Evaluation of Water-Energy-Food-Ecology System Development in Beijing-Tianjin-Hebei Region from a Symbiotic Perspective and Analysis of Influencing Factors

Abstract

Rapid economic and social development has created significant ecological and resource problems in the Beijing-Tianjin-Hebei (BTH) region, making it necessary to identify ways of implementing sustainable regional development. The interactions between water, energy, food, and ecology are characterized by a high degree of relevance and complexity. In studying the relationships between the four systems in depth and choosing representative indicators for each system, a comprehensive development model of the water-energy-food-ecology (WEFE) system in the BTH region has been established. The coupling coordination degree model was used to analyze the coupling synergy relationship between the WEFE systems in the BTH region from 2001 to 2020. The primary contributing elements determining the development of linked synergy in the WEFE system were investigated using a gray correlation model. According to the findings, Beijing’s total coupling coordination development level shows a gradual upward trend and is in excellent coordination; Hebei has progressed the most, experiencing a significant change from little coordination to good coordination; and Tianjin has had the least improvement, only improving from basic to good coordination. The exploitation of water resources and ecological protection of the environment are the aspects that have the greatest impact on the WEFE system. Additionally, the linked and synergistic growth of the WEFE system in the BTH region is significantly influenced by economic, social, and technological advancements in the industrial and agricultural sectors. The coupling coordination development of regional WEFE systems, which takes into consideration the synergistic optimization of many subsystems, is provided by this study as a scientific foundation.

1. Introduction

In recent years, the most prominent non-traditional security issues in the world have been water, energy, food, and ecology security, and the WEFE nexus has increasingly drawn widespread attention from the international community [1]. As basic resources, water, energy, and food enable human prosperity, while ecology is the essential foundation for sustainable regional development [2]. At the same time, the accompanying rapid economic development, population growth, and accelerated urbanization and climate change are putting enormous pressure on the availability of these resources and will have a negative impact on regional ecology [3]. Since the reform and opening up, China has made remarkable breakthroughs in development at the cost of resource depletion and environmental damage. There is no doubt that the extensive development model has been accompanied by serious resource depletion and environmental degradation [4]. For instance, long-term transitional groundwater exploitation in Beijing-Tianjin-Hebei has led to significant mining leakage in the North China Plain, and a severe water shortage has resulted in serious ecological issues such as a decline in the number of biological populations in ground subsidence and the disconnection of river channels [5,6]. Therefore, supporting future regional sustainable development to encourage the harmonious development of resources and the ecological environment under the direction of green development is of major academic value and practical relevance.

The majority of earlier research on essential resources concentrated on the connection between a particular resource and urbanization, economic development, and ecological preservation [7,8] Moreover, since the concept of the water-energy-food nexus was first introduced at the Bonn Conference in 2011 defining the high correlation between water, energy, and food [9],it has led to a high level of interest in the opportunities and challenges [10,11] surrounding this issue, and scholars have paid close attention to the approaches and models used in the WEF system study [12,13,14]. Since then, the study focus has shifted from specific systems to correlations between the WEF system as a whole and other resources [15,16], as well as correlations involving resources within the WEF system [17,18,19,20]. Water resource security and the health of the water landscape pattern are ensured by a healthy ecological environment, which is also an important guarantee for local residents to engage in productive life. The photosynthesis of vegetation in the external ecosystem can reduce greenhouse gas emissions from energy generation, which has the impact of reducing carbon emissions. For the production of food, fertile soils and ample water supplies are essential, and a superior ecological environment can guarantee the sustainability of soil and water resources [21]. As a result, ecology should be the key element of the WEF system, and the resulting WEFE system is a synergistic evolution system with reciprocal cooperation and restrictions.

Most of the existing studies focus on water-energy-food (WEF) systems [22,23], but there are few studies that introduce ecology into the system. This is primarily due to the challenge of modeling the water, energy, food, and ecological nexus in terms of reducing multidimensional and interdependent uncertainty [24,25,26]. Water, energy, food, and ecology as an interconnected and mutually influencing organic whole has a strong bond, and its development is clearly subject to the overall changes in the socio-economic and resource environment [27]. At present, the main methods for quantitative study of composite systems include: the system dynamics model [28], life cycle assessment method [29,30], data envelopment analysis model [31], and coupling coordination degree model [21,32].

Since the water-energy-food-ecology (WEFE) system relationship is a complex system composed of multiple subsystems, a more comprehensive consideration of each subsystem is required in selecting indicators and assessment results to prevent the simplicity and one-sidedness of the WEFE system and to ensure the accuracy and comprehensiveness of the results. The coupling coordination degree model has a strong relevance in solving the synergistic development of complex systems and has achieved good results in the application of water resource carrying capacity evaluation [33] and the assessment of integrated socio-economic and resource development [34]. Therefore, the coupling coordination degree model was chosen as a model to evaluate the synergistic development of the WEFE system in the Beijing-Tianjin-Hebei (BTH) region.

The goal of this study is to provide a thorough description of the dynamic relationships that exist among the various subsystems of the WEFE system, which is essential for promoting the sustainable development of local resources and ecology. In addition, most previous studies have been limited to studying changes in coupling coordination relationships within small regions. Few studies have examined the development of coordinated states of administrative units that have close ties to each other on a larger spatial scale but are at the same level, which is essential for resource sharing and mutual ecological benefits between neighboring regions. In order to further the synergistic development of resources and ecological environment in each region, this study selected the BTH region as the study area, which is conducive to a better study of the evolutionary characteristics and main determining variables of the synergistic development of the regional WEFE system.

2. Study Area and Research Methods

2.1. Study Area

The Beijing-Tianjin-Hebei (BTH) region is one of the most important economic zones in China, including the municipalities of Beijing, Tianjin, and Hebei, with a total land area of 218,000 km² (Figure 1). As the most densely populated region in China, the total population of BTH will reach 120 million in 2020, accounting for 8.5% of the total population of China and only 5.3% of the land area. Due to the large population and limited resources in the region, development of the BTH region is seriously restricted and faces many serious problems, including natural environment degradation, land subsidence caused by excessive exploitation of groundwater, inefficient utilization of resources, and serious waste. The BTH region’s per capita water resources were only 117.6 m³/person in 2020, far below the national average of 2239.8 m³/person. This shows that the region’s water resources are under a lot of pressure and will lead to a number of water and soil problems. The production and consumption of food and energy are likewise highly out of balance in the BTH region, with food production being 2.69 times higher than consumption. In contrast, energy consumption accounts for 10% of the nation’s total energy production, whereas production accounts for just 3% of energy consumption and has a 28% effective utilization rate. Increasing food production over the years has led to an increase in fertilizer application and water consumption, resulting in arable land degradation, water pollution, groundwater extraction leakage, and other ecological problems. Excessive energy use may unavoidably worsen the ecological environment in the region, leading to a significant imbalance in regional growth and other cascade effects and endangering the peaceful coexistence of man and nature. Local governments are gradually realizing the importance of treating WEFE as a whole as the WEFE nexus issue is further studied in depth. This realization has in turn increased demands for synergistic development across sectors, which has resulted in the introduction of increasingly coordinated governance policies across sectors. In the near future, this will have a significant impact on the effectiveness of resource integration and ecological protection.

2.2. Research Framework

A symbiotic perspective on the WEFE system nexus is shown in Figure 2. The symbiotic unit synergy of the water, energy, food, and ecological individual subsystems, the symbiotic relationship synergy between the two subsystems, as well as the symbiotic environmental synergy that includes economic, social, and natural conditions were all taken into consideration when studying the WEFE system relationship synergy [35]. Within the constraints of the symbiotic environment, the entire sustainable state of the system is reflected in the synergistic action of the symbiotic environment. The subsystems interact with each other in the process of synergistic development in space and time, ultimately leading to a relative balance of sustainable regional development.

Ecology as an objective response to the state of development of water, energy, and food resources can provide a correction to the healthy state of development of resources, and ecology can serve as a validation for the harmonious development of resources and socio-economics. Energy consumption is also necessary for the extraction, transportation, and purification of water resources. Processes such as cooling and hydropower generation for thermal power require large amounts of water resources. Food is heavily supported by water resources throughout its life cycle. All phases of food production require energy, and biomass created from food can be used as a sustainable energy resource. The exploitation of water, energy, and food produces a large number of pollutants such as greenhouse gases, toxic chemicals, and a fragmented ecology; improving the ecology plays an important role in effectively mitigating warming trends and reducing the occurrence of natural disasters. The basic state of the four regional subsystems and their mutual synergy will generate feedback on external socio-economic development and trigger the attention of relevant governmental decision-making authorities.

2.3. Indicator System Establishment

The coupling coordination development of water, energy, food, and ecology systems is characterized by complexity and uncertainty. As in the previous study [36], we first identified the water resource subsystem, containing total indicators represented by total water resource and consumption indicators that reflect the basic state of regional water resource development. Secondly, the energy aspect in the BTH region is mainly concerned with the production and consumption of energy. Then, the per capita food yield and total power of agricultural machinery were selected as representative indicators of yield and production indicators with due consideration of food cultivation in the BTH area. Finally, we chose ecological quality, environmental management, and emission indicators to reflect the quality of regional ecological development [34].

According to the scientific connotation of the WEFE system relationship and with reference to the relevant literature [37], a comprehensive evaluation index of the WEFE system in the BTH region was constructed, which includes 4 subsystems and 26 specific indicators (

Table 1. The indicators selected for each system layer of the WEFE system.

System Layer	Criterion Layer	Indicator Layer	Unit	Properties
Water Resources	Total metrics	C1: Water resources per capita	m3/per	+
		C2: Total water resources	109 m3	+
		C3: Total water supply	109 m3	-
		C4: Water consumption	109 m3	-
	Consumption indicators	C5: Agricultural water consumption	109 m3	-
		C6: Industrial water consumption	109 m3	-
		C7: Ecological water consumption	109 m3	+
		C8: Water consumption of 10,000 Yuan GDP	m3/104 Yuan	-
Energy	Production index	C9: Total energy production	10,000 tons	+
		C10: Energy self-sufficiency rate	%	+
		C11: Energy consumption per capita	tons	-
	Consumption indicators	C12: Energy consumption per unit of GDP	tons/104 Yuan	-
		C13: Industrial energy consumption	10,000 tons	-
Food	Yield index	C14: Food yield per unit area	tons/hm2	+
		C15: Food production per capita	tons	+
	Production conditions	C16: Food cultivation area	10,000 hm2	-
		C17: Effective irrigated area	10,000 hm2	+
		C18: Total power of agricultural machinery	10,000 kw	+
		C19: Rural electricity consumption per capita	kw/h	-
Ecology	Ecological quality	C20: Urban greening coverage	%	+
		C21: Green space per capita	per/m2	+
	Environmental management	C22: Sewage treatment rate	%	+
		C23: Domestic waste disposal rate	%	+
	Emission indicators	C24: Chemical oxygen demand emission	10,000 tons	-
		C25: Fertilizer application amount	10,000 tons	-
		C26: Sulfur dioxide emissions	10,000 tons	-

). In addition, indicators such as ecological water consumption, agricultural water consumption, water consumption of 10,000 Yuan GDP, and fertilizer application are used as internal correlation indicators of the WEFE system to effectively reflect the interactions between water, energy, food, and ecology. It makes the internal relationship of the WEFE system tighter and more complex and places higher demands on the system to develop interdependently. In order to ensure the accuracy of the study results, the data in this paper were obtained from the China Statistical Yearbook, China Energy Statistical Yearbook, China Environmental Statistical Yearbook, the Beijing, Tianjin, and Hebei Statistical Yearbooks, and the Water Resources Bulletin.

2.4. Data Pre-Processing and Index Weighting

The raw data of the selected indicators were first dimensionless processed to eliminate the inconvenience caused by the model calculation process and the impact on the analysis results, as follows:

Positive indicators:

$$X_{ij}^{\prime }=\left(x_{ij}-\min x_j\right)/\left(\max x_j-\min x_j\right)$$

(1)

Negative indicators:

$$X_{ij}^{\prime }=\left(\max x_j-x_{ij}\right)/\left(\max x_j-\min x_j\right)$$

(2)

where $X_{ij}^{\prime }$ and $x_{ij}$ represent the original and standardized dimensionless values of indicator $j$ in the $i^{th}$ year; $\max x_j$ and $\min x_j$ represent the maximum and minimum values of the actual values of the original data before data processing in the calendar year, respectively.

In terms of index weight determination, combining the entropy method with the coefficient of variation method will have a positive effect on the determination of index weights [38]. The entropy method is an objective assignment method and its steps to calculate the index weights are shown below:

$$P_{ij}=\frac{X_{ij}{}^{\prime }}{{\displaystyle \sum _{i=1}^n{X}_{ij}{}^{\prime }}}$$

(3)

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

(4)

$${\mathrm{g}}_j=1-e_j$$

(5)

$$w_{j1}=\frac{g_j}{{\displaystyle \sum _{i=1}^n{g}_j}}$$

(6)

where the weight $P_{ij}$ of the indicator accounted for by the $j$ indicator in year $i$, and then the entropy value $e_j$ of the $j$ indicator, the coefficient of variation $g_j$ of the $j$ indicator and the weight $w_{j1}$.

The coefficient of variation method directly uses the information contained in each indicator to calculate the weight of the indicator by standardizing the indicator, and the steps to determine the weight of the indicator are as follows:

$$w_{j2}=\frac{v_j}{{\displaystyle \sum _{i=1}^n{v}_j}}$$

(7)

$${\mathrm{v}}_j=\frac{{\sigma }_j}{\overline{x_j}}$$

(8)

The weights combining the entropy weight method and the coefficient of variation method are as follows:

$$w_j=\frac{1}{2}(w_{j1}+w_{j2})$$

(9)

where $n$ is the number of statistical years; $e_j$ is the entropy value of the indicator $(0\le e_j\le 1)$; $v_j$ is the coefficient of variation of the $j$ indicator; ${\sigma }_j^{}$ is the standard deviation of the $j$ indicator; ${\overline{x}}_j$ is the mean of the $j$ indicator; $w_{j1}$ is the weight calculated by the entropy weight method; $w_{j2}$ is the weight calculated by the coefficient of variation method; and $w_j$ is the final weight.

2.5. Coupling Coordination Degree (CCD) Model

First, based on the integrated development indicators of each subsystem, the formula for calculating the integrated development level of the WEFE system is obtained as follows:

$$T=\eta W_a+\gamma E_{\mathrm{n}}+\mu F_o+\beta E_c$$

(10)

where $T$ is the integrated development index of the WEFE system, and $\eta$, $\gamma$, $\mu$, $\beta$ are the weights of the four subsystems in the composite system, where the four subsystems of water, energy, food, and ecology are equally important [21]. According to the formula $\eta =\gamma =\mu =\beta =1/4$ in this study, the greater the combined development index, the better the regional WEFE system development.

System coupling originates from physics and usually refers to the phenomenon that two or more systems are united through mutual influence and interaction [39]. Coupling degree is used to describe the interaction between the system and the system from the disorderly state to the orderly state system role of the degree of measurement; the greater the coupling degree, the better the coupling effect and the stronger the interaction between the systems [40].

$$C=\frac{n\sqrt[n]{W_a{E}_n{F}_o{E}_c}}{W_a+E_n+F_o+E_c}$$

(11)

where $C$ is the coordination degree of the WEFE system. The value of $C$ falls between 0 and 1, with $n$ serving as the adjustment coefficient. When $C$ is closer to 1, it indicates that the overall coupling of the system is better. Where the coupled system includes water, energy, food, and ecology subsystems, $n$ is taken as 4 [20]; however, the value of the coupling degree can only represent the strong and weak relationship between one subsystem and cannot reflect the degree of coordinated development. Thus, the coupling coordination degree (CCD) model is introduced in this paper [15]. Finally, the formula for the WEFE system relationship is obtained as follows:

$$D=\sqrt{CT}$$

(12)

where $D$ is the coupling coordination degree, $0\le D\le 1$, and the closer $D$ is to 1, the better the coupling coordination degree of the WEFE system. The regional WEFE system CCD can be divided into five stages [41], which are no coordination, little coordination, basic coordination, good coordination, and excellent coordination, as shown in

Table 2. The classification criteria of the CCD.

Type	Coordination Stage	CCD Value	Development Level
Coordinated	Excellent coordination	0.90~1.00	Quality coordinated development
		0.80~0.89	Well-coordinated development
	Good coordination	0.70~0.79	Intermediate coordinated development
		0.60~0.69	Primary coordinated development
Transition	Basic coordination	0.50~0.59	Barely coordinated development
		0.40~0.49	Near dysfunctional recession
Recession disorders	Little coordination	0.30~0.39	Mild dysfunctional recession
		0.20~0.29	Moderate dysfunctional recession
	No coordination	0.10~0.19	Severe dysfunctional recession
		0.00~0.09	Extreme dysfunctional recession

.

2.6. Influence Factor Analysis Using Gray Correlation Model

Gray correlation analysis is a quantitative analysis method of dynamic indicators, which can measure the degree of correlation among factors by analyzing and comparing the similarity of development trends among factors in the midst of incomplete information; the higher the value of gray correlation between two series, the stronger the correlation [42].

Suppose the number of observation years is m, and there is a reference series (using the synoptic-level eigenvalues of the WEFE relationship as the reference series) and an n comparison series (using the evaluation metrics as the comparison series). Where m = 20 and n = 26, the reference and comparison sequences are described as shown in the following equations:

For gray correlation analysis, the data series of the dependent variable and the comparative data series of the independent variables consisting of the influencing factors are noted as follows:

$$X_0\left(k\right)=\left[X_0(1),X_0(2),\cdots ,X_0(20)\right], \left(k=0,1,\cdots ,20\right)$$

(13)

$$X_i\left(k\right)=\left[X_i(1),X_i(2),\cdots ,X_i(26)\right], \left(i=0,1,2,\cdots ,26\right)$$

(14)

where $X_i(k)$ is the data series of independent variables, and $n$ is the series of independent variables of influencing factors.

The physical significance and units of measurement of the data for each influence factor may differ, resulting in different magnitudes. In order to facilitate statistical calculations and analytical comparisons, the data were primed by adopting the method, and the equations are as follows:

$$X_0^{\prime }=X_0/X_0(1)$$

(15)

$$X_i^{\prime }=X_i/X_i(1)$$

(16)

where $X_i^{\prime }$ and $X_0^{\prime }$ are the values of $X_i$ and $X_0$ after initialization. The absolute values of the data series of the dependent variable $X_0^{\prime }(k)$ and the data series of the independent variable comparison $X_i^{\prime }(k)$ constitute the difference series ${\Delta }_{0i}(k)$, and then we find the maximum absolute difference ${\Delta }_{\max }$ and the minimum absolute difference ${\Delta }_{\min }$ between the two poles as follows:

$${\Delta }_{0i}(k)=\left|X_0^{\prime }(k)-X_i^{\prime }(k)\right|$$

(17)

$${\Delta }_{\max }={\max }_i{\max }_k\left|X_0^{\prime }(k)-X_i^{\prime }(k)\right|$$

(18)

$${\Delta }_{\min }={\min }_i{\min }_k\left|X_0^{\prime }(k)-X_i^{\prime }(k)\right|$$

(19)

Finally, the gray correlation coefficients of each influencing factor and the correlation ${\gamma }_{0i}$ of the data series $X_{i(k)}$ of the respective variables to the dependent variable $X_{0(k)}$ are calculated as follows:

$${\xi }_{0i(k)}^{}=\frac{{\min }_i{\min }_k\left|X_{0(k)}-X_{i(k)}\right|+\xi \cdot {\max }_i{\max }_k\left|X_{0(k)}-X_{i(k)}\right|}{{\Delta }_{0i(k)}+\xi \cdot {\max }_i{\max }_k\left|X_{0(k)}-X_{i(k)}\right|}$$

(20)

$${\gamma }_{0i}=\frac{1}{N}{\displaystyle \sum _{k=1}^N{\xi }_{0i(k)}}$$

(21)

where $\xi$ is the resolution coefficient, generally taken as $\xi$ = 0.5 [6]. The degree of influence on the dependent variable is determined by the magnitude of the correlation ${\gamma }_{0i}$ calculated from the gray correlation analysis, and the resulting ${\gamma }_{0i}$ is ranked from smallest to largest.

The magnitude of the correlation ${\gamma }_{0i}$ calculated from the gray correlation analysis is used to determine the degree of influence on the coupling coordination development of the WEFE system. When the gray correlation degree ${\gamma }_{0i}<0.6$ has poor correlation, $0.6<{\gamma }_{0i}\le 0.7$ has fair correlation, $0.7<{\gamma }_{0i}\le 0.8$ has good correlation, and ${\gamma }_{0i}>0.8$ has excellent correlation [43], it means that the indicator significantly affects the synergistic effect of the WEFE system relationship.

3. Results

3.1. Comprehensive Development Level Analysis

The comprehensive development level of the WEFE system in the Beijing, Tianjin, and Hebei region from 2001 to 2020 was calculated and the development trend was mapped, as shown in Figure 3.

The total growth of water resources in Beijing and Hebei exhibits a fluctuating rising tendency. Beijing has had a clear upward trend from 2001–2008 and a W-shaped development trend from 2008–2016, reaching a maximum development level in 2012 and showing a slightly lower fluctuating change from 2016–2020. The comprehensive development level of water resources in Hebei from 2001–2008 changed more drastically; the 2008–2014 changes in development were relatively stable. Since 2014, except in 2017 and 2019, there has been a slight decline in the overall performance of a rising trend. Except for rather abrupt surges in 2008 and 2012, Tianjin’s degree of water resource development has had relatively little change in general. Water resources in the BTH region have generally undergone extensive growth, with the level increasing steadily from 0.219 in 2001 to 0.655 in 2020.

In terms of energy subsystem development levels, Beijing’s energy development levels continued to decline from 2001–2007, reached a minimum in 2007, fluctuated and increased from 2007 to 2015, began to decline again in 2015–2017, and then continued to rebound after 2017. Between 2001 and 2010, Tianjin had a sizable fluctuating growth, a small fall from 2010 to 2013, and a total energy development pattern that indicates a fluctuating increase after 2013. In Hebei, the degree of energy development exhibits a cyclical decreasing pattern, peaking in 2004 and declining in 2019, with a recovery from 2009 to 2012. On the whole, the BTH region’s energy development level fluctuates less and exhibits significant development trend uncertainty; it goes from 0.512 in 2001 to 0.540 in 2020.

From 2001 to 2003, the level of food development in Beijing declined significantly and then fluctuated after 2003, with no obvious upward or downward trend. Between 2001 and 2006, the level of food development in Tianjin rose slowly but steadily and then began to decline until reaching the lowest point in 2012. The growth of the food subsystem fluctuated significantly between 2012 and 2016, which is mostly attributable to the rise in per capita food production and the fall in rural electricity consumption. The food subsystem evolved in an N-shaped pattern from 2016 to 2019. From 2001 to 2012, Hebei’s level of food development increased most noticeably and peaked in 2012. This was followed by a sharp decline in 2012–2016, which was correlated with a decline in the total amount of agricultural machinery power, and then a modest rise in 2016–2020. From a starting point of 0.339 in 2001 to and ending point of 0.570 in 2020, the degree of food development in the BTH area exhibits a modestly varying rising trend.

The trends in the ecology subsystem are significantly different from the other three subsystems. The development of the ecology subsystem as a whole demonstrates a clear pattern of continuous growth, which underwent a slight drop from 2010 to 2011 due to a spike in chemical oxygen demand emission but essentially maintained a quicker growth in the following years. In the BTH region, the ecological development level has risen steadily over time, from 0.164 in 2001 to 0.945 in 2020. In summary, the general impact of BTH in the WEFE system is exceptional owing to the greater growth of the ecology subsystem, and the ecological subsystem and other subsystems should be more closely connected to promote each other in the future, so that the WEFE system as a whole may be enhanced.

3.2. Coupling Coordination Degree (CCD) Development

The general trend for coupling coordination development in the BTH region from 2001 to 2020 is positive (Figure 4). The temporal distribution of coupling coordination in the BTH region mainly experienced low coordination states accompanied by rapid improvement (2001–2008), moderate coordination states accompanied by steady improvement (2009–2015), and excellent coordination states accompanied by slow improvement (2016–2020).

The development of CCD in Beijing can be divided into the following three stages: (1) the initial CCD value of 0.431 in Beijing was in a state of basic harmony and then underwent a rapid increase of 32.9% to 0.573 in 2002, mainly due to a decrease in water consumption of 10,000 Yuan GDP and an increase in the domestic waste treatment rate; (2) the CCD increased from 2002 to 2005, reaching a value of 0.667 and indicating good coordination, which subsequently fell in 2006 before rising to an intermediate level of development with a CCD of 0.762 from 2008 to 2012; and (3) between 2012 and 2020, the CCD was in an obvious period of repeated upward fluctuations in 2020 when the CCD value reached a maximum of 0.846 already at the good development level of excellent coordination status.

In comparison with Beijing and Hebei, the CCD in Tianjin has altered less dramatically. Tianjin’s CCD value was 0.570 in 2001, which was greater than that of Beijing and Hebei in basic coordination, and from 2001 to 2007, the CCD increased with small fluctuations so that the CCD was always at the basic coordination level, with the CCD value in 2007 being 0.640, which was only 12.3% higher than that in 2001. Since 2008, when it attained the primary development level of good coordination status, Tianjin’s CCD value had not changed significantly. It stayed at this level until 2020, when the Tianjin CCD value of 0.771 was regarded as being at the intermediate development level of good coordination status.

The most challenging sectors for the coupling coordination development of the BTH region were in Hebei, whose CCD values were persistently lower than those of Beijing and Tianjin until 2010. The fundamental cause of the long-term relative imbalance in the WEFE system is that Hebei had never evolved the WEFE system to a balanced condition in terms of its economic and social development. In the period 2001–2002, Hebei’s CCD level was at the lower level of the BTH region as a little coordination status, with a CCD value of only 0.353 at the lowest. The dramatic rise in CCD to 0.559 in 2003, which was necessary to achieve a condition of fundamental harmony, was mostly brought about by the expansion of water supplies and the decline in agricultural water usage. After Beijing and Tianjin, Hebei was the third province to reach the good coordination level with a CCD score of 0.644 in 2008. From 2008 to 2012, the level of CCD development in Hebei continued to rise, but from then until 2014, its CCD was in a declining phase, mainly due to a decrease in the amount of water resources and total energy production. Since then, the Hebei CCD continues to increase significantly, surpassing Tianjin in 2020 to reach a maximum of 0.775, which is still considered to be in the stage of good coordination.

The coupling coordination degree of the BTH region reveals a rapid process of CCD development from a basic coordination stage to a good coordination stage from 2001 to 2020. A significant factor in the changing growth of the WEFE system at this time was the introduction of several laws and regulations on water, energy, agricultural production, and environment in the area. The Chinese government has increased its focus on coordinated sustainable development and environmental preservation since 2012. For instance, the Strictest Water Resources Management System was formally adopted by the nation in 2013, and the Air Pollution Prevention and Control Law was updated in 2015. The implementation of these laws and regulations have been effective in improving the management of resources and in allowing for the improvement and restoration of the ecological environment, which in turn has brought the development of a virtuous cycle of economy, society and nature to a new level. As a result, the local WEFE system has grown from 0.530 in 2001 to 0.814 in 2020, with an average annual growth rate of 14.2%. This has helped the coupling coordination of the regional WEFE system advance from a stage of basic coordination to an excellent coordination status.

3.3. Analysis of the Main Influencing Factors

Based on the already constructed WEFE system evaluation index, multivariate single-series statistical analysis was applied to calculate the gray correlation between the coupling coordination degree and the influencing factor indicators. The gray correlation results for the regions (where the designations 1–26 correspond to the 26 evaluation indicators, c1–c26, in Table 1) are shown in Figure 5. When the gray correlation degree is greater than 0.7, it indicates a good coordination degree. Based on this study, the primary influencing factor for the coupling coordination of the WEFE system is the gray correlation degree of evaluation indices larger than 0.7.

According to the gray correlation analysis of Beijing, the factors influencing the coupling and coordination degree of the WEFE system in Beijing are urban greening coverage (C20), industrial water consumption (C6), domestic waste disposal rate (C23), energy consumption per unit of GDP (C12), green space per capita (C21), agricultural water consumption (C5), water consumption of 10,000 Yuan GDP (C8), and ecological water consumption (C7); most of these influencing factors are between 0.7 and 0.8, which are in the range of good coordination. Therefore, these indicators have the greatest comprehensive influence on the coupling and coordination of the WEFE system in Beijing. The urban greening coverage, green space per capita, ecological water consumption, industrial water consumption, water consumption of 10,000 Yuan GDP, and agricultural water consumption are factors related to ecological environment construction, industrial production, economic development, and agricultural plantation but are also closely related to water resources, which fully demonstrate the driving role of water resources in other subsystems as well as social economy. The regional industrial structure and the level of regional economic development are primarily related to energy consumption per unit of GDP. The main correlation impacts demonstrate that Beijing’s natural environment and economic development are strongly correlated. This demonstrates that Beijing’s WEFE system has strict criteria for environmental management and preservation, technological advancement, and sound socio-economic growth.

The gray correlation influencing factors in Tianjin that are greater than 0.7 include urban greening coverage (C20), sewage treatment rate (C22), chemical oxygen demand emission (C24), effective irrigated area (C17), energy consumption per unit of GDP (C12), agricultural water consumption (C5), per capita water resources (C1), and total water resources (C2). There are some differences between these correlation factors and Beijing, mainly due to the fact that the per capita water resources and total water resources in Tianjin are at the bottom level in China. Although the South-North Water Diversion Project launching and the advancement of desalination technology have had a significant impact on development in Tianjin, it has remained challenging to fundamentally address the city’s water shortage issues. In terms of the energy consumption per unit of GDP, Tianjin pays more emphasis on how energy consumption affects the economy when it comes to its socio-economic growth. Water resources per capita, agricultural water consumption, effective irrigated area, sewage treatment rate, urban greening coverage, and total water resources indicate the crucial importance of water resources in the Tianjin WEFE system, which is also the main driver and risk for the CCD growth of the WEFE system. Thus, managing Tianjin’s connection with its water resources will be a primary priority in its future development.

The primary gray correlation elements of the WEFE system in Hebei include industrial and agricultural processing production as well as urban green and healthy development. Among the factors that reach good coordination status are urban greening coverage (C20), food production per capita (C15), industrial water consumption (C6), total energy production (C9), energy consumption per unit of GDP (C12), agricultural water consumption (C5), total power of agricultural machinery (C18), effective irrigation area (C17), and fertilizer application amount (C25), from which it can be seen that Hebei, as a large agricultural and industrial province, has more water and energy consuming industries. This also affects practical issues such as the ratio of agricultural to industrial water consumption in Hebei, the amount of water used for irrigation per hectare, and how much energy is consumed by agricultural machinery, thus making the WEFE system more synergistic within agricultural production and energy inputs and outputs. The regional ecological environment can be enhanced by increasing the amount of soil restoration and improving water quality by reducing fertilizer application. The continual reduction in energy consumption as a percentage of GDP leads to a decrease in pollutant emissions and a considerable improvement in Hebei’s whole ecological environment, revealing that the interaction between the energy and ecological subsystems is highly synergistic.

The BTH region’s primary influencing variables are concentrated on regional sustainable development, food production, economic growth, and water resources. Specific major influencing factors greater than 0.7 include total water resources (C2), agricultural water consumption (C5), water consumption of 10,000 Yuan GDP (C8), per capita food production(C15), energy consumption per unit of GDP (C12), domestic waste disposal rate (C23), total power of agricultural machinery(C18), urban greening coverage(C20), and green space per capita (C21). These related influencing elements have a significant impact on regional socio-economic growth, and their interdependence can be important in enhancing regional coupling coordination. There are direct correlations between total water resources, agricultural water consumption, and water consumption of 10,000 Yuan GDP, as well as indirect correlations between per capita food production, urban greening coverage, and green space per capita. It is clear that water resources are crucial to the coupling coordination development of regional WEFE systems, making it imperative to address how regional development and water resources are related. The amount of regional economic development and agricultural modernization is measured by the energy consumption per unit of GDP and the total power of agricultural machinery. The advancement of agricultural modernization offers a powerful push for the rise of the energy and industrial sectors, which promotes rapid economic growth and strengthens the positive feedback loop of CCD development in regional WEFE systems.

4. Discussion

The BTH region is the most densely inhabited area in China, and it has experienced tremendous economic growth and a significant concentration of the food and energy industries [36]. At the same time, there are outstanding problems such as the unbalanced distribution of water and soil resources, the prominent contradiction between energy and food and water, waste resources, and poor ecological environment. This imbalanced and disorganized contradiction of regional development is becoming more obvious with the increase in urbanization and the influx of people into cities [44]. The findings indicate that water is a key component of the WEFE system in the BTH region, which is a synergistic system with symbiotic effects that includes water, energy, food, and ecology subsystems. Water resources have been considerably enhanced in terms of quality and quantity with the implementation of various national water management and recharge programs, which has boosted the overall development of the WEFE system in the BTH region. The WEFE system places a high priority on ecological progress, and because the ecology subsystem has generally improved through time and has a stronger ability to reflect the state of other subsystems, this suggests that the WEFE system development is sustainable. The main production area of food production is Hebei, and the food production of Hebei is related to the food security of the whole region, which requires that the production conditions of food be coordinated so the crop has enough water resources and agricultural machinery available during the production period and that the application of chemical fertilizers be minimized in order to maintain the sustainability of water and soil resources. As a major form of energy, industrial production is contributing more to GDP than energy consumption, which is falling. As opposed to Hebei, which still has many problems with industrial development, this trend is mostly concentrated in Tianjin and Beijing. Furthermore, the general development trend demonstrates that the energy business has undergone significant changes within the context of high-quality social economic growth. In the context of technological advancement, the high emissions, high pollution, and high consumption development model is progressively abandoned and replaced by a new model of energy development. Hebei must change as soon as possible to keep up with the present trend of social development. The primary influencing aspects of the WEFE system in the BTH region are water resources and ecological concerns. Tianjin is the region most impacted by water resources, followed by Hebei and Beijing. However, with the implementation of water environment management and the South-North Water Diversion Project, the problems of serious water pollution and groundwater overdraft have been greatly reduced, and the state of water resources has been significantly improved. The interaction of socio-economic development and the other three subsystems leads to the development of the ecology subsystem. For instance, the ecological recovery of North Chinese rivers and lakes and the reopening of the Beijing-Hangzhou Grand Canal can both benefit the environment. Pollutant emissions such as sulfur dioxide and chemical oxygen demand can be efficiently reduced by cutting back on energy production usage and switching to clean energy. With the advancement of agricultural breeding technology, food production is steadily increasing. This can effectively reduce the soil and water pollution brought on by excessive chemical use, which has a positive impact on the improvement of the ecological environment.

The impacts of urbanization, population income, and government policies on the WEFE system were not quantitatively studied since there was a dearth of data on human activities and socio-economic categories; however, these effects will be examined more in the future. Additionally, the evaluation of the CCD of the WEFE system development in various settings, such as ecological scenarios and resource contexts, will recommend additional improvements to the development of the WEFE system in the BTH region.

5. Conclusions and Policy Implications

5.1. Main Conclusions

The integrated development of each subsystem in the BTH region was assessed using a comprehensive development model in this study. The coupled synergistic impacts of the WEFE system in the BTH region were systematically examined using the CCD model, and the coupling coordination development level of the WEFE system from 2001 to 2020 was determined. A gray correlation model was then used to determine the main influences on the CCD of the WEFE system in the BTH region.

First, it was noted that the long-term development level of the WEFE system gradually improved over the course of the study period, with Hebei showing the most improvement, followed by BTH as a whole and Hebei, with Tianjin showing less improvement in this area. The energy subsystem development is severely polarized, with Beijing having the best development, Tianjin coming in second with the least amount of entire fluctuation change, and Hebei having the worst level of development. The food subsystem of development is slowly increasing, with Hebei having the highest level, followed by BTH as a whole and Tianjin, which have smaller growth rates, and Beijing having the lowest level. The ecological subsystem growth trajectory is noticeably better than that of the other three systems, and between 2001 and 2020, it experienced a quick rise in development level, with Beijing experiencing the most progress in that regard, followed by BTH as a whole, Hebei, and Tianjin. Second, from the perspective of synergistic development, the coupling coordination development of the WEFE system in the BTH region from 2001 to 2020 exhibits a varying increasing tendency. Beijing and BTH as a whole developed the best, both starting from a state of basic coordination and eventually reaching a state of excellent coordination, while Tianjin and Hebei reached a state of good coordination from a state of basic and little coordination, respectively. In regard to the major influencing factors, there are some regional characteristics, but the trends of the BTH region as a whole are consistent. Eight indicators make up the primary WEFE system in Beijing’s synergistic impact factors: urban greening coverage, industrial water consumption, domestic waste disposal rate, energy consumption per unit of GDP, green space per capita, agricultural water consumption, water consumption of 10,000 Yuan GDP, and ecological water consumption. The corresponding influencing factors in Tianjin include urban greening coverage, sewage treatment rate, chemical oxygen demand emission, effective irrigated area, energy consumption per unit of GDP, agricultural water consumption, per capita water resources, and total water resources. Urban greening coverage, sewage treatment rate, chemical oxygen demand emission, effective irrigated area, energy consumption per unit of GDP, agricultural water consumption, per capita water resources, and total water resources are the main synergistic influencing factors in Hebei, as measured by a total of eleven indicators. Total water resources, agricultural water consumption, water consumption of 10,000 Yuan GDP, per capita food production, energy consumption per unit of GDP, domestic waste disposal rate, total power of agricultural machinery, urban greening coverage, and green space per capita are the synergistic influencing factors of BTH as a whole.

5.2. Policy Implications

According to the main factors affecting the synergy of the WEFE systems in the BTH regions, the following policy recommendations can be derived.

(1) The efforts to carry out ecological environmental protection and socio-economic development and to improve the synergistic relationship with other resource-based subsystems have been the focus of the development of the WEFE system in Beijing. Building a garden city, reducing energy and water consumption per unit of GDP, improving the rate of sewage treatment and the efficiency of surface water resources utilization, and promoting the recycling of water resources are the main directions of development in Beijing. Simultaneously, enhancing technical research and development to improve the energy industry capacity and preserve energy, as well as lowering sulfur dioxide emissions and chemical oxygen demand emissions, may make regional growth more sustainable.

(2) The development of WEFE in Tianjin is hampered by the inefficient use and pollution of water resources; therefore, improving the current state of water resource development is a crucial prerequisite for the coordinated growth of the WEFE system. In order to improve per capita water resources and assure water security, Tianjin should actively assist the wastewater treatment and desalination businesses. It should also create use scenarios and resource scheduling systems to ensure more effective management of water resources. In order to increase energy use efficiency and decrease wasteful energy consumption, significant investments must also be made in ecological environmental protection and in upgrading the urban living environment.

(3) Industry and agriculture are vital industries in Hebei, and water resources and ecological protection are crucial prerequisites for the sustainable development of industry and agriculture. Water resources are an essential guarantee for the synergistic development of the WEFE system in Hebei; hence, measures should be taken to reduce groundwater extraction and water pollution. In terms of agricultural water consumption, Hebei should encourage effective irrigation technology that saves water, while agricultural science and technology innovation should be promoted to increase food production per unit area for food security. For emphasis on industrial upgrading in industrial development to enhance the competitiveness of the energy sector and for clean energy as an important development direction, Hebei should increase investment in research related to the development of clean energy utilization in pursuit of long-term sustainable use of resources.