimization potential of current land use is crucial for achieving sustainable land development and efficient utilization. This will provide valuable theoretical support for resource management in arid regions.

There has been substantial research on land use efficiency and its coupling coordination degree in both theoretical and empirical contexts [13,14]. Studies have extensively discussed the conceptual implications [15], evaluation methods [16], influencing factors [17], and driving mechanisms related to land use efficiency and its coordination [18]. Land use efficiency is characterized by its multi-dimensional and interrelated nature, where economic output, social welfare, and ecological services together constitute the overall benefits. These dimensions not only mutually promote each other but also present potential conflicts [19]. Most existing studies focus on national and provincial scales. At the national level, research often concentrates on macro strategies and regional coordination, such as the development of the Yangtze River Economic Belt and cross-provincial ecological and economic collaboration [20]. At the provincial level, studies focus on regional disparities, revealing the spatial differentiation of economic gradients and ecological benefits, as exemplified by the “high south, low north” pattern in Jiangsu Province [21]. Multi-scale studies help reveal the mechanisms behind benefit differentiation and provide a basis for targeted policy formulation [22].

However, existing research on land use in the Tarim River Basin has been primarily confined to studies on water resources, habitat quality, and land use changes [23,24,25]. Comprehensive evaluations of the overall land use efficiency and the quantitative characterization of the coupling coordination among multiple benefits across time and space have been relatively insufficient. Land use efficiency is an integrated reflection of the economic, social, and ecological subsystems within the human–land system. These three dimensions are not isolated but interact dynamically, forming a complex coupling system characterized by mutual promotion and restriction. Drawing on the social–ecological systems (SES) theory and the land use multifunctionality framework [26,27], economic efficiency provides the material foundation for social development and ecological protection, while social efficiency promotes institutional support, infrastructure construction, and livelihood improvement, which in turn influence land utilization intensity and structure. Ecological efficiency ensures the environmental capacity and resource sustainability necessary for both economic production and social welfare. The interactions among these subsystems may lead to both synergistic and conflicting outcomes [28]. For instance, economic expansion can enhance social welfare but may exert pressure on ecological systems, whereas ecological restoration may limit short-term economic gains while improving long-term social stability and sustainability. More importantly, the counties and cities within the basin are not isolated units, and their land use efficiency and coordination status are profoundly influenced by spatial interactions with neighboring regions. Therefore, in-depth research on the spatial coordination of land use efficiency and its evolution across the basin will help optimize land resource allocation and promote sustainable development in the region.

To deeply reveal the intrinsic mechanisms and spatial patterns of coordinated land use development in the Tarim River Basin, this study combines the Coupling Coordination Degree (CCD) model and the Gravity Model to systematically explore land use efficiency evaluation and coupling coordination mechanisms in 42 counties and cities within the basin. The Coupling Coordination Degree (CCD) model is widely used to assess the interactions among subsystems [29,30], such as land use, ecology, and economy, particularly in analyzing the coordinated development across multiple dimensions of benefits. This model quantitatively evaluates the degree of coordination between subsystems, providing essential theoretical support for understanding the balanced state of human-land systems. Additionally, the Gravity Model [31] reveals the spatial interactions and linkages between regions, offering a clearer spatial perspective for analyzing the spatial distribution patterns of land use efficiency. By combining the strengths of these two models, this study not only delves into the current state of land use but also uncovers key factors influencing regional coordination, aiming to provide theoretical and practical guidance for the sustainable development of human-land systems in arid areas.

2. Materials and Methods

2.1. Study Area

The Tarim River Basin is situated in the southern part of China’s Xinjiang Uygur Autonomous Region (73°10′–94°05′ E, 34°55′–43°08′ N) and is one of the largest inland river basins in the world. It is surrounded by three major mountain ranges: the Tianshan Mountains to the north, the Kunlun Mountains to the south, and the Altyn-Tagh to the southeast. The basin is characterized by its distinct topography, featuring high mountains, alluvial plains, and the Taklamakan Desert at the center (Figure 1) [32]. Administratively, the basin spans five prefectures—Bayingolin Mongol Autonomous Prefecture, Aksu Prefecture, Kashgar Prefecture, Kizilsu Kirghiz Autonomous Prefecture, and Hotan Prefecture—encompassing 42 counties and cities. County-level statistical data were selected as the basic evaluation units because they are the most complete and comparable data available and correspond to the primary administrative level for land use management and socioeconomic planning in Xinjiang. Although the administrative areas of counties in the Tarim River Basin vary considerably, most of the basin’s interior consists of uninhabited desert. Human settlements and land use activities are concentrated in the oases and riparian zones, where the scale of effectively utilized land is relatively comparable among counties. Therefore, using the county as the evaluation unit is both data-feasible and policy-relevant. The Tarim River Basin covers approximately 1.03 million km² (61.82% of Xinjiang’s total area), making it the largest inland river basin in China. The region exhibits a temperate continental hyper-arid climate, characterized by minimal annual precipitation (<100 mm, and <50 mm in the basin’s core) and extremely high potential evaporation (2000–3000 mm). The region’s population and economic activities are primarily concentrated along the oasis belts that border the desert margins, where irrigated agriculture, urban settlements, and transportation networks are well developed. The oases serve as hubs of social and economic interaction, while the surrounding desert areas remain sparsely populated [33]. This climatic regime, coupled with fragile ecosystems, underscores the basin’s strategic significance for sustainable development in China’s arid northwest and beyond. In recent decades, the Tarim River Basin has experienced profound transformations in land use patterns and eco-environmental conditions due to combined natural and anthropogenic pressures. These changes have triggered cascading impacts on local agricultural productivity, water resource sustainability, and socio-ecological resilience.

Figure 1. The location of the research area: (a) Red region represents the Xinjiang Uygur Autonomous Region; (b) Light blue region represents the Tarim River Basin. Note: White areas represent the Xinjiang Production and Construction Corps (XPCC), which are excluded from the study due to data availability and administrative considerations.

2.2. Data Analysis

With reference to the research achievements of relevant literature [34], and considering the connotation of land use benefits and the actual situation of the Tarim River Basin, land use benefits are defined as the comprehensive outcomes of land use in three distinct but interconnected dimensions: economic benefits, social benefits, and ecological benefits. Economic benefits reflect the land use’s contribution to economic output and growth, primarily focused on industrial development and productivity. Social benefits encompass improvements in social welfare, public services, and quality of life, including factors like infrastructure, accessibility, and overall community well-being. Ecological benefits relate to the land use’s capacity to maintain ecological sustainability and the effective management of natural resources. Building on these definitions, 16 indicators (as shown in Table 1) were selected from three dimensions of land use economic benefits, social benefits, and ecological benefits to construct a land use benefit evaluation index system based on the principles of scientificity and representativeness [35,36]. The data for the land use benefit evaluation system mainly come from the Xinjiang Statistical Yearbook (1990–2021). For the data missing in very individual years, the multiple imputation method was used for completion. To ensure the comparability of results across different periods, the comprehensive evaluation model of land use benefits adopted a consistent indicator framework for all four temporal nodes—1990, 2000, 2010, and 2020. Each of the 16 indicators maintained the same structure and meaning throughout the analysis, and the data were standardized according to identical criteria.

Table 1. Selection of Evaluation Indicators for Land Use Benefits in the Tarim River Basin.

Target Layer	System Layer	Code	Indicator Layer	Attribute	Meaning	${\mathit{W}}_{\mathit{i}\mathit{j}}$
Land Use Benefits	Economic Benefits	E1	Proportion of secondary industry in GDP (%)	Positive	The impact of industrial structure on the economic benefits of land use.	0.045
		E2	Proportion of tertiary industry in GDP (%)	Positive	The impact of industrial structure on the economic benefits of land use.	0.030
		E3	Per capita GDP (Yuan/person)	Positive	Reflect the level of urban economic development and affluence	0.190
		E4	Per capita retail sales of social consumer goods (Yuan/person)	Positive	Key economic indicators reflecting the average consumption level of regional residents	0.187
		E5	Fiscal revenue (10,000 Yuan)	Positive	Reflecting government fiscal capacity and regulatory power	0.274
		E6	Investment in fixed assets (10,000 Yuan)	Positive	Reflecting private investment dynamism and economic growth potential	0.273
	Social Benefits	S1	Grain yield per unit area (Ton/hectare)	Positive	Reflecting agricultural land efficiency and food security resilience	0.090
		S2	Population density (Person/km2)	Negative	The carrying capacity of the population quantity in the land use process.	0.019
		S3	Urban population (10,000 persons)	Positive	Measuring urbanization rate and city scale	0.015
		S4	Construction land area (km2)	Positive	Measuring urban development intensity and land use efficiency	0.460
		S5	Agricultural machinery power per unit land area (kW/unit area)	Positive	Reflecting agricultural mechanization and productivity in regional farming	0.415
	Ecological Benefits	EC1	PM2.5 (μg/m3)	Negative	Reflecting air quality and pollution levels	0.026
		EC2	CO2 emissions (Mt)	Negative	Measuring regional CO2 emission levels	0.003
		EC3	Fertilizer application rate per unit land area (t/km2)	Negative	Key indicators of land health status and ecological effects	0.218
		EC4	Cultivated land area at year-end (thousand hectares)	Positive	Reflect the agricultural resource base and relate to grain production potential	0.581
		EC5	Precipitation (mm)	Positive	Key climatic humidity metric influencing agricultural output and water supply sustainability	0.172

2.3. Methodology

2.3.1. Entropy Weight Method

The entropy weight method is a quantitative approach that determines indicator weights based on information entropy. A lower information entropy indicates higher information content and greater significance of the corresponding indicator. Thus, the entropy weight method quantifies each indicator’s contribution to the total system information through the calculation of information entropy [37,38].

(1)

Data normalization

To ensure comparability and eliminate dimensional effects, all indicators are normalized. Depending on whether an indicator is positively or negatively oriented—defined by its impact on land use efficiency—the normalization formulas are as follows:

$$\mathrm{For} \mathrm{positive} \mathrm{indicators}: A_{ij}=(X_{nm}-X_{min})÷(X_{max}-X_{min})$$

(1)

$$\mathrm{For} \mathrm{negative} \mathrm{indicators}: A_{ij}=(X_{max}-X_{nm})÷(X_{max}-X_{min})$$

(2)

where $A_{ij}$ is the normalized value (ranging from 0 to 1), with 0 representing the worst value and 1 the best. $X_{nm}$is the value of the $m^{th}$ indicator in year $n$, while $X_{max}$ and $X_{max}$ are the maximum and minimum values of the indicator, respectively.

(2)

Calculation of indicator proportion${ P}_{ij}$:

$$P_{ij}={\displaystyle \frac{Y_{ij}}{{\sum }_{j=1}^m{Y}_{ij}}}$$

(3)

where $m$ is the total number of samples, and $P_{ij}$represents the normalized proportion of each indicator.

(3)

Calculation of entropy value $e_j$:

$$e_j=-{\displaystyle \frac{1}{\ln n}}({\sum }_{i=1}^m{P}_{ij}\times \ln P_{ij})$$

(4)

(4)

Determination of indicator weights:

$$W_{ij}={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^n(1-e_j)}}$$

(5)

where $W_j$ denotes the weight of the $j^{th}$ indicator.

In the Tarim River Basin, indicators such as GDP and CO₂ emissions exhibit limited inter-county variation due to the region’s similar agricultural economic structure and relatively low industrialization. Consequently, the entropy algorithm assigns lower weights to these indicators, which reflects the real spatial homogeneity of these variables rather than their conceptual insignificance. This ensures that the weight distribution remains objective and data-driven, reflecting the actual spatial characteristics of the study area.

2.3.2. Comprehensive Evaluation Model

As an objective weighting method, the entropy weight method can effectively avoid the influence of human factors on weight assignment [39]. Therefore, this paper calculates the land use benefits based on the entropy weight method in existing research, uses the entropy weight method to determine the weights of 16 evaluation indicators, and accumulates them to obtain the benefit scores of the three criterion layers of land use economy, society, and ecology [40]. According to the obtained weight values of each indicator, the calculation method is as follows:

$$U_i={\sum }_{j=1}^n{W}_{ij}\times X_{ij}$$

(6)

In the formula: $U_i$ represents the land use benefit score of the i-th county-level city, n is the number of indicators,$n=1,2,\dots ,16; W_{ij}$ represents the weight of the j-th indicator. The land use benefit values of each county-level city are calculated as follows and are divided into the following table. In this study, the land use benefit indices of the Tarim River Basin from 1990 to 2020 were calculated based on a comprehensive evaluation model. Four temporal nodes—1990, 2000, 2010, and 2020—were selected for analysis. The calculated benefit indices were classified into five levels: low, relatively low, medium, relatively high, and high, according to the magnitude of the indices (as shown in Table 2). Spatial distribution maps of the economic, social, and ecological benefits of land use in the Tarim River Basin were then generated based on this classification.

Table 2. Classification of Land Use Benefit.

Closeness Degree (Ui)	Land Use Benefit Level Grades
0 ≤ Ui < 0.1	Low
0.1 ≤ Ui < 0.2	Relatively Low
0.2 ≤ Ui < 0.3	Medium
0.3 ≤ Ui < 0.4	Relatively High
0.4 ≤ Ui < 1	High

2.3.3. Coupling Coordination Model

Coupling refers to the interaction and mutual dependence between two (or more) systems or elements [41]. In this study, it specifically denotes the mutual influence among the economic, social, and ecological systems. Based on the comprehensive evaluation method, the comprehensive evaluation indices of each system are calculated. Furthermore, a coupling degree model and a coupling coordination degree model are established to measure the coupling and coordinated development level of the land use benefit system [42]. The coupling coordination degree can reflect the level of coordination among the economic, social, and ecological subsystems, indicating whether they restrict each other at a low level or promote each other at a high level. The formulas are as follows:

$$C=3\sqrt{{\displaystyle \frac{U_1\times U_2\times U_3}{{\left(\frac{U_1+U_2+U_3}{3}\right)}^3}}}$$

(7)

$$D=\sqrt{CT}$$

(8)

Among them, $C$ is the coupling degree. $U_1, U_2, U_3$ are the comprehensive evaluation values of the three subsystems. Specifically, $U_1$ represents the economic benefit value of land use; $U_2$ represents the social benefit value of land use; $U_3$ represents the ecological benefit value of land use. $T={\alpha }_1{U}_1+{\alpha }_2{U}_2+{\alpha }_3{U}_3$, $D$ is the coupling coordination degree, and T is the comprehensive coordination index of land use benefits. Based on the concept of collaborative development, this study assumes that each subsystem is equally important, ${\alpha }_1={\alpha }_2={\alpha }_3$ = ${\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}$. Referencing existing literature, the classification of coupling degree and coupling coordination degree levels is shown in Table 3:

Table 3. Classification of Coupling Coordination Degrees.

Coordination Degree	Coordination Levels	Coordination Degree	Coordination Levels
0 < D ≤ 0.1	Extremely Disharmonious	0.5 < D ≤ 0.6	Barely Coordinated
0.1 < D ≤ 0.2	Seriously Disharmonious	0.6 < D ≤ 0.7	Primary Coordination
0.2 < D ≤ 0.3	Moderately Disharmonious	0.7 < D ≤ 0.8	Intermediate Coordination
0.3 < D ≤ 0.4	Mildly Disharmonious	0.8 < D ≤ 0.9	Sound Coordination
0.4 < D ≤ 0.5	On the Verge of Disharmony	0.9 < D ≤ 1.0	High-quality Coordination

2.3.4. Coupling Coordination Gravity Model

After obtaining the coupling coordination degree index, this study further analyzes the linkage intensity of the coupling and coordination of the three benefits (economic, social, and ecological) from a spatial dimension, i.e., to analyze the mutual interaction intensity of coupling coordination among different regions [43]. The specific methods are as follows:

$$R_{ij}=K{\displaystyle \frac{D_i{D}_j}{T_{ij}^2}}$$

(9)

In the formula, $Di$ and $Dj$ respectively represent the coupling coordination indices of land use benefits for county-level cities $i$ and $j$. Among them, $T_{ij}^2$ is the shortest time distance between cities $i$ and $j$ calculated based on spatial accessibility, and K is the gravitational constant, typically 1. $R_{ij}$ is the spatial linkage intensity of the coupling coordination level of land use benefits between cities $i$ and $j$.

3. Results

3.1. Evaluation of Land Use Benefits

3.1.1. Spatiotemporal Characteristics of Land Use Economic Benefits

The economic benefit values of land use in each county-level city of the Tarim River Basin increased from 0.020–0.076 in 1990 to 0.007–0.778 in 2020, showing an overall upward trend. The average values in 1990, 2000, 2010, and 2020 were 0.022, 0.035, 0.080, and 0.224, respectively, demonstrating a year-by-year increase. The largest growth occurred between 2010 and 2020, though the overall benefit values remained low. According to the spatial distribution map (Figure 2), land use economic benefits exhibited a spatial pattern of “higher in the east, lower in the west.” High and relatively high value areas were concentrated in the central cities of the five prefectures in the Tarim River Basin and their adjacent county-level cities. In 2020, high-value areas included Korla City, Kashgar City, Aksu City, Kuqa City, and Ruoqiang County, while most other counties remained in the relatively low-value range. In 1990, all 42 counties were in the low-value category. Overall, low and medium-value areas accounted for a large proportion of land use economic benefits in the counties of the Tarim River Basin, with high and relatively high values mainly concentrated in the central cities of each prefecture and their neighboring cities, and the growth trend remained slow.

Figure 2. Spatial and temporal patterns of land use economic benefits.

3.1.2. Spatiotemporal Characteristics of Land Use Social Benefits

From a temporal perspective, the social benefit values of land use in each county-level city of the Tarim River Basin increased from 0.003–0.179 in 1990 to 0.021–0.750 in 2020, showing an overall upward trend. The average values in 1990, 2000, 2010, and 2020 were 0.039, 0.068, 0.121, and 0.163, respectively, with overall low benefit values. In 2020, Shaya County had the highest social benefit value (0.750), while Taxkorgan Tajik Autonomous County had the lowest (0.021). According to the spatial distribution map (Figure 3), land use social benefits exhibited a “higher in the north, lower in the south” pattern. In 2020, the only high-value area for social benefits was Shaya County, while relatively high-value areas included Aksu City, Kuqa City, Korla City, and Yuli County. The spatial clustering of high-value areas around the Taklamakan Desert margins reflects the influence of urbanization and the development of oasis-based settlements. These regions host the largest population concentrations in southern Xinjiang, where agricultural production, transportation networks, and public infrastructure are relatively well developed. Most counties in the relatively low-value and low-value zones were located in the hinterland of the Taklamakan Desert. Social benefit values remain generally low with limited growth. The social benefit index and economic benefit index show significant spatial heterogeneity across counties in the Tarim River Basin, with a notable positive correlation in their spatial distributions. Overall, the spatial pattern of social benefits demonstrates that human activities and infrastructure development are primarily concentrated along the oasis belts, forming a human–land coordination corridor surrounding the Taklamakan Desert.

Figure 3. Spatial and temporal patterns of land use social benefits.

3.1.3. Spatiotemporal Characteristics of Land Use Ecological Benefits

The ecological benefit values of land use in each county-level city of the Tarim River Basin changed from 0.114–0.481 in 1990 to 0.108–0.592 in 2020. The average values in 1990, 2000, 2010, and 2020 were 0.271, 0.290, 0.345, and 0.314, respectively, showing an overall trend of first increasing and then decreasing. In terms of the spatial distribution map (Figure 4), high-value areas of land use ecological benefits are mainly distributed in counties along the Tarim River and around the Taklamakan Desert. As southern Xinjiang is located in an arid and semi-arid region, water resources have a significant impact on the ecological environment, so the concentration of high-value areas along rivers demonstrates the critical role of water sources in the ecology of each county. The figure shows that ecological benefits are mainly distributed in medium and relatively high-value areas, with no low-value areas. In 2020, there were 13 counties in the high-value zone, 16 in the relatively high-value zone, and 13 in the medium-value zone. Overall, the land use ecological benefits in the Tarim River Basin are generally good, but the proportion of high and relatively high-value areas has shown a decreasing trend.

Figure 4. Spatial and temporal patterns of land use ecological benefits.

3.2. Analysis of Coupling and Coordination Relationships of Land Use Benefits

3.2.1. Spatiotemporal Characteristics of Coupling and Coordination Relationships

Based on the coupling coordination degree model, the land use coupling coordination degree indices from 1990 to 2020 were calculated, and spatial distribution pattern maps (Figure 5) were drawn. According to the classification criteria, the coupling coordination degrees in the Tarim River Basin can be divided into six levels. The coupling coordination degree values increased from 0.172–0.335 in 1990 to 0.302–0.687 in 2020, showing an overall upward trend. The average values in 1990, 2000, 2010, and 2020 were 0.231, 0.279, 0.363, and 0.448, respectively. Among them, the highest benefit value in 2020 was observed in Korla City (0.687). From the spatial distribution map, the overall pattern is “higher in the north and lower in the south.” In 1990, only Aksu City, Korla City, and Shache County were in mild disharmony, while the rest were in severe or moderate disharmony. By 2020, Korla City, Aksu City, Kuqa City, and Shaya County had entered the primary coordination stage. Overall, the coupling coordination degree of each county-level city in the Tarim River Basin shows an upward trend, with significant growth in most counties, while growth in fewer counties is relatively gentle. The overall differences in coupling coordination degree are remarkable. Each county-level city needs to improve land use benefit values to achieve balanced development across the basin.

Figure 5. Spatial and temporal patterns of the coupling and coordinating relationship between land use benefits.

3.2.2. Analysis of Restrictive Factor Types

Based on the analysis of the three land use benefits and their coupling coordination degree, significant spatial differences exist in the land use benefits and their coupling coordination degree among county-level cities. To further explore the differences in land use benefit levels across regions and identify the restrictive factors of coupling coordination, taking 2020 as the research case, different regions were divided into seven lag types according to the land use benefit values of each county-level city (Table 4). As shown in Table 4, only 10 county-level cities are affected by single-subsystem lag, including 3 cities with economic benefit lag, 3 with social benefit lag, and 4 with ecological benefit lag. Additionally, 14 county-level cities are affected by the lag of two land use benefits, mainly dominated by the “economic and social benefit lag type”. There are 13 county-level cities in the “comprehensive lag type”, indicating that county-level cities in the Tarim River Basin are largely influenced by compound factors, and economic benefits and ecological benefits are critical factors affecting the improvement of land use benefit coordination. Among them, 5 county-level cities belong to the “comprehensive good type”, including Wensu County, Baicheng County, Korla City, and Hejing County. There are 9 county-level cities in the economic and social benefit lag type. Considering the common characteristics of these areas, the possible reasons include: remote geographical location and inconvenient transportation, which restrict economic development. Meanwhile, due to economic backwardness, investment in social infrastructure such as education and healthcare may be insufficient, further affecting social benefits. Additionally, these counties rely on traditional agriculture or animal husbandry, with a single industrial structure and a lack of industrial and service sector development, leading to low economic efficiency. There are 13 county-level cities in the comprehensive lag type, accounting for a relatively large proportion, indicating that the development level of most counties urgently needs improvement. These areas not only have a single industrial structure dominated by agriculture but also suffer from weak infrastructure, fragile ecological environments, and severe land degradation. Long-term poverty has resulted in weak foundations in infrastructure, education, healthcare, etc., forming a vicious cycle of “poverty-inefficiency-poverty.” Therefore, in the future, it will be necessary to formulate systematic revitalization strategies for specific counties to promote the comprehensive improvement of economic, social, and ecological benefits.

Table 4. Classification of Restrictive Factors for Coupling and Coordination Relationships Among Economic, Social, and Ecological Benefits of Land Use.

Classification of Restrictive Factor Types	Classification Criteria	Cities Included in 2020
Economic Benefit Lag Type	$U_1<\overline{U_1},U_2>\overline{U_2},U_3>\overline{U_3}$	Awat County, Yuli County, Shache County
Social Benefit Lag Type	$U_1>\overline{U_1},U_2<\overline{U_2},U_3>\overline{U_3}$	Hejing County, Akto County, Wuqia County
Ecological Benefit Lag Type	$U_1>\overline{U_1},U_2>\overline{U_2},U_3<\overline{U_3}$	Shaya County, Luntai County, Kashgar City, Shule County
Economic and Social Benefits Lag Type	$U_1<\overline{U_1},U_2<\overline{U_2},U_3>\overline{U_3}$	Wushi County Yanqi Hui Autonomous County, Hoxud County, Hotan County, Pishan County, Yecheng County, Jiashi County, Taxkorgan Tajik Autonomous County, Akqi County
Economic and Ecological Benefits Lag Type	$U_1<\overline{U_1},U_2>\overline{U_2},U_3<\overline{U_3}$	Xinhe County, Bachu County
Social and Ecological Benefits Lag Type	$U_1>\overline{U_1},U_2<\overline{U_2},U_3<\overline{U_3}$	Ruoqiang County, Bohu County, Hotan City
Comprehensive Lag Type	$U_1<\overline{U_1},U_2<\overline{U_2},U_3<\overline{U_3}$	Keping County, Qiemo County, Moyu County, Lop County, Qira County, Yutian County, Minfeng County, Shufu County, Yingjisha County, Zepu County, Makit County, Yopurga County, Artux City

Note: $U_1,U_2,U_3$ represent the economic, social, and ecological benefits of land use in each county-level city for the corresponding year, respectively. $\overline{U_1},\overline{U_2},\overline{U_3}$ denote the arithmetic means of the three land use benefits in each county-level city for the corresponding year, respectively.

3.3. Analysis of Spatial Linkage Intensity of Land Use Benefit Coupling and Coordination

The gravitational model was used to calculate the spatial linkage intensity of land use benefit coupling coordination degree, which was divided into five levels: strong linkage, relatively strong linkage, general linkage, relatively weak linkage, and weak linkage (Figure 6). Due to the large number of county-level cities in this study, the generated spatial gravitational lines are numerous, causing clutter in the spatial distribution map; therefore, weak linkage intensity is not displayed. As shown in Figure 6, the spatial linkage intensity of land use benefit coupling coordination in the Tarim River Basin counties has shown an upward trend over the years. Significant changes occurred in the spatial linkage pattern from 1990 to 2020, with enhanced overall coordination: more counties have formed linkages with central cities and their neighboring counties, the gravitational gap between cores and peripheries has narrowed, and spatial linkages have comprehensively strengthened. Specifically, the spatial linkage intensity in the Tarim River Basin exhibits an evolutionary trajectory of “core polarization-axis belt expansion-networked coordination,” primarily concentrated in the linkages between the central cities of the five prefectures in the Tarim River Basin and their surrounding counties. In the network pattern, areas with relatively strong linkages are mainly concentrated in the oasis irrigation districts of the Tarim River Basin, with linkage axes linearly distributed along the main stream of the Tarim River. However, the hinterland of the Taklamakan Desert remains a weak linkage zone.

Figure 6. Spatial network pattern of the coupling coordination degree of land use benefit.

4. Discussion

(1)

Under limited land resources, the rational utilization of resources is of great significance for ensuring regional economic, social, and ecological benefits, especially in arid and semi-arid regions with water scarcity, low ecological vulnerability, and significant economic development gaps, such as southern Xinjiang [44]. Meanwhile, its geostrategic position is crucial for the economic development, social stability, and ecological protection of the entire Xinjiang region and the country. This study reveals significant gaps in land use benefits among counties and cities in the Tarim River Basin, with unbalanced regional development, particularly notable differences in the economic and social benefits of land use. The main reasons are as follows: The Tarim River Basin is dominated by agriculture, and counties and cities based on oasis agriculture have a clear dependence on water and land resources [45]. The shortage of water and land resources severely restricts the development of oasis agriculture and towns. At the same time, differences in spatial location lead to transportation disparities among counties and cities. Counties and cities with convenient transportation (such as Korla and Aksu) are more likely to boost economic development. Compared with social and economic benefits, the differences in ecological benefits of land use are smaller, but it is also an important subsystem affecting the improvement of the coupling and coordination degree of land use benefits. The uneven distribution of water resources in the basin leads to differences in land development intensity. In counties and cities with water shortages, soil desertification and salinization are severe, limiting benefits [46]. Therefore, in the future, it is necessary to optimize industrial layouts according to the water resource conditions of each county and city, strengthen policy support, and prioritize ecological restoration investments in ecologically fragile areas (such as the lower reaches of the Tarim River).

(2)

Based on relevant literature and the characteristics of land use and its location in the Tarim River Basin, the main factors influencing the spatial linkage pattern of the coupling and coordination level of land use benefits are analyzed. First, there are natural geographical factors: the Tarim Basin is surrounded by high mountains such as the Tianshan and Kunlun Mountains, with a flat interior, forming a closed basin topography. This topography has led to the concentration of population and economic activities in the oasis zones along the edge of the basin (such as Korla and Aksu), forming a spatial agglomeration effect [47]. Second, there are socioeconomic factors: economically developed areas (such as Korla and Aksu) have become gravitational cores due to industrial concentration and abundant employment opportunities, attracting large inflows of population and capital [48]. Meanwhile, the construction of transportation networks (such as railways and highways) has strengthened the connections between core cities and surrounding areas. Additionally, there are policy factors: the Western Development Strategy, the large-scale development of the Tarim Oilfield, border trade, and the deepening of the “Belt and Road” initiative in the Kashgar Economic Belt have all enhanced spatial gravitation. In the future, it is necessary to achieve a balance between ecological protection and economic development and ultimately realize sustainable human-land coordination through optimizing spatial layouts (such as the Korla model), promoting green transitions (such as the South-to-North Water Diversion Project), and strengthening regional collaboration.

(3)

Compared with studies on land use coordination in other regions of China, the Tarim River Basin exhibits distinctive characteristics shaped by its arid environment and resource constraints. In the Yangtze River Delta and Pearl River Delta, land use efficiency and coupling coordination levels are generally higher due to advanced urbanization, industrial agglomeration, and well-developed transportation and infrastructure networks that enhance both land use adequacy and spatial connectivity [49,50,51]. Similarly, in the Yellow River Basin and Loess Plateau, coordinated development has improved significantly under ecological restoration policies such as the Grain for Green program, which has strengthened the interaction between ecological and economic benefits [52,53]. In contrast, the Tarim River Basin, located in southern Xinjiang, faces a dual challenge of resource scarcity and spatial isolation. The region’s dependence on oasis agriculture, together with its closed basin topography, restricts cross-regional linkages and creates a strong dependence on water and land resources. Consequently, while the coupling coordination degree of land use benefits has gradually improved, its overall level remains lower than that of eastern and central China. The spatial connectivity pattern identified by the gravity model is also distinct: instead of a dense and circular network of urban clusters, the Tarim River Basin displays a linear, oasis-centered linkage structure that follows the Tarim River and its tributaries. This pattern underscores the decisive role of water availability, transportation accessibility, and policy support in shaping human–land coordination in arid inland basins. These comparisons underline that although land use coordination in the Tarim River Basin lags behind developed regions, it follows a unique human–land adaptation pathway, where ecological constraints, resource management, and policy-driven development jointly determine spatial linkages and sustainability outcomes.

(4)

In this study, the evaluation of land use benefits uses counties and cities as the basic analysis units, making it difficult to capture the heterogeneous characteristics of different internal regions of the Tarim River Basin (such as oasis edges, urban-rural fringes, and ecological protection areas). In the future, it will be necessary to analyze the coupling and coordination relationships among the economic, social, and ecological benefits of land use in the Tarim River Basin from different scales, enrich small-scale regional research, and provide more reference insights for the development of southern Xinjiang.

5. Conclusions

This study takes the Tarim River Basin as the research object, establishes a land use benefit evaluation index system composed of 16 evaluation indicators, and applies a reasonable evaluation model to evaluate the land use benefits and their coupling coordination degree in the Tarim River Basin from 1990 to 2020. The conclusions are as follows.

(1)

The benefits of each subsystem of land use in the Tarim River Basin show different change trends. Among them, both economic benefits and social benefits exhibit an upward trend, and spatially, they both show a distribution pattern of “higher in the north and lower in the south”. Ecological benefits are generally high, but most counties and cities show a declining trend, with high-value areas distributed along the main stream area of the Tarim River Basin.

(2)

The coupling and coordination levels among the economic, social, and ecological benefits of land use in the Tarim River Basin generally show an upward trend. Most counties and cities have shown significant growth, while fewer counties and cities have grown more gently. At the spatial level, it also generally presents a “higher in the north and lower in the south” distribution pattern, similar to the spatial distribution patterns of economic and social benefits.

(3)

The restrictive factors of the coupling and coordination degree of land use benefits in the Tarim River Basin are divided into seven types. Among them, only 10 counties and cities are affected by the lag of a single subsystem, mainly dominated by the ecological benefit lag type; 14 counties and cities are affected by the lag of two land use benefits, mainly economic and social benefits; and 13 counties and cities are affected by comprehensive lag. This indicates that the influence of composite factors is the most important factor restricting the coupled and coordinated development of land use benefits.

(4)

The spatial linkage of the coupling and coordination of land use benefits in the Tarim River Basin presents an evolutionary feature of “core polarization, axis belt expansion and networked coordination”, mainly a spatial linkage pattern dominated by central cities in five prefectures in southern Xinjiang and their neighboring counties and cities. Areas with strong linkages are mainly concentrated in the oasis irrigation areas of the Tarim River Basin, while the hinterland of the Taklamakan Desert is a weak linkage area.