Evaluation of the Coupled Coordination of the Water–Energy–Food System Based on Resource Flow: A Case of Hubei, China

Abstract

External environmental changes, such as climate, industrial expansion, and population growth, threaten the sustainable development of the water–energy–food (WEF) system. Clarifying the intricate nonlinear relationships within this system and revealing the degree of coupling coordination and evolutionary trends within the WEF system can provide feasible pathways for regional sustainable development. Taking Hubei Province as the study area, this research quantified resource flows between dual systems from a resource consumption perspective. It then analyzed the temporal evolution characteristics of resource interactions within the WEF system from 2003 to 2023. In addition, this WEF system was evaluated by an evaluation index system according to the resource utilization level of the single system and the resource flow level of the dual system, and the CRITIC method was employed to assess the coordinated development of the WEF system in Hubei Province from 2003 to 2023. Finally, the coupling coordination degree for 2025 to 2040 was predicted through the grey GM (1,1) model. The results show that the comprehensive development evaluation index exhibited a trend of initial decline followed by an increase from 2003 to 2023. Among these, the water resources system demonstrated the relatively optimal comprehensive development status, while the energy system performed the worst. The WEF system remained in a high-level coupling stage, with its degree of coupling coordination showing a pattern of initial decline followed by an increase, reaching its peak in 2023 and entering a moderately coordinated stage. Within the dual-coupling systems, the water–food (WF) system achieved the highest level of coordinated development, reaching the good coordination stage. The GM (1,1) model indicates that Hubei Province’s WEF system can gradually achieve a good coordinated stage between 2024 and 2040.

1. Introduction

Water, energy, and food, as strategic foundational resources, hold vital significance for human survival and development. However, under the combined pressures of intensifying global climate change, sustained population growth, and rapid socioeconomic development, the supply–demand conflicts and security risks surrounding these three resources are exhibiting pronounced “chain-linked” and “networked” characteristics. Relevant studies have shown that by 2030, water scarcity will increase by 30%, food consumption will rise by 50%, and energy consumption will triple [1]. By 2050, the Food and Agriculture Organization of the United Nations (FAO) estimates that with a global population reaching 9.7 billion [2], approximately 5 billion people will face water scarcity [3], food demand will increase by about 60% [4], and primary energy demand will rise to 30 billion tce [5]. The water–energy–food (WEF) system exhibits complex dynamic interdependencies, where any imbalance in the supply of its subsystems can compromise the stability of the entire WEF system. The specific manifestations are as follows: Approximately 70% of the world’s water resources are used for irrigation in food production [6], while 15% are used for energy production and electricity generation [7]. Approximately 8% of the global energy is used for water distribution and recycling, while 30% is consumed in food production [5]. Moreover, multiple external factors also constrain the coordinated development of the WEF system. These external factors primarily stem from economic, environmental, geopolitical, social, and technological domains [8]. Specifically, the economic aspect includes underinvestment in or failure of key infrastructure, which reduces food production, hinders water supply, and raises the price of fossil fuel energy [9,10]. Environmental factors include extreme weather events, environmental degradation, and major natural disasters. Geopolitical factors include failures in national or regional governance and large-scale terrorist attacks. Social factors include failures in urban planning and the spread of infectious diseases [11]. Technological aspects refer to the adverse consequences resulting from technological progress [12]. This has made the security of water, energy, and food a significant challenge. Therefore, the coupling coordination degree of the WEF system is being constrained by multiple adverse external factors. It is imperative to establish a scientific indicator system to conduct a systematic assessment of the WEF system’s coordinated development, thereby preparing for future dynamic external environments.

In the initial phase of research on the WEF nexus, exploration was primarily conducted through qualitative analysis. In 1972, in their pioneering book “The Limits to Growth”, Meadows [13] revealed that if a population and an industry grow and develop without restraint, resources such as water, energy, and food will face depletion at some point in the future. In 1976, Grenon [14] attempted to conduct quantitative research on water–energy issues. They designed an impact matrix to assess the resource quantities—including water, energy, land, capital, and labor—required to convert primary energy into end products, aiming to broaden the scope for comparing different energy strategies. However, this research concept failed to develop into a usable model [15]. In 1983, the United Nations University (UNU) launched a study on the interaction between food and energy. By the 1990s, the World Bank had held conferences on the linkages between “water, food, and trade,” conducting related studies on the water–energy–agriculture nexus in India and Mexico [16]. In 2009, the Royal Institute of Technology (KTH) and the International Atomic Energy Agency (IAEA) collaborated to propose the CLEWs (climate, land, energy, and water) model framework, but it was not applied to specific decision-making studies at that time [17]. A significant turning point for WEF research arrived in 2011, when the WEF nexus was formally introduced at the Bonn Conference in Germany [18]. Since then, quantitative research on the WEF nexus has gradually increased.

Currently, quantitative research on the WEF system can be categorized into two types based on research perspectives: One type adopts a holistic systems perspective, focusing on the overall development level and integrated operational status of the WEF system. This includes quantifying the level of coupled and coordinated development [19], identifying barrier factors [20], analyzing security risks [8], and measuring system efficiency [21]. For instance, Wang [22] first assessed the safety level of the WEF system, then employed an obstacle degree assessment model to identify key constraints, and finally analyzed the joint risk probability of WEF using Vine–Copula functions. Hao [23] employed the SBM-DEA model and the Meta-frontier model to measure the efficiency of the WEF system and analyzed the factors influencing its efficiency.

Another type adopts a local interconnections perspective, leveraging feedback mechanisms among WEF subsystems to focus on resource flows and material exchanges. General research methods include system dynamics (SD) models [24], Agent-based models [25], input–output (IO) models [26], life cycle assessment (LCA) models [27], mathematical programming [28], etc. SD models can identify key causal relationships within and between water, energy, and food systems. By describing causal relationships and feedback loops among elements in subsystems, they simulate the developmental changes in water–energy–food systems under various future scenarios [29]. Agent-based models can reflect the dynamic evolution of water–energy–food systems over time [30]. IO models can account for resource and material flows between WEF systems and quantify inter-system linkage effects [31]. LCA models can quantify resource consumption across WEF systems, typically measuring indicators such as water footprints in energy production and food production processes [32]. Mathematical programming models address the optimization of resource allocation within WEF systems at any time scale [33]. Another specific integrated model is the WEF Nexus Tool 2.0. This platform evaluates resource allocation strategies for water, energy, and food security. It visualizes and compares local food production, water and energy availability and demand, available technologies, and land requirements across different scenarios. Consequently, it calculates the sustainability index of the WEF system for each scenario [34]. The Q-Nexus model can account for changes in demand, technology, and resource allocation to calculate both direct and indirect usage of WEF resource systems under different scenarios and policies [35]. The MuSIASEM model, grounded in metabolic concepts, describes the flows of energy, food, and water and their interrelationships. It assesses the desirability, feasibility, and development scenarios of actual metabolic processes within socioeconomic systems, as well as the viability of policy options, providing useful quantitative analysis for governance [36].

In the operation of WEF coupled systems, subsystems both constrain and promote each other. Coupling coordination degree can be measured by establishing a WEF system evaluation indicator system to assess the tightness of system interconnections and the state of coordinated development. Its calculation requires two steps: first, constructing an appropriate evaluation indicator system; second, determining weights through suitable methods. The principles for constructing an evaluation indicator system must be scientifically grounded. Most scholars conduct multidimensional quantitative assessments from perspectives such as resilience, reliability, coordination, elasticity, and stability within the WEF framework. Li [37] employed reliability, coordination, and resilience as the criterion layer. Beyond considering the natural endowments of water, energy, and food, the subsystem also selected indicators from economic, social, and environmental subsystems. Their results indicated that resilience significantly impacts the sustainable development of the WEF system. Wang [22] studied 11 cities along the Yangtze River Economic Belt. In addition to the 3 criterion layers of reliability, coordination, and resilience, they also considered 57 indicators in the system pressure subsystem. The results showed that there is a significant positive coupling between reliability and coordination, while resilience and system pressure exhibit a significant negative coupling. Some scholars have also established evaluation frameworks through the “Pressure–State–Response” (PSR) model. Qian [38] constructed the indicator system of China’s WEF system based on the PSR theoretical framework, sustainable development goals, and China’s 14th Five-Year Development goals and used the obstacle degree model to analyze obstacle factors. The results showed that the main obstacles in most provinces come from the pressure subsystem and the response subsystem. Yin [3] selected evaluation indicators directly from water, energy, and food subsystems, with results indicating that per capita GDP contributed most significantly to coupling coordination. Mondal [39] constructed an evaluation indicator system for India’s WEF using the United Nations Sustainable Development Goals SDG2, SDG6, and SDG7 as the criterion layer. The results indicated that SDG2 shows superior development compared to SDG6 and SDG7. After constructing the WEF system evaluation indicator system, the AHP method, the CRITIC method [40], and the entropy method [41] can be employed to determine the weights. Among these, the CRITIC method calculates weights based on data correlation and variability, while the entropy method calculates weights based on data information content; both belong to objective weighting methods. The AHP method relies on decision-maker scoring to obtain weights, exhibiting significant subjectivity. Therefore, the AHP method is typically combined with objective weighting methods [42]. In summary, current research not only lacks quantification of WEF relationships but also lacks linkage indicators in evaluation systems that rigorously reflect the concept of pairwise system interactions within WEF systems. Therefore, this paper establishes and quantifies the relationships among water–energy, water–food, and energy–food systems to comprehensively understand resource flows within WEF systems. Based on this, an indicator system reflecting the concept of pairwise system interactions within WEF systems is developed to analyze the level of coordinated development in WEF system coupling.

Hubei Province is rich in water resources, and within its territory, Danjiangkou city serves as the core water source area for the Middle Route of the South-to-North Water Diversion Project, a strategic inter-basin water transfer project in China, continuously supplying clean water to North China. In terms of energy, Hubei faces a scarcity of fossil fuels and relies on imports from other provinces. However, it possesses significant advantages in hydropower and serves as a key electricity transmission hub for Central China. Additionally, Hubei is a major national grain-producing base and a significant grain-exporting province, as well as a dominant production area for rice and wheat. Given this context, as a major exporter of water resources, electricity, and grain, the sustainable development of Hubei Province’s WEF system is not only crucial for advancing the province’s economic, social, and ecological progress in tandem but also holds irreplaceable strategic significance for ensuring the balanced operation and secure stability of China’s overall WEF system. Therefore, this paper selects Hubei Province, China, as the research area.

2. Materials and Methods

2.1. Study Area

Hubei Province is situated in the middle reaches of the Yangtze River, between 108°21′42″ and 116°07′50″ east longitude and 29°01′53″ and 33°06′47″ north latitude (Figure 1). It enjoys abundant rainfall, plentiful surface water, and numerous rivers. Beyond the Yangtze and Han rivers, the province hosts 4229 rivers exceeding 5 km in length, forming the backbone of China’s national water network. As one of China’s thirteen major grain-producing regions, Hubei serves as a vital commercial grain production base and net exporter of grain, excelling particularly in rice and wheat cultivation. Hubei Province is deficient in fossil fuels, such as raw coal, crude oil, and natural gas, generally classified as an inland province with “scarce coal, limited oil, and insufficient gas.” In 2023, its total production of raw coal, crude oil, and natural gas accounted for only 0.017%, 0.26%, and 0.079% of the national total, respectively. Abundant hydropower resources make hydroelectricity one of the province’s major sources of electricity. Numerous hydropower stations, such as the Three Gorges Dam and Gezhouba Dam, harness their formidable generating capacity to deliver a steady flow of electricity to all parts of the country. Therefore, Hubei Province shoulders the critical responsibility of supplying water, electricity, and grain to other provinces, playing a pivotal role in the nation’s resource allocation, energy supply, and food security systems. Ensuring the security of Hubei’s electricity, water resources, and grain not only holds key importance for sustaining Hubei’s WEF system but also carries profound and far-reaching significance for safeguarding national resource security, stabilizing energy supply, guaranteeing food security, and promoting coordinated regional development.

2.2. Methodology

This paper first comprehensively analyzes the boundaryless coupling mechanism of the WEF system. Based on this coupling mechanism, a bounded quantitative formula for the dual system is derived. Taking Hubei Province as the study area, it calculates resource flow volume between subsystems: specifically, the water footprint of fossil fuel extraction and processing alongside thermal power generation within the energy system; the blue and green water footprints of grain crop production; energy consumption in the social water cycle process; energy consumption during grain cultivation; and biomass energy derived from crop straw conversion. Subsequently, an evaluation framework for WEF system coupling coordination is established using these indicators and data, with weights assigned to each indicator via the CRITIC method. Finally, the future trajectory of Hubei’s coupling coordination is forecasted using the grey model (GM (1,1)). The overall research framework is illustrated in Figure 2.

2.2.1. The Coupling Mechanism of the WEF System

Water–Energy

The processing and extraction of fossil fuels and power generation processes rely on water resources. Specifically, water consumption for oil extraction and processing varies depending on technology and reservoir geology. Primary oil recovery, where crude oil naturally flows into production wells, has a minimal water footprint. Secondary oil recovery involves injecting large volumes of water into reservoirs to boost oil production. Another highly water-intensive oil extraction technique is tertiary oil recovery, which uses CO₂ injection. Conventional natural gas extraction requires only a small amount of water during the drilling and cementing stages. Water consumption in coal mining increases with mining depth, and the coal washing and beneficiation process also consumes significant amounts of water. The water requirements for biomass fuels are closely related to crop types, geographical location, climatic conditions, and soil characteristics. Second-generation biomass fuels derived from crop residues and third-generation biomass fuels derived from algae consume less water than first-generation biomass fuels derived from food crops. Furthermore, second-generation and third-generation biomass fuels do not conflict with or compete against food production. The Rankine cycle technology used in thermal power plants and coal-fired power plants relies heavily on water, which is heated to produce the steam needed to drive turbines for electricity generation. In nuclear power generation, water is used not only for cooling but also to regulate the temperature of the uranium fission process. Furthermore, uranium mining and processing require significant amounts of water. In coal-fired and gas-fired power plants coupled with CCUS technology, water is used to separate CO₂ from flue gas. Wind power generation utilizes wind to rotate turbine blades, converting mechanical energy into electrical energy via induction motors. Consequently, its operation involves virtually no water consumption. Solar power generation is divided into solar photovoltaic power and solar thermal power. The water consumption in photovoltaic power generation involves using water resources to clean the surface of battery components, while the water consumption in thermal power generation is for cooling purposes. Hydropower generation’s water consumption stems from evaporation losses in reservoirs, which vary depending on reservoir size and climate conditions.

Water forms a cycle within the socioeconomic system encompassing abstraction, treatment, distribution, consumption, drainage, and wastewater reuse, which is known as the social water cycle. Every stage of this cycle involves energy consumption. The abstraction stage requires the use of pumps, whose operation consumes electricity or fossil fuels. Reclaimed water and seawater require secondary treatment before being supplied to various departments. Different production sectors have varying water quality requirements. After being drawn from the source, the water travels through raw water pipelines to the primary intake pumping station and then is transported to the water treatment plant. There, it undergoes processes such as coagulation, sedimentation, and disinfection before being delivered to domestic and industrial departments. Residential water consumption primarily involves heating energy and mechanical energy. Heating energy is mainly used to meet residents’ demand for hot water, while mechanical energy is primarily converted into mechanical power. Industrial water use reflects the interdependence between water and energy, which will not be elaborated upon here. Drainage encompasses processes such as sewage collection, conveyance, treated water recycling, and discharge. Treated wastewater is reintroduced for reuse in applicable sectors.

Water–Food

Energy is utilized across multiple activities within the food system. Agricultural machinery requires fuels such as diesel and gasoline to operate, while irrigation systems draw groundwater using electricity or diesel-powered pumps. Food processing, packaging, transportation, and refrigeration also consume coal, natural gas, or electricity.

Both grain crops and their residues can serve as feedstock for biofuels. First-generation biofuels are produced from sugar crops, starch crops, and oilseeds. For example, sugarcane contains an extremely high sugar content, which can be fermented using microorganisms such as yeast to convert its sugars into ethanol. Starchy crops like wheat and corn require amylase to convert starch into glucose, which is then further converted into ethanol. Oils from crops such as rapeseed and soybeans can be broken down into biodiesel through ester exchange processes. Additionally, crop residues like stalks, branches, and leaves left after harvest can be directly burned as fuel for biomass power generation.

Energy–Food

In terrestrial ecosystems, all primary and secondary production relies on water. During growth, crops in agriculture, trees in forestry, and forage in animal husbandry all obtain moisture through irrigation or rainfall. From the moment seeds germinate, adequate water is required to activate various enzymes and promote the physiological and biochemical reactions within the seeds. Throughout the entire crop growth cycle, water participates in physiological processes, such as photosynthesis, respiration, and transpiration. In photosynthesis, water serves as a raw material that combines with carbon dioxide under the influence of light energy to synthesize organic substances, providing the energy and material foundation for crop growth. Compared to crop farming and animal husbandry, fisheries exhibit distinct water usage patterns and processes, requiring greater water storage capacity and higher levels of water circulation.

In the process of grain production, the application of pesticides and chemical fertilizers is crucial for increasing grain yields. However, residues of these substances in farmland can flow into surface water or groundwater through irrigation, rainfall, surface runoff, or soil infiltration, thereby contaminating the source water. Nitrogen and phosphorus elements in pesticides and chemical fertilizers are the primary causes of water eutrophication. Water quality degradation resulting from nitrogen and phosphorus loads can also trigger algal blooms. Additionally, improperly treated manure and wastewater from livestock farming can pollute surrounding water bodies.

The WEF system coupling mechanism is shown in Figure 3.

2.2.2. The Quantification of the WEF System

Water for Energy

The water demands of energy production primarily include water consumption during fossil fuel extraction and processing, as well as water usage in the cooling systems of thermal power plants. Water consumption from cleaning photovoltaic panels and surface evaporation at hydroelectric stations, along with seepage from reservoirs, is not included in the calculations [43,44].

$$EW={\displaystyle \sum _{i}E{M}_i}\times EW{Q}_i$$

(1)

where $EW$ (m³) is total water consumption in energy production, $i$ corresponds respectively to raw coal mining, raw coal washing, crude oil extraction, crude oil processing, natural gas extraction, natural gas processing, and thermal power generation, $E{M}_i$ (kg or m³) is the energy input in the energy production process of $i$, and $EW{Q}_i$ (m³/kg or m³/m³) is water quota for energy production process of $i$. See

Table 1. Water quota for energy production processes.

Energy	Type	Value	References
Raw coal	Raw coal mining	0.33 m3/t	[45]
	Raw coal washing	0.17 m3/t	[45]
Crude oil	Crude oil extraction	7 m3/t	[46]
	Crude oil processing	2.37 m3/t	[47]
Natural gas	Natural gas extraction	0.003 m3/m3	[44]
	Natural gas processing	0.00024 m3/m3	[44]
Power	Thermal power generation	2.75 m3/MWh	[48,49]

for the specific values.

Water for Food

Water use for grain cultivation and growth generally involves the following four processes: ① transporting irrigation water from the water source to farmland via irrigation channels or water conveyance pipelines, which is the water delivery process; ② after entering farmland, utilizing specific irrigation techniques to evenly distribute the water across the farmland surface, which is the irrigation process; ③ irrigation water/precipitation distributed on the surface of farmland infiltrates into the soil under the influence of gravitational potential and matric potential, undergoing both vertical and lateral infiltration within the soil, which is the soil infiltration process; and ④ crops absorb and utilize soil water through their root systems for crop transpiration, tissue growth, and maintaining other physiological activities, ultimately forming the crop’s economic yield, which is the plant utilization process. The water footprint of crops is the sum of the blue water footprint and the green water footprint. The Penman–Monteith formula embedded within the CROPWAT model developed by FAO was employed to estimate the crop water footprint [50].

$$FW={\displaystyle \sum _c\left(FW{B}_c+FW{G}_c\right)}$$

(2)

$$FW{B}_c=E{T}_{blue}\times A_c\times B$$

(3)

$$FW{G}_c=E{T}_{green}\times A_c\times B$$

(4)

$$E{T}_{blue}=\max \left(0,E{T}_a-p_e\right)$$

(5)

$$E{T}_{green}=\min \left(E{T}_a,p_e\right)$$

(6)

$$E{T}_a=K_s\times K_c\times E{T}_0$$

(7)

$$E{T}_0={\displaystyle \frac{0.408\delta \left(R_n-G\right)+\gamma \frac{900}{T+273}{U}_2\left(e_s-e_a\right)}{\delta +\gamma \left(1+0.34U_2\right)}}$$

(8)

where $FW$ (m³) is total water footprint of crops, $c$ refers to wheat, rice, corn, soybeans, and tubers, $FW{B}_c$ (m³) and $FW{G}_c$ (m³) are, respectively, the blue water footprint and the green water footprint of $c$, $A_c$ (ha/km²) is grain crop planting area, $B$ is the unit conversion constant, taken as 10 [51], $E{T}_a$ (mm) is actual crop evapotranspiration, and $E{T}_0$ (mm) is reference crop evapotranspiration.

Energy for Water

The demand for water to produce energy manifests as natural water resources flowing through conveyance, purification, and distribution processes to serve production and daily life sectors. This requires consuming energy sources such as electricity and diesel as driving forces. This interdependent process permeates the entire social water cycle, encompassing water intake, water treatment, water conveyance, water utilization, and water drainage.

$$WE=W{E}_I+W{E}_T+W{E}_C+W{E}_U+W{E}_D$$

(9)

where $WE$ (kWh) is the total energy consumption of the social water cycle process and $W{E}_I$, $W{E}_T$, $W{E}_C$, $W{E}_U$, and $W{E}_D$ are, respectively, total energy consumption in the stages of water intake, water treatment, water conveyance, water utilization, and water drainage.

Water intake involves conveying surface water, groundwater, or reclaimed water to water treatment plants or water-consuming departments. Surface water intake methods include impoundment, diversion, pumping, and inter-basin water transfer. The main energy consumption of water storage projects is the potential energy loss generated by water flowing through transmission pipelines. Water diversion projects generally rely on gravity to transport water resources from the source to users, so energy consumption is negligible. The main energy consumption in water lifting projects stems from the energy expended by electromechanical pumps to raise water resources from lower elevations to higher ones. Water transfer projects transport water resources from water-abundant regions to water-scarce areas. Groundwater extraction relies on electromechanical pumps or diesel pumps as driving tools, with energy consumption determined by groundwater depth, extraction volume, and pump type. According to Zou’s [52] research, electromechanical pumps and diesel pumps account for 76% and 24% of water pumping, respectively. The water intake engineering calculation formula is as follows:

$$W{E}_I=W{E}_{sur}+W{E}_{under}$$

(10)

$$W{E}_{sur}=W{E}_s+W{E}_l$$

(11)

$$W{E}_s=E{I}_s\times W_s/\rho$$

(12)

$$W{E}_l={\displaystyle \frac{W_{l}g{h}_l}{3.6\times {10}^6\times {\epsilon }_1}}$$

(13)

$$W{E}_{under}={\displaystyle \frac{W_{under}g{h}_{under}}{3.6\times {10}^6\times {\epsilon }_1\times \lambda }}$$

(14)

where $W{E}_{sur}$ (kWh), $W{E}_{under}$ (kWh), $W{E}_s$ (kWh), and $W{E}_l$ (kWh) are, respectively, the energy consumption of surface water intake, groundwater intake, water storage projects, and water lifting projects; $E{I}_s$ (kWh/m³) is the energy intensity of water storage projects, set to 0.14 kWh/m³ [53]; $W_s$ (kg), $W_l$ (kg), and $W_{under}$ (kg) are, respectively, the water quantity of storage projects, lifting projects, and groundwater extraction; $\rho$ (kg/m³) is the density of water; $g$ (N/kg) is gravitational acceleration; $h_l$ (m) and $h_{under}$ (m) are, respectively, the lifting project’s pump head and groundwater depth, set to 29.37 m and 47.77 m [53]; ${\epsilon }_1$ is the pump efficiency, where the electric pump is 40% and the diesel pump is 15% [49]; and $\lambda$ is water intake efficiency, set to 95% [49].

Natural water or recycled water undergoes multiple complex treatments at water treatment plants, including clarification, screening, sedimentation, disinfection, deodorization, taste removal, iron removal, and softening. Water resources meeting potable water quality standards are then delivered from the plant to the water distribution network via pressurized pumping stations and, finally, transported to end users through the distribution network. The energy consumption formulas for water production and distribution are as follows:

$$W{E}_T=I{E}_T\times W_T/\rho$$

(15)

$$W{E}_C=I{E}_C\times W_C/\rho$$

(16)

where $I{E}_T$ (0.286 kWh/m³) [54] and $I{E}_C$ (0.394 kWh/m³) [54] are, respectively, the energy intensity of water treatment and water conveyance, and $W_T$ (kg) and $W_C$ (kg) are, respectively, the quantity of water treatment and water conveyance.

Water use energy consumption encompasses energy consumption associated with water use in the agricultural, industrial, domestic, and ecological sectors. Agricultural water use energy consumption is already included in the energy consumption of the water intake stage and is not counted again in the water utilization stage. Compared to the energy consumption of groundwater extraction, the energy consumption of irrigation facilities is relatively small and can therefore be disregarded [55]. Industrial water consumption includes energy consumption from water use in manufacturing, mining, and the power sector. Domestic water consumption, due to variations in usage, encompasses household water consumption and public service water consumption. Ecological and environmental water consumption is virtually zero [55]. The energy consumption formulas for water utilization are as follows:

$$W{E}_U=W{E}_{U-D}+W{E}_{U-I}$$

(17)

$$W{E}_{U-D}=I{E}_{U-DH}\times W_{U-DH}/\rho +I{E}_{U-DP}\times W_{U-DP}/\rho$$

(18)

$$W{E}_{U-I}={\displaystyle \frac{W_{ther}/\rho \times T\times c_w}{3.6\times {10}^6\times {\epsilon }_2}}+Q_{ther}\eta +{\displaystyle \frac{W_{indus}\times I{E}_{indus}/\rho }{{\epsilon }_3}}$$

(19)

where $W{E}_{U-D}$ (kWh) and $W{E}_{U-I}$ (kWh) are, respectively, the energy consumption of the domestic sector and the industrial sector; $I{E}_{U-DH}$ (12.62 kWh/m³) [56], $I{E}_{U-DP}$ (11.6 kWh/m³) [57], and $I{E}_{indus}$ (5.63 kWh/m³) [53] are, respectively, the energy intensity of household water utilization, public service water utilization, and industrial cycle water utilization; $W_{U-DH}$ (kg), $W_{U-DP}$ (kg), $W_{ther}$ (kg), and $W_{indus}$ (kg) are, respectively, the quantity of household water utilization, public service water utilization, thermal power generation water utilization, and industrial cycle water utilization; $T$ (°C) is the heating temperature, set to 375 °C [57]; $c_w$ (kJ/kg °C) is the specific heat capacity of water, set to 4.2 kJ/kg °C [57]; ${\epsilon }_2$ is the water efficiency in thermal power generation, set to 0.75 [57]; $Q_f$ (kWh) is thermal power generation; $\eta$ is the electric power consumption efficiency of water pumps in circulating cooling systems, set to 1.55% [57]; and ${\epsilon }_3$ is the circulating cooling system’s operating efficiency, set to 50% [57].

The drainage process encompasses two stages, namely, wastewater collection and wastewater treatment, representing the final phase of the urban water cycle. The collection and treatment of wastewater constitute vital components of urban infrastructure and environmental protection. This involves the centralized gathering and conveyance of domestic sewage, industrial effluent, stormwater, and other wastewater streams through drainage pipelines, pumping stations, and related facilities to wastewater treatment plants. The objective is to separate pollutants, purify water quality, and enable reuse. The energy consumption calculation formula for the drainage process is as follows:

$$W{E}_D=W{E}_{D-C}+W{E}_{D-T}$$

(20)

$$W{E}_{D-C}=I{E}_{D-C}\times W_{D-C}/\rho$$

(21)

$$W{E}_{D-T}=I{E}_{D-T}\times W_{D-T}/\rho$$

(22)

where $W{E}_{D-C}$ (kWh) and $W{E}_{D-T}$ (kWh) are, respectively, the energy consumption of wastewater collection and wastewater treatment; $I{E}_{D-C}$ (0.08 kWh/m³) [53] and $I{E}_{D-T}$ (0.25 kWh/m³) [56] are, respectively, the energy intensity of wastewater collection and wastewater treatment; and $W_{D-C}$ (kg) and $W_{D-T}$ (kg) are, respectively, the quantity of wastewater collection and wastewater treatment.

Energy for Food

Energy inputs in grain production are categorized into direct and indirect inputs. Direct energy inputs include diesel and electricity used in agricultural machinery and water pumping for irrigation. Indirect energy inputs encompass pesticides and chemical fertilizers. Diesel consumption in agricultural machinery activities covers land tillage, seeding, fertilization, pesticide application, and harvesting processes. The process of applying electricity to agricultural machinery includes water intake and irrigation. The fertilizers applied comprise four types, namely, nitrogen fertilizer, phosphorus fertilizer, potassium fertilizer, and compound fertilizer, with different energy consumption equivalent values for different fertilizer types. The energy consumption calculation formula for grain production is as follows:

$$FE={\displaystyle \sum _{j}FE{M}_j}\times E{V}_j$$

(23)

where $FE$ (MJ) is the total energy consumption of grain production; $j$ refers to the types of energy inputs used in grain production, including electricity, diesel fuel, pesticides, nitrogen fertilizers, phosphate fertilizers, potash fertilizers, and compound fertilizers; $FE{M}_j$ (kg) is the consumption of energy input of $j$; and $E{V}_j$ (kg/MJ or kWh/MJ) is the energy equivalent of $j$. See

Table 2. Energy equivalent value of agricultural input.

Types of Agricultural Inputs		Energy Equivalent Value	Reference
Electricity		3.6 MJ/kWh	[58]
Diesel		56.31 MJ/L	[58]
Pesticide		219.54 MJ/kg	[59]
Fertilizer	Nitrogen fertilizer	60.6 MJ/kg	[60]
	Phosphate fertilizer	11.1 MJ/kg	[60]
	Potassium fertilizer	6.7 MJ/kg	[60]
	Compound fertilizer	8.4 MJ/kg	[60]

for the specific values.

Food for Energy

Biomass energy serves as the link between energy and food security. First, the straw-to-grain ratio method [61] is used to estimate the quantity of crop straw resources. A portion of the collected straw is utilized for feed, organic fertilizer, industrial raw materials, etc., while another portion is converted into biomass energy. According to the relevant literature [55], 45% of grain crop straw is converted into biomass energy. The formula for converting grain crop straw into biomass energy is as follows:

$$F{S}_c=Y_c\times r_c\times s_c$$

(24)

$$EF=0.45\times {\displaystyle \sum _c\left(F{S}_c\times {\lambda }_c\right)}$$

(25)

where $F{S}_c$ (kg) is the actual resource quantity of grain crop straw of $c$, $Y_c$ (kg) is the grain crop yield of $c$, $r_c$ is the straw-to-grain ratio of grain crops of $c$, $s_c$ is the grain crop straw collection coefficient, ${\lambda }_c$ (kgce/kg) is the straw folding coefficient for grain crops, and $EF$ (kgce) is total biomass energy from crop straw conversion. The parameter values used to estimate straw resources from grain crops are listed in

Table 3. Parameter values for estimating straw resources from grain crops.

Crop	${\mathit{r}}_{\mathit{c}}$	${\mathit{s}}_{\mathit{c}}$	${\mathit{\lambda }}_{\mathit{c}}$	References
Rice	1.28	0.74	0.429	[62,63]
Wheat	1.38	0.73	0.5	[62,63]
Corn	2.05	0.85	0.529	[62,63]
Soybean	1.68	0.56	0.543	[62,63]
Tuber	1.16	0.73	0.486	[62,63]

.

2.2.3. Indicator System Construction

Based on the principles of scientific rigor, comprehensiveness, and data accessibility, and drawing upon the relevant literature [3,41,64,65,66], combined with the research framework of this paper and the actual conditions of Hubei Province, an evaluation index system for the coupling coordination degree of the WEF system in Hubei Province was constructed. This study selected a total of 27 indicators, as shown in

Table 4. Comprehensive evaluation indicator system of the WEF system in Hubei Province.

System	Indicator Layer	Explanation	Unit	Directions
Water	Water consumption of fossil energy extraction and processing	Water consumption in the extraction and processing of raw coal, crude oil, and natural gas	m3	−
	Water consumption of thermal power generation	NA	m3	−
	Unit green water footprint of grain crops	Total green water footprint of crops/Grain crop yield	m3/kg	−
	Water consumption for thermal power generation	Total blue water footprint of crops/Grain crop yield	m3/kg	−
	Total water resources	NA	m3	+
	Per capita water consumption	Total water consumption/Population	m3/person	−
	Effective utilization efficiency of farmland irrigation	The degree to which irrigation water is effectively utilized	%	+
	Water consumption per ten thousand yuan of industrial added value	Industrial water efficiency	m3/104 CNY	−
	Proportion of surface water supply	Surface water supply/Total supply	%	+
	Proportion of groundwater supply	Groundwater supply/Total supply	%	−
	Proportion of recycled water supply	Reclaimed water supply/Total supply	%	+
Energy	Energy consumption per unit area for grain planting	Total energy consumption for grain planting/Grain crop sown area	tce/hm2	−
	Agricultural machinery power per unit area	Total power of agricultural machinery/Cultivated land area	kW/hm2	−
	Energy consumption per unit water supply in social water cycle	Total energy consumption of social water cycle/Total water supply	kgce/m3	−
	Self-sufficiency rate of coal	Coal production/Coal consumption	%	+
	Self-sufficiency rate of crude oil	Crude oil production/Crude oil consumption	%	+
	Self-sufficiency rate of natural gas	Natural gas production/Natural gas consumption	%	+
	Self-sufficiency rate of electricity	Electricity production/Electricity consumption	%	+
	Energy consumption per unit of GDP	Total energy consumption/Regional gross domestic product	tce/CNY	−
Food	Energy equivalent converted from unit grain crops	Total biomass converted from grain crops/Crop yield	kgce/kg	−
	Grain sown area	NA	hm2	+
	Multiple cropping index	Sown area of crops/Cultivated land area	NA	+
	Grain production	NA	104 t	+
	Per capita grain production	Grain Production/Population	kg/person	+
	Irrigation area	NA	hm2	+
	Pesticide usage	NA	104 t	−
	Fertilizer usage	NA	104 t	−

. In the water system, water consumption in the energy department and the food department, per capita water consumption, and water consumption per CNY 10,000 of industrial added value reflect the water resource consumption intensity of various sectors. All these indicators are negative, meaning lower values indicate a more efficient water resource system. The total water resources, surface water supply ratio, groundwater supply ratio, and reclaimed water supply ratio reflect Hubei Province’s water resource endowment. Except for the groundwater supply ratio indicator, all other indicators are positive. An increase in the groundwater supply ratio would cause irreversible damage to the region’s ecological environment and disrupt the water resource system’s equilibrium. Therefore, this indicator is negative. The effective utilization efficiency of farmland irrigation reflects the efficiency of water resource utilization. Higher efficiency benefits the water resource system; hence, this attribute is positive.

In the energy system, energy consumption per unit area of grain cultivation, power consumption per unit area of agricultural machinery, energy consumption per unit water supply in the social water cycle, and energy consumption per unit GDP reflect energy usage patterns. These indicators are all negative; lower values indicate a more effective energy transition and higher utilization efficiency in Hubei Province. The fossil fuel self-sufficiency rate and electricity self-sufficiency rate reflect Hubei Province’s energy supply situation. Higher values indicate greater benefits for the energy system.

In the food system, the conversion of grain crops into energy equivalents reflects grain consumption, with a negative indicator attribute. The grain sown area, grain output, and per capita grain output reflect grain supply levels, with a positive indicator attribute; higher values indicate a more robust grain system. The cropping index and irrigated area are crucial for sustaining agricultural productivity [67]. Increased pesticide and fertilizer usage can lead to unsustainable cultivated land in the region, threatening food security; therefore, this indicator has a negative attribute.

All data were from the Hubei Provincial Water Resources Bulletin, Hubei Provincial Statistical Yearbook, China Energy Statistical Yearbook, China Rural Statistical Yearbook, China Electric Power Statistical Yearbook, and Urban Water Supply Statistical Yearbook from 2003 to 2023. Meteorological data for the calculation of crop water footprints were from the CHM-Drought dataset.

2.2.4. CRITIC Weighting Methods

The CRITIC weighting method is an objective weighting approach, which determines weights by calculating the standard deviation of indicators and their correlation coefficients to reflect the magnitude of information content and the degree of conflict among indicators [68]. To eliminate interference caused by differences in data units and scales, the raw data were first standardized [69].

Positive indicators:

$$r_{ij}=\left(x_{ij}-x_i^{\min }\right)/\left(x_i^{\max }-x_i^{\min }\right)$$

(26)

Negative indicators:

$$r_{ij}=\left(x_i^{\max }-x_{ij}\right)/\left(x_i^{\max }-x_i^{\min }\right)$$

(27)

The weight formula is (28)–(31):

$${\rho }_{ik}={\displaystyle \frac{{\displaystyle \sum _j^n\left(r_{ij}-{\overline{r}}_i\right)\left(r_{ik}-{\overline{r}}_k\right)}}{\sqrt{{\displaystyle \sum _j^n{\left(r_{ij}-{\overline{r}}_i\right)}^2{\left(r_{ik}-{\overline{r}}_k\right)}^2}}}}i,k=1,2,\cdots ,m$$

(28)

$${\sigma }_i=\sqrt{{\displaystyle \frac{1}{n-1}}{\displaystyle \sum _j^n{\left(r_{ij}-{\overline{r}}_i\right)}^2}}$$

(29)

$$c_i={\sigma }_i{\displaystyle \sum _k^m\left(1-{\rho }_{ik}\right)}$$

(30)

$$w_i={\displaystyle \frac{c_i}{{\displaystyle \sum _i^m{c}_i}}}$$

(31)

2.2.5. Comprehensive Development Evaluation Index Model

Using the CRITIC method, standardized data and weights (Appendix A,

Table A1. Weights of each indicator.

System	Indicator Layer	Unit	Directions	Weight
Water	Water consumption of fossil energy extraction and processing	m3	−	0.0768
	Water consumption of thermal power generation	m3	−	0.0893
	Unit green water footprint of grain crops	m3/kg	−	0.0686
	Water consumption for thermal power generation	m3/kg	−	0.0713
	Total water resources	m3	+	0.0581
	Per capita water consumption	m3/person	−	0.0758
	Effective utilization efficiency of farmland irrigation	%	+	0.0805
	Water consumption per ten thousand yuan of industrial added value	m3/104 CNY	−	0.094
	Proportion of surface water supply	%	+	0.1284
	Proportion of groundwater supply	%	+	0.1281
	Proportion of recycled water supply	%	+	0.129
Energy	Energy consumption per unit area for grain planting	tce/hm2	−	0.0982
	Agricultural machinery power per unit area	kW/hm2	−	0.0898
	Energy consumption per unit water supply in social water cycle	kgce/m3	−	0.1354
	Self-sufficiency rate of coal	%	+	0.108
	Self-sufficiency rate of crude oil	%	+	0.1076
	Self-sufficiency rate of natural gas	%	+	0.1025
	Self-sufficiency rate of electricity	%	+	0.0885
	Energy consumption per unit of GDP	tce/CNY	−	0.27
Food	Energy equivalent converted from unit grain crops	kgce/kg	−	0.2862
	Grain sown area	hm2	+	0.0926
	Multiple cropping index	NA	+	0.0853
	Grain production	104 t	+	0.0917
	Per capita grain production	kg/person	+	0.089
	Irrigation area	hm2	+	0.1
	Pesticide usage	104 t	−	0.1011
	Fertilizer usage	104 t	−	0.1541

) for each indicator were calculated. Based on this, a comprehensive evaluation of the water–energy–food system was conducted using the linear weighting method, obtaining the comprehensive development evaluation index of the WEF system $T$. $f_i\left(x_i\right)$ is the comprehensive evaluation value of the subsystem of $i$. In this paper, $n=3$, $f_1\left(x_1\right)$, $f_2\left(x_2\right)$, and $f_3\left(x_3\right)$ are, respectively, comprehensive evaluation values for the water resources system, the energy system, and the food system.

$$f_i(x_i)={\displaystyle \sum _i{w}_i{r}_{ij}}$$

(32)

$$T={\displaystyle \sum _i^3{\alpha }_i}{f}_i\left(x_i\right)$$

(33)

2.2.6. Coupled Coordination Degree Model

The coupling degree reflects the degree of mutual influence between subsystems. A higher coupling degree indicates greater sensitivity between subsystems, while a lower coupling degree indicates weaker sensitivity.

$$C=n{\displaystyle \frac{{\left[{\displaystyle \prod _i^n{f}_i\left(x_i\right)}\right]}^{\frac{1}{n}}}{{\displaystyle \sum _i^n{f}_i\left(x_i\right)}}}$$

(34)

where $C$ is the coupling degree, and $0\le C\le 1$.

The coupling degree can only reflect the degree of association between subsystems, not the coordinated development of the entire system. Therefore, the coupling coordination degree model is introduced.

$$D=\sqrt{CT}$$

(35)

where $D$ is the coupling degree, and $0\le D\le 1$.

The coupling degree and coupling coordination degree calculated from Equations (34) and (35) were categorized into different types, as shown in

Table 5. Division of the coupling degree and coupling coordination degree criteria.

Class	Type of Coupling Degree	Class	Type of Coordination	Type of Coupling Coordination Degree
[0, 0.3]	Low coupling	(0, 0.09]	Uncoordinated Declination	Extreme unbalance
		(0.09, 0.19]		Serious unbalance
(0.3, 0.5]	Moderate coupling	(0.19, 0.29]		Moderate unbalance
		(0.29, 0.39]		Mild unbalance
		(0.39, 0.49]	Transitional Development	Imminent unbalance
(0.5, 0.8]	High coupling	(0.49, 0.59]		Near coordination
		(0.59, 0.69]		Primary coordination
(0.8, 1]	Coordinated coupling	(0.69, 0.79]	Coordinated Development	Moderate coordination
		(0.79, 0.89]		Good coordination
		(0.89, 1]		Extreme coordination

.

2.2.7. Gray GM (1,1) Forecasting Model

The grey prediction model is an uncertainty system forecasting method proposed by Chinese scholar Professor Deng [70] in 1982. Its core principle involves processing partially known information to uncover underlying patterns within the data, thereby enabling predictions about a system’s future development trends.

$${\displaystyle \frac{d{X}_1}{dt}}+a{X}_1=\mu$$

(36)

where $X_0\left\{x_0\left(1\right),x_0\left(2\right),\cdots ,x_0\left(n\right)\right\}$ is the original time series, $X_1\left\{x_1\left(1\right),x_1\left(2\right),\cdots ,x_1\left(n\right)\right\}$ is the new sequence generated after accumulating the original time series $X_0$, $a$ is the development gray numbers, and $\mu$ is the endogenous control gray number. Let the estimated value of $a$ be $\widehat{a}$, where $\widehat{a}=\left({\displaystyle \frac{a}{\mu }}\right)$. Solving using the least squares method yields $\widehat{a}=\left(B^{T}B-1\right)B^T{Y}_n$. The predictive model was solved as follows:

$$x_1^T{\widehat{x}}_1\left(k+1\right)=\left[x_0\left(1\right)-{\displaystyle \frac{\mu }{a}}\right]e^{-ak}+{\displaystyle \frac{\mu }{a}}$$

(37)

where $k=1,2,\cdots ,n$. Finally, an accuracy test was conducted on the grey prediction model.

3. Results

3.1. Resource Flow of the WEF Subsystem

3.1.1. Water

In terms of overall water consumption in energy production, the water usage associated with the extraction and processing of fossil fuels accounts for a relatively small proportion of total energy production water consumption (Figure 4a). The primary water-consuming sector is thermal power generation, which has consistently represented over 87% of the total energy production water consumption on average over many years. From 2003 to 2023, the total water consumption in energy production showed a fluctuating upward trend, increasing from 131.5 × 10⁶ m³ to 448.9 × 10⁶ m³, an increase of 241.4%. Within the fossil fuel production sector, crude oil production consumed the most water, followed by raw coal, with natural gas consumption being the lowest. Water consumption in raw coal production significantly decreased with adjustments to the mining scale. Following the sharp decline in Hubei Province’s raw coal extraction volume since 2015, water consumption in raw coal production correspondingly decreased from 2.1 × 10⁶ m³ to 0.3 × 10⁶ m³. Water consumption in crude oil and natural gas production, however, showed a “fluctuating growth” trend. Crude oil water consumption increased from 20.3 × 10⁶ m³ to 38.7 × 10⁶ m³, representing an 88% increase. Natural gas water consumption rose from 0.3 × 10⁶ m³ to 0.8 × 10⁶ m³. As the primary driver of water consumption in energy production, thermal power generation’s share of total water use continued to rise, reaching 91.2% in 2023.

From 2003 to 2023, the total water footprint of grain crops in Hubei Province showed a fluctuating upward trend, driven by sustained growth in grain production (Figure 4b). The total water footprint increased from 194.6 × 10⁸ m³ to 279.8 × 10⁸ m³, representing a cumulative increase of 43.8%. Among them, the green water footprint, as the main component of the total water footprint, showed an increasing trend with fluctuations, maintaining a high overall level, increasing from 164.3 × 10⁸ m³ to 216.8 × 10⁸ m³, with a growth rate of 32%, and its multi-year average accounted for 82.5% of the total water footprint. Although the blue water footprint had a lower overall level, it showed a rapidly increasing trend, growing from 30.3 × 10⁸ m³ to 63 × 10⁸ m³, achieving a doubling of growth.

3.1.2. Energy

From the perspective of overall energy consumption in grain production, the period from 2003 to 2023 exhibited distinct phased characteristics: the total energy consumption for grain production increased from 568 × 10⁴ tce to 679.2 × 10⁴ tce, following an overall “peak-shaped” trajectory (Figure 4c). Among these, 2012 marked the peak year for energy consumption, with total energy use reaching 792.1 × 10⁴ tce. Prior to 2012, the total energy consumption grew at a relatively rapid pace, increasing by 39.4% over a decade. After 2012, with the exception of a slight rebound in 2014, consumption declined in all subsequent years, though at a relatively moderate pace, resulting in a cumulative decrease of 14.2%. Meanwhile, the energy consumption structure of agricultural inputs underwent significant adjustments, with the proportions of various energy consumption categories exhibiting a pronounced “rise and fall” pattern. In 2003, fertilizer energy consumption accounted for as much as 57.4% of the total energy consumption. This proportion then declined steadily, falling to 33.1% by 2023, with a cumulative decrease of 42.1%. Pesticide energy consumption also showed a downward trend, decreasing from 13.2% to 9.2%, a reduction of 30.8%. In contrast, the share of electricity and diesel consumption continued to rise, where electricity consumption gradually surpassed fertilizer consumption, surging from 13.6% to 37.5%, a rise of 164.3%. Diesel consumption also climbed from 15.8% to 20.2%, representing a 25% growth. The reason for this phenomenon is that before 2012, the agricultural department blindly pursued economic benefits in agriculture, while ignoring that the input of agriculture and chemical fertilizers would lead to a series of chain reactions, such as the destruction of soil ecosystems, water pollution, and threats to biodiversity. Since the implementation of Hubei Province’s 12th Five-Year Plan for Agricultural Development, policy-level rigid constraints have been imposed on agricultural resource utilization and environmental protection. Concurrently, efforts to gradually enhance agricultural mechanization levels have been made to achieve sustainable agricultural development.

From 2003 to 2023, the total energy consumption of Hubei Province’s social water cycle process showed a steady upward trend, ranging between 709.7 × 10⁴ tce and 1014.5 × 10⁴ tce, with a cumulative increase of 25.4% (Figure 4d). Analyzing the distribution of energy consumption across different stages reveals distinct characteristics: water usage dominated, supply accounted for a low proportion, and drainage exhibited rapid growth. Energy consumption in each stage of water resource supply (water intake, water treatment, and water conveyance) accounted for a relatively small proportion and showed a trend of modest increases amid fluctuations. Energy consumption in these stages ranged from 41.2 × 10⁴ tce to 56.5 × 10⁴ tce, 8.8 × 10⁴ tce to 12 × 10⁴ tce, and 9.9 × 10⁴ tce to 13.8 × 10⁴ tce, respectively. Water utilization was the main contributor to total energy consumption, with an average annual proportion of 91%, increasing from 643.5 × 10⁴ tce to 795.4 × 10⁴ tce, a growth rate of 34.7%. Among water utilization sectors, the industrial sector had the highest water-related energy consumption, with an average annual proportion between 62.1% and 77.6%, followed by residential household water use, with an average annual proportion between 17.2% and 31.7%, while public service water consumption had the smallest proportion, ranging between 4.4% and 7.3% (Figure 4e). With advancements in wastewater treatment technology and the expansion of reclaimed water utilization, energy consumption in the drainage sector showed a rapid growth trend: between 2003 and 2023, energy consumption in the drainage sector increased by 3.7 times, while its proportion also rose from 0.4% to 1.5%.

3.1.3. Food

From 2003 to 2023, the total biomass energy converted from grain crop straw in Hubei Province showed an overall upward trend, ranging between 373.9 × 10⁴ tce and 594.6 × 10⁴ tce (Figure 4f). The cumulative increase reached 47.9%, with a slight decline in recent years. Among these, as the primary grain crop in Hubei Province, biomass energy derived from rice straw conversion consistently held a dominant position, increasing from 245.3 × 10⁴ tce to 343.8 × 10⁴ tce, a rise of 40.1%. However, its proportion showed a gradual downward trend, with a cumulative decrease of 5.2%. The biomass energy conversion from corn and wheat straw exhibited “rapid growth,” with their share significantly increasing. Biomass energy converted from corn straw rose from 69.5 × 10⁴ tce to 132 × 10⁴ tce, an increase of 90%, while its proportion grew from 18.6% to 23.9%, marking a 28.4% rise. Although wheat yields were comparable to corn yields, its straw-to-grain ratio and collection coefficient were lower, resulting in a lower amount of convertible biomass energy than corn. The biomass energy converted from wheat straw increased from 18.9 × 10⁴ tce to 46.9 × 10⁴ tce, a 1.5-fold increase. Its proportion rose from 5.1% to 8.5%, representing a 67.7% growth. Both the biomass energy conversion volume and share from soybean and tuber straw showed declining trends. Affected by reduced yields, biomass energy from soybean straw conversion decreased from 10.3 × 10⁴ tce to 9.8 × 10⁴ tce, with its share falling from 2.7% to 1.8%. The biomass energy converted from sweet potato straw decreased from 30 × 10⁴ tce to 20.6 × 10⁴ tce, with its share falling from 8% to 3.7%.

3.2. Analysis of the Comprehensive Development Evaluation Index for the WEF System

From 2003 to 2023, the comprehensive development evaluation index of Hubei Province’s WEF system exhibited a phased pattern of initially stable decline followed by fluctuating increases, with overall fluctuations ranging between 0.34 and 0.59 (Figure 5). Specifically, from 2003 to 2011, $T$ declined steadily from 0.5 to its lowest point of 0.34, representing a cumulative decrease of 32%. After 2011, $T$ exhibited a trend of fluctuating upward, rising from 0.34 to 0.59, an increase of 73.5%. The primary cause of this phenomenon is the concurrent decline in evaluation indices for both the energy and food systems. Within the energy system, negative indicators such as energy consumption per unit of grain cultivated and energy consumption per unit of water supplied in the social water cycle have continued to increase. Simultaneously, positive indicators like self-sufficiency rates for raw coal, crude oil, and natural gas have steadily decreased. In the food system, rising usage of chemical fertilizers and pesticides has further exacerbated these negative effects.

From 2003 to 2023, Hubei Province’s water resources development evaluation index showed a fluctuating yet increasing trend, ranging between 0.42 and 0.67 (Figure 5). Its phased characteristics were highly correlated with the status of water supply and demand and changes in the external environment. The lowest value occurred in 2011, a dry year when total water resources were significantly scarce, while water consumption for fossil fuel extraction and thermal power generation was strong, and per capita water use was at a high load level. The year 2020 marked the peak value, characterized by ample total water resources. Concurrently, production activities were halted due to the COVID-19 pandemic, leading to a significant decline in industrial water demand. This effectively alleviated the pressure on the water supply and demand.

The development evaluation index for Hubei Province’s energy system showed a fluctuating downward trend, with fluctuations ranging between 0.35 and 0.63 (Figure 5). The year 2019 marked the trough of the index, primarily due to significant declines in coal self-sufficiency and natural gas self-sufficiency, which fell by 68.4% and 80.9%, respectively. In contrast, 2008 marked the peak year for the index, primarily due to high levels of energy supply. This demonstrates that fossil fuel supply and electricity supply are the core drivers influencing the development evaluation index of Hubei Province’s energy system.

The food development evaluation index for Hubei Province exhibited a pattern of decline followed by growth, fluctuating within a range of 0.21 to 0.72 (Figure 5). Changes in the index were highly correlated with adjustments in agricultural production methods and policy interventions. The year 2011 marked the trough of the index, coinciding with peak usage of chemical fertilizers and pesticides. This high-input, high-pollution production model directly dragged down the comprehensive development evaluation level of the food system. After 2011, Hubei Province implemented strict restrictions on pesticide and fertilizer usage in both its 12th and 13th Five-Year Plans for agricultural development. Concurrently, the introduction of the “Four Subsidies” policy effectively boosted grain crop yields and planting areas, leading to a gradual recovery in the province’s food system development evaluation index.

3.3. Analysis of the Degree of System Coupling and Coupling Coordination

As shown in Figure 6, from 2003 to 2023, Hubei Province’s WEF system exhibited characteristics of high-intensity coupling, with the coupling index consistently above 0.93 and fluctuations within a range of 0.06. This indicates that the province’s three major subsystems—water, energy, and food—formed a highly interconnected and stable state. Changes in any subsystem will significantly impact the others.

The coupling coordination level of the WEF system exhibited a highly convergent trend with its comprehensive development evaluation index from 2003 to 2023. This indicates that the increase in D primarily stemmed from the rise in T (Figure 6). The fluctuation range of D remained between 0.57 and 0.76 (Figure 6), progressing through stages of near coordination, primary coordination, and moderate coordination development.

The temporal variation characteristics of the coupling coordination degree in the binary system exhibited similarities to those of the WEF system, though the degree of coupling synergy differed slightly. The WE system demonstrated the narrowest fluctuation range in coupling coordination, oscillating between 0.64 and 0.73 (Figure 7). It fluctuated between the primary coordination and moderate coordination stages, mainly due to the decline in the energy system development evaluation index. Due to the simultaneous increase in the evaluation indices for water resource systems and food systems, the WF system exhibited the highest level of coupling coordination, achieving a good coordination level with fluctuations ranging between 0.55 and 0.82 (Figure 7). The coupling coordination degree of the EF system progressed through stages of near coordination, primary coordination, and moderate coordination, fluctuating within a range of 0.54 to 0.75 (Figure 7). The year 2011 marked a pivotal turning point in the transition toward coordinated development. Prior to 2011, although Hubei Province had relatively large-scale fossil fuel extraction and a relatively high capacity for energy supply security, it suffered from low water resource utilization efficiency. Water was wasted in sectors such as agricultural irrigation and industrial production. Energy utilization efficiency was inadequate, with high-energy-consuming industries accounting for a large proportion of the economy. Agricultural production efficiency was low, and grain yields per unit area remained low due to resource and technological constraints. Since 2011, Hubei Province has continuously enhanced the coupling and coordination of its WEF system through multi-sectoral policy coordination. This includes restructuring water supply sources by reducing reliance on groundwater while gradually increasing the proportion of surface water and reclaimed water. The province has promoted water-saving irrigation technologies to improve agricultural irrigation efficiency. Through industrial restructuring and technological advancement, energy consumption per unit of GDP has been steadily reduced. The adoption of intensive agricultural management models has steadily increased grain crop yields and land utilization efficiency. Concurrently, reduced pesticide and fertilizer usage has ensured green agricultural development, facilitating the transition from “high-input, high-pollution” to “high-quality, sustainable” agricultural production. This multi-sectoral coordination has advanced the coupling and coordination of the WEF system.

3.4. Forecasting Degree of WEF System Coupling Coordination

The prediction results from the GM (1,1) model indicate that from 2024 to 2040, the coordination level of the WEF system will exhibit a continuous trend toward higher coordination. Between 2024 and 2036, it will maintain a sustained increase within the moderately coordinated phase. Starting in 2037, it will enter the good coordination stage and continue to increase steadily. By 2040, this index will reach 0.82. The projected change values are shown in

Table 6. Prediction of the coupling coordination degree of the WEF system from 2024 to 2040.

Year	Actual Value	Predicted Value	Residual Value
2005	0.7	0.64	−0.06
2010	0.59	0.66	0.07
2015	0.71	0.68	−0.03
2020	0.73	0.71	−0.02
2025	0.74	0.74	0
2030		0.78
2035		0.79
2040		0.82

.

4. Discussion

4.1. Comparison

Hubei Province is located in the core region of the Yangtze River Basin. Existing studies often treat it as a sub-object within the broader research on the entire basin. In terms of research applicability [1,22,66], the WEF system evaluation framework constructed in this paper is not only suitable for Hubei Province but can also be extended to cities within the Yangtze River Basin urban agglomerations. In the arid regions of water-scarce northwest China, the methods for selecting indicators and determining their weights should be revised [71]. This study reveals that from 2003 to 2023, the WEF system in Hubei Province exhibited characteristics of high-intensity coupling, with the coupling coordination degree progressively advancing toward higher coordination stages. This finding aligns with the results of Tian’s [72] research. This study indicates that the comprehensive evaluation indicators for Hubei Province’s WEF system and its subsystems show an upward trend amid fluctuations, consistent with Qi’s [73] research findings on Hubei Province. Wang [22] examined the coupling coordination degree of the Yangtze River Economic Belt from 2012 to 2022. Their results indicate that Hubei Province’s comprehensive evaluation index for its food system showed significant growth, with the WEF system coupling degree remaining above 0.9. The coupling coordination degree increased from 0.61 in 2012 to 0.72 in 2022. Although the study period of this paper differs from that of the aforementioned research, the findings of this paper align with those of the aforementioned studies during the overlapping research period.

In the current development of evaluation metrics for WEF system coupling and coordination, existing indicator systems predominantly focus on the independent attributes of WEF subsystems. There is a lack of interdependent metrics capable of accurately mapping the interconnected concepts within WEF subsystems. Compared with other studies [38,39,40,69,70,71,72], this research combines a comprehensive quantification of WEF relationships with an evaluation metric for coupling coordination. It not only visually demonstrates resource flow pathways within the WEF system but also uses this metric to assess the overall coordination level of the system. From a theoretical perspective, this study provides methodological insights and references for expanding the WEF system (e.g., the water–energy–food–carbon system) and similar research. From a practical perspective, it offers theoretical foundations and data support for coordinating the development pathways of Hubei Province’s WEF system.

4.2. Limitation

This study has several limitations. First, the spatial scale of the research area is limited to a single province, failing to reflect the levels of coupled and coordinated development across cities within Hubei Province. Future research could collect data from all 17 cities in Hubei to explore the spatial heterogeneity in the development levels of the WEF system coupling across these cities. Additionally, all cities along the middle and lower reaches of the Han River are located within Hubei Province. Future research can explore the level of coordinated development between water, energy, and food systems in the middle and lower reaches of the Han River basin. Second, in the quantification of WEF relationships, the energy consumed by electricity and diesel inputs during crop cultivation overlaps with the energy consumed in the water withdrawal stage of the social water cycle in statistical terms. However, due to limitations in data availability, it is challenging to accurately separate and isolate this portion of overlapping data. Furthermore, estimates indicate that the duplicate values are minimal, and their interference with the overall quantitative results remains within an acceptable range. Finally, the CRITIC method relies excessively on the inherent dispersion and correlation within the data itself, making it incapable of incorporating expert experience or industry-specific prior knowledge. It is also sensitive to outliers, with data quality directly determining the reliability of assessment results [38]. Furthermore, future approaches could adopt input–output methods or LCA methods to account for resource flows between WEF systems.

5. Conclusions and Suggestions

5.1. Conclusions

This paper first elaborates on the coupling mechanism of the WEF system, describing the processes of facilitation and constraint among generalized, unbounded WEF subsystems, along with the pathways of resource and material flows. Subsequently, based on this coupling mechanism, a quantitative formula for a narrowly defined, bounded WEF system is proposed. Using Hubei Province as a case study, the resource flow between WEF binary systems is quantified. Specifically, this includes the mutual resource consumption between water and energy systems, the mutual consumption between energy and food systems, and the unidirectional consumption between water and food systems. Secondly, using the aforementioned indicators and resource utilization level indicators for subsystems, an evaluation indicator system for Hubei’s WEF system was established. The CRITIC method was employed to calculate the weights of each indicator, analyzing the temporal evolution characteristics of the coupling coordination degree of Hubei’s WEF system from 2003 to 2023. Finally, the GM (1,1) model is employed to forecast the trend of WEF system coupling coordination in Hubei Province from 2024 to 2040. Quantifying the WEF system can clearly identify the flow of resources among departments, pinpointing precisely which department and which link consumes the most of a particular resource, and providing data support for formulating targeted policies. Integrating the interrelationships among WEF subsystems into a coupled coordination evaluation framework allows for the selection of key indicators for each subsystem. This approach clarifies lagging factors within the WEF system during coordinated development, aiding in the precise resolution of systemic synergy challenges.

(1) From the perspective of resource flow pathways, water consumption within the energy system increased by 2.4 times during the study period, with over 80% of the water resources directed toward the thermal power generation sector. Concurrently, the blue water footprint within the food system grew rapidly, reflecting increased agricultural irrigation water usage. The rapid growth in water consumption within the energy sector not only places pressure on regional water resource endowments but also, under total water resource constraints, reduces the water availability for other critical water-using sectors. In the food system, total energy consumption increased by 19.6%, and the energy flow structure underwent significant changes. The dominant position of fertilizer energy consumption shifted, with agricultural electricity consumption ranking first at 37.5%, nearly tripling, followed by agricultural diesel energy consumption. Total energy consumption in the water resources system increased by 25.4%, with the primary energy-consuming process occurring during water use. Water use accounted for an average of over 90% of the total energy consumption, with the industrial sector having the highest share at a multi-year average of 71.3%, followed by residential household water use. Rice was the main grain crop flowing into the energy system, with its straw contributing the highest amount of biomass energy, accounting for over 60%. Corn followed, with an average share exceeding 20%.

(2) From 2003 to 2023, the comprehensive development evaluation index of Hubei Province’s WEF system exhibited a pattern of initial steady decline followed by fluctuating growth, rising from 0.496 to 0.594—an increase of 19.9%. The water and food systems developed well, while the comprehensive development evaluation index for the energy system lagged behind, constituting the primary constraint on the overall development of the WEF system. The coupling degree of the WEF system exceeded 0.93, indicating that Hubei Province’s WEF system formed a highly coupled state with deep interconnections. Changes in the coupling coordination degree of Hubei’s WEF system primarily stemmed from shifts in its comprehensive development evaluation index, with both exhibiting convergent trends.

From 2003 to 2023, the coupling coordination level of the WEF system in Hubei Province experienced development stages of moderate coordination–primary coordination–near coordinated–primary coordination–moderate coordination, with the coupling coordination degree decreasing from 0.7 to 0.57 and then increasing to 0.76. Constrained by the lagging development level of the energy system, the coupling coordination level between the WE system and the EF system was significantly weaker than that of the WF system; the WF system’s coupling coordination level reached a well-coordinated stage. It is worth noting that although the WE coordination level is relatively low, its overall stability is comparatively good, with the smallest fluctuation range in coupling coordination degree, showing strong anti-interference capability.

(3) Given the Hubei Provincial Government’s sustained coordination and management of water resources, energy, and food systems, the WEF system coupling coordination gradually progressed toward a more coordinated phase, achieving a good coordination level by 2037 and reaching 0.82 by 2040.

5.2. Policy Recommendations

For the water system, over 80% of the water consumption in energy production is attributed to the thermal power generation sector. As a high-water-consumption industry, it should intensify research and development of cooling water conservation technologies, reduce water quotas for thermal power generation, and enhance water resource utilization efficiency in the industrial sector. Meanwhile, the blue water footprint of the food system has doubled since 2003, indicating increased irrigation water usage. This necessitates upgrading water-saving technologies in agriculture and promoting modern irrigation methods, such as field sprinkler systems and improved furrow irrigation. Additionally, water supply structures should be optimized to gradually reduce the proportion of groundwater supply while enhancing water recycling efficiency to decrease excessive reliance on traditional water sources. Finally, through regular water conservation awareness campaigns and the implementation of incentive policies, such as tiered water pricing and water-saving subsidies, public awareness of water conservation should be comprehensively enhanced.

For the energy system, between 2003 and 2023, Hubei Province’s comprehensive water resources development evaluation index rose from 0.45 to 0.63, and the comprehensive grain development evaluation index rose from 0.49 to 0.72, but the comprehensive energy development evaluation index fell from 0.54 to 0.43. This indicates that energy system development lags behind the water and food systems, the root cause being insufficient fossil energy endowments and reliance on external inputs. Therefore, Hubei should build a more adaptive energy development pathway, adjust the energy supply structure, and—while consolidating the advantages of hydropower—vigorously develop renewables such as wind and photovoltaic power. In addition, the water-use stage of the social water cycle accounts for over 90% of the average energy consumption, with the main water-consuming and energy-consuming sectors being industry and residential use. Thus, attention should focus on end-use water devices in the industrial and residential sectors, such as high-efficiency energy-saving pumps for industrial circulating water, to reduce energy consumption per unit of water while ensuring water function.

For the food system, the expansion of cultivated land and increased cropping intensity have significantly boosted grain production. However, excessive use of chemical fertilizers and pesticides will constrain the development of the food system. Therefore, the government should strictly enforce the red line for farmland protection, curb all forms of farmland encroachment, and solidify the resource foundation for grain production. Simultaneously, it should promote the intensive development of agricultural production and optimize crop layout; control the total volume and intensity of pesticide, fertilizer, and agricultural film usage; and ensure grain output while reducing impacts on soil and the ecological environment to achieve sustainable development of the food system.