Introduction

With the intensification of human activities and global climate change, water resources shortage, energy depletion, high carbon emission and ecological environment deterioration have become major threats to the multi-dimensional sustainable development of ESE (economy-society-ecological environment) in the YRB (Yellow River basin) and even in China1. In order to achieve the dual carbon goals, the YRB as the core area of China’s “Three Zones and Four Zones” ecological security barrier, faces the contradiction between energy transformation and ecological governance. Ensuring water resources and energy security, reducing carbon emissions, restoring the ecological environment and coping with global warming have become urgent needs for global sustainable development.

Water resources are the source of life, the basis of ecology and the basis of production. The uneven distribution of water resources in time and space and the uncoordinated allocation of water resources in the YRB directly determine the sustainability of agricultural irrigation, industrial water use, residential life and ecological base flow2. Energy is the engine and power source of social and economic development, and rational use of energy can promote coordinated economic growth3. The management of carbon emission and carbon sink is related to regional climate stability and ecological balance. The ecological environment is related to human well-being and is the ultimate bottom line of sustainable development. When the four systems of water resources, energy, carbon and ecological environment exist alone, they are the natural systems that human beings rely on for survival. In real life, they interact with each other. The WECEc coupling system is a highly correlated complex system. The sub-systems achieve sustainable conversion and dynamic balance through material flow, energy conversion and functional interaction, which not only cooperate with each other but also cause chain contradictions4. Therefore, a comprehensive and precise comprehension of the WECEc coupling system and its intricate interrelations within the YRB is of paramount practical significance. Such understanding is instrumental in elucidating the complex interactions among multi-dimensional elements, discerning critical barriers impeding system development, and thereby facilitating the formulation of evidence-based strategic policies tailored to the region’s sustainable development objectives5.

In recent years, scholars have conducted extensive research on regional development under the influence of resource factors in different research regions from different perspectives. For the coupling of multiple subsystems, most of them focus on the coupling of two or three subsystems. Some scholars used a recursive dynamic calculation of the general equilibrium model to investigate the coupling effect of China’s energy tax and water tariff policies6. Some people used the water evaluation and planning (WEAP) platform to study the energy consumption, water storage and water source structure of Beijing from 2001 to 20197. And a comprehensive investigation on the relationship between residential water use, energy structure, and carbon emissions in urban areas8. Base on water-energy-carbon (WEC) calculation of different land use types, someone analyzed the impact of urbanization on the WEC relationship system in Zhengzhou9. Someone established a water-energy-carbon (WEC) analysis framework for urban ecological transformation in the Beijing-Tianjin-Hebei (BTH) region10.

For the method used to study the coupled system, someone constructed multiple future change scenarios that couple climate and socio-economic development, and developed a multidimensional land use spatial optimization (LUSO) model related to water-energy-carbon11. Based on the coupling coordination evaluation model and projection tracking model, the coupling coordination scheduling (DCC) of water-energy-food-ecology (WEFE) was analyzed12. Estimation of spatiotemporal changes in provincial WEF carbon emissions in China from 2011 to 2020 using an improved WEF correlation index (WEFNI) and land use carbon emission assessment method13. Quantitative assessment and spatiotemporal evolution analysis of the vulnerability of water-energy-food (WEF) ecological relationships in the Yangtze River economic belt using SPA-TOPSIS and ArcGIS 10.714.

On the coupled system study of the YRB, someone proposed a theoretical framework for the relationship between water-energy-food (WEF) and poverty15. Overview of the relationship between water-energy-carbon (WEC) and value flow from the perspective of regional trade in some studies16. A study constructed a resource dependent framework between WLE and LCD using non collinear indexing systems and multiple linear regression17. And the evaluation level, coupling status, and coupling coordination status of industrial water-energy-CO2 (WEC) in the YRB from 1999 to 201918. Establishing an indicator system for the water-energy-food (WEF) system, and analyzing the coupling relationship of the WEF system using coupling coordination and decoupling models19.

In summary, the academic community has conducted extensive research on the coupled development of regional multi-subsystems driven by resource factors, covering typical systems such as water-energy-food (WEF) and water-energy-carbon (WEC). In terms of research methods, coupling coordination degree models, spatio-temporal analysis methods, and system modeling techniques have been widely applied to quantify the interactions and dynamic evolution among systems. Regarding studies on the YRB, although some scholars have explored from perspectives such as resource dependence, value flow association, and poverty relations, most of the research is limited to the coupling analysis of a few subsystems, and there is a lack of systematic identification of the obstacles to coupled and coordinated development. Compared with regions like the Yangtze River Economic Belt and the Beijing-Tianjin-Hebei region, there is still a significant gap in the holistic research on the WECEc system in the YRB. It is urgent to establish a multi-dimensional evaluation system, deeply analyze the coupling coordination mechanism and key constraints of the system, and provide scientific support for ecological protection and high-quality development of ESE in the basin20. Focusing on the WECEc comprehensive system in the YRB, this paper selects 2013–2022 as the research period and conducts the following studies: Firstly, the systematic characteristics of water, energy, carbon and ecological environment and the regional industrial structure were comprehensively considered, and the comprehensive evaluation index system of WECEc system was constructed, and the CAI (Comprehensive Assessment Index) was calculated by using EWM-AHP (Entropy weight method and analytic hierarchy process) combined weights. Secondly, the comprehensive development trend of WECEc system in the YRB from 2013 to 2022 is analyzed, and the dynamic change trend of the system development level is explored from the time, space and subsystem levels respectively. Thirdly, the CCD (Coupling Coordination Degree) model is used to explore the coupling coordination types of the four-dimensional WECEc system, determine the evaluation threshold, point out the CCD level, and analyze the spatio-temporal evolution trend of the CCD from the perspective of basin and province. Finally, the OD (Obstacle Degree) model is adopted to identify the obstructive factors affecting the coordinated development of the WECEc system.

This study innovatively establishes a four-dimensional WECEc system for the YRB, integrating water and energy resources, carbon emissions, and the ecological environment to investigate regional coordination. By combining objective weights through the Minimum Entropy Method, we adopt the novel application of EWM-AHP for weight determination. This methodological advancement can significantly enhance the scientific validity of our findings on CCD and OD.

Materials and methods

Research framework

To study the relationship between WECEc and high-quality development of ESE in the YRB, we propose an interactive framework for the coordinated development of the two in Fig. 121. From the perspective of the influence of WECEc on the high-quality development of ESE, the coupling and coordination degree of WECEc system in the YRB is analyzed with the high-quality development as the result orientation. The WECEc system was classified layer by layer into target layer (WECEc system development level), criterion layer (water, energy, carbon, ecological environment systems), dimension layer (supply and demand, resources and consumption, carbon storage and carbon emission, protection and exploitation) and index layer (26 specific indicators), and the coupling coordination degree evaluation framework was constructed. Considering the relationship between the nine provinces and the development of the YRB, the coupling coordination study focusing on the nine provinces and regions can clearly quantify the comprehensive development level of the YRB.

Fig. 1
figure 1

The analytical framework of the WECEc system in the YRB.

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Study area

As the second longest river in China, the Yellow River not only carries China’s important hydropower resources, but also the ecological security barrier in China. Its sustainable development is related to the realization of China’s water resources, energy, carbon cycle, ecological security and dual carbon goals22. The main stream of the Yellow River is mainly distributed in northwest China and North China, with a basin between 96° ~ 119° E and 32° ~ 42° N. It originates from the northern foot of Bayankala Mountain on the Qinghai-Tibet Plateau, flows through nine provinces (autonomous regions) of Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan and Shandong, and finally flows into the Bohai Sea, with a total length of 5,464 km. The river basin covers about 795,000 square kilometers, covering 8.3% of the country’s land area. The annual average precipitation is about 476 mm, concentrated in June to September, prone to extreme weather such as heavy rains and floods. The main stream of the Yellow River passes through the Tibetan Plateau and mountains in the upper reaches, the Loess Plateau in the middle reaches, the alluvial plain of the Yellow River in the lower reaches and the hills in the middle reaches of Shandong Province, spanning the three major topographic terraces in China, covering multiple ecosystems such as the alpine region, the plain region and the estuarine wetland (Fig. 2)23. Although the upper reaches of the Yellow River are the core areas of water conservation, problems such as glacier retreat and grassland degradation caused by overgrazing have weakened the upper reaches of water conservation capacity. Water resources in the middle and lower reaches are relatively abundant, but the rapid development of plain agriculture and urban agglomeration has greatly consumed water resources. In terms of energy security, Shanxi and Inner Mongolia, as the core coal producing areas, have great potential for wind and light resources, and the increasing energy consumption mainly based on coal makes the regional carbon emission intensity still higher than the national average level. In the carbon cycle, high-carbon industries in the basin have been transformed into low-carbon industries, while high-energy-consuming industries have expanded along with the economic development, which has caused carbon emission problems affecting the green and sustainable development of the YRB24. In terms of ecological security, although the area of forest and wetland in the YRB was relatively abundant, soil and water loss, desertification and biodiversity loss became increasingly difficult20. As an important ecological barrier and grain production base in China, the integrated development level of water resources, energy, carbon and ecological environment in the nine provinces of the YRB is of great significance to regional sustainable development.

Fig. 2
figure 2

Location of the study area. The base map was obtained from the standard map service system of the ministry of natural resources of China, map review number: GS(2022)1873. The maps were generated using ArcGIS software version 10.7 (https://www.arcgis.com/index.html).

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Data collection

The water resources index data of the nine provinces and regions of the YRB from 2013 to 2022 are derived from China Water Resources Statistical Yearbook, Urban Water Supply Statistical Yearbook and related provincial water resources communiques. Energy production and consumption data were obtained from the China Energy Statistical Yearbook. Carbon cycle assessment indicators were obtained from China environmental statistical yearbook and China carbon emission database (CEADs). The ecological and environmental indicators come from the data of China Urban Statistical Yearbook. During the study period, the missing data in nine provinces were supplemented by linear interpolation method.

Construction of evaluation index system

The WECEc system is a complex system in which the subsystems of water resources, energy, carbon and ecological environment interact with each other. Water resources are the basic resources for economic and social development and ecosystem maintenance in the YRB. The accessibility and quality of water resources exert a profound influence on energy development, carbon emission management, and the well–being of the ecological environment. They serve as the pivotal linchpin for achieving harmonious coupling and coordination within the system25. Energy is the driving force for regional economic and social development, and there is a close coupling relationship between energy production and water resources utilization. Meanwhile, structural optimization and efficiency improvement of the energy system have an important impact on promoting water resources conservation and carbon emission reduction of the system. Carbon cycle is a key process connecting natural ecosystem and human activities. The study of carbon cycle subsystem plays an important role in realizing the coordinated and sustainable development of water resources, energy and ecological environment system. As the basis for sustainable development of the YRB, ecological environment’s health status directly affects water resources availability, energy exploitation potential and carbon cycle balance, and is an important guarantee for system coupling and coordination.

The multi-dimensional indicators of WECEc system should be selected in accordance with the principles of scientificity, systematisms, dynamics and operability. The indicators should also objectively reflect the development of the subsystems of water resources, energy, carbon and ecological environment in the nine provinces and regions of the YRB. Secondly, the index data should be available and recognized by the public and scholars in the field26.

This paper takes the comprehensive development level of WECEc system as the target layer, divides four subsystems, and takes water resources subsystem, energy subsystem, carbon subsystem and ecological environment subsystem as the criterion layer. The water resources subsystem consists of two layers: supply layer and demand layer. The energy subsystem includes resource layer and consumption layer. The carbon subsystem consists of two levels: carbon storage and carbon emission. The ecological environment subsystem includes two levels: protection layer and exploitation layer. By reading literature and referring to the relevant studies of scholars, this paper finally selected a total of 26 representative indicators to construct a comprehensive evaluation index system for the WECEc system of the Yellow River Basin, including 1 target layer, 4 criterion layers, 8 dimensions layers and 26 specific indicators (Fig. 3). There are two types of indicators. Positive indicator (+) is an indicator that promotes the development of the system, while negative indicator (−) is an indicator that hinders the development of the system.

Fig. 3
figure 3

Evaluation index system.

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Indicator weight

Weights determined by entropy weight method

“Entropy” is a physical quantity used to measure the disorder of a system. The more different the values, the more disordered the system is, the more information it contains, and the more influence it may have on the results. Using the EWM (Entropy weight method) to calculate the weight of indicators, it is first necessary to standardize and normalize the indicators to eliminate the influence of different units on the results27. Formula (1) is used to standardize the positive indicators, and formula (2) is used to standardize the negative indicators28.

$$x{^{\prime}_{ij+}}=\frac{{{x_{ij}} - \mathop {\hbox{min} }\limits_{j} {x_{ij}}}}{{\mathop {\hbox{max} }\limits_{j} {x_{ij}} - \mathop {\hbox{min} }\limits_{j} {x_{ij}}}}$$
(1)
$$x{^{\prime}_{ij - }}=\frac{{\mathop {\hbox{max} }\limits_{j} {x_{ij}} - {x_{ij}}}}{{\mathop {\hbox{max} }\limits_{j} {x_{ij}} - \mathop {\hbox{min} }\limits_{j} {x_{ij}}}}$$
(2)

Where i and j are indicators and years respectively; \({x_{ij}}\) is the original data of index i in year j; \(x{^{\prime}_{ij+}}\),\(x{^{\prime}_{ij - }}\) is standardized index; it’s value ranges from 0 to 1.

Calculate index information entropy and weights29:

$${p_{ij}}=\frac{{x{^{\prime}_{ij}}}}{{\sum\limits_{{j=1}}^{m} {(x{^{\prime}_{ij}})} }}$$
(3)
$${E_i}= - \frac{{\sum\limits_{{j=1}}^{m} {\left({p_{ij}}\ln {p_{ij}}\right)} }}{{\ln m}}$$
(4)
$${G_i}=1 - {E_i}$$
(5)
$${W_{ei}}=\frac{{{G_i}}}{{\sum\limits_{{i=1}}^{n} {{G_i}} }}$$
(6)

Where \({p_{ij}}\) is the proportion of indicator i in year j; m is the number of observation periods (total years); \({E_i}\) is the index of information entropy; \({G_i}\) is entropy redundancy; \({W_{ei}}\) is the index weight; n indicates the total number of indicators.

Weights determined by analytic hierarchy process

As a multi-objective decision-making method, AHP (Analytic Hierarchy Process) establishes a hierarchical structure model to gradually determine the weight of factors at each level, laying a foundation for the comprehensive evaluation of a multi-level system30.

  1. (1)

    Construct hierarchical structure and judgment matrix. Following the established evaluation index system, the AHP hierarchy is successively divided into target layer, criterion layer, dimension layer and index layer.

The judgment matrix represents the relative importance of elements at one level relative to those at the previous level. The judgment matrix is constructed as follows31:

$${\mathbf{U}}=\left[ {\begin{array}{*{20}{c}} {{u_{11}}}&{{u_{12}}}& \cdots &{{u_{1n}}} \\ {{u_{21}}}&{{u_{22}}}& \cdots &{{u_{2n}}} \\ \vdots & \vdots & \ddots & \vdots \\ {{u_{n1}}}&{{u_{n2}}}& \cdots &{{u_{nn}}} \end{array}} \right]$$
(7)

Assuming that element A of the previous layer (layer A) is related to \({S_1},{S_2}, \ldots ,{S_n}\) in the next layer (layer B), the relative importance of \({S_i}\) and \({S_j}\) for the previous layer (layer A) is expressed as \({u_{ij}}\) ,and the value of \({u_{ij}}\) is determined by the method shown in Table 1.

Table 1 Scaling principles of AHP judgment matrix.
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  1. (2)

    Determine the order of importance. According to the judgment matrix, the eigenvector \({\mathbf{W}}\) corresponding to the maximum eigenvalue is obtained, which reflects the importance of the evaluated element, and its weight is32:

$${W_{ai}}=\frac{{\sqrt[n]{{\prod\nolimits_{{j=1}}^{n} {{u_{ij}}} }}}}{{\sum\limits_{{i=1}}^{n} {\sqrt[n]{{\prod\nolimits_{{j=1}}^{n} {{u_{ij}}} }}} }}$$
(8)
$${\mathbf{W}}={\left(\begin{array}{*{20}{c}} {{W_1}}&{{W_2}}& \cdots &{{W_n}} \end{array}\right)^T}$$
(9)

Where \({W_i}\) is the weight of indicators of each layer.

  1. (3)

    Consistency check33:

$${\lambda _{\hbox{max} }}=\frac{1}{n}\sum\limits_{{i=1}}^{n} {\frac{{{{({\mathbf{UW}})}_i}}}{{{W_i}}}}$$
(10)
$$C.I.=\frac{{{\lambda _{\hbox{max} }} - n}}{{n - 1}}$$
(11)
$$C.R.=\frac{{C.I.}}{{R.I.}}$$
(12)

Where \({\lambda _{\hbox{max} }}\) is the maximum eigenvalue of the judgment matrix; \({({\mathbf{UW}})_i}\) is the ith component of the matrix \({\mathbf{UW}}\); \(C.I.\) is the consistency index of judgment matrix; \(R.I.\) is a random consistency indicator and can be obtained from Table 2. \(C.R.\) is the consistency ratio of the judgment matrix; when \(C.R. \leqslant 0.1\), the judgment matrix was considered to have consistency, indicating that the weights of the indicators were reasonable. Otherwise, the judgment matrix must be adjusted and the above steps repeated until the condition is met34.

Table 2 Random consistency index values of the judgment matrix.
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Combinational weights determined by minimum entropy method

To avoid a single method and obtain one-sided weight values, this paper combines the subjective weight determined by AHP method and the objective weight determined by EWM method, and adopts the principle of minimum entropy to calculate the comprehensive weight of indicators to ensure the scientific and rational weight distribution (Fig. 4). The calculation formula is as follows:

$${W_i}=\frac{{\sqrt {\left({W_{ei}} \times {W_{ai}}\right)} }}{{\sum\nolimits_{{i=1}}^{n} {{W_{ei}} \times {W_{ai}}} }}$$
(13)

Where \({W_{ai}}\) is the subjective weight determined by AHP; \({W_{ei}}\) is the objective weight determined by EWM; \({W_i}\) is the comprehensive weight of indicators; n indicates the number of indicators.

Fig. 4
figure 4

Indicator weights. Note: In the figure, ‘S’ and ‘D’ respectively represent the ‘supply’ and ‘Demand’ of the Water subsystem, ‘R’ and ‘Co’ represent ‘Resource’ and ‘Consumption’ of the Energy subsystem respectively, ‘Cs’ and ‘Ce’ respectively represent ‘Carbon storage and sink’ and ‘Carbon emission’ of the Carbon subsystem, ‘P’ and ‘Ex’ respectively represent ‘Protection’ and ‘Exploitation’ of the Ecological environment subsystem.

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Comprehensive assessment index method

The CAI of each system reflects the comprehensive development level of the system. The higher the value, the higher the development level, and the lower the development level. The linear weighting model was used to calculate the CAI of the system35:

$${U_j}=\sum\nolimits_{{r=1}}^{q} {{U_r}=} \sum\nolimits_{{r=1}}^{q} {\left(\sum\nolimits_{{t=1}}^{p} {\left(\sum\nolimits_{{i=1}}^{z} {{W_i}{x_{ij}}'} \right)} \right)}$$
(14)
$$T=\alpha {U_1}+\beta {U_2}+\gamma {U_3}+\mu {U_4}$$
(15)

Where \({U_j}\) is the development level of WECEc system in yearj; \({U_r}\) is the development level of each subsystem; q is the number of criterion layers or subsystems of WECEc system; p is the number of dimensions contained in each criterion layer; z is the number of indicators in each dimension layer; T is the CAI of WECEc system; \({U_1}\), \({U_2}\), \({U_3}\) and \({U_4}\) represents the development level of water resources subsystem, energy subsystem, carbon subsystem and ecological environment subsystem respectively. \(\alpha ,\beta ,\gamma\) and \(\mu\) are the weight factors of the corresponding subsystems. This paper holds that the four subsystems are equally important, \(\alpha =\beta =\gamma =\mu =\frac{1}{4}\).

Coupling coordination degree model

“Coupling Degree” can reflect the degree of mutual influence between two or more systems, and use coupling degree to reflect the interaction effect between systems36. “Coupling Coordination Degree” further reflects the degree of benign coupling in the coupling between systems, which can better reflect the coordination status and quality of the system, and its change trend can effectively reflect the effectiveness and challenges of various regions in sustainable development37. In this paper, the CCD model is used to quantitatively reflect the interaction and synergistic effect of WECEc system, to explore the coupling relationship and development level of the system more directly. The steps of establishing the CCD model are as follows:

$$C=\sqrt[4]{{\frac{{{U_1}{U_2}{U_3}{U_4}}}{{{{\left[ {\frac{{{U_1}+{U_2}+{U_3}+{U_4}}}{4}} \right]}^4}}}}}$$
(16)
$$D=\sqrt {CT}$$
(17)

Where C is the coupling degree of WECEc system, \(0 \leqslant C \leqslant 1\), the greater the value of C, the closer the connection between systems; D is the CCD of the WECEc system. According to the value of D, the CCD of the system is divided into 6 levels (Fig. 5).

Fig. 5
figure 5

Classification of coupling coordination degree.

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Obstacle degree model

The coordinated development of WECEc system is affected by multiple factors. Since each factor has different influence on the system, based on the analysis of CCD, this paper introduces the OD model to diagnose the obstacle factors of WECEc system and analyze the obstacle factors affecting the CCD of the system. The calculation formula is as follows38:

$${L_i}=1 - x{^{\prime}_{ij}}$$
(18)
$${O_i}=\frac{{{L_i} \times {W_i}}}{{\sum\nolimits_{{i=1}}^{n} {{L_i} \times {W_i}} }} \times 100\%$$
(19)
$${V_t}=\sum\nolimits_{{i=1}}^{j} {{O_i}}$$
(20)

Where \({L_i}\) is the index deviation degree; \({O_i}\) is the OD of each single index to the CCD of WECEc system; \({V_t}\) is the OD of each subsystem to the coupling coordination degree of WECEc system; j is the number of individual indicators contained in each subsystem.

Results and discussion

Comprehensive development level of WECEc system in nine provinces of the YRB

Spatio-temporal evolution analysis

From the perspective of time, from 2013 to 2022, the regional average of CAI of WECEc system in the nine provinces and regions of the YRB increased from 0.3072 to 0.3805, with an increase rate of about 23.9%, showing a fluctuating upward trend in general, which is consistent with the research results of Shi and He (2024)39. This growth trend shows that the integrated management and improvement of the YRB in terms of water resources security, energy security, carbon emission reduction and ecological protection has achieved remarkable results. From 2020 to 2021, the average level of CAI rose from 0.3777 to 0.3989, an increase of 5.6%, reflecting the gradual effect of policies on ecological protection and high-quality development in the YRB in recent years (Fig. 6).

From the spatial perspective, the comprehensive development level of the nine provinces of the YRB has significant spatial heterogeneity among provinces. The CAI level of the middle and upper reaches of Sichuan, Inner Mongolia, Shaanxi, Shanxi and Shandong provinces has always been higher than the regional average of the nine YRB provinces, while the CAI value of the lower reaches of Qinghai, Gansu and Ningxia provinces has been below the average for a long time in this decade. Sichuan’s CAI increased from 0.4655 in 2013 to 0.5412 in 2022, an increase of about 16.3%. Despite the decline in 2022 compared with 2021, its CAI value is still in a leading position in the YRB. Inner Mongolia’s CAI increased from 0.4638 in 2013 to 0.5548 in 2022, an increase of about 19.6%. As an important province in the middle reaches of the YRB, the improvement of Inner Mongolia’s CAI is mainly due to the optimization of the energy industry and the promotion of ecological protection projects. Shaanxi’s CAI increased from 0.3595 in 2013 to 0.4527 in 2022, an increase of about 25.9%. The implementation of the strategy of “Ecological protection and high-quality development in the Yellow River Basin” has a certain promoting effect on the improvement of the CAI of Shaanxi Province. The CAI of Shanxi increased from 0.3066 in 2013 to 0.3902 in 2022, an increase of about 27.3%. As a major energy province, remarkable achievements in energy structure transformation and ecological restoration have promoted the comprehensive development of Shanxi Province year by year. Shandong’s CAI increased from 0.2673 in 2013 to 0.3594 in 2022, an increase of 34.5%. Shandong is located in the lower reaches of the YRB, and the rapid improvement of the CAI is mainly due to the modernization of agriculture and the optimization of energy structure. In contrast, the CAI of Qinghai, Gansu and Ningxia is relatively low, although the overall improvement, but the increase is relatively small. The CAI of the three provinces from 2013 to 2022 was 0.2865, 0.2535 and 0.1763 respectively, which were lower than the average of 0.3461 in the YRB, reflecting the problems of low utilization efficiency of key basic resources such as water resources and energy, insufficient carbon emission control and fragile ecological environment in the upper Yellow River region where the three provinces are located. There may also be a waste of resources or mismanagement.

From the perspective of spatiotemporal correlation, the differences in the comprehensive development level of the WECEe system among the nine provinces/autonomous regions in the YRB are the results of the joint action of resource endowments, industrial structures, and policy responses. In the temporal dimension, the significant improvement from 2020 to 2021 is closely linked to the dual carbon goals and the basin’s ecological protection strategy, reflecting the driving role of policies. However, the fluctuating trend also reveals the issue of policy dependence. Spatially, central and upper-reach provinces have led in development by relying on resource advantages and early-initiated industrial upgrading. Shandong, a lower-reach province, has achieved breakthroughs through the optimization of agricultural and energy structures. In contrast, upstream provinces such as Qinghai, Gansu, and Ningxia have lagged in development due to high water-consuming industries, inefficient energy utilization, and insufficient ecological compensation. This indicates that future governance of the basin needs to strengthen differentiated positioning and establish cross-regional resource allocation and benefit-sharing mechanisms to promote the basin’s transition from local optimization to overall synergy.

Fig. 6
figure 6

Change trend of the CAI value of the nine provinces of the YRB from 2013 to 2022.

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Analysis of subsystem development level

From the perspective of the water resources subsystem, the CAI of the water resources subsystem shows a fluctuating upward trend from 2013 to 2022. The average value of CAI of water resources subsystem of the nine provinces in the YRB increased from 0.1272 in 2013 to 0.1457 in 2022, an increase of about 14.5%. Among them, the CAI value of water system peaked at 0.1673 in 2021 and decreased slightly in 2022. This overall upward trend is due to the efforts made by provinces in recent years to improve the efficiency of water use according to local conditions, such as the improvement of water infrastructure in Sichuan province and the implementation of the Outline of the National Medium and Long-term Plan for Food Security in Henan Province, a major agricultural province. The development trend of the water resources subsystem in the past ten years is the most consistent with the overall development trend of WECEc system, which indicates that the improvement and promotion of water resources utilization capacity plays a crucial role in the benign development of WECEc system.

From the perspective of the energy subsystem, the CAI of this subsystem showed a slow and steady upward trend from 2013 to 2022. The regional mean of the energy subsystem increased from 0.0680 in 2013 to 0.0730 in 2022, an increase of about 7.4%, which is the least statistically significant of the four subsystems. Among the nine provinces in the YRB, Inner Mongolia Autonomous Region and Shaanxi Province are the major energy production and consumption provinces. The application of clean energy technology and optimization of energy structure in each province have played a certain role in promoting the improvement of regional energy security level. However, the sustained and rapid development of heavy industry, coal and chemical industry and other energy-consuming industries has slowed down the rise rate of the CAI of energy system. In the context of the dual carbon goals, the endeavor to restructure the energy system and optimize corresponding policies in the YRB remains a formidable and protracted challenge.

From the perspective of carbon subsystem, the CAI of this subsystem shows a steady upward trend between 2013 and 2022. The average CAI of the subsystem in the nine provinces of the YRB rose from 0.0779 in 2013 to 0.0992 in 2022, an increase of about 27.3%. Taking Shanxi Province and Shandong Province as examples, the CAI of carbon emission subsystem in Shanxi increased from 0.0414 in 2013 to 0.0556 in 2022, with an increase of about 34.3%. The continuous growth of total carbon emissions in Shanxi Province during this decade may be related to factors such as increased energy consumption and expansion of industrial activities. The CAI of Shandong’s carbon subsystem increased from 0.0441 in 2013 to 0.0525 in 2022. Shandong is an important industrial province in the lower reaches of the YRB, with heavy chemical industry (such as steel, chemical industry, building materials, etc.) accounting for a relatively large proportion and high carbon emission intensity, resulting in an increase in the overall carbon emission level. It also shows that the economic growth of Shandong Province still relies on the high-carbon model, and has not yet realized the decoupling of economic development and carbon emissions.

From the perspective of the ecological environment subsystem, the CAI of this subsystem showed a significant upward trend from 2013 to 2022. The mean value of ecological environment subsystems in the nine provinces of the YRB increased from 0.0341 in 2013 to 0.0625 in 2022, an increase of 83.3%. This shows that with the improvement of regional ecological environment and the implementation of the “Three-North Shelterbelt Project” and “Desertification Prevention and Control Project”, the overall optimization effect of ecological environment is obvious. The ecological environmental subsystem CAI of Qinghai Province and Gansu Province has increased by 51.6% and 100% respectively in the past ten years, which is inseparable from the relevant departments in the upper reaches of the Yellow River to effectively coordinate the integrated protection and systematic management of “mountains, rivers, forests, fields, lakes, grasses and sand”.

However, complex synergistic and restrictive relationships exist among the subsystems. The development of the water resources subsystem provides fundamental support for energy production and ecological restoration. For example, efficient water resource utilization can meet the cooling water demand in energy extraction processes, reducing pressure on the ecological environment. Conversely, improvements in the ecological environment subsystem help conserve and purify water resources, forming a benign cycle. However, the contradiction between the energy subsystem and the carbon subsystem is particularly prominent. Energy system growth driven by high-energy-consuming industries directly exacerbates carbon emissions, offsetting part of the achievements in ecological environment governance and indirectly impacting the water resources system, for instance, water pollution issues during energy extraction. Such imbalanced relationships among subsystems necessitate strengthening the concept of multi-system collaborative governance in the sustainable development planning of the YRB. It is essential to comprehensively promote the economical and intensive use of water resources, low-carbon transformation of the energy structure, precise control of carbon emissions, and protection of the ecological environment system, breaking down development barriers among subsystems and building a new development pattern of mutual promotion and symbiosis.

Development of CCD of WECEc system in nine provinces of the YRB

Spatial and Temporal distribution characteristics

In the time dimension, it is noted that from 2013 to 2022, the mean CCD of the basin gradually increased from 0.5061 to 0.5673, and the overall WECEc system of the YRB showed a steady growth trend, with an annual growth rate of about 1.2%. Among them, the growth rate of CCD from 2013 to 2016 was relatively low, with an average annual growth rate of about 1.1%; From 2017 to 2021, the growth of CCD accelerated, with an annual growth rate of 1.5%; In 2022, the overall CCD of the YRB decreased slightly, and this downward trend is related to the increased pressure on the ecological environment caused by economic development in some regions.

In spatial dimension, the CCD of Inner Mongolia, Sichuan, Shaanxi and other upstream provinces is generally high, and the coordination type is mostly “General Coordination”, forming a high-value agglomeration area of coupling coordination. This level of coordination reflects the better balance between ecological protection and economic development in the upstream regions, as well as the remarkable results achieved in ecological protection and economic transformation in these regions. The CCD of Shanxi, Gansu, Qinghai and other provinces located in the middle reaches is mostly at the medium level, and the coordination type is concentrated as the state of “Near Imbalance” gradually improved to “Primary Coordination”, and it is represented as the median accumulation area of coupling coordination. It shows that these regions have made some progress in coordinated development, but still need to be further optimized. In the downstream provinces of Shandong and Henan, although the CCD is low, most of them are of the “Near Imbalance” type, which is reflected in the accumulation area of low coupling coordination value, but the CCD has shown a steady rising trend in the past ten years, indicating that these regions are facing greater pressure on water resources, energy and ecological environment in their economic development. The improvement in coupling coordination of multidimensional systems is slow (Fig. 7).

From a comprehensive perspective of spatiotemporal evolution, the changes in the coupling coordination degree of the WECEe system in the YRB not only reflect the divergent development paths across regions but also mirror the periodic effects of policy implementation. Temporally, the accelerated growth period from 2017 to 2021 closely aligns with the policy cycle of strengthening ecological civilization construction and promoting green development during China’s “The thirteenth Five-Year Plan” (2016–2020), demonstrating that top-down policy design has significantly propelled the coordinated development of the systems. However, the brief decline in 2022 serves as a warning that extensive economic recovery may backfire on the ecological environment, underscoring the critical need for policy continuity and precision. Spatially, the gradient differences in coordination levels among the upper, middle, and lower reaches are deeply rooted in regional resource endowments. Upper-reach provinces, supported by abundant ecological resources and low development intensity, hold a natural advantage in ecological protection. Middle-reach regions, as energy and chemical industry hubs, exhibit slow coordination improvement due to lagging industrial transformation. Lower-reach areas, characterized by dense populations and industrial agglomeration, face the dilemma of resource-environmental carrying capacity nearing saturation. This spatial heterogeneity suggests that it is urgent to promote the formation of a development pattern of complementary advantages and coordinated progress in the entire basin.

Fig. 7
figure 7

Spatial and temporal distribution of CCD in nine provinces of the YRB. The base map was obtained from the standard map service system of the ministry of natural resources of China, map review number: GS(2022)1873. The maps were generated using ArcGIS software version 10.7 (https://www.arcgis.com/index.html).

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Provincial variation trend

Most of the nine provinces showed a steadily improving form of CCD (Fig. 8). Among them, Inner Mongolia Province, which has the highest mean value of CCD, presents a fluctuating growth trend. The coordination degree decreased by 3.87% from 2013 to 2015, and gradually increased at a growth rate of 2.53% every year from 2015 to 2021, reaching a peak of 0.7319 in 2021. However, the CCD decreased slightly in 2022. The potential negative impact of energy development and carbon emissions on water resources and ecological environment should be vigilant. The average CCD of Ningxia Hui Autonomous Region is 0.3941, which is the lowest among the nine provinces in the YRB, and its CCD value rises from 0.3753 in 2013 to 0.4071 in 2022, with an average annual growth rate of about 0.80%, which is related to the realistic basis of Ningxia’s shortage of water resources, high energy dependence and great pressure on ecological environment. Ningxia should strengthen the protection of water resources, promote the low-carbon energy structure, reduce carbon emissions, and strengthen ecological and environmental governance.

Fig. 8
figure 8

Variation trend of CCD in nine provinces of the YRB.

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Factors hindering the CCD of WECEc system in nine provinces of the YRB

According to the calculation results of OD, the regional mean represents the obstacle degree of the YRB. In this paper, the top 8 obstacle factors are selected and analyzed according to the order of OD of indicator layer from high to low (Fig. 9). In the YRB, the ranking of the top 8 degrees of influence on the CCD of the WECEc system is as follows: Total water resources > Per capita carbon storage > Coal production > Water production modulus > Carbon storage of forest and grass > Total energy production > Carbon sink of forest and grass vegetation > Domestic water consumption.

Fig. 9
figure 9

Heat map of obstacle factors diagnosis for WECEc system in the YRB.

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The OD of total water resources is increasing by 1.5% per year and will peak in 2022 (14.33%). It is speculated that this is related to the fluctuation of precipitation and the increase of economic development demand. The increasing OD of this index directly reflects the worsening water shortage problem in the YRB, indicating the slow improvement of regional water resources utilization efficiency and the persistent water pressure in agriculture and industry. The OD of per capita carbon storage increased from 10.67% in 2013 to 10.71% in 2022, with a slow upward trend, indicating that the improvement of carbon sink capacity is limited. This is related to low vegetation coverage, overgrazing and development in ecologically fragile areas of the YRB. Coal mining and thermal power industry development in Inner Mongolia, Shanxi and other places will also lead to increased carbon emissions and weaken carbon sink capacity, thus affecting regional coordinated development. The annual average value of OD of coal production is 8.71%, and the annual growth rate of this variable is about 0.3%. This result may be due to the increase of carbon emission pressure caused by excessive dependence on coal economy in coal-rich areas. Meanwhile, the long coal industry chain involves high-carbon emission industries such as coking and thermal power, which will aggravate the pressure on the ecological environment subsystem. The OD of the water production modulus increased from 7.37% in 2013 to 8.21% in 2022, with an average annual growth rate of about 1.2%, reflecting the increasing pressure of water resources development year by year. The proportion of agricultural water in Henan Province is more than 60%, the development of water resources is over, the ecological water is squeezed, and the low water production modulus leads to insufficient irrigation water and aggravates the shortage of water resources. The OD of carbon storage and carbon sink capacity of forest and grassland vegetation on the system is about 7%, which is related to the degradation of grassland caused by overgrazing in the province with large animal husbandry, and the decrease of carbon sink capacity indirectly caused by the reduction of forest and grass vegetation caused by farmland expansion in the province with large agriculture. The OD of total energy production decreased from 6.5% in 2013 to 6.43% in 2022, showing a slow downward trend. The total output of energy industry in coal-dominated energy provinces is higher, and the hydropower development in Qinghai, Sichuan and other places is more intensive, which leads to the overall energy production as an obstacle factor affecting the coordinated development of regions. The annual average value of OD of domestic water is about 6.12%, which is related to the increasing demand for domestic water in densely populated areas, aging and imperfect water supply facilities in urban and rural areas (Fig. 10).

Fig. 10
figure 10

Sankey diagram of the contribution of major obstacle factors to the development of nine provinces in the YRB.

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Conclusion

Conclusion

In this paper, CAI, CCD and OD model are used to study the comprehensive development, spatio-temporal evolution of coupled coordination and blocking factors of WECEc system in the YRB from 2013 to 2022. The main research results include:

  1. (1)

    The comprehensive development level of WECEc system in the YRB has been significantly improved, but the development of subsystems is uneven and regional differences are prominent. The average CAI of WECEc system increased from 0.3072 in 2013 to 0.3805 in 2022, an increase of 23.9%, showing a fluctuating upward trend. The growth rate of the ecological environment subsystem was 83.3%, the growth rate of the water resources and energy subsystem was 14.5% and 7.4% respectively, and the CAI of the carbon subsystem was 27.3% annually. The CAI of the middle and downstream regions is high and the growth rate is fast, while the CAI of the upstream provinces such as Qinghai, Gansu and Ningxia are lower than the average of the basin for a long time, and the problem of regional development imbalance needs to be solved urgently.

  2. (2)

    The CCD of WECEc system generally increased steadily, with significant spatio-temporal differentiation. The mean CCD of the basin rose from 0.5061 to 0.5673, showing a “IV” and “Primary Coordination”, and the space showed a “High upstream, Low downstream” ladder characteristic: Inner Mongolia, Sichuan and other upstream provinces were mostly “V” and “General coordination”, and Shandong, Henan and other downstream areas were still in the “III” and “Near Imbalance” state.

  3. (3)

    The improvement of CCD in the YRB is restricted by multiple obstacle factors, which are mainly constrained by water and energy resources and high carbon emission. According to the calculation results of the average barrier degree in ten years, WS1 (OD is13.14%), CS4 (10.85%), ER2 (8.71%), WS2 (7.58%), CS1 (7.28%), ER1 (16.74%), CS2 (6.73%) and WD3 (6.11%) were the main obstacles to the coordinated development of WECEc system.

Policy recommendations

According to the coupling coordination characteristics and obstacle factors of WECEc system in the YRB revealed in this study, the following suggestions are put forward for high-quality development of ECE in the YRB:

  1. (1)

    The YRB should continue to strengthen the protection of the ecological environment, promote the green development of the industry, further optimize the production structure of water resources and energy, and strengthen the coordinated development of multi-dimensional systems. Especially in the upper reaches, ecological restoration efforts should be strengthened to enhance the production capacity of water resources and energy, narrow the gap with the middle and lower reaches, and realize the overall high-quality ECE development in the YRB.

  2. (2)

    Improve the inter-provincial coordination mechanism, design a differentiated ecological compensation system, and build a watershed industry linkage mechanism. Formulate the upstream and downstream division of labor and cooperation in the industrial chain, the upstream provides clean energy and ecological raw materials for the downstream, and the downstream transfers low-carbon technologies to the upstream. Strengthen the sharing of economic benefits between upstream ecological protection areas and downstream industrial belts, and promote policy linkage and resource allocation among provinces.

  3. (3)

    Guide industrial transformation by regions. The upstream regions of Qinghai, Gansu, Ningxia, etc., with ecological priority as the core, develop regional characteristics of agriculture and animal husbandry and ecological tourism, establish a national ecological compensation fund, and quantitatively compensate the carbon sink and water conservation services of the upstream ecological protection areas. The middle reaches focus on the transformation of the energy structure and the upgrading of the industrial chain, promote the clean utilization of coal in Shanxi, Shanxi and Inner Mongolia, and support the transformation of heavy industry to low-carbon processes. The downstream provinces of Shandong and Henan should strengthen the leading role of technology-intensive industries and modern agriculture, develop new energy equipment and promote water-saving agriculture.

Limitations

This study has certain limitations. In terms of data acquisition, the provincial-scale panel data adopted fails to capture intra-regional heterogeneity at prefectural or county levels. Additionally, while the static obstacle degree model identifies key factors (e.g., WS1, ER2), it cannot quantify nonlinear feedback mechanisms among subsystems through approaches like System Dynamics (SD) or Agent-Based Modeling (ABM). Future studies could employ higher-resolution databases to deepen regional disparity analyses and develop SD-ABM coupled models to simulate cross-system interactions within the WECEc nexus.