Investigation of the Coupling and Coordination Relationship of Water–Energy–Food–Ecology and the Driving Mechanism in Dalad Banner

Abstract

Aiming at the complex problems of water resources, energy, food, and ecology in ten parallel tributaries directly joining the Yellow River in Ordos City, Inner Mongolia Autonomous Region (the Ten Kongduis), the coordination of the water–energy–food–ecology (WEFE) system in Dalad Banner and its townships was studied from the perspective of system coupling in time and space. First, the evaluation index system of WEFE coupling coordination is constructed, and then the coupling coordination degree model, grey relationship degree model, and geographical detector are used to quantitatively evaluate the development level and spatio-temporal evolution characteristics of WEFE coordination in Dalad Banner and its townships and explore its driving mechanism. The results suggest that (1) the WEFE comprehensive evaluation index of Dalad Banner and its townships showed a slowly rising trend on the whole. The growth trends for the WEFE subsystems and integrated assessment are 0.65%, 21.02%, 17.01%, 1.17%, and 9.96%, respectively. This shows that the energy subsystem occupies the main position. (2) The coupling degree of WEFE in Dalad Banner and its townships is high; the mean value is 0.967, which is in the high-level coupling stage. But the coordination degree is low; the mean value is 0.668, which is in the elementary coordination stage. The spatial development is unbalanced and uncoordinated in Dalad Banner; this phenomenon mainly exists in the eastern part of Dalad Banner, with a gradual change from the west to the center of the area with higher harmonization. (3) The coordination of the WEFE system of Dalad Banner and its townships shows a good trend. The average coupling coordination degree in Wangaizhao town has the largest increasing trend, which is 12.69%. Fengshuiliang Town has the smallest growth trend, which is −10.37%. The region is gradually developing to the stage of well coordination, and each township has basically entered the middle-rank coordination stage. (4) In general, in the degree of system impact, the ecological impact is the largest and the grain is the weakest. In terms of spatial differences, energy has the greatest impact, while water has the least explanatory power. In terms of interaction, the interaction among water resources, grain, and ecology has the greatest explanatory power. The influence of WEFE in Dalad Banner coupling coordination changes obviously with time, and the driving mechanism and stability are relatively weak. The results of this study can provide a decision basis for the coordinated development of WEFE and the high-quality and sustainable development of Dalad Banner and its townships.

1. Introduction

“Water–energy–food–ecology” (WEFE) is an important system on which human survival and development depend, and good coupling within this system can strongly contribute to the sustainable development of the region [1,2]. Global demand for water, food, and energy is projected to increase by 50 %, 35 %, and 70 % by 2050, respectively, compared with current demand [3,4,5]. Against the backdrop of population growth, resource scarcity, and global warming, the safety of the W-E-F-E system has attracted widespread attention in both domestic and international societies [6]. For water, energy, and food, changes in one sector can have cascading effects on others. Failure in these sectors can lead to vulnerabilities and crises regarding water scarcity, energy shortages, or food insecurity [7]. Since the concept of the “water–energy–food” (WEF) nexus was first proposed [8], scholars at home and abroad have mainly carried out WEF research from the connotation of nexus relationships and related mechanisms, theoretical frameworks, and the current situation of regional nexus [9,10]. In terms of research content, it mainly includes the analysis of the temporal evolution and spatial difference in the coupling coordination degree, the assessment of the current situation of the region, and the prediction of the future coupling coordination degree [11]. For example, Mabhaudhi, Mpandeli, and Nhamo et al. [12] developed an integrated WEF analytical model based on a hierarchical approach. They evaluated the current status of South Africa’s irrigated agriculture and suggested possibilities for improving irrigated agriculture in the region. Duan et al. [13] considered the impact of external factors such as climate change and population growth on the WEF nexus, simulated different socio-economic development scenarios, and projected changes in water and food demand in Turkmenistan. In terms of research methodology, it mainly includes the input–output method [14,15], the system dynamics model [16], the coupled coordination degree model [17], and the geographically weighted regression model. For example, Karamian et al. [18] used the WEFNI model to quantitatively explain crop WEF nexus relationships in agricultural cropping systems in the Miandarband Plain, Iran. Wang et al. [19] and Chen et al. [20] proposed the WEF nexus “sustainability” assessment system based on the PSR technique, the matter–element model, and the coordination degree model. Then, they assessed the sustainability of the “water–energy–food” system in China and Northwest China. They concluded that China’s resource systems in Northwest China in 2015 were relatively fragile and poorly coordinated among systems.

However, the development and utilization of water, energy production, and food production all have a direct impact on ecology and the environment. So, the basic requirements of ecological environmental protection should be taken into account in the study of water–food–energy, which is the shift from WFE to WEFE [21]. Dalad Banner is located in the Ten Kongduis, and its geomorphology type is extremely complex, with upper reaches of gullies and ravines. It is covered in arsenic sandstone, the “cancer of the earth”. The soil in the watershed has weak resistance to erosion, the structure of forest and grass vegetation is single, soil erosion is serious, and the ecological environment is seriously deteriorated [20]. The ecosystem, an important carrier of water, energy, food, and other resource activities, has an interdependent and interactive relationship with the WEF, and its matching and coupling with the WEF is related to regional development and stability. Therefore, it is necessary to carry out a coupled WEFE study in Dalad Banner in the Ten Kongduis [22]. Nowadays, most of the related research studies are about the WEF system; there are relatively few research studies on the WEFE system, but some scholars at home and abroad have carried out research in this area. For example, Shi et al. [23] explored the water–energy–food–ecology nexus in the Aral Sea basin based on Bayesian networks. In recent years, research on the influencing factors has also gradually increased, and scholars have mainly used methods such as gray correlation and the geographical detector to explore the driving factors of the system. For example, Zhang et al. [24] used gray correlation analysis with coefficient of variation weights to analyze the factors affecting moisture, protein, and fat contents of Semen sesami nigrum (steamed at atmospheric pressure or high pressure). Wang et al. [25] used gray correlation analysis and the entropy weight method to evaluate the influencing factors of tea tree varieties and trait indicators comprehensively, which provided a way to promote a genetic improvement in the selected good varieties. Bin et al. [26] used the Mann–Kendall test and the geographical detector to explore the drivers of flash floods in Hainan Island, China, from the following three major categories: natural, social, and rainfall, totaling 14 factors. Quan et al. [27] identified the driving capacity of different factors on the atmospheric hydrological cycle in the Inner Mongolia Autonomous Region by using the geographical detector. That study provides a scientific basis for identifying driving mechanisms of and preventing disasters in the atmospheric process of the water vapor cycle in the Inner Mongolia Autonomous Region.

Therefore, the aim of this paper is to explore the coupling relationship between the WEFE system and its driving mechanism in Dalad Banner and its townships. The main purposes of this paper are as follows: (1) Complete a comprehensive evaluation of the current status of the subsystems of the WEFE in Dalad Banner. (2) Complete a spatio-temporal analysis of the coupling coordination degree of the WEFE system in Dalad Banner and its townships. (3) Reveal the driving mechanism of the WEFE system in Dalad Banner and its townships. In summary, by analyzing the WEFE linkage and constraint characteristics of Dalad Banner and its townships and revealing the interactive coupling mechanism and driving mechanism among water, energy, food, and ecology, we can further optimize the layout of ecological protection, food production, energy development, and water resource deployment in the Ten Kongduis and provide an overall basis for decision-making for the sustainable development of the region.

2. Study Area

“The Ten Kongduis” are located in Dalad Banner, Ordos City, Inner Mongolia Autonomous Region, and consists of ten parallel tributaries flowing from south to north directly into the Yellow River. Originating in the Ordos platform, they flow through the Kubuqi Desert and the alluvial plain into the Yellow River. From west to east, the Ten Kongduis are composed of Maopra Kongdui, Bolsetai Gully, Heilai Gully, Xiliu Gully, Hantaichuan River, Haoqing River, Hashlachuan River, Muhua Gully, Dongliu Gully, and Hustai River, with a total drainage area of 8200 square kilometers [28]. In terms of topography and geomorphology, the upper reaches of the Ten Kongduis belong to the Ordos Plateau Loess Hills and Gullies area. The surface is covered with thin wind-formed sand with coarse grains, and the underlying strata have large areas of arsenic sandstone outcrops, which are highly susceptible to erosion. The middle reach of Kongduis is the Kubuqi Desert, which crosses Kongdui to the east and west, with mostly mobile dunes in the west and semi-fixed sands in the east. Under the action of wind, quicksand is mostly piled up on both sides of the riffle and is discharged with water flow in the case of floods. The lower reaches of the Ten Kongduis are flood plains and alluvial plains, which are flat and fertile, with slight soil erosion; however, the river channel is highly oscillating, and flooding has very serious consequences [29]. In terms of hydrometeorology, the climate of the Ten Kongduis is temperate continental, the region is dry all year round with little rainfall, and the annual precipitation is 200~400 mm in the form of heavy rainfall concentrated in the summer.

The scope of the Ten Kongduis includes Hangjin Banner, Dalad Banner, Dongsheng District, and Jungar Banner of Ordos City, involving eighteen townships. It includes eight townships and one Sumu in Dalad Banner, which accounts for 50% of the total, and Dalad Banner covers all the Kongduis in the watershed. Under the unique geographical conditions and the influence of climate change and human activities, there is a serious shortage of water resources, a prominent contradiction among the energy industry, agriculture, and ecological water use. This results in regional wind and water erosion, interwoven sandstorm and sediment processes, a seriously fragile ecological environment, and other problems in the Ten Kongduis. These problems seriously restrict the ecological protection and high-quality development of the region. Therefore, it is important to fully realize ecological protection and high-quality development in the Ten Kongduis basin, strengthen the problem orientation, adhere to ecological priority and green development, and promote the intensive and economical utilization of water resources and the comprehensive treatment of mountains, rivers, forests, fields, lakes, grass, and sand. This is of great strategic significance to ensure the strategic areas of national energy security and build a strong ecological barrier in the north of our country. To summarize, this paper takes Dalad Banner as the research object to identify the current situation and development trend of the Ten Kongduis more comprehensively. Figure 1 shows the distribution of the Dalad Banner and its townships, and

Table 1. Basic information of Dalad Banner.

Dalad Banner	Name	Area (km2)
		Hilly and gully areas in the upper reaches	The sandstorm area in the middle reaches	The lower plains
	Maopra Kongdui		292.96	133.51
	Bolsetai Gully	266.67	532.00	125.33
	Heilai Gully	452.64	548.00	187.63
	Xiliu Gully	634.78	480.00	249.47
	Hantaichuan River	463.47	345.00	365.41
	Haoqing River	10.80	260.00	388.84
	Hashlachuan River	542.62	351.00	214.15
	Muhua Gully	192.00	269.00	156.75
	Dongliu Gully	130.00	301.00	100.96
	Hustai River	9.29	203.99	13.82

shows the distribution of the Ten Kongduis within the Dalad Banner.

3. Data and Methods

3.1. Data

The original data needed for this study came from the “China Water Resources Bulletin” (http://app.gjzwfw.gov.cn/jmopen/webapp/html5/szygbxxcx/index.html, accessed on 15 March 2024), “Inner Mongolia Statistical Yearbook” (http://slt.nmg.gov.cn/xxgk/zfxxgkzl/fdzdgknr/gbxx/szygb/index.html, accessed on 15 March 2024), “Dalad Banner Water Resources Bulletin” (http://www.dlt.gov.cn/dltqrmzf2023/zfxxgk2023/fdzdgknr_151557/jgjj_151715/zfbm_151716/202305/t20230525_3399401.html, accessed on 15 March 2024), web data from 2000 to 2022, etc. Some missing data were interpolated by the linear fitting method. Following the principles of scientificity, comprehensiveness, and systemization, the comprehensive evaluation index system of this region was constructed by referring to the existing research results and combining them with the mutual feed mechanism of the WEFE nexus relationship. Meanwhile, the correlation test of the evaluation indicators in the system was carried out, and the indicators with a correlation coefficient of less than 0.5 were retained. Finally, twenty-three evaluation indicators were selected. The comprehensive evaluation index system of WEFE is shown in

Table 2. Evaluation index system of WEFE in Dalad Banner.

Dimension		Indicator	Unit	Type	Weight
Water subsystem	Water resource supply	Water supply per capita	m3/person	+	0.0864
		Water production modulus	104 m3/km2	+	0.0781
	Water resource consumption	Water consumption per capita	m3/person	−	0.1481
	Water efficiency	Water consumption of RMB 10 thousand Gross Domestic Product	m3/(RMB 104)	+	0.1677
	Water consumption structure	Proportion of agricultural water	%	−	0.1030
		Proportion of industrial water	%	−	0.1077
		Proportion of domestic water	%	−	0.2461
		Proportion of eco-environmental water	%	−	0.0630
Energy subsystem	Energy supply situation	Coal production per capita	ton/person	+	0.2316
	Energy consumption	Coal consumption per capita	ton/person	−	0.2086
	Energy supply structure	Proportion of coal production	%	+	0.2226
	Energy consumption structure	Annual power generation	109 kW·h	+	0.1106
		Proportion of renewable energy consumption	%	+	0.1434
	Efficiency of energy utilization	Energy consumption of RMB 10 thousand Gross Domestic Product	ton/(RMB 104)	−	0.0720
Food subsystem	Grain production	Per capita output of grain	ton/person	+	0.1141
		Food output per unit area	kg/hm2	+	0.1218
	Grain planting structure	Grain planting area	hm2	+	0.2050
		Arable land per capita	hm2/person	+	0.1138
	Grain demand	Natural population growth rate	%	−	0.0607
Ecology	Ecological environment level	Vegetation coverage	%	+	0.2865
	Environmental pollution situation	Industrial wastewater discharge	103 ton	−	0.1805
	Pressure on the ecological environment	Population density	persons/km2	−	0.2122
	Degree of ecological environment improvement	Soil erosion control area	km2	+	0.1536
		Water-saving irrigation area	km2	+	0.1691

. Total resources, consumption structure, and economic benefits were the starting points for selecting indexes of water resources and energy subsystems. In the evaluation index of the water resources subsystem, per capita water resource use is an important index to represent the abundance of regional water resources. Water consumption of less than RMB 10 thousand Gross Domestic Product and lower industrial added value result in a more optimized regional water resources consumption structure and higher ecological benefits. In the evaluation index of the energy subsystem, the higher the proportion of industry, agriculture, and life in energy consumption, the more unreasonable the regional energy consumption structure. The smaller the elasticity coefficient of energy consumption (a technical and economic index reflecting the relationship between energy and national economic development), the higher the degree of energy consumption security. The grain subsystem selects indexes from the two aspects of production and consumption. The character of the grain production index is positive, and the higher the value, the more adequate the food supply. The nature of the fertilizer load index is negative, and the smaller the value, the more reasonable the energy consumption in the process of grain production.

3.2. Indicator System

In order to reflect the coupling coordination level of water resources, energy, food, and ecology in Dalad Banner, representative indicators were selected, and an evaluation index system was established. This system was based on the current situation of water, energy, food, and ecological development in the Dalad Banner region and the field research and data collected, combined with the characteristics of each township. The WEFE evaluation index system was constructed with two levels and twenty-three indicators by referring to the existing literature [30,31] and following the principles of objectivity, systematicity, rationality, and data availability. In order to make the data comparable, water consumption of RMB 10 thousand Gross Domestic Product and energy consumption of RMB 10 thousand Gross Domestic Product were calculated based on the 2015 constant price in USD. In this paper, the improved range normalization method was used to standardize the index, and the entropy weight method was used to determine the index weight [32].

3.3. Data Normalization and Weight Calculation

Since the evaluation indicators of each subsystem have different scales, it was necessary to standardize the raw data to obtain dimensionless values so that the data were comparable and measurable. According to the different properties of the indicators, they were divided into positive indicators and negative indicators and then standardized. The detailed steps are as follows.

The formula for positive indicators is as follows:

$$X_i^{\prime }=\frac{X_i-X_{\min }}{X_{\max }-X_{\min }}$$

(1)

The formula for negative indicators is as follows:

$$X_i^{\prime }=\frac{X_{\max }-X_i}{X_{\max }-X_{\min }}$$

(2)

where $X_i^{\prime }$ is the index value after the standardization of index “i”, $X_i$ is the raw data of indicator “i”, $X_{\max }$ and $X_{\min }$ are the maximum and minimum values of each evaluation index, respectively. After the data were normalized according to the above formula, there were some cases where the data were zero and the assigned number was meaningless, so data translation was carried out so that A = 1. The formula is as follows:

$$Z_i=X_i^{\prime }+A$$

(3)

where $Z_i$ is the translated value of the index “i” and A is the assignment when the data are shifted. Formulas (1)–(3) refer to reference [33].

In this paper, the entropy weight method was used to objectively assign weights to each index, which avoids the subjectivity of artificial weight to a certain extent. Entropy weight is a method that calculates the entropy of indicators by analyzing the degree of dispersion among indicators, according to the information carried by each indicator. The smaller the entropy value, which indicates less variability among indicators, the greater the weight of the indicator within each subsystem, and vice versa [34].

The weight calculation results of each evaluation index are shown in Table 2.

3.4. Coupling Harmonious Degree Model of WEFE

The coupling degree can reflect the degree of interaction and mutual influence among subsystems, and it is a powerful basis for measuring the influence among systems or elements [35]. When it is difficult to reflect the synergistic effect and the overall efficiency among different systems using the coupling degree, it is necessary to introduce the coupling coordination degree to quantitatively describe the coordination development level among systems.

This paper draws on sustainable development and coupling coordination theories to evaluate the interaction degree and coordination development of the WEFE system by constructing coupling degree and coupling coordination degree models [36,37].

(1)

Evaluation index of the subsystem development level

In order to directly measure the development level of each subsystem of the WEFE system, this paper uses the linear weighting method to calculate the development level evaluation index of each subsystem. The formula is as follows:

$$f(x)={\displaystyle \sum _{j=1}^6{w}_j{x}_{ij}^{\prime }}$$

(4)

$$g(y)={\displaystyle \sum _{j=1}^6{w}_j{y}_{ij}^{\prime }}$$

(5)

$$h(z)={\displaystyle \sum _{j=1}^6{w}_j{z}_{ij}^{\prime }}$$

(6)

$$q(e)={\displaystyle \sum _{j=1}^5{w}_j{e}_{ij}^{\prime }}$$

(7)

where $x_{ij}^{\prime }$, $y_{ij}^{\prime }$, $z_{ij}^{\prime }$, and $e_{ij}^{\prime }$ are the standardized values of each index in the subsystem of water resources, energy, food, and ecology. $w_j$ is the weight of each indicator.

(2)

Comprehensive evaluation index

The formula for the comprehensive evaluation index is as follows:

$$T=\alpha f(x)+\beta g(y)+\gamma h(z)+\phi q(e)$$

(8)

where T is the comprehensive evaluation index, which represents the comprehensive development capability of the system. The larger the value of T, the better the system development status. $\alpha$, $\beta$, $\gamma$, and $\phi$ are the weights of the water resources, energy, food, and ecological subsystems. This paper assumes that the four subsystems are mutually restricted and of equal importance, so $\alpha$ = $\beta$ = $\gamma$ = φ = 1/4.

(3)

The coupling degree model and the coupling coordination degree model

The formulas for the coupling degree model and the coupling coordination degree model are as follows:

$$C=\frac{4\times \sqrt[4]{f(x)\times g(y)\times h(z)\times q(e)}}{f(x)+g(y)+h(z)+q(e)}$$

(9)

$$D=\sqrt{C\times T}$$

(10)

where C is the coupling degree, and C ∈ [0, 1]. The larger the value of C, the stronger the coupling degree of the system and the closer the correlation, which is weaker on the contrary. D is the coupling coordination degree, and D ∈ [0, 1]. The larger the value of D, the better the coordination of the system, and it indicates poor coordination on the contrary. Formulas (4) to (10) refer to reference [38]. The classification of C and D is shown in the following

Table 3. Coupling degree and coupling coordination degree classification.

C	Coupling Level	D	Degree of Coupling Coordination	Coordination Type
[0, 0.3]	Low level coupling	[0, 0.1)	Dissonant decay	Extreme unbalance
(0.3, 0.5]	Antagonistic stage	[0.1, 0.2)		Serious unbalance
(0.5, 0.8]	Break-in stage	[0.2, 0.3)		Moderate unbalance
(0.8, 1]	High level coupling	[0.3, 0.4)		Mild unbalance
		[0.4, 0.5)	Transitional type	Borderline unbalance
		[0.5, 0.6)		Borderline coordination
		[0.6, 0.7)	Coordinated development	Elementary coordination
		[0.7, 0.8)		Middle rank coordination
		[0.8, 0.9)		Well coordination
		[0.9, 1]		Superior coordination

[33,36,37,38].

3.5. Grey Relationship Degree Model

The coupling and coordination of the “water–energy–food–ecology” system in Dalad Banner and its townships is a complex system problem, and clarifying primary and secondary factors is a common concern in systems analysis. Grey relationship analysis is a statistical analysis technique mainly used to analyze the closeness of the relationship between parent factors and sub-factors in a system and identify the main and secondary factors that cause development and change in a system [39]. This is a quantitative and comparative analysis method for the dynamic development of the system. Compared with traditional mathematical statistical analysis methods (such as regression analysis, variance analysis, principal component analysis, etc.), the grey relationship analysis method is equally applicable to sample size and sample irregularity and it requires minimal calculation. So, there will be no inconsistency between the quantitative results and the quantitative analysis results, which makes up for the defects caused by the systematic analysis using the mathematical statistics method [40]. Therefore, this paper uses the grey relationship degree model to analyze the driving factors of the coupled and coordinated development level of these four systems.

The basic idea of grey relationship analysis is to use the similarity in geometric shapes of sequence curves to show the closeness of the relationship between different sequences. The formula is as follows:

$$x_0(k)=\left\{x_0^1, x_0^2, \cdots , x_0^m\right\} k=1, 2, \dots , m$$

(11)

$$x_i(k)=\left\{x_1^1, x_1^2, \cdots , x_1^n\right\} k=1, 2, \dots , m$$

(12)

where x₀(k) is the reference sequence and x_i(k) is the comparative series. The coupling coordination degree of the WEFE system in Dalad Banner and its townships is taken as the reference sequence, and each driving factor is the comparative series. When calculating the grey correlation coefficient, {x₀(t)} is the series of numbers after nondimensionalization, and its subseries is {x_i(t)}. When t = k, the grey correlation between {x₀(t)} and {x_i(t)} is calculated. The formula is as follows:

$${\zeta }_{0i}(k)=\left|\frac{{\Delta }_{\min }+\rho {\Delta }_{\max }}{{\Delta }_{0i}(k)+\rho {\Delta }_{\max }}\right|$$

(13)

where ${\Delta }_{0i}(k)$ is the absolute difference between two sequences of item k. A is the maximum absolute difference in each item. ${\Delta }_{\max }$ is the maximum absolute difference in each item. ${\Delta }_{\min }$ is the minimum absolute difference in each item.

The grey correlation degree is further calculated according to the correlation coefficient. The formula is as follows:

$$E_{0i}=\frac{1}{m}{\displaystyle \sum _{k=1}^m{\zeta }_{0i}(k)}$$

(14)

Formulas (11) to (14) refer to reference [33].

3.6. Geographical Detector Model

Considering the autocorrelation of many factors affecting the fit degree, there is a need to explore the influence of different factors on the interaction of the WEFE system. This paper selects a geographic detector to analyze the driving factors causing the spatial differentiation of the coupling coordination degree of the WEFE system in Dalad Banner and its townships. The geographical detector method is a spatial analysis method used to detect spatial differentiation and reveal the driving force behind it. The geographical detector includes factor detection, interactive detection, ecological detection, and risk detection, among which factor detection and interactive detection are widely used in driving force analysis and factor analysis [41]. The geographical detector is based on the assumption that the independent variable has a significant influence on the dependent variable, accompanied by changes in spatial distribution [42]. Statistical analysis of differentiation using geographic detectors has two advantages. First, factor detectors can detect unlimited types of data, both numerical and qualitative. Second, they can not only judge the action intensity of a single factor but also the action intensity of the interaction of a double factor. If the double-factor action intensity is greater than the single-factor action intensity, both factors should develop simultaneously [43]. On the contrary, we should try to avoid the simultaneous effect of double factors. In addition to this, interaction detectors can provide important insights into the formulation of policy recommendations.

In this study, factor detection, interactive detection, and ecological detection are used to study the driving factors of the spatial differentiation of the WEFE coupling coordination degree in Dalad Banner and its townships. The formula for factor detection is as follows:

$$q=1-\frac{{\displaystyle {\sum }_{h=1}^L{N}_h{\sigma }_h^2}}{N{\sigma }^2}=1-\frac{SSW}{SST}$$

(15)

where “h = 1, 2, …, L” is the stratification of variables, i.e., classification or partitioning. N_h is the number of units in layer “h”, and N is the number of units in the whole area. ${\sigma }_h^2$ is the variance in the “Y” values of layer “h”, and ${\sigma }^2$ is the variance in the “Y” values of the whole area. SSW is the sum of intra-layer variances, and SST is the total variances in the whole region. The range of “q” is [0, 1]; the higher its value, the more explanatory power the argument “X” has for the attribute “Y”. Formula (15) refers to reference [41].

4. Results

4.1. Analysis of the Comprehensive Development Level of the WEFE System

Firstly, the evaluation index, comprehensive evaluation index, coupling degree, and coupling coordination degree of each subsystem of the WEFE system in Dalad Banner and its townships from 2000 to 2022 were calculated according to Formulas (1)–(10). Based on the data, the time series evolution charts of the evaluation index, comprehensive evaluation index, and coupling coordination degree of each subsystem in the Dalad Banner area were drawn.

The calculated data of Dalad Banner and its townships are shown in

Table 4. Comprehensive evaluation index, coupling degree, and coupling coordination degree mean values of Dalad Banner and its townships in the WEFE system.

Region	$\mathit{f}(\mathit{x})$	$\mathit{g}(\mathit{y})$	$\mathit{h}(\mathit{z})$	$\mathit{q}(\mathit{e})$	T	C	D	Coordination Type
Dalad Banner	0.544	0.500	0.346	0.464	0.463	0.967	0.668	Elementary coordination
Shulinzhao Town	0.564	0.521	0.346	0.493	0.481	0.965	0.679	Elementary coordination
Bainijing Town	0.535	0.446	0.228	0.477	0.421	0.917	0.620	Elementary coordination
Zhonghexi Town	0.553	0.508	0.285	0.468	0.454	0.947	0.653	Elementary coordination
Jigesitai town	0.543	0.435	0.338	0.456	0.443	0.949	0.647	Elementary coordination
Wangaizhao Town	0.549	0.471	0.305	0.467	0.448	0.940	0.671	Elementary coordination
Zhaojun Town	0.545	0.440	0.314	0.466	0.441	0.950	0.645	Elementary coordination
Engebei Town	0.560	0.503	0.335	0.464	0.465	0.961	0.667	Elementary coordination
Fengshuiliang Town	0.552	0.336	0.319	0.313	0.380	0.897	0.582	Borderline coordination
Zhandanzhao Sumu	0.557	0.501	0.350	0.443	0.463	0.963	0.665	Elementary coordination

.

From the perspective of time evolution, the overall comprehensive evaluation index of the Dalad Banner area showed a slow rising trend (Figure 2); it rose from 0.356 in 2000 to 0.575 in 2022. The evaluation indexes of the energy and food subsystems generally fluctuated upward, while the water resources and ecology subsystems fluctuated upward and downward. This shows that in the past 20 years, the energy and food situation has gradually improved, but the water resources and ecological situation have not improved [9]. The inter-annual variation range in the water resources subsystem was the largest, rising to 0.674 in 2007, which reached the maximum. That is mainly because the implementation of relevant policies in Dalad Banner at this time led to improved water resource utilization efficiency, and the per capita water consumption and the proportion of agricultural water use decreased [44]. The energy subsystem rose to 0.747 in 2022, reaching the maximum, mainly because the level of energy development and utilization in Dalad Banner increased year by year, resulting in an increase in coal production and per capita energy production [45]. In general, the grain subsystem showed an upward trend, which fluctuated greatly in 2000–2002 and 2011–2013 mainly because of the decrease in grain output in Dalad Banner due to climate, precipitation, and other factors. The overall fluctuation in the ecological subsystem is obvious, and the fluctuation amplitude was larger in 2008–2013 and 2017–2018. This shows that the ecological and environmental conditions of the Ten Kongduis and people’s awareness and actions to protect the environment and ecology need to be improved.

From the perspective of the spatial dimension, the comprehensive evaluation index of each town in Dalad Banner was not high, except for Fengshuiliang Town. The comprehensive evaluation index of the other towns was between 0.4 and 0.5. Shulinzhao Town had the highest comprehensive evaluation index, reaching 0.592 in 2022. Fengshuiliang Town had the lowest, only 0.316 in 2018. The uneven distribution of water and soil resources is the direct cause of the obvious differences in the evaluation indexes of the subsystems in the townships of Dalad Banner. The water resources evaluation index of Shulinzhao Town and Engebei Town were higher, which is related to their abundant groundwater reserves. The water consumption per capita of the two towns was about 1300~1600 m³/person. The water resources evaluation index of Bainijing Town was relatively low, which may have been caused by unreasonable water use structure and a large proportion of agricultural water use. Shulinzhao Town had the highest energy evaluation index, mainly because Shulinzhao Town has abundant hydropower resources and large hydroelectric power generation [46], which leads to a high proportion of renewable energy consumption. Shulinzhao Town and Zhandanzhao Sumu are the main grain planting bases in Dalad Banner. The grain evaluation index of Shulinzhao Town was high because of its large grain output and wide planting area, and the small population and wide planting area of Zhandanzhao Sumu are reasons for the high food evaluation index [10]. The higher ecological subsystem evaluation index of Shulinzhao Town and Bainijing Town was mainly attributed to the lower discharge of industrial wastewater. In general, the development level and comprehensive evaluation index of each township in Dalad Banner are close, which is related to similarities in the resource base and geographical environment.

4.2. Spatiotemporal Analysis of the Coupling Coordination Degree of the WEFE System

On the whole, the coupling degree and coupling coordination degree of Dalad Banner and its townships were basically in the range of 0.8~1 and 0.5~0.8. This indicates that the interaction and correlation between the water, energy, food, and ecosystem systems of Dalad Banner and its townships are strong, but the coordination and development level need to be improved.

4.2.1. Time Evolution Analysis

(1)

Coupling degree (C)

From 2000 to 2022, the coupling degree of the WEFE system in Dalad Banner and its townships showed a trend of fluctuation on the whole (Figure 3); it increased from 0.884 in 2000 to 0.960 in 2022, and the increase was more obvious in 2007–2009. The coupling degree of the WEFE system in Dalad Banner and its towns was at a high-level coupling stage (the mean value was 0.946), which shows that water, energy, food, and ecology in Dalad Banner have a strong interaction and stable correlation. Except Fengshuiliang Town, the coupling degree of each town in Dalad Banner had a certain spatial difference and decreased year by year. The coupling degree of Wangaizhao Town increased most significantly from 2000 to 2019, rising from 0.793 to 0.994 and reaching a high level of coupling stage, which shows that the relationship between the subsystems is close and the interaction trend is good. The coupling degree level of Shulinzhao Town was the most stable and remained at a high level, roughly fluctuating in the range of 0.92~0.99. Fengshuiliang Town as a whole showed a sharp downward trend because Fengshuiliang Town was only completed in 2016 and all aspects of its resources and facilities are not mature.

(2)

Coupling coordination degree (D)

From 2000 to 2022, the coupling coordination degree of the WEFE system in Dalad Banner and its townships showed a slow growth trend (Figure 4); it increased from 0.530 in 2000 to 0.733 in 2022. From the numerical point of view, the coupling coordination of the system gradually improved from borderline coordination in 2000–2005 to middle-rank coordination in 2022, indicating that the coordination between various resources and the system made considerable progress. The main reason is the increase in the comprehensive evaluation index of each township. The root cause is the optimization of farmland water conservancy facilities in the Dalad Banner area, the continuous increase in the effective irrigation area, the increase in water consumption for the ecological environment, the improvement in the development and utilization of soil and water energy resources, and the enhancement in the awareness of ecological environment protection. The above factors led to an improvement in the development level of each subsystem [47]. Except Fengshuiliang Town, which showed a downward trend, the other towns showed an upward trend to varying degrees. The growth rate of Wangaizhao Town was larger, increasing from 0.484 (borderline unbalance) in 2000 to 0.763 (middle-rank coordination) in 2022. In contrast to Fengshuiliang Town’s borderline coordination, the other towns had elementary coordination, which shows that the coordinated development relationship of the WEFE system in Dalad Banner and its townships is relatively stable. Among them, the coupling coordination degree of Bainijing Town was poor compared with other towns except Fengshuiliang Town, the development of subsystems was unbalanced, and they had a mutual inhibition relationship.

4.2.2. Spatial Difference Analysis

It can be seen from the subsystem evaluation index that the WEFE system of Dalad Banner and its townships is always in an unbalanced state. There are three main factors restricting the development of coupling and coordination as follows: ① Bainijing Town and Zhonghexi Town belong to the grain lag type, which indicates that the contribution of the grain subsystem to the total system is less. ② The water resources of each township are more balanced, but Bainijing Town is relatively low. ③ Bainijing Town, Jigesitai Town, Zhaojun Town, and Fengshuiliang Town belong to the energy lag type, although they are rich in fossil energy, the proportion of renewable energy consumption is low, so the energy subsystem is relatively lagging. There is no great coordinated development among the subsystems of each township, and the lagging development of a single resource will affect the coordination level of the whole system.

There were some differences in the spatial development of the coordination degree of each township in Dalad Banner (Figure 5). Based on the data from 2000, 2005, 2011, 2017, and 2022, the overall coordination type of Shulinzhao Town was the best, the development level difference among its subsystems was small, and the development was more balanced. The coordination level between Zhaojun Town and Zhandanzhao Sumu was higher, while the coordination level between Fengshuiliang Town and Bainijing Town was relatively lower. Specifically, the coordination degree of Dalad Banner in 2000 was roughly between 0.49 and 0.56, and the mean coupling coordination degree was 0.530. Except for Bainijing Town and Wangaizhao Town, which were in the stage of borderline unbalance, all the other towns were in the stage of borderline coordination. After 5 years of evolution and development, the coordination degree of Shulinzhao Town and Wangaizhao Town increased rapidly, and the other towns had no obvious change, but overall, they showed an upward trend, and the mean coupling coordination degree was 0.553. In 2011, the coupling coordination degree of each township in Dalad Banner improved comprehensively, ranging from 0.66 to 0.74, and the mean coupling coordination degree was 0.704. The coordination of the WEFE system in each township was closer, and the overall development was better balanced. Compared with 2011, the coordination degree of each town in 2017 had no significant change, roughly ranging from 0.64 to 0.77, and the mean coupling coordination degree was 0.701. In 2022, the coordination degree of each township in Dalad Banner improved, which was roughly maintained in the range of 0.56~0.76, and the mean coupling coordination degree was 0.730. At this time, except Fengshuiliang Town, which was in the stage of borderline coordination, the other towns entered middle-rank coordination. On the whole, the coordination of the WEFE system in each township of Dalad Banner showed a good trend and gradually developed to the well-coordination stage [48].

4.3. WEFE System Coupling Coordination Driving Mechanism in Dalad Banner

4.3.1. Grey Relationship Analysis

In order to promote the transformation of the WEFE system of Dalad Banner and its townships to a higher quality coordination state, this study referred to the existing literature, analyzed the driving contributions of different subsystems using the grey relationship degree model, and further explored the driving mechanism of the coupled coordination of the WEFE system of Dalad Banner and its townships.

The correlation degree between the coupling coordination degree of the WEFE system and four driving factors in Dalad Banner and its rural areas was calculated using the grey relationship degree model (

Table 5. Correlation between the coupling coordination degree and driving factors of the WEFE system in Dalad Banner and its townships.

Region	Water	Energy	Food	Ecology
Dalad Banner	0.847	0.642	0.598	0.867
Shulinzhao Town	0.833	0.671	0.643	0.812
Bainijing Town	0.834	0.810	0.709	0.672
Zhonghexi Town	0.775	0.575	0.773	0.629
Jigesitai town	0.845	0.835	0.632	0.769
Wangaizhao Town	0.904	0.912	0.609	0.876
Zhaojun Town	0.789	0.754	0.603	0.664
Engebei Town	0.750	0.587	0.630	0.641
Fengshuiliang Town	0.788	0.711	0.683	0.737
Zhandanzhao Sumu	0.828	0.724	0.642	0.731
Mean value	0.816	0.731	0.658	0.726

). The greater the value of the correlation degree, the greater the influence of corresponding driving factors on the coupling coordination degree of the WEFE system. Through the model calculation, it was determined that the driving factors of Dalad Banner and its townships are ranked from large to small as follows: ① Dalad Banner: ecology > water > energy > food. ② Shulinzhao Town: water > ecology > energy > food. ③ Bainijing Town: water > energy > food > ecology. ④ Zhonghexi Town: water > food > ecology > energy. ⑤ Jigesitai Town: water > energy > ecology > food. ⑥ Wangaizhao Town: energy > water > ecology > food. ⑦ Zhaojun Town: water > energy > ecology > food. ⑧ Engebei Town: water > ecology > food > energy. ⑨ Fengshuiliang Town: water > ecology > energy > food. ⑩ Zhandanzhao Sumu: water > ecology > energy > food.

In terms of water resources, the overall correlation value of Dalad Banner is 0.847. The reason is that water resources directly affect the agricultural ecological landscape pattern and industrial and agricultural development mode in Dalad Banner, so water resources have a greater impact on the coupling and coordinated development of the WEFE system [20].

Among all the towns in Dalad Banner, the correlation degree value of Wangaizhao Town is the highest, which has the greatest influence on the water resources system of Dalad Banner. The reason is that Kubuqi Desert is south of Wangaizhao Town, the landform is a desert beach state, and the annual rainfall is low, so the scarcity of water resources has become the main factor restricting the further development of WEFE in Wangaizhao Town. The correlation values of Zhonghexi Town, Zhaojun Town, Engebei Town, and Fengshuiliang Town are lower than the mean value. This indicates that the water resources factor is less important to the coupling coordination of these four towns, and the development of other subsystems can be considered in the coordinated development.

In terms of energy, the overall correlation value of Dalad Banner is 0.642. The possible reason is that modern energy exploration and mining machinery provide a powerful tool for coupling and coordinated development. However, with the proposal of China’s “Carbon Peaking and Carbon Neutrality” goal and the extensive use and development of new energy, the traditional energy industry led by coal has been impacted. Therefore, the overall proportion of energy in the coordinated development is relatively reduced.

The energy correlation degree of Wangaizhao Town is the highest among all the towns, which has the greatest influence on the energy system of Dalad Banner. The reason is that the proven underground mineral deposits in Wangaizhao Town are mainly dominated by mirabilite, which provides less energy for production and development. Therefore, the coordinated development of Wangaizhao Town has a strong dependence on energy [20]. In addition to Bainijing Town, Jigesitai Town, Wangaizhao Town, and Zhaojun Town, the correlations of the other towns are lower than the average. This shows that the influence of energy on coupling coordination has decreased in recent years, and the towns with abundant energy and low correlations can have a siphon effect on population by virtue of mineral resources and topography. Through the transfer of industry and population, the pressure of agricultural production can be reduced to protect the original fragile ecological environment of Dalad Banner and promote the harmonious and stable development of water, energy, food, and ecology.

In terms of grain, the overall correlation value of Dalad Banner is 0.598, which has the least influence on the system. The reason is that although high-quality and sustainable development cannot be separated from a high-quality agricultural ecological environment, Dalad Banner is limited by natural geographical factors such as terrain and climate. This leads to relatively weak agricultural production resources. Therefore, the influence of agricultural development on the coupling and coordinated development of the WEFE system in Dalad Banner is relatively small [18].

The grain correlation degree of Zhonghexi Town is the highest, which has the greatest influence on the grain system of Dalad Banner. The reason is that Zhonghexi Town has abundant reserves of organic fertilizer, which is most favorable for food production. Except for Bainijing Town, Zhonghexi Town, and Fengshuiliang Town, the correlation degree of the other towns is lower than the average. This shows that the importance of the food system to the coordinated development of WEFE is relatively low in both the whole and the part. Dalad Banner has a temperate continental climate, dry temperatures with little rain, and cold winters and hot summers, so the food system is one of the main reasons restricting overall high-quality development.

In terms of ecology, the overall correlation value of Dalad Banner is 0.867, which has the greatest impact on the system. “Lucid waters and lush mountains are invaluable assets”. Ecology is the basic condition to support high-quality and sustainable development, and it can play the most fundamental guiding and regulating role in coupling and coordination [11]. Dalad Banner is located in the Ten Kongduis; its water resource situation is severe, the ecosystem is fragile, and soil erosion is serious, so ecology has the greatest impact on the coupling and coordinated development of the WEFE system in Dalad Banner. To sum up, ecology is one of the decisive forces for the coordinated development of the WEFE system.

The ecological correlation degree of Wangaizhao Town is the highest among all townships, and it has the greatest impact on the ecosystem of Dalad Banner. The reason is that in recent years, Wangaizhao Town vigorously carried out the ecological regulation policy of the Yellow River basin, making coordinated efforts to improve the ecological environment of state levees and their sides. Among them, the correlation values of Bainijing Town, Zhonghexi Town, Zhaojun Town, and Engbei Town are lower than the mean value. This shows that ecological factors are less important to the coupling coordination of these four towns, and the development of other subsystems can be considered in coordinated development.

4.3.2. Driving Factor Analysis

Stratified heterogeneity reflects the nature of geographical elements, and exploring their temporal and spatial properties can reveal their evolution process and potential formation mechanism [42]. With the help of a geographical detector, the driving factors of spatial differentiation of WEFE coupling coordination degree in Dalad Banner and its townships were analyzed.

Risk detection: The risk detector reveals the differences between the internal stages of each driver [41]. This paper selects four factors including water resources, energy, food, and ecology. The four types of data were sampled separately in the geographical detector as independent variables X₁~X₄ and the coupling coordination degree as Y. Finally, the detection results of each driving factor were obtained by input into the geographic detector in the form of (Y, X). The results show that there are internal differences in each driving factor, and they have different abilities to explain the spatial stratification heterogeneity in the coupling coordination degree of the WEFE system in Dalad Banner.

Factor detection: Factor detection analysis was used to explain the spatial stratification heterogeneity in the coupling coordination degree of the WEFE system in Dalad Banner by selecting four influencing factors including water resources, energy, food, and ecology. It is represented by “q” [41]. Based on the factor detection result (

Table 6. Driving factor explanatory power detection results.

	X1	X2	X3	X4
q	0.199	0.913	0.795	0.729
p	0.857	0.000	0.008	0.027

), it can be seen that the explanatory power of spatial differentiation of the coupling coordination degree of the WEFE system in Dalad Banner is as follows: X₂ (energy: 0.913), X₃ (food: 0.759), X₄ (ecology: 0.729), and X₁ (water resources: 0.199). Only water resources are below the 50% level, and the remaining factors are above the 70% level. In addition to energy having the greatest influence on the spatial differentiation of coupling coordination degree of the WEFE system in Dalad Banner, food and ecology also have a significant influence on it, while water resources have the least explanatory power. It is inferred that the difference in groundwater reserves is not large among towns in Dalad Banner, and the effect on the spatial differentiation of coupling coordination is minimal. Therefore, the influence of water resources on the spatial differentiation of coupling coordination is minimal.

Interactive detection: Interactive detectors can assess whether the interaction among driving factors increases or decreases the explanatory power of the dependent variable. If the explanatory power of a single factor is less than that of a double-factor interaction, then it can be interpreted as enhanced [41]. According to

Table 7. Driving interaction detection results.

	X1	X2	X3	X4
X1	0.199
X2	0.996	0.913
X3	1	0.966	0.795
X4	1	0.990	0.970	0.729

, it can be seen that the change in the coupling coordination degree is the result of the comprehensive action of energy factors and ecological factors as follows: X₁ (water resources) ∩ X₃ (food), X₁ (water resources) ∩ X₄ (ecology). The interaction between these two pairs of factors has the greatest explanatory power, indicating that the combination of water resources and ecological factors, grain, and ecological factors has a strong influence on the overall coupling coordination of Dalad Banner. Next is X₁ (water resources) ∩ X₂ (energy). In addition, the interaction between any double factors will enhance the interpretation of the coupling coordination degree of the WEFE system in a nonlinear way. X₁ (water resources) and X₄ (ecology) have weak explanatory power, but the explanatory power of their interaction is even greater than that of X₂ (energy), which has the strongest explanatory power. This shows that the spatial differentiation of the WEFE system in Dalad Banner is the result of many influencing factors that play a synergistic role. Therefore, we should pay attention to the relationship among the influence factors and consider the influence of the synergistic interaction among the factors on the spatial differentiation of WEFE coupling coordination in Dalad Banner.

Ecological detection: The following table (

Table 8. Driving factors ecological detection results.

	X1	X2	X3	X4
X1
X2	Y
X3	Y	Y
X4	Y	Y	N

) can be used to compare whether there are significant differences in the influence of driving factors on the spatial differentiation of the WEFE system coupling coordination degree. If the difference is significant, it is “Y”. Otherwise, it is “N” [41]. The results show that the spatial distribution of WEFE coupling coordination in Dalad Banner is significantly different among the other factors except X₃ (food) and X₄ (ecology). This shows that the influence of the above factors on the coupling coordination of WEFE in Dalad Banner will change obviously with time, and the driving mechanism and stability are relatively weak.

5. Discussion

The results show that the coupling coordination of the WEFE system in Dalad Banner and its townships is relatively poor, its development level needs to be improved, and there are some differences in the development of spatial coordination degree among the townships. In a study on the Amu Darya River (ADR) Basin, the three countries within the basin (Tajikistan, Uzbekistan, and Turkmenistan) also showed differences in coordination due to spatial factors [49]. In studies on coupled coordination in the Yangtze River Basin, the spatio-temporal pattern of WEFE coupling coordination degree among provinces in the Yangtze River Basin Economic Belt also showed significant differences [50,51]. The reason for this is that Dalad Banner is located in the western part of the Inner Mongolia Autonomous Region and the northeastern part of Ordos City, on the south bank of the middle reaches of the Yellow River and the northern end of the Ordos Plateau. The region is unique in its geographic location, with its arid climate and fragile ecosystems. The region is rich in natural resources, especially energy resources, but has very minimal water resources, and the spatial distribution of water, energy, and land resources is highly uneven. Many scholars have also conducted studies on the influence of environmental factors such as climate and energy on the degree of coupling coordination [52,53]. Given the unique natural geographic conditions and geopolitical environment, the rapid development of Dalad Banner and its townships has resulted in intermingled water–energy–food–ecological, different distributions of resources and resource demands, and so on [54]. The over-exploitation of water, energy, soil, and other resources has further exacerbated diverse ecological and environmental problems. This in turn has led to prominent contradictions among the subsystems of the WEFE in the entire Dalad Banner, restricting the high-quality development of the region [55]. Based on this study, it can be concluded that the socio-economic development needs of Dalad Banner and its townships influence the level of WEFE coordination within the region, while the sustainable development of the townships is also inseparable from the coordination and stabilization of the WEFE system in Dalad Banner [56]. Therefore, it is necessary to analyze the coupled coordination of Dalad Banner. In summary, compared with only considering water resources, energy, and food in the three-dimensional perspective, the systematic study that integrates the four-dimensional perspective of water–energy–food–ecology can more comprehensively reveal the level of multi-factor coupling and coordination of the sustainable socio-economic development of Dalat Banner [57]. However, compared with the study at the level of time span alone, the study of the coupling relationship among water–energy–food–ecology elements in Dalad Banner at the level of spatial area by townships is more helpful in providing a decision-making basis for the high-quality and sustainable development of Dalad Banner, Ordos, and even Inner Mongolia. Many scholars have also analyzed and evaluated the WEFE system from different perspectives such as sustainable development and spatio-temporal dynamics [58,59,60].

This study found that the comprehensive evaluation index and coupling coordination level of the four subsystems of water resources, energy, food, and ecology in Dalad Banner are relatively low and have certain spatial differences. It is mainly restricted by the unbalanced development of towns and villages in Dalad Banner and the geographical environment conditions [61]. Dalad Banner is rich in mineral resources, but the development and coordination levels of its subsystems are different because of the different degrees of development and utilization in different townships. Because of geographic factors and climatic influences, the scarcity of water resources constrains the development of irrigated agriculture and the economy of Dalad Banner, while the structural contradiction between upstream and downstream water use further exacerbates energy, food, and ecological security [62,63]. The WEFE evaluation index system of Dalad Banner proposed in this paper is constructed based on the principles of scientificity and representativeness, with full consideration of the relationship among the impacts of water resources, energy, food, and ecological elements in the study area [64]. Individual indicators may be missing because of data availability. However, by comparing the results of this paper with the results of the existing literature, the indicator system is able to reflect the characteristics of the various aspects of the WEFE system in Dalad Banner to a certain extent in a more comprehensive manner.

In summary, it is both feasible and valuable to study the coupled harmonization of bonding relationships in Dalad Banner and its townships. This is conducive to the protection of people’s lives, property, production, and living safety downstream of the Ten Kongduis and to the protection of Dalad Banner, and even Ordos City, to realize industrialization, urbanization, industrialization of agriculture, and animal husbandry. It is of great significance for promoting the comprehensive, coordinated, and sustainable development of the local economy and society and protecting the safety and benefits of the downstream water conservancy hub project [65]. At the present stage, we should further address the serious water resources situation, fragile ecosystems, serious soil erosion, irrational agricultural cultivation structure, and high concentration of industrial wastewater in the Ten Kongduis. This includes systematically studying the evolution of water, energy, food, and life cycles and the driving mechanisms in the Ten Kongduis under changing environments [66]. Relevant optimized allocation and regulation technologies should be proposed to provide important technical support for the implementation of major national strategies such as “strengthening ecological environmental protection and promoting high-quality development of the Yellow River Basin”. Ultimately, this will realize the goal of making a significant leap from “parallel running” to “leading” this field of research in China on a global scale.

In order to realize regional sustainable development and strengthen the coordination level among the systems, based on the WEFE coupling coordination development status of Dalad Banner and its townships, we propose the following suggestions:

(1)

Promote coordinated development. Based on the different resource endowments and development status of different parts of Dalad Banner, targeted development strategies are formulated, overall consideration is taken, and the development goals of water, energy, food, and ecology are coordinated. The most important thing is to improve the development level of lagging subsystems in low-coordination areas, promote the sustainable use of resources, improve the level of coordination, and achieve high-quality economic development and overall competitiveness.

(2)

Introduce advanced technology, strengthen comprehensive management, and break restrictive factors. We should improve the efficiency of water resource utilization, optimize the allocation of water resources and the energy structure, develop clean energy, and enhance the technological innovation capacity of industrial enterprises. This includes developing water-saving irrigation technology, improving early warning and emergency response capabilities for natural disasters, and strengthening the protection of cultivated land. Finally, we should strengthen regional ecological protection, improve pollution control, and promote energy conservation and emission reduction.

6. Conclusions

In this paper, a comprehensive evaluation index system of the WEFE system is constructed. The coupling coordination degree model, grey relationship degree model, and a geographical detector are used to analyze the temporal and spatial changes in the coupling coordination degree and driving factors of the WEFE system in Dalad Banner and its townships. The main conclusions are as follows:

(1)

The comprehensive evaluation index of water–energy–food–ecology in Dalad Banner and its townships showed a slowly rising trend on the whole. The development trend of the energy and food subsystems is basically the same as that of the comprehensive evaluation index, while the development level of the water resources and ecological subsystems changes little. Bainijing Town and Zhonghexi Town have relatively rich water resources and large hydroelectric power generation, and the development level of water resources and energy subsystems is great, so they belong to the grain lag type. The Fengshuiliang Town and Jigesitai Town grain planting area is wide and the grain subsystem development level is great, so they belong to the energy lag type. In terms of water resources, each township is balanced, but Bainijing Town lags behind.

(2)

The coupling degree of water–energy–food–ecology in Dalad Banner and its townships is high, but the coordination degree is low, and the spatial development is unbalanced and uncoordinated. The coupling degree of Dalad Banner and its townships is mostly in the high-level coupling stage, and the correlation degree among subsystems is strong. The other towns show a rising trend except Fengshuiliang Town. The coupling coordination degree of Dalad Banner and its townships shows a trend of fluctuation and rise, which is basically in the elementary coordination stage, and the development of subsystems has a mutual restriction relationship.

(3)

The coordination of the WEFE system in each township of Dalad Banner shows a good trend and gradually develops to a good coordination stage. In 2000, except for Bainijing Town and Wangaizhao Town. which were in the stage of borderline unbalance, all the other towns were in the stage of borderline coordination. In 2005, the coupling coordination degree of each town was on the rise. In 2011, the coupling coordination degree of all townships improved. Compared with 2011, the coordination degree of each town in 2017 had no significant change. In 2022, the coordination degree of each township in Dalad Banner improved, and except for Fengshuiliang Town, which is located in the borderline coordinated stage, the rest of the townships entered the middle-rank coordination stage.

(4)

The influence degree of the four driving factors of water, energy, food, and ecology on the coupling coordination degree of the WEFE system in Dalad Banner and its townships is as follows: ecology > water > energy > food. In terms of water resources, energy, and ecology, the correlation degree of water resources in Wangaizhao Town is the highest. In terms of food, the food correlation degree of Zhonghexi Town is the highest. For the spatial differentiation of the coupling coordination degree of the WEFE system in Dalad Banner and its townships, energy has the greatest influence, followed by food and ecology, and water resources have the least explanatory power. In terms of the interaction detector, the interaction among water resources and food, water resources, and ecology has the greatest explanatory power, followed by the interaction between water resources and energy. In terms of interactive detection, there is no significant difference among ecology, water resources, and energy, but there is a significant difference between ecology and food.