Coupled Water–Energy–Carbon Study of the Agricultural Sector in the Great River Basin: Empirical Evidence from the Yellow River Basin, China

Abstract

In the context of sustainable development, water resources, energy, and carbon emissions are pivotal factors influencing the rational planning of economic development and the secure establishment of ecological barriers. As a core food production area, how can the Great River Basin balance the pressure on the “water–energy–carbon” system (WEC) to realize the coordinated development of “nature–society–economy”? Taking the Yellow River Basin in China as the research object, this paper explores the coupling characteristics and virtual transfer trends of WEC in the agricultural sector under the condition of mutual constraints. The results show the following: (1) On the dynamic coupling characteristics, W-E and E-C are strongly coupled with each other. The optimization of water resource allocation and the development of energy-saving water use technology make the W-E consumption show a downward trend, and the large-scale promotion of agricultural mechanization makes the E-C consumption show an upward trend. (2) On the spatial distribution of transfer, there is an obvious path dependence of virtual WEC transfer, showing a trend of transfer from less developed regions to developed regions, and the coupling strength decreases from developed regions to less developed regions. The assumption of producer responsibility serves to exacerbate the problem of inter-regional development imbalances. (3) According to the cross-sectoral analysis, water resources are in the center of sectoral interaction, and controlling the upstream sector of the resource supply will indirectly affect the synergistic relationship of WEC, and controlling the downstream sector of resource consumption will indirectly affect the constraint relationship of WEC. This study provides theoretical and methodological references for the Great River Basin to cope with the resource and environmental pressure brought by global climate change and the effective allocation of inter-regional resources.

1. Introduction

The 2023 Development Report released by UN Water indicates that the water–energy–food–ecology nexus offers a systematic approach to understanding the interconnections and trade-offs among systems. Its essence lies in balancing the diverse goals, interests, and needs of humanity and the environment within the framework of sustainable development, and in seeking better strategies for the synergistic development of multiple systems [1]. Agriculture has long been an important economic foundation in the Great River Basin, thanks to its favorable climate, fertile soil, and flat terrain [2]. Nevertheless, under the combined impacts of climate change and human activities, issues such as water scarcity, limited potential for balanced water allocation, prominent energy supply–demand contradictions, and fragile ecosystems are becoming more pronounced. The mutual constraints and transformations among water, energy, and carbon are restricting food security, further magnifying the security risks of the basin system, and hampering the sustainable socio-economic development of the Great River Basin [3]. As a result, with the advancement of the sustainable development goals (SDGs), agricultural production in the Great River Basin has entered a new stage of development, which demands a holistic approach to balancing various production factors [4].

Water resources, energy, and carbon emissions, as vital components of the “nature–society–economy” complex system, are interdependent, mutually constrained, and undergo transformation and adaptation, thus forming a complex “water–energy–carbon” system (WEC) [5]. Since the WEC system was first put forward in 1997, research on this three-factor coupled system has attracted the attention of numerous scholars. Gradually, it has become evident that this research holds significant guiding importance for mitigating climate change, balancing ecosystem evolution, and promoting economic and social development [6,7,8]. In recent years, many scholars have explored the WEC system from multiple perspectives. Regionally, they have studied it at the household [9], urban [10], and global [11] levels. From an industry perspective, they have delved into fields like the electric power system [12] and agricultural trade [13]. Their research mainly focuses on the associated characteristics, degree of synergy, driving factors, and spatial features of the WEC system.

In the field of WEC system research, scholars have been committed to exploring its associated characteristics. Initially, many scholars conducted research from the perspective of interactive changes in subsystems. They focused on water resources and, through the comparative analysis of energy intensity and carbon emission levels in different links and processes of the water cycle system, discovered that ice production and heating projects in the water system are the main sources of carbon emissions [14]. At the same time, they found that the energy consumption of water use and water withdrawal are increasing significantly [15]. However, these early studies mainly concentrated on the static characteristics of single systems. To reveal the dynamic interaction characteristics of the WEC system, scholars then studied this system from the perspective of coupled synergy. They tried to explore the high–low characteristics of the WEC system coupling degree by evaluating the synergy degree of elemental coupled systems [16,17]. The evaluation indexes mainly included stability [18], adaptability [19], sustainability [20], etc. These indexes were selected, evaluated, and analyzed to uncover the external adaptability and internal interaction characteristics of the WEC system. When it comes to exploring the driving factors of the WEC system, some scholars, based on the ESDA method and the gray correlation model, found that the urbanization rate, population size, and the level of economic development are the main factors causing changes in the WEC system [21]. A large number of case studies have shown that climate, technology, and geography have a significant influence on the coupling strength and distribution characteristics of the WEC system [22,23]. Some scholars, using system dynamics modeling, have also found that the energy structure, industrial structure, and GDP growth rate have an increasingly important impact on the WEC system. In terms of exploring the spatial correlation and structural characteristics of the WEC system, some scholars used the MRIO model to analyze the interaction characteristics of WEC in different industries. They found that the power sector and the transportation sector play a key role in the synergistic optimization of the WEC system [24]. When analyzing the decoupling effect between regional carbon load and economic growth with the Tapio decoupling model, it was found that the WEC system shows significant differences between economically developed and underdeveloped regions [25]. Meanwhile, considering the economic trade between regions, the coordination level and distribution pattern of the WEC system present different characteristics [26].

Agriculture is a sector that is highly reliant on water resources. It has a notable impact on the energy system and plays a pivotal role in carbon emission reduction. Moreover, it serves as a crucial link connecting the natural, economic, and social realms. Consequently, numerous scholars have endeavored to study the WEC system within the agricultural sector. Their aim is to alleviate resource shortages and promote the development of a green economy. The research mainly centers around two key aspects: food security [27] and synergistic strategies for carbon emission reduction [28]. Typically, the constraint linkages among various resources like land, water, and energy in the agricultural sector are explored through multiple analytical methods. These include life cycle analysis [29], ecological network analysis [30], system dynamics analysis [31], and input–output modeling [32]. Some studies have concentrated on the crop production process, aiming to elucidate the interaction process of the WEC system. For instance, crop production requires a large amount of water, and at the same time, it releases carbon dioxide through cultivation and fermentation processes [33]. Additionally, some research has employed the indicator of linkage strength to examine the WEC relationship in agricultural trade. By investigating drivers such as import/export ratios and GDP per capita, these studies provide valuable insights for managing cross–border trade relationships, with the ultimate goal of improving resource use efficiency and reducing carbon emissions [34].

Previous studies have offered valuable references for research perspectives and methods regarding the WEC system within the agricultural sector. Nevertheless, as the process of climate change accelerates, the pressures on resources and the environment have become increasingly prominent. Moreover, the contradictory characteristics of the three elements in the WEC system under mutual constraints have become more conspicuous. At present, existing research has not yet shifted to a perspective that aligns with the concept of sustainable development. It has also failed to clearly elucidate the coupling characteristics and virtual transfer trends of the WEC system in the agricultural sector under conditions of mutual constraints.

This paper takes the Yellow River Basin in China, a typical region with the most prominent contradictions in WEC ties, as the research object to explore this realistic problem: when the development factors are mutually constrained, how can the agricultural sector in the Great River Basin balance the WEC coupled system to achieve the coordinated development of “nature–society–economy”? Based on the framework of sustainable development goals, this study constructs the research content of WEC coupling in the agricultural sector from the aspects of dynamic coupling characteristics, spatial transfer laws, and cross-sectoral analysis. Starting from the constraints of green development, we explore the current situation of the agricultural sector’s WEC system in the Yellow River Basin, reveal the relationships and system characteristics among the three elements, and clarify the WEC multidimensional risk coupling mechanism and sectoral control relationships. In terms of research significance, this study helps to uncover the dynamic coupling mechanism and trans-regional transfer path among water resources, energy, and carbon emissions. It further clarifies the sustainable development law of the “nature–society–economy” system in the Great River Basin, represented by the Yellow River Basin. Moreover, it provides references and decision-making bases for ensuring food security, promoting water resource conservation and intensive utilization, and facilitating the transformation towards a low-carbon economy and society.

Compared with existing studies, this research has the following marginal contributions: (1) From a dynamic perspective, this study clarifies the characteristics of WEC coupling and coupling intensity within the agricultural sector. (2) It establishes a theoretical analytical framework for the WEC coupling mechanism. (3) This study integrates the multiregional input–output approach and the ecological network approach to identify the control dependencies between the agricultural sector and other sectors.

The remainder of this study is structured as follows. Section 2 elaborates on the analysis of WEC coupling mechanisms in the agricultural sector. Section 3 presents the research model, methodology, and data. Section 4 presents the study results and analyzes them in terms of dynamic coupling characteristics, spatial transfer patterns, and cross-sectoral analysis. Section 5 summarizes the research conclusions. Section 6 conducts an extended discussion of the conclusions and provides targeted recommendations.

2. The Mechanism of Coupling Mechanism of WEC in the Agricultural Sector

With regard to the interconnections and interactions between resource utilization and carbon emissions, the agricultural sector can be compartmentalized into three subsystems: the water system, the energy system, and the carbon emission system. The water system primarily encompasses the potable water supply, water storage, water production, water transportation, water usage, and wastewater treatment; the energy system primarily encompasses energy extraction, processing, transmission, and consumption; and the carbon emission system primarily pertains to the greenhouse gas emissions resulting from the utilization of water resources and energy during agricultural production (Figure 1).

The production of agricultural products constitutes a complex process that encompasses various stages, including the development of water resources, land utilization, and the deployment of artificial energy. These stages are interconnected through a series of primary pathways, which can be delineated as follows: (1) The development and utilization of water resources. The development and application of water resources for agricultural production (e.g., water diversion, storage, transport, and irrigation) require auxiliary energy inputs from human activities, thereby leading to carbon emissions. (2) Land development and utilization. Processes such as land development, plowing, irrigation, weeding, and harvesting necessitate water and energy resource inputs, consequently producing associated carbon emissions. (3) Energy generation and input. Agricultural energy inputs include both solar and artificial sources, with solar energy being the predominant source. Anthropogenic energy inputs are predominantly focused on the utilization of water resources, land development and cultivation, and fertilizer production, among other activities. (4) Carbon sequestration during crop growth. In addition to carbon emissions from anthropogenic activities, photosynthesis in crops enables a degree of carbon absorption. Simultaneously, carbon exchange transpires between crops and soil. These four processes collectively define the WEC nexus within the agricultural sector (Figure 2).

The process of WEC coupling is primarily influenced by the intricate interplay of natural, economic, and social factors. These include the following: (1) Natural factors encompass natural conditions and environmental alterations. The prevailing natural conditions in a region dictate the endowment and distribution patterns of water, energy, and other resources. It is therefore evident that changes in these conditions will affect the supply and combination of resources. Furthermore, environmental changes will bring about the evolution of water resource distribution and ecosystems, resulting in a change in the relationship between the WEC system in the region. (2) Economic factors, comprising economic development, industrial structure, production efficiency, and technology level, serve as critical determinants in regional WEC coupling. The level of regional economic development is a principal factor influencing the intensity of regional water and energy resource consumption and carbon emission intensity. The industrial structure, in turn, shapes the fundamental spatial pattern of WEC coupling and the interrelationship between factor combinations. Moreover, the production efficiency and technology level influence the efficiency of regional resource coupling and carbon emission efficiency. (3) Social factors include population growth, governmental policy-making, and climate change. A rise in the global population leads to an increased demand for water and energy resources. This will have a significant impact on the agricultural system, placing further strain on the regional natural environment. Governments play a crucial role in shaping the WEC coupling system through diverse mechanisms, including the regional industrial layout, soil and water resource development and planning and management, the development and utilization of new energy resources, environmental protection policies, and ecological construction. Climate change affects the carrying capacity and structure of the coupled WEC system by altering the water cycle and affecting water–energy infrastructure (Figure 2).

3. Research Models and Methods

3.1. Study Area

The Yellow River originates in the Bayan Khara mountain range in Qinghai, traversing nine provinces: Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong. It eventually drains into the Bohai Sea at Dongying City, Shandong Province (Figure 3).

The Yellow River Basin serves not only as a crucial ecological barrier in China but also as a vital core area for food production and a region rich in energy resources. In terms of resource distribution, the spatial mismatch between the WEC system, food production, and socio−economic factors in the Yellow River Basin is highly pronounced. It is one of the regions in China where the most contradictory WEC nexus relationships are most concentrated and prominent [35]. The upper and middle reaches of the Yellow River Basin are distant from the ocean, mostly consisting of arid and semi-arid areas with fragile ecosystems. The regional water resources in this area account for merely 4% of the country’s total, yet its grain production accounts for approximately 35.6% of the national output, holding a significant position in China’s grain production landscape. The Fenwei Plain, the Huanghuai Irrigation District, and the Hetao Irrigation District within the Yellow River Basin are important grain-producing areas in China and are integral components of the strategic pattern of agricultural development as outlined in China’s Main Functional Areas Plan. According to the statistics on grain output from the China Bureau of Statistics, the total grain output of the Yellow River Basin region in 2023 exceeded 240 million tons, with a yield of 45,834 kg per hectare. Meanwhile, the Yellow River Basin has a concentrated and large-scale energy distribution. It contributes 45% of the country’s coal production, 31% of its natural gas production, and 18% of its power generation. The proposal of China’s “carbon peak and carbon neutral” goal has provided new impetus for promoting the green and low−carbon transformation of the Yellow River Basin and has also set new requirements for driving the high−quality development of this region [36].

With the rapid development of modern agriculture, food security will drive the continuous increase in water demand and carbon emissions. Meanwhile, water scarcity will restrict energy supply and food security in the agricultural sector, resulting in the coercive impact of agricultural production on the ecosystem [37]. In general, the resource endowment and development situation of the Yellow River Basin determine that the conflicts within the WEC system in the agricultural sector of this region are particularly prominent and concentrated. Therefore, it has become a top priority for the high-quality development of the Yellow River Basin to accelerate the construction of a sustainable WEC coupled system in the agricultural sector and promote the economical and intensive utilization of resources.

3.2. WEC-MRIO Modeling

A multiregional input–output model is defined as an input–output model that is constructed based on a single-region input–output model, incorporating interregional labor flows and commodity trade. The WEC-MRIO model utilized in this study is a hybrid input–output model that integrates three environmental pressure indicators—namely, energy consumption, water consumption, and carbon emissions—with a multiregional economic system, based on a monetary multiregional IO model [38,39,40]. The specific framework of the model is as follows: within a multiregional economic system comprising $z$ regions, each with $n$ sectors, the following row equilibrium relationships exist:

$$\begin{array}{c}{X}_i^r={\displaystyle \sum _{s=1}^z}{\displaystyle \sum _{j=1}^n}{x}_{ij}^{rs}+{\displaystyle \sum _{s=1}^z}{Y}_i^{rs}\end{array}$$

(1)

$$\begin{array}{c}{a}_{ij}^{rs}={\displaystyle \frac{x_{ij}^{rs}}{x_j^{rs}(i,j=\mathrm{1,2},\dots ,n)}}\end{array}$$

(2)

In this context, $r$ and $s$ represent any two regions, $X_i^r$ denotes the total output of region $r$, $x_{ij}^{rs}$ signifies the transfer of sector production from region $s$ to region $r$, and $Y_i^{rs}$ represents the final demand between regions $s$ and $r$. Furthermore, $a_{ij}^{rs}$ represents the direct consumption coefficients, and $A=\left(a_{ij}^{rs}\right)$ represents the direct consumption coefficient matrix. Let ${X=(x}_i^r), Y=(y_i^{rs})$; then, the MRIO can be expressed as follows:

$$\begin{array}{c}AX+Y=X\end{array}$$

(3)

$$\begin{array}{c}X={\left(I-A\right)}^{-1}Y\end{array}$$

(4)

In this context, $X$ represents the column vector of total output, $Y$ denotes the column vector of final demand, $I$ is the unit matrix, and ${(I-A)}^{-1}$ signifies the Leontief inverse matrix.

The MRIO method provides a more effective means of assessing the extent of WEC coupling between industrial sectors. Additionally, the indirect coefficients can be utilized to examine the spatial characteristics of the WEC system. To enhance the understanding of the flow within the WEC system among industries, beyond the direct energy consumption coefficient matrix and the final demand vector, this study computes the indirect coefficient, as presented in the subsequent formula:

$$e_j^{sd}=e_j^s/x_j^s$$

(5)

$$\begin{array}{c}{w}_j^{sd}=w_j^s/x_j^s\end{array}$$

(6)

$$\begin{array}{c}{c}_j^{sd}=c_j^s/x_j^s\end{array}$$

(7)

The variables $e_j^{sd}$, $w_j^{sd}$, and $c_j^{sd}$ represent the energy consumption intensity, water consumption intensity, and carbon emission intensity, respectively.

$$E^{em}=E{I}^d{\left(I-A\right)}^{-1}{Y}^{diag}$$

(8)

$$\begin{array}{c}{W}^{em}=W{I}^d{\left(I-A\right)}^{-1}{Y}^{diag}\end{array}$$

(9)

$$\begin{array}{c}{C}^{em}=C{I}^d{\left(-A\right)}^{-1}{Y}^{diag}\end{array}$$

(10)

The following variables are defined: $E^{em}$ is the indirect energy consumption matrix; $W^{em}$ is the indirect water consumption matrix; $C^{em}$ is the indirect carbon emission matrix; $E{I}^d$ is the indirect energy consumption coefficient matrix; $W{I}^d$ is the indirect water consumption coefficient matrix; $C{I}^d$ is the indirect carbon emission coefficient matrix; and $Y^{diag}$ is the diagonal matrix of the final demand column vector transformation.

$$\begin{array}{c}IE=E^{em}-E\end{array}$$

(11)

$$\begin{array}{c}IW=W^{em}-W\end{array}$$

(12)

$$\begin{array}{c}\begin{array}{c}IC=C^{em}-C\end{array}\end{array}$$

(13)

In this context, IE represents the indirect energy consumption matrix, IW denotes the indirect water consumption matrix, and IC signifies the direct energy consumption coefficient matrix.

3.3. Ecological Network Analysis of the WEC System

Ecological network analysis is a systematic analysis method that utilizes a network to investigate the transfer of substances within a system. This method is capable of identifying and analyzing the direct and indirect interactions between all system components in a systematic manner [31]. To examine the connections and interactions within the WEC network, this study employed the ecological network analysis method. The network control dependence coefficient, as an indicator in the ecological network analysis method, facilitates the quantitative assessment of intersectoral control and dependence relationships in factor flows [32]. In this study, the intersectoral control matrix is denoted as CN, and the specific formula is presented below:

$$\begin{array}{c}{G}^e={\left[g_{ij}^e\right]}_{n\times n}={\left[{\displaystyle \frac{e_{ij}}{e_j^d}}X\right]}_{n\ast n}\end{array}$$

(14)

$$\begin{array}{c}{G}^{e^{\prime }}={\left[g_{ij}^{e^{\prime }}\right]}_{n\times n}={\left[{\displaystyle \frac{e_{ij}}{e_i^d}}X\right]}_{n\ast n}\end{array}$$

(15)

$$\begin{array}{c}{G}^w={\left[g_{ij}^w\right]}_{n\times n}={\left[{\displaystyle \frac{w_{ij}}{w_j^d}}X\right]}_{n\ast n}\end{array}$$

(16)

$$\begin{array}{c}{G}^{w^{\prime }}={\left[g_{ij}^{w^{\prime }}\right]}_{n\times n}={\left[{\displaystyle \frac{w_{ij}}{w_i^d}}X\right]}_{n\ast n}\end{array}$$

(17)

$$\begin{array}{c}{G}^c={\left[g_{ij}^c\right]}_{n\times n}={\left[{\displaystyle \frac{c_{ij}}{c_j^d}}X\right]}_{n\ast n}\end{array}$$

(18)

$$\begin{array}{c}{G}^{c^{\prime }}={\left[g_{ij}^{c^{\prime }}\right]}_{n\times n}={\left[{\displaystyle \frac{c_{ij}}{c_i^d}}X\right]}_{n\ast n}\end{array}$$

(19)

$$\begin{array}{c}N={\left[n_{ij}\right]}_{n\times n}={\left(1-G\right)}^{-1}\end{array}$$

(20)

$$\begin{array}{c}{N}^{\prime }={\left[n_{ij}^{\prime }\right]}_{n\times n}={\left(1-G^{\prime }\right)}^{-1}\end{array}$$

(21)

$$\begin{array}{c}CN=\left[{cn}_{ij}\right]=\left\{\begin{array}{c}{n}_{ij}/{n^{\prime }}_{ij}<1,{cn}_{ij}=1-n_{ij}/{n^{\prime }}_{ij}\\ n_{ij}/{n^{\prime }}_{ij}>1,{cn}_{ij}=0\end{array}\right.\end{array}$$

(22)

In this context, $G^e$ denotes the dimensionless matrix of direct energy flows among sectors with an input orientation, $G^w$ denotes the dimensionless matrix of direct water resource flows among sectors with an input orientation, and $G^c$ denotes the dimensionless matrix of direct carbon emissions among sectors with an input orientation. The matrices $G^{e\prime }$, $G^{w\prime }$, and $G^{c\prime }$ correspond to the dimensionless direct energy, water resource, and carbon emission flows, respectively, among sectors with an output orientation. The elements ${g^e}_{ij}$, ${g^w}_{ij}$, and ${g^c}_{ij}$ within these matrices indicate the direct energy, water resource, and carbon emission flows, respectively, from sector $i$ to sector $j$ within the input-oriented sectoral framework. Conversely, ${g^{e\prime }}_{ij}$, ${g^{w\prime }}_{ij}$, and ${g^{c\prime }}_{ij}$ denote the direct energy, water resource, and carbon emission flows, respectively, from sector $i$ to sector $j$ within the output-oriented sectoral framework. Furthermore, $N$ encapsulates the dimensionless overall flow of elements among sectors with an input orientation, while $N^{\prime }$ encapsulates the dimensionless overall flow of elements among sectors with an output orientation.

3.4. Data Sources and Data Processing

The study utilizes three primary categories of data. The first category includes water resource data, sourced primarily from the China Environmental Statistics Yearbook, China Water Resources Bulletin, and China Water Resources Statistics Yearbook. The second category includes energy and carbon emission data, sourced from the CEADS database. The third category includes economic development data from the China Statistical Yearbook.

In the water resources data, the “water production and supply sector” includes tap water storage and supply, and the data are from the tap water withdrawal volume in the China Environmental Statistics Yearbook. Water use in the “service sector” includes urban water use and rural domestic water use, and water use in the sub-sector is allocated in the same proportion with reference to the proportion of the added value of the service sub-sector and calibrated based on the total domestic water use in the China Environmental Statistics Yearbook. Data on agricultural water consumption in each province are obtained from the China Water Resources Bulletin. Industrial water use data are difficult to obtain due to the lack of officially published industrial water use in differentiated sectors in each region, and the following steps were adopted in this study with reference to Lei and Huang (2015) [41]: (1) Calculate water consumption by province and by industry, using the China Water Conservancy Statistical Yearbook; (2) calculate the yearly industrial water intensity of each province based on the GDP and industrial added value of each province; (3) calculate the average annual growth rate of the industrial water intensity of each province separately; and (4) assuming that there is no change in the annual growth rate of the water intensity within the interval and no difference in the growth rate of the water intensity among the industrial industries, extrapolate the water consumption of each industry in each province (

Table 1. List of research data sources and processing methods.

Data Type	Data Name	Data Processing Method	Data Source
Water resource data	Production and supply of water	Water intake of tap water	China Environmental Statistics Yearbook
	Water use in the service industry	Proportional distribution according to the proportion of added value in service industries	China Environmental Statistics Yearbook; China Statistical Yearbook
	Water use in agriculture	Water resource consumption	China Water Resources Bulletin
	Water use in industry	Lei and Huang (2015) [41]	China Water Resources Bulletin; China Water Resources and Hydropower Statistics Yearbook
Energy data	Energy consumption	Xu et al. (2024) [38], Guan et al. (2021) [39], Shan et al. (2020) [40]	ceads.net (20 December 2024)
Carbon emission data	Carbon dioxide emissions	Xu et al. (2024) [38], Guan et al. (2021) [39], Shan et al. (2020) [40]	ceads.net (20 December 2024)
Economic development data	GDP	-	China Statistical Yearbook
	Value added of industries	-	China Statistical Yearbook

).

Notes on the data and research scope are as follows: (1) The research time nodes are 2012, 2015, and 2017; the reason for this selection is firstly, the IO table in the CEADS database is updated once at a fixed time, and the latest update was in 2017; secondly, this research focuses on the dynamic coupling characteristics of WEC and the trend of the transfer path in the Yellow River Basin, and in the long time scale, the economic structure of the Yellow River Basin and the basic indexes such as industrial characteristics do not change significantly. In the long time scale, the economic structure and industrial characteristics of the Yellow River Basin have not changed significantly, and the dynamic evolution law and transfer trend in the results of the study are also applicable to the present. (2) Since there are only 30 provinces in China in the CEADS database, only the WEC trade balance among 30 provinces in China is analyzed in this paper. (3) According to the research focus of this study and the industry classifications already studied in the National Economic Industry Classification (GBT 4754-2017) [39], the 42 sectors in China’s IO table are summarized into 13 sectors (

Table 2. Comparison of 13 major industries with 42 industries.

Serial Number	14 Departments	42 Departments
S1	agriculture	agricultural, forestry, and fishery products and services
S2	extraction industry	coal mining products
		oil and gas extraction products
		metal ore mining products
		non-metallic and other mineral extraction products
S3	light industry	food and tobacco
		fabrics
		textile, clothing, shoes, hats, leather, down, and their products
		woodwork and furniture
		paper, printing, and educational and sporting goods
S4	petroleum, coking products, and processed nuclear fuels	petroleum, coking products, and processed nuclear fuels
S5	chemical products	chemical products
S6	non-metallic industry	non-metallic mineral products
S7	metal industry	metal smelting and rolling products
		metalwork
S8	machinery and equipment manufacturing	general equipment
		specialized equipment
		transportation equipment
		electrical machinery and equipment
		communications equipment, computers, and other electronic equipment
		instrumentation
S9	other industries	other manufacturing products
		scrap
		metalwork, machinery, and equipment repair services
S10	energy supply	production and supply of electricity and heat
		gas production and supply
		water production and supply
S11	construction	construction
S12	transportation, storage, and postal services	transportation, storage, and postal services
S13	services sector	wholesale and retail
		information transmission, software, and information technology services
		financial
		real estates
		leasing and business services
		scientific research and technical services
		water, environment, and utilities management
		residential services, repairs, and other services
		teaching
		health and social work
		culture, sports, and recreation
		public administration, social security, and social organizations

).

4. Results and Analysis

4.1. Analysis of WEC Dynamic Coupling in the Yellow River Basin

4.1.1. Evolution of WEC Footprint of the Agricultural Sector

Figure 4 analyses the dynamic interaction characteristics of the WEC system, and it can be seen that the agricultural sector in the Yellow River Basin has a high degree of coupling between W–E and E–C. Specifically, the agricultural sector in the Yellow River Basin is characterized by a “water–energy”, “energy–carbon ”, and “water–carbon ” sector, with the latter exhibiting relatively weaker coupling between water and carbon. The agricultural sector in the Yellow River Basin exhibits a high level of water consumption, with the irrigation of farmland, drinking water for livestock, and water for fisheries representing the primary avenues for water utilization. The region is predominantly arid and semi–arid, marked by low annual precipitation and constrained water resources. Additionally, the maturation of crops relies on artificial light irrigation, further contributing to the sector’s significant water consumption. The intensity of energy consumption in the agricultural sector (S1) is 0.25 million tons of standard coal, surpassed only by other industries (S9), which is 0.16 million tons of standard coal. Such a level of energy consumption is relatively low when compared to the 13 sectors. The primary energy consumption in the agricultural sector is for harvesting, sowing, irrigation, heat preservation, refrigeration, lighting, and other processes involving agricultural machinery and equipment. Additionally, energy is consumed for drying, crushing, and other agricultural product-processing machinery and facilities. In comparison to other sectors, energy consumption in the agricultural sector is relatively small. However, it is a sector with high energy intensity. Carbon emissions from the agricultural sector (S1) were 37.90 Mt in 2017, representing 1% of the total carbon emissions. This is a significant discrepancy from the energy supply sector (S10), which had the highest carbon emissions at 1715.80 Mt. It is also at a lower level among the 13 sectors. The primary sources of carbon emissions from the agricultural sector are tailpipe emissions from agricultural machinery, methane emissions from rice paddies, nitrous oxide emissions from the application of nitrogen fertilizer on farmlands, methane emissions from animal intestines, and methane and nitrous oxide emissions from livestock and poultry manure, with direct emissions being relatively low.

Analyzed from the perspective of footprint evolution, the agricultural sector has shown a significant increase in water use, an annual decrease in energy consumption, and a steady fluctuation in carbon emissions. Energy consumption decreased by 0.19 million tons of standard coal in five years, with an annual reduction rate of 43%. On the one hand, the water consumption of the mining industry (S2), petroleum coking products and nuclear fuel processing products (S4), the non-metallic industry (S6), and the machinery and equipment manufacturing industry (S8) shows a decrease. Under the national policy of high-quality development and conservation and intensive use of water resources, the economic structure of the Yellow River Basin provinces has begun to shift from heavy industries such as coal mining, petroleum coking products, and nuclear fuel processing, which consume large amounts of water, to service industries, light industries, and other industries. The reduced consumption of water resources in other sectors has led to an increase in available water resources in the agricultural sector. Conversely, the energy intensity (energy consumption per unit of water) of agricultural water resources (S1) has exhibited a gradual decline. The advancement of agricultural large-scale planting and processing technology, coupled with the implementation of energy-saving policies, has enhanced the energy utilization efficiency of the agricultural sector. Concurrently, agricultural water-saving technology has evolved towards greater energy conservation. In summary, the three elements of WEC in the agricultural sector of the Yellow River Basin demonstrate a downward trend in water-energy consumption, largely due to the implementation of policies such as optimized water resource allocation and energy-efficient water use technology. Additionally, the large-scale promotion of agricultural mechanization has contributed to a gradual decline in energy–carbon consumption. The large-scale dissemination of agricultural mechanization has resulted in an upward trajectory of “energy–carbon” consumption. Despite improvements in energy utilization efficiency, there is considerable scope for further reductions in agricultural emissions, particularly given the relatively low baseline levels of carbon emissions in this sector.

4.1.2. Intersectoral Analysis of Coupled WEC Elements

The volume of water flowing in the Yellow River Basin in 2012 differed significantly from that observed in 2015 and 2017. From Figure 5 it can be seen that 43% of the water consumed by the agricultural sector (S1) was allocated to the light industrial sector (S3), which constituted the primary downstream sector for water use in the agricultural sector. The consumption of water by the agricultural sector increased significantly between 2012 and 2017. Furthermore, its share of indirect water consumption increased substantially, becoming the primary supplier of this resource to the light industry (S3), services (S13), and chemicals (S5). This suggests that the rise in agricultural water consumption is largely attributable to indirect water resources consumption. Furthermore, the agricultural sector provides raw materials for a multitude of industries, thereby becoming the primary upstream sector for indirect water use. Agriculture (S1) serves as the energy demand sector for numerous other sectors. From 2012 to 2017, the highest shares of chemical products (S5) and the light industry (S3) accounted for 42% and 29% of the energy output of the agricultural sector, respectively. The indirect energy consumption of the agricultural sector necessitates the use of manufactured products from the chemical products sector, including pesticides and fertilizers, as well as manufactured products from the light industry sector, such as mulch and timber. Consequently, agriculture (S1) bears indirect responsibility for energy consumption in the chemical products (S5) and light industry (S3) sectors. The primary energy-consuming sector of the agricultural industry (S1) is the light industry (S3). Indirect energy consumption accounts for 47% of the energy output of the agricultural sector. This is due to the fact that a significant quantity of food, vegetables, livestock, and other agricultural products are transferred to the light industry sector for further processing. The transfer of carbon emissions is relatively uninterrupted, as agriculture (S1) represents one of the downstream sectors of the energy supply (S10). This is attributed to the fact that it engages in both direct and indirect consumption of energy sector products throughout the production process. Consequently, it shares the responsibility of an indirect consumer of carbon emissions from the energy supply sector. Conversely, the light industry sector (S3) is the primary contributor to carbon emissions from the agricultural sector, due to its reliance on raw materials derived from agriculture.

The supply sectors of the agricultural sector in the Yellow River Basin that synergize WEC factors include the light industry (S3), chemical products (S5), and energy supply (S10). These sectors bear the responsibility of indirect consumers for higher carbon emissions. The WEC factors in the agricultural sector are subject to inter-sectoral flows due to the supply of raw materials, processing, and production activities. Among them, the light industry sector plays the most significant role. The water flows between 2012 and 2017 demonstrated notable alterations, with the proportion of water consumption in agriculture representing a considerable increase in the sector’s indirect water consumption. Conversely, the flow of energy and carbon emissions showed a trend toward stabilization. In light of the preceding section’s findings regarding the gradual rise in the water utilization rate, it becomes evident that the expansion of water consumption within the agricultural sector is largely attributable to the sectoral indirect water consumption. Consequently, there is a pressing need for the industrial sector, particularly the light industry sector, to undergo a technological upgrade in order to enhance its water-saving capabilities. Furthermore, the current level of efficiency and effectiveness of industrial water use must be brought up to a higher standard.

4.2. MRIO-Based WEC Virtual Transfer Analysis

4.2.1. Virtual WEC Balance Analysis

The virtual WEC transfer from the agricultural sector in the Yellow River Basin refers to the amount of water and energy transfers from within the nine provinces of the Yellow River Basin that must be made to meet the final demand of provinces outside the nine provinces of the Basin, as well as the resulting transfer of carbon emissions.

Although the nine provinces and regions situated along the Yellow River rely on it, the arid and semi–arid climate with low annual precipitation means that the amount of available water resources in each province is limited. Consequently, the region is regarded as being more water–scarce overall. From Figure 6 it can be seen that virtual water transferred out of the Yellow River has been increasing annually, while the amount transferred in has fluctuated. Overall, the net transfer of virtual water out of the region is significantly greater than the net transfer in. This illustrates that the agricultural sector in the Yellow River Basin continues to bear the burden of being both a producer and a provider of agricultural products to other provinces. Meanwhile, a considerable volume of virtual water is transported to other provinces through trade, thereby exacerbating the water scarcity issue faced by the nine provinces within the Yellow River Basin. The virtual energy transfer in and out of the agricultural sector demonstrates a fluctuating trend, with a decline followed by an increase. In 2017, the virtual energy transfer in the agricultural sector reached 688.84 tons of standard coal, while the transfer out amounted to 2229.12 tons of standard coal. In consideration of the sector as a whole, the virtual energy transfer out of the agricultural sector is greater than the transfer in. The net virtual energy transfer out of the agricultural sector in 2017 was 1540.28 tons of standard coal. The agricultural sector acts as a producer in the balance of virtual energy trade within the nine provinces and regions along the Yellow River. Virtual energy from these regions is transported to other provinces through the agricultural sector’s trade activities. The virtual carbon emission transfer balance of the agricultural sector within the nine Yellow River provinces and regions mirrors the energy transfer balance, displaying a pattern of initial decline and subsequent increase over the period from 2012 to 2017. The transfer out is markedly larger than the transfer in, with a net transfer out of 2. The high net transfer out of the carbon emissions level in 2012, 2015, and 2017, has respective values of 5.37 Mt, 15.39 Mt, and 27.33 Mt. The nine Yellow River provinces and regions assume the role of producers in the virtual carbon emission trade balance.

Consequently, the agricultural sector in the nine provinces and regions along the Yellow River represents a prototypical coupled WEC production sector, facing the pressures of a coupled factor transfer in cross-regional trade. Given the constraints of the region’s development conditions, including water scarcity and fragile environmental carrying capacity, the agricultural sector in the Yellow River Basin continues to shoulder the responsibility of providing water and energy resources and mitigating carbon emissions. This imposes greater demands on the development of the agricultural sector in the provinces and regions along the Yellow River. The question of how to efficiently and intensively utilize the resources in the region and balance the WEC coupling has become an important factor constraining the high-quality development of the Yellow River Basin.

4.2.2. Spatial Characterization of Virtual WEC Transfer

In Figure 7, The distribution of virtual water transfer among provinces reveals that Xinjiang and other regions along the Yellow River in the agricultural sector receiving the largest volume, 670,800 cubic meters, accounting for 25% of the total transfer, are the primary recipients. Next are Heilongjiang and Hebei, which received 462,000 and 156,900 cubic meters, respectively. In the case of Jilin, the respective figures are 218,500 cubic meters, 462,000 cubic meters, and 156,900 cubic meters. It is assumed that these resources are to be transferred to Beijing, Tianjin, Shanghai, and other developed areas.

Regarding virtual energy transfer in and out of the agricultural sector of the nine provinces and regions along the Yellow River, which are engaged in regional bilateral trade, the virtual energy primarily flows to the coastal provinces, including Zhejiang, Guangdong, and Jiangsu. Concurrently, a considerable volume of virtual energy originates from Hebei, Heilongjiang, Xinjiang, and other provinces, contributing to a more extensive energy trade. However, with regard to the absolute value of the transfer in and out, the virtual energy outflow is greater than the transfer in. This indicates that the agricultural sector of the Yellow River Basin is a net exporter of virtual energy and bears consumer responsibility for developed regions such as Beijing, Tianjin, Shanghai, and Guangdong.

The transfer of virtual carbon in the agricultural sector of the Yellow River Basin does not align with the virtual energy transfer law in the context of regional bilateral trade. Heilongjiang is the primary province among the nine provinces and regions along the Yellow River where virtual carbon is transferred in, representing 20% of the total transfer volume. Additionally, the provinces of Hebei, Jilin, Zhejiang, and Xinjiang also contribute a notable amount of virtual carbon transferred into the aforementioned nine provinces and regions through trade. The provinces and regions along the Yellow River that are responsible for the transfer of virtual carbon emissions are primarily developed provinces and municipalities, including Beijing, Tianjin, Jiangsu, Zhejiang, Guangdong, and Chongqing. These provinces and regions act as consumers of the transferred carbon emissions from the nine provinces and regions along the Yellow River.

Overall, the virtual WEC transfer in the Yellow River Basin exhibits a clear path dependence, demonstrating a trend of flowing from less developed regions to developed ones. The coupling intensity decreases from developed regions to less developed ones, exacerbating the issue of inter–regional development imbalance. The four provinces of Hebei, Jilin, Heilongjiang, and Xinjiang engage in frequent virtual WEC transactions with substantial transfer volumes. In these provinces, the outward transfers consistently exceed the inward transfers. Despite facing water resource scarcity, they shoulder the responsibility of carbon emission transfer from the nine provinces and regions along the Yellow River. The inter–provincial disparities stem from the notable differences in industrial and energy structures between developed and less developed regions. In less developed provinces, the industrial structure is relatively homogeneous, predominantly centered around high energy and high water-consuming resource extraction and primary processing industries. The water use efficiency is generally low, causing virtual carbon emissions to flow inward. Conversely, developed provinces are dominated by high-end manufacturing and service industries, boasting a more diversified and innovative industrial structure. They have higher water use efficiency and are actively shifting towards cleaner energy in terms of energy structure, leading to the outward transfer of virtual carbon emissions. As a result, significant inter–provincial disparities have emerged.

To thoroughly explore the WEC linkages among the provinces and regions along the Yellow River and other regions, and to facilitate synergistic governance, this study carried out an analysis of WEC transfers between the agricultural sector in the Yellow River Basin and typical development regions in China. The Yangtze River is the largest river in China. The WEC linkages between the provinces along the Yangtze River and those along the Yellow River are of great significance. (Given that both the Yellow River and the Yangtze River flow through Qinghai and Sichuan provinces, these two provinces are excluded from the analysis of the provinces along the Yangtze River). In general, the Yangtze River Basin is closely related to the agricultural sector of the Yellow River Basin in terms of WEC. It can be described as a region of “net inward transfer of water resources and net outward transfer of energy and carbon emissions” for the agricultural sector of the Yellow River Basin. The abundant water resources of the Yangtze River Basin are transferred to the Yellow River Basin provinces through the trade of the agricultural sector, thereby providing a substantial quantity of water resources to the water-scarce Yellow River Basin provinces. Concurrently, energy from the Yellow River Basin is transferred into the Yangtze River Basin through trade in the agricultural sector, supplying virtual energy for the development of the agricultural sector in the Yangtze River Basin. Furthermore, this transfer of energy assumes indirect responsibility for the carbon emissions of the Yellow River Basin.

The Beijing–Tianjin–Hebei urban agglomeration is geographically bordered by the middle and lower reaches of the Yellow River Basin, and trade exchanges are close. Therefore, a systematic study of the WEC transfer patterns between the two regions will facilitate the coordination of resource distribution and industrial layout. In general, the Beijing–Tianjin–Hebei region can be considered to belong to the category of regions exhibiting a net transfer out of water resources, a net transfer in of energy resources, and a net transfer out of carbon emissions within the agricultural sector of the Yellow River Basin. The Yellow River Basin serves as a significant energy base in China. The transfer of virtual energy from the agricultural sector may be attributed to the concentration of downstream agricultural industry chain activities, such as agricultural product processing and agricultural product terminal retailing, within the Beijing–Tianjin–Hebei region. Concurrently, the agricultural sector within the Yellow River Basin transfers a considerable quantity of virtual water to the Beijing–Tianjin–Hebei region, thereby assuming responsibility for virtual carbon emissions. The Beijing–Tianjin–Hebei region is particularly notable for its imbalance of WEC transfer. The Yellow River Basin, which is scarce in water resources and relatively rich in energy, still requires the transfer of virtual water and virtual energy to meet the production demands of the agricultural sector and to bear the responsibility of being a consumer of virtual carbon emissions. This imbalanced resource consumption not only presents a novel challenge to the efficient deployment of resources between regions, but also introduces new requirements for industrial transformation and high-quality development in the Yellow River Basin.

4.3. Analysis of WEC Ecological Network Control in the Agricultural Sector in the Yellow River Basin

Figure 8 illustrates the control relationship of the WEC ecological network for each industrial sector within the Yellow River Basin. With respect to water resources, the dependence of the agricultural sector (S1) on other sectors is relatively low and is diminishing annually. At this juncture, the agricultural sector remains reliant on petroleum coke products and nuclear fuel processing products (S4), and energy supply (S10) to a certain extent. The modernization of the agricultural sector and the large-scale adoption of intelligent agricultural machinery and facilities have contributed to this interdependence, particularly with regard to coke products and nuclear fuel processing products, as well as the energy supply sector. The agricultural sector occupies a central position within the water factor network, exhibiting a tendency towards increasing control over other sectors, which subsequently declines. The promotion of water-saving technologies in the agricultural sector may be a contributing factor to this phenomenon.

With respect to the energy factor network, there is a notable and growing interdependence between the agricultural sector and energy-related industries. Specifically, the dependence of the agricultural sector (S1) on the extraction industry (S2), petroleum coking products and processed nuclear fuel products (S4), chemical products (S5), energy supply (S10), transportation (S12), and the service industry (S13) exceeds 80%. With the exception of the extractive industries, the dependence on all related sectors is increasing. The degree of control exerted by the agricultural sector over other sectors is relatively stable and has not exhibited significant fluctuations. In particular, the controllability of the non-metal industry (S6), machinery and equipment manufacturing (S8), and construction (S11) is all above 90%. However, the trend of change over time is not significant. Thus, the agricultural sector in the energy factor network is becoming increasingly dependent on other sectors. Agricultural modernization initiatives, including the mechanization of agriculture, the scientificization of production technology, the industrialization of agriculture, and the informatization of agriculture, may be contributing factors to this strengthening dependence.

In regard to the regulation of carbon emissions within a network context, the agricultural sector exhibits a pronounced interdependence with both the heavy industry and transportation sectors, exhibiting minimal variation. Specifically, the agricultural sector (S1) is closely intertwined with the extraction industry (S2), petroleum coking products and processed nuclear fuel products (S4), the metal industry (S7), machinery and equipment manufacturing (S8), the energy supply (S10), and transportation (S12). Consequently, the reduction in carbon emissions from the agricultural sector can be achieved by promoting the reduction in carbon emissions from the relevant dependent industries. The agricultural sector (S1) exerts considerable influence over a number of other economic activities, including the light industry (S3), machinery and equipment manufacturing (S8), other industries (S9), construction (S11), and services (S13). Consequently, the reduction in carbon emissions from the agricultural sector will positively affect the emission reduction targets of these sectors.

In general, the agricultural sector is dependent on the energy supply sector in the water factor network, which is incrementally dependent on the energy consumption sector in the energy factor network, and strongly dependent on the heavy industry and transportation sectors in the carbon factor network. In the context of the dependency–control relationship, the agricultural sector can be considered a sectoral dependency in the WEC network. Additionally, it occupies a central position in the water resources network, exerting a significant control over the water consumption patterns of other sectors. This suggests that the agricultural sector plays a significant role in the transfer of factors within the WEC network. Factors flow through the network using sectoral products as a medium, and the agricultural sector prioritizes and controls the transfer of factors within the entire water network.

5. Conclusions

This study delved into the dynamic coupling characteristics and spatial transfer paths of WEC in the agricultural sector of the Yellow River Basin from the perspectives of “mechanism analysis, feature revelation, and cause clarification”. The main conclusions are as follows:

(1) The agricultural sector in the Yellow River Basin exhibits strong coupling between W–E and E–C, but weak coupling between W–C. Over time, it shows high sensitivity to water resources within the WEC system. Owing to the development trend of optimizing water resource allocation and the widespread adoption of energy-saving water utilization technologies, the dynamic coupling characteristics between W–E in the Yellow River Basin’s agricultural sector are on a downward trend. Meanwhile, the large-scale promotion of agricultural mechanization leads to an upward trend in the dynamic coupling characteristics of E–C. In the future, continuous attention should be paid to the integration of water resources and energy consumption. Water resources should be taken as the core of integration to enhance energy utilization efficiency and the carbon reduction potential of water use technologies.

(2) In the context of WEC cross-regional coupled transfer, the agricultural sector of the Yellow River Basin is subject to the pressure of a factor coupled transfer, with the net virtual water transfer exhibiting an upward trend on an annual basis. Furthermore, the inward and outward transfers of virtual energy and carbon demonstrate a decrease initially, followed by an increase. In the analysis of the coupled transfer of national key regions, the Yellow River Basin as a whole assumes the producer responsibility of WEC transfer. The analysis of factor flows reveals a clear path dependence in virtual WEC transfer in the agricultural sector, demonstrating a trend of transfer from less developed to more developed regions. The industrial and energy structures of provinces have been identified as contributing factors to inter–provincial differences.

(3) The agricultural sector is pivotal in articulating upstream and downstream sectors within the sectoral flow of WEC in the Yellow River Basin. The primary upstream sectors include the light industry, chemical products, and energy supply, among others. These sectors contribute resources to the agricultural sector and concurrently generate substantial carbon emissions. The predominant downstream sector is the light industry. Concurrently, the agricultural sector performs a pivotal function in factor transfer within the WEC network, thereby occupying a central position within the control framework of the water factor network. It is evident that the upstream sector of the resource supply exerts a direct influence on the synergistic relationship of “water–energy” coupling in the agricultural sector, while the downstream sector of resource consumption indirectly affects the constraint relationship of WEC coupling in the agricultural sector.

6. Discussion and Recommendations

This paper discussed the WEC coupling characteristics, spatial and temporal transfer distribution, and the analysis of the sectoral flow mechanism of the agricultural sector in the Great River Basin, represented by the Yellow River Basin. This study revealed that the agricultural sector in the Great River Basin, characterized by water resources as the primary constraint, exhibits a consistent footprint evolution in the dynamic coupling of WEC. The development of modern agriculture, marked by the adoption of large–scale cultivation and water–saving technologies, has contributed to ensuring food security to a certain extent. However, the resulting W–E and E–C couplings necessitate that river basin managers prioritize the synergistic security of the river basin’s economy and ecology and achieve a balance between the synergistic relationship between ecological carbon sinks, energy, and food through more rational water allocation (economy–ecology).

Furthermore, the agricultural sector in the Great River Basin tends to be subject to greater levels of unfairness due to the limitation of development conditions. This study concludes that the less developed regions with a high degree of WEC mutual coercion must bear more producer responsibilities and transfer more virtual resources to the developed regions in exchange for economic benefits, and the sustainable development of the basins is restricted. Concurrently, due to the characteristics of agricultural sector producers, WEC coupling is associated with upstream and downstream sectors of the industrial chain, and the constraints and control relationships demonstrate interdependence. Consequently, the level of inter–sectoral coordination and adaptability should be incorporated into the consideration of the sustainable development of the watershed.

Different from previous evaluative research articles on WEC systems, this paper focuses more on the exploration of WEC coupling mechanisms and the analysis of spatio-temporal characteristics. In the field of WEC system research, scholars have traditionally concentrated on constructing WEC system adaptive evaluation subsystems and indicator systems from different dimensions based on the theory of complex adaptive systems. They have also applied comprehensive co-evolutionary models to evaluate the adaptive levels and grades of regional WEC systems. However, there is a paucity of research exploring the coupling mechanism and evolution law of the WEC system itself, and the intrinsic connection between the characteristics of WEC coupling in the economic zone and sustainable development goals is not explored from the evaluative indicators. The coordination index is combined to learn about the degree of coordination between various subsystems and the adaptability of the WEC system, to explore the spatial correlation characteristics of the adaptability of the WEC system, and lacks the analysis of the spatial transfer paths under the consideration of virtual trade, which fails to make clear the correlation between the WEC coupling, the ecosystems, and economic development.

Therefore, this paper pays more attention to the dynamic coupling characteristics of WEC, trans-regional transfer paths and inter-sectoral constraints and control relationships, and clarifies the WEC bonding relationships and coupling mechanisms through the study, so as to provide a reference for the synergistic “nature–society–economy” elements of the large river basin for the goal of sustainable development, which will help to enhance the capacity of the river basin in terms of sustainable development and more secure food security. This will help the river basin to improve its capacity for sustainable, greener, and safer food security.

However, this study has its own research limitations. It focuses on the WEC coupling within the agricultural sector in the Yellow River Basin and extends the study of WEC factor flows between this sector and others, and between the Yellow River Basin and other regions. It operates under the assumption that China is a closed region; it does not consider trade activities between the nine provinces of the Yellow River Basin and foreign countries. This may lead to an underestimation of factor transfer in the region. The study uses the Yellow River Basin as a representative case for the agricultural sector in the broader Great River Basin, acknowledging the uncertainty and heterogeneity of this region. Extending the research scale and drawing more generalized conclusions are worthy directions for future research.

In light of the conclusions and discussions presented in this paper, the following recommendations are put forth for the coordination and sustainable development of the elements comprising the Great River Basin.

Firstly, it is vital to achieve synergies in development, resolve any existing conflicts, and promote a balanced WEC system. The agricultural sector within the Yellow River Basin has been effective in reducing energy consumption and controlling carbon emissions. However, efforts to improve the utilization of water resources and reduce the amount of water used are clearly insufficient. To address these discrepancies, the upstream, midstream, and downstream regions must be anchored to distinct endowments of resources and industrial structures, fostering a harmonized integration of resource elements through effective coordination. The construction of a low–carbon and water–saving energy system would reduce the dependence of energy activities on water resources, thereby better adapting to the current development requirements of a low-carbon society. Furthermore, protecting the ecological environment and promoting the interaction of ecosystems and water systems would ensure a balanced low-carbon energy structure and climate-resilient agricultural system, thus promoting the high–quality development of the basin.

Secondly, it is imperative to elucidate the positioning, establish a compensation mechanism, and advocate for the equitable distribution of regional resources. The virtual transfer characteristics of the WEC system in the Yellow River Basin must be given full consideration, along with the role of producers. It is recommended that an intra–regional carbon emission responsibility system and horizontal compensation mechanism be constructed according to local conditions. The exploration of intra–regional carbon compensation programs by provinces and regions within the Yellow River Basin is recommended prior to the introduction of the national carbon compensation mechanism, with due consideration given to factors such as energy and water resources. A horizontal compensation mechanism should be explored, according to the role and extent of the transfer of resources into and out of the region. This approach will incentivize the rational flow and efficient allocation of resources, providing a model for the promotion of the national unified carbon compensation mechanism.

Thirdly, the focus should be on strengthening technology and building systems to ensure the sustainable development of river basins. In terms of water resource utilization, there will be a continuation of the research and development and promotion of highly efficient water–saving irrigation technologies, such as drip irrigation and sprinkler irrigation, as well as intelligent management systems for agricultural water use, so as to realize the fine management and efficient deployment of agricultural and energy water use. In terms of energy system construction, there will be a vigorous development of low–carbon and water–saving clean energy sources, including solar and wind energy. There will be a gradual reduction in the dependence of agricultural production and energy activities on traditional high water–consuming energy sources, in line with the development needs of a low–carbon society. In terms of ecological environmental protection, there will be an enhancement of the protection and restoration of the ecosystems of the Yellow River Basin, with the synergistic effects of the ecosystems and the water system being brought into full play. Furthermore, a low–carbon energy structure and a climate–resilient agricultural production system will be constructed, so as to establish a solid foundation for the high–quality sustainable development of the Yellow River Basin.