Coupling Coordination Analysis of Water Resources-Social Economy-Ecological Environment in the Yellow River Golden Triangle Area

Coupling Coordination Analysis of Water Resources-Social Economy-Ecological Environment in the Yellow River Golden Triangle Area | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Coupling Coordination Analysis of Water Resources-Social Economy-Ecological Environment in the Yellow River Golden Triangle Area Zhao Kou, Linjuan Xu, Zhanqiao Wang, Xiangyu Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4573159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Water resources, social economy, and ecological environment are interrelated and interacting complex systems, and the relationship among them affects the sustainable development of the region. To explore the interactive relationship and driving factors between water resources, social economy, and ecological environment in the Yellow River Golden Triangle region, taking the Yellow River Golden Triangle region as the research object in this paper. By constructing a coupling coordination evaluation index system of water resources, social economy, and ecological environment system, the coupling coordination development of this region from 2011 to 2021 is studied using the coupling coordination degree model, and the influencing factors of coupling coordination development are identified by grey relational analysis. The results show that from 2011 to 2021, the comprehensive evaluation index of water resources, social economy, and ecological environment in the Yellow River Golden Triangle region shows a trend of steady development followed by a gradual increase. The water resources subsystem restricts the development of the coupling system. The coupling coordination degree increased from a barely coordinated stage in 2011 to a well-coordinated stage in 2021. The coupling and coordinated development of Yuncheng and Linfen cities is better than that of Sanmenxia and Weinan cities. The social economy subsystem and water resources subsystem are the main factors affecting the coordinated development of the coupling system. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences water resources social economy ecological environment Yellow River Golden Triangle coupling coordination degree Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Water resources, as the material basis for human survival, play a crucial role in the development of social economy and the protection and governance of regional ecological environment 1 . However, with the development of society, issues such as water resources and ecological environment have become key factors hindering social and economic development 2 . The Yellow River Golden Triangle (hereinafter referred to as the "Golden Triangle"), located along the Yellow River at the junction of Henan, Shaanxi, and Shanxi provinces, includes Sanmenxia, Weinan, Yuncheng, and Linfen cities. Currently, the Golden Triangle region faces challenges such as water scarcity, excessive exploitation and utilization of water resources, low-level repeated construction of industries, and a grim situation in regional ecological environmental protection and governance. The contradictions between water resources, social economy 3 , and ecological environment restrict the development of the Golden Triangle. Therefore, a systematic analysis of water resources, social economy, and ecological environment, studying the coupling and coordination relationship between water resources-social economy-ecological environment (WSE), and analyzing the constraining factors of system coordination development are of great practical significance for promoting the rapid development of the Golden Triangle and pushing forward the process of regional integration. Coupling refers to the mutual relationship, interaction, and mutual restraint that exist between two or more systems 4 . Early research on the coupling of multiple systems mainly focused on the engineering field and gradually extended to the economic and ecological fields 5,6 . In recent years, with the development of social economy, people have paid more attention to the ecological environment, and the coupling of water resources, social economy, and ecological environment 7 has gradually become a research hotspot. For example, Wen et al. 8 used the entropy weight method and coupling coordination degree model to explore the symbiotic relationship between water resources, economy, and ecology in key provinces along the "Belt and Road" from 2006 to 2015. Sui et al. 9 evaluated the coupling coordination relationship between water resources, economy, and ecology in nine provinces of the Yellow River Basin from 2002 to 2022 using the coupling coordination degree model and geographic detector model. Wang et al. 10 studied the coordinated coupling relationship of water resources, economy, and ecology in the Henan section of the Yellow River Basin from 2000 to 2019 using the coupling coordination degree model, grey relational degree method, and Sparrow Search Algorithm optimized BP neural network (SSA-BP) joint prediction model. Su et al. 11 analyzed the spatio-temporal evolution characteristics and influencing factors of the coupling coordination between water resources, economy, and ecology in Hunan Province from 2005 to 2020 through the coupling coordination degree model and obstacle degree analysis model. He et al. 12 analyzed the spatial correlation characteristics of the coupling coordination degree between water resources, social economy, and ecological environment in China from 2011 to 2020 using spatial autocorrelation methods. Cui et al. 13 proposed a mechanical model to identify the coupling coordination state of water resources, social economy, and ecological environment in 16 cities in Anhui Province from 2011 to 2020. Currently, there are abundant research results on the coupling of water resources, social economy, and ecological environment, but there are still some shortcomings. In terms of research methods, most weight determination methods use a single entropy weight method 14 , and the appropriateness of weight determination remains to be considered. In terms of research content, most studies focus on coupling coordination degree analysis of research objects, but seldom involve the influencing factors of coupling coordination degree. This study takes the Golden Triangle as the research object, constructs an evaluation index system for the water resources-social economy-ecological environment system, determines the index weights using entropy weight method and analytic hierarchy process, conducts coupling coordination analysis on the water resources-social economy-ecological environment system in the Golden Triangle from 2011 to 2021, and explores the factors affecting the coupling and coordinated development of the Golden Triangle using a grey relational analysis model. It provides a theoretical basis for sustainable development planning of water resources, social economy, and ecological environment in the Golden Triangle. Data Sources and Methods Study Area The Yellow River Golden Triangle (located at 108°58′-112°34′E, 33°31′-36°57′N) is situated in the middle reaches of the Yellow River, at the junction of Shanxi, Shaanxi, and Henan provinces. It encompasses Weinan City in Shaanxi Province, Linfen and Yuncheng Cities in Shanxi Province, and Sanmenxia City in Henan Province (see Fig. 1 ). With a total area of 57,900 Square kilometers, it accounts for 10.95% of the combined area of the three provinces. At the end of 2021, the permanent resident population of the four cities was 15.31 million, and their GDP reached 763.23 billion yuan, accounting for 8.84% and 6.86% of the three provinces' total population and GDP, respectively. The Yellow River Golden Triangle stands at the junction of central and western China, connecting North China, Northwest China, and the Central Plains. It boasts a dense railway network and extensive road system. Rich in mineral resources and land, the area enjoys advantageous agricultural production conditions, making it a significant grain production base. Additionally, it has a solid industrial foundation, forming an industrial system focused on energy and raw material production such as coal, electricity, and non-ferrous metals, as well as equipment manufacturing and agricultural product processing. However, the total water resources in the Yellow River Golden Triangle are limited, with per capita water resources amounting to only 375m³, far below China's per capita water resources. Water resource utilization is relatively extensive, and agricultural water use efficiency is low. Industrial wastewater and agricultural pollution emissions cause water pollution and ecological damage. The deterioration of the ecological environment and water scarcity, in turn, restrict industrial development, hindering the sustainable economic development of the region. Therefore, it is essential to study the coupling coordination degree of water resources, socio-economics, and the ecological environment in the Yellow River Golden Triangle region. Research Methods Data Sources This study encompasses subsystems for water resources, socio-economics, and the ecological environment. Data for the water resources subsystem indicators were sourced from the "Water Resources Bulletin of Henan Province," "Water Resources Bulletin of Shanxi Province," and "Water Resources Bulletin of Shaanxi Province." Data for the socio-economic and ecological environment subsystem indicators were obtained from the "Henan Statistical Yearbook," "Shanxi Statistical Yearbook," "Shaanxi Statistical Yearbook," various city-specific statistical yearbooks, and the "China City Statistical Yearbook." Additionally, during the calculation of long-term sequence data, missing values for certain indicators were interpolated to ensure completeness. Indicator Preprocessing Due to the different dimensions of the original data for each indicator, direct comparison between them is not feasible. Therefore, it is necessary to standardize the original data of each indicator to eliminate the dimensional differences. In this paper, the range method is used to standardize the original data of each indicator: \({\text{x}}_{{ij}}^{\prime }=\frac{{{x_{ij}} - \hbox{min} \left( {{x_j}} \right)}}{{\hbox{max} \left( {{x_j}} \right) - \hbox{min} \left( {{x_j}} \right)}}\) (Positive indicator) ( 1 ) \(x_{{ij}}^{\prime }=\frac{{\hbox{max} \left( {{x_j}} \right) - {x_{ij}}}}{{\hbox{max} \left( {{x_j}} \right) - \hbox{min} \left( {{x_j}} \right)}}\) (Negative indicator) ( 2 ) Where \({x_{ij}}\) represents the original data value of the indicator j in the year i, \(x_{{ij}}^{\prime }\) represents the standardized value of the indicator j in the year i, and \(\hbox{max} \left( {{x_j}} \right)\) and \(\hbox{min} \left( {{x_j}} \right)\) are the maximum and minimum values of the indicator j. Weight Determination Method In this paper, a combined weighting method based on entropy weight and Analytic Hierarchy Process (AHP) is used to determine the weight of each indicator. The entropy weight method is an objective weighting method that determines the weight of each indicator based on the differences between the indicator data. That is, the smaller the entropy value of an indicator, the higher the degree of dispersion, and the greater the indicator weight 15 . However, the entropy weight method mainly relies on data analysis and calculation and cannot reflect subjective value judgments. The AHP is a subjective weighting method that determines the judgment matrix of each subsystem through expert scoring, thereby deriving the subjective weights of each indicator 16 . The AHP is subject to human influence and relies heavily on expert experience and judgment. Therefore, combining these two methods in a combined weighting approach can avoid the limitations of both subjectivity and objectivity, making the calculation results more accurate. The specific calculation formulas are as follows 17 : Entropy Weight Method $${p_{ij}}=\frac{{x_{{ij}}^{\prime }}}{{\sum\limits_{{i=1}}^{m} {x_{{ij}}^{\prime }} }}$$ 3 $${E_j}= - \frac{1}{{\ln m}}\sum\limits_{{i=1}}^{m} {{p_{ij}}\ln {p_{ij}}}$$ 4 $${\omega _j}=\frac{{1 - {E_j}}}{{n - \sum\limits_{{j=1}}^{n} {{E_j}} }}$$ 5 Where \({p_{ij}}\) represents the proportion of the indicator j in the year i , and m represents the time scale of the data, where m = 9, \({E_j}\) represents the information entropy of the indicator j. If \({p_{ij}}=0\) , then let \({p_{ij}}\ln {p_{ij}}=0\) , \({\omega _j}\) represents the weight of the indicator j in the subsystem, and n represents the number of indicators in the subsystem. Analytic Hierarchy Process (AHP) The AHP is a method that decomposes elements related to decision-making into levels such as objectives, criteria, and alternatives, and performs qualitative and quantitative analysis based on this structure 18 . The main steps are as follows 19 : Establish a hierarchical structure model. When applying Analytic Hierarchy Process (AHP) to analyze decision-making problems, it is necessary to rationalize and stratify the problems, and construct a multi-layer structural model including the objective layer, the criterion layer, and the scheme layer. Construct a judgment matrix. In this paper, the criterion layer represents the indicators of each subsystem. Due to differences in the importance of each indicator within the subsystem, numbers from 1 to 9 and their reciprocals are used to judge the importance level between two indicators (Table 1 ). Table 1 Judgment matrix scale definition 20 scale Explanation 1 The two indicators are of equal importance 3 Compared to the latter, the former indicator is slightly more important 5 Compared to the latter, the former indicator is significantly more important 7 Compared to the latter, the former indicator is strongly more important 9 Compared to the latter, the former indicator is extremely more important 2,4,6,8 Intermediate values between the two adjacent judgments reciprocal If the importance ratio of indicator i to indicator j is any of the aforementioned numbers, then the importance ratio of indicator j to indicator i is the reciprocal of that number Hierarchical single sorting and consistency check. Calculation of consistency index: $$CI=\frac{{{\lambda _{\hbox{max} }} - n}}{{n - 1}}$$ 6 Where CI represents the consistency index of the judgment matrix, \({\lambda _{\hbox{max} }}\) is the largest eigenvalue of the judgment matrix, and n is the order of the judgment matrix. Look up the average random consistency index RI (Table 2 ). Table 2 RI of low order judgment matrix 21 m 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 $$CR=\frac{{CI}}{{RI}}$$ 7 Where CR is consistency ratio, When \(CR<0.10\) , it can be considered that the judgment matrix meets the consistency requirement; if \(CR \geqslant 0.10\) , the judgment matrix should be modified to meet the consistency requirement. When the consistency check is passed, the eigenvector corresponding to the largest eigenvalue is the weight vector. Standardizing the weight vector gives the weight of the indicator \({w_j}\) . Calculation of combined weights The objective weights \({\omega _j}\) obtained through the entropy weight method and the subjective weights \({w_j}\) obtained through the analytic hierarchy process are linearly combined to derive the combined weights \(\omega\) . $$\omega =\alpha {\omega _j}+\left( {1 - \alpha } \right){w_j}$$ 8 Where \(\omega\) represents the combined weight, and \(\alpha\) represent the relative importance of the two weight calculation methods, satisfying \(0 \leqslant \alpha \leqslant 1\) . Here, \(\alpha =0.6\) . Comprehensive Evaluation Index of WSE Subsystem Comprehensive Evaluation Index: $$\left\{ {\begin{array}{*{20}{c}} {f\left( x \right)=\sum\limits_{{a=1}}^{m} {{\omega _a}x_{a}^{\prime }} } \\ {g\left( y \right)=\sum\limits_{{b=1}}^{n} {{\omega _b}x_{b}^{\prime }} } \\ {h\left( z \right)=\sum\limits_{{c=1}}^{k} {{\omega _c}x_{c}^{\prime }} } \end{array}} \right.$$ 9 Where f (x)、 g (y) and h (z) represent the comprehensive evaluation indices for the water resource system, socio-economic system, and ecological environment system, respectively. m, n, k denote the number of indicators in each subsystem, where in this case, m = 8, n = 9, k = 7. The combined weights of each indicator in the respective subsystems are denoted as \({\omega _a}\) , \({\omega _b}\) , and \({\omega _c}\) . The standardized values of each indicator are represented by \(x_{a}^{\prime }\) , \(x_{b}^{\prime }\) , and \(x_{c}^{\prime }\) . $$T=\alpha f\left( x \right)+\beta g\left( y \right)+\gamma h\left( z \right)$$ 10 Where T represents the comprehensive evaluation index of the water resources-socioeconomic-ecological environment system. \(\alpha\) , \(\beta\) , and \(\gamma\) represent the relative importance of the three subsystems. In this case, they are set as equal, i.e., \(\alpha =\beta =\gamma =\frac{1}{3}\) . Coupling Coordination Degree Model Constructing a coupling coordination degree model for the water resources-socioeconomic-ecological environment system 22 : $$C=\frac{{3\sqrt[3]{{f\left( x \right)g\left( y \right)h\left( z \right)}}}}{{f\left( x \right)+g\left( y \right)+h\left( z \right)}}$$ 12 $$D=\sqrt {CT}$$ 13 Where C represents the coupling degree. When C ∈ [0, 0.3), the system is in a low-level coupling stage; when C ∈ [0.3, 0.5), the system is in an antagonistic phase; when C ∈ [0.5, 0.8), the system is in a running-in phase; when C ∈ [0.8, 1], the system is in a high-level coupling stage. D represents the coupling coordination degree, \(0 \leqslant D \leqslant 1\) . Based on existing research results 23,24 , the coupling coordination degree is classified as shown in Table 3 . Table 3 classification of coupling coordination degree Coupling coordination degree Coupling Coordination Level Coupling coordination degree Coupling Coordination Level [0.0 ~ 0.1) Extreme disorder [0.5 ~ 0.6) Barely coordinated [0.1 ~ 0.2) Severe disorder [0.6 ~ 0.7) Preliminary coordination [0.2 ~ 0.3) Moderate disorder [0.7 ~ 0.8) Intermediate coordination [0.3 ~ 0.4) Mild disorder [0.8 ~ 0.9) Good Coordination [0.4 ~ 0.5) Nearly dysfunctional [0.9 ~ 1.0] High quality coordination Grey Relational Analysis Grey relational analysis is a multi-attribute decision-making method proposed by Kuo et al. 25 This method judges the closeness of different sequences by the similarity of the geometric shape of the sequence curves. Through grey relational analysis, the key factors affecting the coupling coordination degree can be identified. The grey relational degree between the coupling coordination degree and the selected indicators of each city in the Golden Triangle of the Yellow River is calculated, with the coupling coordination degree selected as the reference sequence and each indicator as the comparison sequence. Results Indicator System Construction Based on principles of scientificity, systematicness, comprehensiveness, and indicator accessibility 26 , this paper constructs three subsystems for the four cities in the Golden Triangle of the Yellow River region: water resources, socio-economic, and ecological systems. For the water resource system, eight indicators are selected from three aspects: water resource endowment, water usage structure, and water resource utilization degree. For the socio-economic system, nine indicators are chosen from two perspectives: regional economic structure and social development level. For the ecological environment system, seven indicators are picked from ecological conditions and environmental pressure. Altogether, 24 indicators are selected to construct the evaluation indicator system for the water resource-socioeconomic-ecological environment system in the Golden Triangle of the Yellow River, as shown in Table 4 . Table 4 The coupling evaluation index system of WSE system in the Yellow River Golden Triangle region Target layer Criterion layer Indicators Attribute unit Combined weight Water resources - social economy - ecological environment system evaluation index system Water resources Per capita water resources /X 1 + m³ 0.247 Proportion of industrial water use / X 2 + % 0.092 Proportion of domestic water use /X 3 + % 0.107 Per capita water consumption /X 4 + m³ 0.149 Water production modulus /X 5 + 10,000 m³/km 2 0.193 Irrigation water per mu of farmland /X 6 - m³ 0.072 Water consumption per 10,000 yuan of GDP / X 7 - m³/ 10,000 yuan 0.050 Proportion of groundwater supply /X 8 - % 0.090 Social economy Per capita GDP /Y 1 + Yuan/ person 0.187 The proportion of primary industry in GDP /Y 2 - % 0.095 The proportion of secondary industry in GDP /Y 3 + % 0.160 The proportion of tertiary industry in GDP /Y 4 + % 0.139 Per capita net income of rural residents /Y 5 + Yuan 0.109 Per capita disposable income of urban residents /Y 6 + Yuan 0.101 Urbanization rate /Y 7 + % 0.080 Population density /Y 8 + Person/ km 2 0.060 Total retail sales of consumer goods /Y 9 + 10 8 yuan 0.069 Ecological environment Green coverage rate of built-up area /Z 1 + % 0.071 Proportion of water used for ecological environment /Z 2 + % 0.207 Per capita green park area /Z 3 + ㎡ 0.106 Comprehensive utilization rate of solid waste /Z 4 + % 0.130 Waste water discharge per 10,000 yuan of GDP /Z 5 - Tons/ 10,000 yuan 0.197 Sulfur dioxide emissions per 10,000 yuan of GDP /Z 6 - Tons/ 10,000 yuan 0.172 Nitrogen oxide emissions per 10,000 yuan of GDP /Z 7 - Tons/ 10,000 yuan 0.117 Comprehensive Evaluation of the WSE System in the Golden Triangle Region of the Yellow River The comprehensive evaluation index reflects the integrated development level of water resources, socioeconomics, ecological environment, and their coupled system in the Golden Triangle Region of the Yellow River, as shown in Fig. 2. According to Fig. 2(a), the development level of water resources in the Golden Triangle Region of the Yellow River is not high, generally below 0.5 from 2011 to 2019, but it has significantly improved from 2020 to 2021. The development of water resources in Sanmenxia City is relatively unstable, showing a fluctuating state overall. It reached the worst development level in 2017 (0.223) and then fluctuated upward, reaching the best development level (0.739) in 2021. The development of water resources in Weinan City showed a trend of decreasing first and then increasing. After reaching the worst development level in 2016 (0.152), it gradually improved to the best level (0.769), with an increase of 406%. The water resources in Yuncheng City showed a steady upward trend overall. In 2012, it reached the lowest water resources development index value of 0.122 among the four cities, and then gradually increased to the highest water resources development index value of 0.913 among the four cities, with an increase of 648%. The development of water resources in Linfen City was relatively stable before 2020, but the development level was not high. It began to increase significantly in 2020. According to Fig. 2(b), the socioeconomic development in the Golden Triangle Region of the Yellow River shows a trend of gradual improvement overall. The socioeconomic subsystem development level of Sanmenxia City is the best among the four cities, with the socioeconomic evaluation index increasing from 0.289 in 2011 to 0.762 in 2021. Weinan City has the fastest socioeconomic development among the four cities, with the socioeconomic evaluation index increasing from 0.161 in 2011 to 0.651 in 2021, an increase of 304%. The socioeconomic development of Yuncheng City and Linfen City is similar, but Yuncheng City's development is more stable. From 2011 to 2021, the socioeconomic evaluation index never decreased, steadily increasing from 0.265 in 2011 to 0.732 in 2021. According to Fig. 2(c), the ecological environment development in the Golden Triangle Region of the Yellow River shows an upward trend overall. The development level of the ecological environment subsystem in the four cities was not high before 2016, generally below 0.4. However, after 2016, the development level of the ecological environment in the four cities rapidly improved. Among them, the development level of the ecological environment in Sanmenxia City was the worst among the four cities before 2016, and its ecological environment evaluation index was all below 0.2. The comprehensive evaluation index of the ecological environment in Weinan City has grown steadily, showing a steady upward trend overall. The development level of the ecological environment in Yuncheng City was higher than that in Linfen City before 2014. After 2014, the ecological environment of Yuncheng City showed a gradual growth trend, while the ecological environment of Linfen City showed an upward trend overall, but the upward trend was unstable. According to Fig. 2(d), the development of the water resources-socioeconomic-ecological environment system in the Golden Triangle Region of the Yellow River can be divided into two stages: the low-level development stage from 2011 to 2016 and the rapid development stage from 2016 to 2021. Before 2016, the comprehensive evaluation index of the WSE coupling system has been less than 0.4, which is at a relatively low development level. During this period, the comprehensive evaluation index of the coupling system in Linfen City and Yuncheng City fluctuated somewhat. After 2016, the comprehensive evaluation index of the coupling system in the four cities rose rapidly. Sanmenxia City has been in a leading position before 2020, but due to the impact of water resources, the comprehensive evaluation index has decreased, putting it in a backward position. Linfen City has developed the fastest, with an increase from 0.260 in 2011 to 0.836 in 2021, an increase of 221%. Analysis of WSE Coupling Coordination Degree Based on the coupling coordination degree model, the coupling degree and coupling coordination degree of the WSE coupling system for various cities in the Golden Triangle of the Yellow River region from 2011 to 2021 are calculated, as shown in Figs. 3 to 5. According to Fig. 3 , the WSE coupling degrees of the four cities in the Golden Triangle of the Yellow River region from 2011 to 2021 are all higher than 0.8, indicating a high-level coupling stage. This suggests that the water resources subsystem, socio-economic subsystem, and ecological environment subsystem in each city of the Golden Triangle of the Yellow River region are closely connected, and the subsystems strongly interact with each other. However, there are fluctuations in the coupling degree of each city during development. For example, the WSE coupling degree of Sanmenxia City dropped from 0.963 in 2019 to 0.894 in 2020. This indicates that although the WSE system in each city is well-coupled, the subsystem development is not stable, which affects the coordinated development of the WSE coupling to some extent. According to Fig. 4 , the coupling coordination degree of the WSE system in the Golden Triangle of the Yellow River region shows a trend of steady development followed by a gradual increase. From 2011 to 2015, there was little change in coupling coordination degree, but it rose from a barely coordinated stage in 2016 to a well-coordinated stage in 2021. As can be seen from Fig. 5, before 2016, the subsystems of water resources, socio-economic, and ecological environment in the four cities of the Golden Triangle of the Yellow River were not highly developed, keeping the coupling coordination degree of the WSE system at the verge of imbalance or barely coordinated stage. However, since 2016, with the economic growth and improvement of the ecological environment in various cities, the coupling coordination degree of the WSE system in each city has begun to gradually increase. Among them, Yuncheng and Linfen have seen the largest increase in coupling coordination degree, rising from a barely coordinated stage to a high-quality coordinated stage. Although the coupling coordination degree of the WSE system in Sanmenxia has increased, it declined in 2020 due to the influence of the water resources subsystem, showing a fluctuating upward trend. In 2021, the development of water resources, socio-economic, and ecological environment in the four cities was relatively similar. The three subsystems interacted and promoted development together, resulting in a significant increase in the coupling coordination degree of the four cities. Discussion The evaluation indices for water resources, socio-economic, ecological environment, and the comprehensive evaluation index of the coupling system in the Golden Triangle of the Yellow River region indicate that there were minimal differences among the systems before 2016. However, after 2016, the comprehensive evaluation indices for socio-economic and ecological environment increased. With the implementation of the 13th Five-Year Plan, the government has strengthened its supervision of high-polluting enterprises, significantly reducing wastewater and exhaust emissions, leading to a substantial increase in the ecological environment evaluation index. Simultaneously, as the industrial structure gradually shifts from "secondary, tertiary, primary" to "tertiary, secondary, primary," with the tertiary industry surpassing the secondary industry in proportion, the service industry in the Golden Triangle of the Yellow River region has gradually become the leading industry driving economic growth, further promoting economic development. The water resources evaluation index remained at a relatively low level until 2020. The increase in the evaluation index from 2020 to 2021 was primarily attributed to abundant rainfall, with the average rainfall in 2021 increasing by more than 50% compared to the multi-year average. As economic growth and urbanization accelerate, the demand for water resources has further increased. However, due to the limitation of total water resources, the water resources evaluation index remains low, exacerbating the contradiction between socio-economic development, ecological environmental protection, and water resource development and utilization. The lack of water resources has hindered the development of the Golden Triangle of the Yellow River. The coupling coordination degree of the WSE system in the cities of the Golden Triangle of the Yellow River region generally shows a trend of steady development followed by a gradual increase. From 2011 to 2015, the coupling coordination degree of the four cities in the Golden Triangle of the Yellow River was in a barely coordinated stage. During this period, the economic development of the Golden Triangle of the Yellow River was backward, and the urbanization rate was low. To achieve rapid economic growth, protection of the ecological environment was ignored, resulting in massive emissions of wastewater and exhaust. Additionally, due to water scarcity, unreasonable water use structure, and insufficient supervision of water-intensive industries, water resource utilization was inefficient. From 2016 to 2021, the coupling coordination degree of the WSE system in the cities of the Golden Triangle of the Yellow River rose from a barely coordinated stage to a well-coordinated stage. During this period, with the implementation of the 13th Five-Year Plan, the economy developed rapidly, the urbanization rate continuously increased, and ecological environmental governance achieved remarkable results. The comprehensive evaluation indices of the socio-economic subsystem and ecological environment subsystem increased, elevating the coupling coordination degree of the WSE system. In 2021, abundant rainfall in the Golden Triangle of the Yellow River region, coupled with improvements in water equipment in water-intensive industries, led to improved water resource allocation. This resulted in a notable upward trend in the comprehensive evaluation index of water resources, which further promoted economic development and ecological environmental improvement, ultimately leading to a well-coordinated stage of coupling coordination. The influencing factors of the coupling coordination degree of the WSE system in the cities of the Golden Triangle of the Yellow River are shown in Fig. 6. Indicators with a gray correlation degree exceeding 0.9 are selected as the main influencing factors. The socio-economic subsystem is the most critical subsystem affecting the coupled and coordinated development of the WSE system in Sanmenxia City. The socio-economic subsystem has the largest proportion of indicators (42.9%), while the ecological environment subsystem has the smallest proportion (25.2%). Among them, the indicator with the highest correlation degree is Y1 (per capita GDP), followed by Y6 (per capita disposable income of urban residents). Most indicators of the socio-economic subsystem have a correlation degree of 0.9. The proportion of subsystem indicators in Weinan, Yuncheng, and Linfen is similar, with the water resources subsystem accounting for approximately 34%, the socio-economic subsystem accounting for approximately 41%, and the ecological environment subsystem accounting for approximately 25%. Among the factors influencing the coupling coordination degree of the WSE system in Weinan City, the indicator with the highest gray correlation degree is Y6 (per capita disposable income of urban residents), followed by Y1 (per capita GDP), Y7 (urbanization rate), and Z3 (per capita park green area). X3 (proportion of domestic water use), Y4 (proportion of tertiary industry in GDP), and Y5 (per capita net income of rural residents) also have a correlation degree of 0.9. Among the factors affecting the coupling coordination degree of the WSE system in Yuncheng City, the indicators with the highest gray correlation degree are Y6 (per capita disposable income of urban residents) and Y9 (total retail sales of social consumer goods), followed by Y1 (per capita GDP). For the coupling coordination degree of the WSE system in Linfen City, the indicator with the highest gray correlation degree is Y1 (per capita GDP), followed by Y6 (per capita disposable income of urban residents), Y9 (total retail sales of consumer goods), and X3 (proportion of domestic water use). The identification of impact factors indicates that the indicators of the socio-economic subsystem are the main factors affecting the coupled and coordinated development of the WSE system in the Golden Triangle of the Yellow River region, mainly including indicators such as GDP per capita, the proportion of the tertiary industry in GDP, disposable income per capita of urban residents, net income per rural resident, and total retail sales of social consumer goods. At the same time, indicators such as the proportion of domestic water consumption and green park area per capita also greatly affect the coupled and coordinated development of the WSE system in the Golden Triangle of the Yellow River. Therefore, while accelerating economic development, optimizing industrial structure, and increasing residents' income, the Golden Triangle of the Yellow River should also rationally develop and utilize water resources, optimize water resource allocation, and focus on the development of water resources and ecological environment while emphasizing economic development. Conclusion Based on the coupling coordination degree model of water resources, social economy, and ecological environment in the Golden Triangle of the Yellow River, an evaluation index system for the coupled and coordinated development of the water resources, social economy, and ecological environment system in the Golden Triangle of the Yellow River was constructed. The comprehensive evaluation index, coordination degree, and coupling coordination degree trends of the water resources, social economy, and ecological environment systems of various cities from 2011 to 2021 were studied. Grey correlation analysis was used to study the influencing factors that affect the coupled and coordinated development of the water resources, social economy, and ecological environment system in the Golden Triangle of the Yellow River. The main conclusions are as follows: ( 1 ) The comprehensive evaluation index of the Golden Triangle of the Yellow River indicates that the comprehensive evaluation index of the socio-economic subsystem, ecological environment subsystem, and coupling system shows a trend of steady development followed by a gradual increase. There are large fluctuations in the ecological environment subsystems of Yuncheng and Linfen during their development, and the water resources subsystem was poorly developed before 2020, restricting socio-economic development and ecological environmental protection and governance. ( 2 ) The coupling degree of the three subsystems in the Golden Triangle of the Yellow River has always been at a high level of coupling stage. The three subsystems are closely connected and strongly influence each other. The coupling coordination degree of the coupling system shows a trend of steady development followed by a gradual increase, rising from a barely coordinated stage to a well-coordinated stage. Among them, Yuncheng and Linfen have risen to a superior coordination stage, showing a good overall development trend. ( 3 ) According to grey correlation analysis, the socio-economic subsystem has the greatest impact on the coupled and coordinated development of water resources, social economy, and ecological environment in the Golden Triangle of the Yellow River, followed by the water resources subsystem. Among them, indicators such as GDP per capita, the proportion of the tertiary industry in GDP, disposable income per capita of urban residents, net income per rural resident, and water consumption per capita are the most important factors affecting the coupled and coordinated development of water resources, social economy, and ecological environment in the Golden Triangle of the Yellow River. Declarations Competing interests The authors declare no competing interests. Funding This research was funded by the National Natural Science Foundation of China (No. 42041006), the Major Science and Technology Project of Henan Province (No. 231100320100), Henan Province Natural Science Foundation Project (No.222300420013, No.242300420039), the Significant Science and Technology Project of Ministry of Water Resources (No. SKS-2022011), the Excellent Young Talents Project of Yellow River Conservancy Commission (No. HQK-202309). Author Contribution Z.K. methodology, software, data curation, writing-original draft. L.X. conceptualization, proofreading. Z.W. proofreading. X.G. data collection. All authors have read and agreed to the published version of the manuscript. Data Availability The data presented in this study are available on request from the corresponding author. References Marston, L., Cai, X. M. An overview of water reallocation and the barriers to its implementation. WIREs Water. 3, 658–677, https://doi.org/10.1002/wat2.1159 (2016). Luo, Z. L., Zuo, Q. T. Evaluating the coordinated development of social economy, water, and ecology in a heavily disturbed basin based on the distributed hydrology model and the harmony theory. Journal of Hydrology. 574, 226–241, https://doi.org/10.1016/j.jhydrol. 2019.04.042 (2019). National Development and Reform Commission. 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J., Ma, C. M., Huang, P. & Guo, X. Ecological vulnerability assessment based on AHP-PSR method and analysis of its single parameter sensitivity and spatial autocorrelation for ecological protection – A case of Weifang City, China. Ecological Indicators. 125, 107464, https://doi.org/10.1016/j.ecolind.2021.107464 (2021). Saaty, T. L. How to make a decision: the analytic hierarchy process. European journal of operational research. 48(1), 9–26, https://doi.org/10.1016/0377-2217(90)90057-I (1990). Saaty, T. L. Decision making - the analytic hierarchy and network processes (AHP/ANP). Journal of Systems Science and Systems Engineering. 01, 1–35, https://doi.org/10.100 7/s11518-006-0151-5 (2004). Wang, S. J., Kong, W., Ren, L. et al. Research on misuses and modeification of coupling coordination degree model in China. Journal of Natural Resources. 36(3), 793–810(2021) (in Chinese). Deng, M., Chen, J., Tao, F., Zhu, J. L. & Wang, M. On the Coupling and Coordination Development between Environment and Economy: A Case Study in the Yangtze River Delta of China. International Journal of Environmental Research and Public Health. 19(1), 586, https://doi.org/10.3390/ijerph19010586 (2022). Yu, L. et al. Effects of agricultural activities on energy-carbon-water nexus of the Qinghai-Tibet Plateau. Journal of Cleaner Production. 331, 129995, https://doi.org/10.1016/j. jclepro.2021.129995 (2022). Kuo, Y., Yang, T. & Huang, G. W. The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & industrial engineering. 55(1), 80–93, https://doi.org/10.1016/j.cie.2007.12.002 (2008). Peng, Z. L., Zhang, A. P., Wang, S. F. & Bai, Y. The Design Principles and Construction Process of a Comprehensive Evaluation Indicator System. Science Research Management. 38(S1), 209–215(2017) (in Chinese). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4573159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320802156,"identity":"8fef6f03-43e6-4add-a367-9752e2c2520b","order_by":0,"name":"Zhao Kou","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Kou","suffix":""},{"id":320802157,"identity":"175de038-eea4-4e2f-a919-3620e51eac41","order_by":1,"name":"Linjuan Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie2RMQoCMRBFBxaiRSCdZBH0CiPbWHiYCYI2W+wRhG3XXvESe4RIQBs9gU3AC6RcQdCktHFTCubV/83wZwASiV9kAMoSLjgTtdaui1EyWKKtVhMhT+q4b+IUlltninxTFmbIIgw0YCVhplp9cQY4TMVIf1fyGggJmWrNtjXVHGb7A31XRAZEhNxvubZmx/2AW4/CvKIJpVdKaziLUPwWtSHEUB/iFN9lGdqEI6M/suzvgudm/eier/DKu3PdYirGPQoA/0jIvnhgoGNSiUQi8c+8AZYUSOoOrL48AAAAAElFTkSuQmCC","orcid":"","institution":"Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission","correspondingAuthor":true,"prefix":"","firstName":"Linjuan","middleName":"","lastName":"Xu","suffix":""},{"id":320802158,"identity":"862913f3-c5c4-4dd8-9ba6-e98673c7af9d","order_by":2,"name":"Zhanqiao Wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhanqiao","middleName":"","lastName":"Wang","suffix":""},{"id":320802159,"identity":"8daa993e-39ae-4696-ae9e-87d0dff2fd57","order_by":3,"name":"Xiangyu Gao","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2024-06-13 03:02:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4573159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4573159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59390293,"identity":"13ee637b-efd7-4c9d-b472-745cea1b1e39","added_by":"auto","created_at":"2024-07-01 07:55:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306724,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area. (a) China, (b) The three provinces of Shanxi, Shaanxi and Henan, (c) Yellow River Golden Triangle region. Created by ArcGIS 10.2 software (https://www.arcgis.com/).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/80749cad0083d7d8cd8a0eb9.png"},{"id":59390296,"identity":"a8444c20-300e-4d7f-b104-9bd79570c2f6","added_by":"auto","created_at":"2024-07-01 07:55:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81710,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive evaluation index of each subsystem and coupling system of WSE\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/77c78d7bac13d036d45876d4.png"},{"id":59390292,"identity":"20170de5-ed42-4541-9487-2916c556909f","added_by":"auto","created_at":"2024-07-01 07:55:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73455,"visible":true,"origin":"","legend":"\u003cp\u003eWSE coupling degree of four cities in Yellow River Golden Triangle region\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/bbac914b474ffa8e7db938f0.png"},{"id":59390294,"identity":"6c4e3fb6-b2dc-4a75-a194-394cb47677b4","added_by":"auto","created_at":"2024-07-01 07:55:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45255,"visible":true,"origin":"","legend":"\u003cp\u003eWSE coupling degree and coupling coordination degree of Yellow River Golden Triangle\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/bc4daccbc75acd861c693663.png"},{"id":59390893,"identity":"c65949f9-2774-45ea-8bdb-acfca654ecd1","added_by":"auto","created_at":"2024-07-01 08:03:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":153350,"visible":true,"origin":"","legend":"\u003cp\u003eWSE coupling coordination degree in Yellow River Golden Triangle region\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/279c5642e6eb97c96b17b29b.png"},{"id":59390291,"identity":"2586e444-83ce-4e13-88e2-7f7379be95d3","added_by":"auto","created_at":"2024-07-01 07:55:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":71179,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence factors of coupling coordination degree of WSE system in cities of Yellow River Golden Triangle region\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/3f6d2df6ddf21527f8cc8856.png"},{"id":66039106,"identity":"f75efeef-3879-427a-844f-abff6cdc31ae","added_by":"auto","created_at":"2024-10-07 05:24:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1314727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4573159/v1/81a562ba-64f7-4e30-889c-d85d64010b39.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coupling Coordination Analysis of Water Resources-Social Economy-Ecological Environment in the Yellow River Golden Triangle Area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater resources, as the material basis for human survival, play a crucial role in the development of social economy and the protection and governance of regional ecological environment\u003csup\u003e1\u003c/sup\u003e. However, with the development of society, issues such as water resources and ecological environment have become key factors hindering social and economic development\u003csup\u003e2\u003c/sup\u003e. The Yellow River Golden Triangle (hereinafter referred to as the \"Golden Triangle\"), located along the Yellow River at the junction of Henan, Shaanxi, and Shanxi provinces, includes Sanmenxia, Weinan, Yuncheng, and Linfen cities. Currently, the Golden Triangle region faces challenges such as water scarcity, excessive exploitation and utilization of water resources, low-level repeated construction of industries, and a grim situation in regional ecological environmental protection and governance. The contradictions between water resources, social economy\u003csup\u003e3\u003c/sup\u003e, and ecological environment restrict the development of the Golden Triangle. Therefore, a systematic analysis of water resources, social economy, and ecological environment, studying the coupling and coordination relationship between water resources-social economy-ecological environment (WSE), and analyzing the constraining factors of system coordination development are of great practical significance for promoting the rapid development of the Golden Triangle and pushing forward the process of regional integration.\u003c/p\u003e \u003cp\u003eCoupling refers to the mutual relationship, interaction, and mutual restraint that exist between two or more systems\u003csup\u003e4\u003c/sup\u003e. Early research on the coupling of multiple systems mainly focused on the engineering field and gradually extended to the economic and ecological fields\u003csup\u003e5,6\u003c/sup\u003e. In recent years, with the development of social economy, people have paid more attention to the ecological environment, and the coupling of water resources, social economy, and ecological environment\u003csup\u003e7\u003c/sup\u003e has gradually become a research hotspot. For example, Wen et al.\u003csup\u003e8\u003c/sup\u003e used the entropy weight method and coupling coordination degree model to explore the symbiotic relationship between water resources, economy, and ecology in key provinces along the \"Belt and Road\" from 2006 to 2015. Sui et al.\u003csup\u003e9\u003c/sup\u003e evaluated the coupling coordination relationship between water resources, economy, and ecology in nine provinces of the Yellow River Basin from 2002 to 2022 using the coupling coordination degree model and geographic detector model. Wang et al.\u003csup\u003e10\u003c/sup\u003e studied the coordinated coupling relationship of water resources, economy, and ecology in the Henan section of the Yellow River Basin from 2000 to 2019 using the coupling coordination degree model, grey relational degree method, and Sparrow Search Algorithm optimized BP neural network (SSA-BP) joint prediction model. Su et al.\u003csup\u003e11\u003c/sup\u003e analyzed the spatio-temporal evolution characteristics and influencing factors of the coupling coordination between water resources, economy, and ecology in Hunan Province from 2005 to 2020 through the coupling coordination degree model and obstacle degree analysis model. He et al.\u003csup\u003e12\u003c/sup\u003e analyzed the spatial correlation characteristics of the coupling coordination degree between water resources, social economy, and ecological environment in China from 2011 to 2020 using spatial autocorrelation methods. Cui et al.\u003csup\u003e13\u003c/sup\u003e proposed a mechanical model to identify the coupling coordination state of water resources, social economy, and ecological environment in 16 cities in Anhui Province from 2011 to 2020.\u003c/p\u003e \u003cp\u003eCurrently, there are abundant research results on the coupling of water resources, social economy, and ecological environment, but there are still some shortcomings. In terms of research methods, most weight determination methods use a single entropy weight method\u003csup\u003e14\u003c/sup\u003e, and the appropriateness of weight determination remains to be considered. In terms of research content, most studies focus on coupling coordination degree analysis of research objects, but seldom involve the influencing factors of coupling coordination degree. This study takes the Golden Triangle as the research object, constructs an evaluation index system for the water resources-social economy-ecological environment system, determines the index weights using entropy weight method and analytic hierarchy process, conducts coupling coordination analysis on the water resources-social economy-ecological environment system in the Golden Triangle from 2011 to 2021, and explores the factors affecting the coupling and coordinated development of the Golden Triangle using a grey relational analysis model. It provides a theoretical basis for sustainable development planning of water resources, social economy, and ecological environment in the Golden Triangle.\u003c/p\u003e"},{"header":"Data Sources and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe Yellow River Golden Triangle (located at 108\u0026deg;58\u0026prime;-112\u0026deg;34\u0026prime;E, 33\u0026deg;31\u0026prime;-36\u0026deg;57\u0026prime;N) is situated in the middle reaches of the Yellow River, at the junction of Shanxi, Shaanxi, and Henan provinces. It encompasses Weinan City in Shaanxi Province, Linfen and Yuncheng Cities in Shanxi Province, and Sanmenxia City in Henan Province (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). With a total area of 57,900 Square kilometers, it accounts for 10.95% of the combined area of the three provinces. At the end of 2021, the permanent resident population of the four cities was 15.31\u0026nbsp;million, and their GDP reached 763.23\u0026nbsp;billion yuan, accounting for 8.84% and 6.86% of the three provinces' total population and GDP, respectively. The Yellow River Golden Triangle stands at the junction of central and western China, connecting North China, Northwest China, and the Central Plains. It boasts a dense railway network and extensive road system. Rich in mineral resources and land, the area enjoys advantageous agricultural production conditions, making it a significant grain production base. Additionally, it has a solid industrial foundation, forming an industrial system focused on energy and raw material production such as coal, electricity, and non-ferrous metals, as well as equipment manufacturing and agricultural product processing. However, the total water resources in the Yellow River Golden Triangle are limited, with per capita water resources amounting to only 375m\u0026sup3;, far below China's per capita water resources. Water resource utilization is relatively extensive, and agricultural water use efficiency is low. Industrial wastewater and agricultural pollution emissions cause water pollution and ecological damage. The deterioration of the ecological environment and water scarcity, in turn, restrict industrial development, hindering the sustainable economic development of the region. Therefore, it is essential to study the coupling coordination degree of water resources, socio-economics, and the ecological environment in the Yellow River Golden Triangle region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThis study encompasses subsystems for water resources, socio-economics, and the ecological environment. Data for the water resources subsystem indicators were sourced from the \"Water Resources Bulletin of Henan Province,\" \"Water Resources Bulletin of Shanxi Province,\" and \"Water Resources Bulletin of Shaanxi Province.\" Data for the socio-economic and ecological environment subsystem indicators were obtained from the \"Henan Statistical Yearbook,\" \"Shanxi Statistical Yearbook,\" \"Shaanxi Statistical Yearbook,\" various city-specific statistical yearbooks, and the \"China City Statistical Yearbook.\" Additionally, during the calculation of long-term sequence data, missing values for certain indicators were interpolated to ensure completeness.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIndicator Preprocessing\u003c/h2\u003e \u003cp\u003eDue to the different dimensions of the original data for each indicator, direct comparison between them is not feasible. Therefore, it is necessary to standardize the original data of each indicator to eliminate the dimensional differences. In this paper, the range method is used to standardize the original data of each indicator:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{x}}_{{ij}}^{\\prime }=\\frac{{{x_{ij}} - \\hbox{min} \\left( {{x_j}} \\right)}}{{\\hbox{max} \\left( {{x_j}} \\right) - \\hbox{min} \\left( {{x_j}} \\right)}}\\)\u003c/span\u003e \u003c/span\u003e (Positive indicator) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(x_{{ij}}^{\\prime }=\\frac{{\\hbox{max} \\left( {{x_j}} \\right) - {x_{ij}}}}{{\\hbox{max} \\left( {{x_j}} \\right) - \\hbox{min} \\left( {{x_j}} \\right)}}\\)\u003c/span\u003e \u003c/span\u003e (Negative indicator) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x_{ij}}\\)\u003c/span\u003e\u003c/span\u003erepresents the original data value of the indicator j in the year i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{{ij}}^{\\prime }\\)\u003c/span\u003e\u003c/span\u003e represents the standardized value of the indicator j in the year i, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\hbox{max} \\left( {{x_j}} \\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\hbox{min} \\left( {{x_j}} \\right)\\)\u003c/span\u003e\u003c/span\u003e are the maximum and minimum values of the indicator j.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWeight Determination Method\u003c/h2\u003e \u003cp\u003eIn this paper, a combined weighting method based on entropy weight and Analytic Hierarchy Process (AHP) is used to determine the weight of each indicator. The entropy weight method is an objective weighting method that determines the weight of each indicator based on the differences between the indicator data. That is, the smaller the entropy value of an indicator, the higher the degree of dispersion, and the greater the indicator weight\u003csup\u003e15\u003c/sup\u003e. However, the entropy weight method mainly relies on data analysis and calculation and cannot reflect subjective value judgments. The AHP is a subjective weighting method that determines the judgment matrix of each subsystem through expert scoring, thereby deriving the subjective weights of each indicator\u003csup\u003e16\u003c/sup\u003e. The AHP is subject to human influence and relies heavily on expert experience and judgment. Therefore, combining these two methods in a combined weighting approach can avoid the limitations of both subjectivity and objectivity, making the calculation results more accurate. The specific calculation formulas are as follows\u003csup\u003e17\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003eEntropy Weight Method\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${p_{ij}}=\\frac{{x_{{ij}}^{\\prime }}}{{\\sum\\limits_{{i=1}}^{m} {x_{{ij}}^{\\prime }} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${E_j}= - \\frac{1}{{\\ln m}}\\sum\\limits_{{i=1}}^{m} {{p_{ij}}\\ln {p_{ij}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\omega _j}=\\frac{{1 - {E_j}}}{{n - \\sum\\limits_{{j=1}}^{n} {{E_j}} }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p_{ij}}\\)\u003c/span\u003e\u003c/span\u003erepresents the proportion of the indicator \u003cem\u003ej\u003c/em\u003e in the year \u003cem\u003ei\u003c/em\u003e, and \u003cem\u003em\u003c/em\u003e represents the time scale of the data, where \u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E_j}\\)\u003c/span\u003e\u003c/span\u003erepresents the information entropy of the indicator j. If\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p_{ij}}=0\\)\u003c/span\u003e\u003c/span\u003e, then let\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p_{ij}}\\ln {p_{ij}}=0\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega _j}\\)\u003c/span\u003e\u003c/span\u003erepresents the weight of the indicator \u003cem\u003ej\u003c/em\u003e in the subsystem, and \u003cem\u003en\u003c/em\u003e represents the number of indicators in the subsystem.\u003c/p\u003e \u003cp\u003eAnalytic Hierarchy Process (AHP)\u003c/p\u003e \u003cp\u003eThe AHP is a method that decomposes elements related to decision-making into levels such as objectives, criteria, and alternatives, and performs qualitative and quantitative analysis based on this structure\u003csup\u003e18\u003c/sup\u003e. The main steps are as follows\u003csup\u003e19\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003eEstablish a hierarchical structure model. When applying Analytic Hierarchy Process (AHP) to analyze decision-making problems, it is necessary to rationalize and stratify the problems, and construct a multi-layer structural model including the objective layer, the criterion layer, and the scheme layer.\u003c/p\u003e \u003cp\u003eConstruct a judgment matrix. In this paper, the criterion layer represents the indicators of each subsystem. Due to differences in the importance of each indicator within the subsystem, numbers from 1 to 9 and their reciprocals are used to judge the importance level between two indicators (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJudgment matrix scale definition\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003escale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe two indicators are of equal importance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the latter, the former indicator is slightly more important\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the latter, the former indicator is significantly more important\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the latter, the former indicator is strongly more important\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the latter, the former indicator is extremely more important\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,4,6,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate values between the two adjacent judgments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereciprocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf the importance ratio of indicator i to indicator j is any of the aforementioned numbers, then the importance ratio of indicator j to indicator i is the reciprocal of that number\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHierarchical single sorting and consistency check. Calculation of consistency index:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$CI=\\frac{{{\\lambda _{\\hbox{max} }} - n}}{{n - 1}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eCI\u003c/em\u003e represents the consistency index of the judgment matrix, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\lambda _{\\hbox{max} }}\\)\u003c/span\u003e\u003c/span\u003e is the largest eigenvalue of the judgment matrix, and \u003cem\u003en\u003c/em\u003e is the order of the judgment matrix.\u003c/p\u003e \u003cp\u003eLook up the average random consistency index RI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRI of low order judgment matrix\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$CR=\\frac{{CI}}{{RI}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere CR is consistency ratio, When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CR\u0026lt;0.10\\)\u003c/span\u003e\u003c/span\u003e, it can be considered that the judgment matrix meets the consistency requirement; if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(CR \\geqslant 0.10\\)\u003c/span\u003e\u003c/span\u003e, the judgment matrix should be modified to meet the consistency requirement.\u003c/p\u003e \u003cp\u003eWhen the consistency check is passed, the eigenvector corresponding to the largest eigenvalue is the weight vector. Standardizing the weight vector gives the weight of the indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w_j}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCalculation of combined weights\u003c/p\u003e \u003cp\u003eThe objective weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega _j}\\)\u003c/span\u003e\u003c/span\u003e obtained through the entropy weight method and the subjective weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w_j}\\)\u003c/span\u003e\u003c/span\u003eobtained through the analytic hierarchy process are linearly combined to derive the combined weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\omega\\)\u003c/span\u003e\u003c/span\u003e.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\omega =\\alpha {\\omega _j}+\\left( {1 - \\alpha } \\right){w_j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\omega\\)\u003c/span\u003e\u003c/span\u003e represents the combined weight, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003erepresent the relative importance of the two weight calculation methods, satisfying\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0 \\leqslant \\alpha \\leqslant 1\\)\u003c/span\u003e\u003c/span\u003e. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha =0.6\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive Evaluation Index of WSE\u003c/h2\u003e \u003cp\u003eSubsystem Comprehensive Evaluation Index:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\left\\{ {\\begin{array}{*{20}{c}} {f\\left( x \\right)=\\sum\\limits_{{a=1}}^{m} {{\\omega _a}x_{a}^{\\prime }} } \\\\ {g\\left( y \\right)=\\sum\\limits_{{b=1}}^{n} {{\\omega _b}x_{b}^{\\prime }} } \\\\ {h\\left( z \\right)=\\sum\\limits_{{c=1}}^{k} {{\\omega _c}x_{c}^{\\prime }} } \\end{array}} \\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ef\u003c/em\u003e(x)、\u003cem\u003eg\u003c/em\u003e(y) and \u003cem\u003eh\u003c/em\u003e(z) represent the comprehensive evaluation indices for the water resource system, socio-economic system, and ecological environment system, respectively. \u003cem\u003em, n, k\u003c/em\u003e denote the number of indicators in each subsystem, where in this case, m\u0026thinsp;=\u0026thinsp;8, n\u0026thinsp;=\u0026thinsp;9, k\u0026thinsp;=\u0026thinsp;7. The combined weights of each indicator in the respective subsystems are denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega _a}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega _b}\\)\u003c/span\u003e\u003c/span\u003e, and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega _c}\\)\u003c/span\u003e\u003c/span\u003e. The standardized values of each indicator are represented by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{a}^{\\prime }\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{b}^{\\prime }\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x_{c}^{\\prime }\\)\u003c/span\u003e\u003c/span\u003e.\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$T=\\alpha f\\left( x \\right)+\\beta g\\left( y \\right)+\\gamma h\\left( z \\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eT\u003c/em\u003e represents the comprehensive evaluation index of the water resources-socioeconomic-ecological environment system. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003eand\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003erepresent the relative importance of the three subsystems. In this case, they are set as equal, i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha =\\beta =\\gamma =\\frac{1}{3}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCoupling Coordination Degree Model\u003c/h2\u003e \u003cp\u003eConstructing a coupling coordination degree model for the water resources-socioeconomic-ecological environment system\u003csup\u003e22\u003c/sup\u003e:\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$C=\\frac{{3\\sqrt[3]{{f\\left( x \\right)g\\left( y \\right)h\\left( z \\right)}}}}{{f\\left( x \\right)+g\\left( y \\right)+h\\left( z \\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$D=\\sqrt {CT}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eC\u003c/em\u003e represents the coupling degree. When \u003cem\u003eC\u003c/em\u003e \u0026isin; [0, 0.3), the system is in a low-level coupling stage; when \u003cem\u003eC\u003c/em\u003e \u0026isin; [0.3, 0.5), the system is in an antagonistic phase; when \u003cem\u003eC\u003c/em\u003e \u0026isin; [0.5, 0.8), the system is in a running-in phase; when \u003cem\u003eC\u003c/em\u003e \u0026isin; [0.8, 1], the system is in a high-level coupling stage. \u003cem\u003eD\u003c/em\u003e represents the coupling coordination degree, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(0 \\leqslant D \\leqslant 1\\)\u003c/span\u003e\u003c/span\u003e. Based on existing research results\u003csup\u003e23,24\u003c/sup\u003e, the coupling coordination degree is classified as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eclassification of coupling coordination degree\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoupling coordination degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoupling Coordination Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoupling coordination degree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoupling Coordination Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.0\u0026thinsp;~\u0026thinsp;0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtreme disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.5\u0026thinsp;~\u0026thinsp;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBarely coordinated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.1\u0026thinsp;~\u0026thinsp;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.6\u0026thinsp;~\u0026thinsp;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePreliminary coordination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.2\u0026thinsp;~\u0026thinsp;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.7\u0026thinsp;~\u0026thinsp;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate coordination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.3\u0026thinsp;~\u0026thinsp;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.8\u0026thinsp;~\u0026thinsp;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood Coordination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.4\u0026thinsp;~\u0026thinsp;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNearly dysfunctional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.9\u0026thinsp;~\u0026thinsp;1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh quality coordination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGrey Relational Analysis\u003c/h2\u003e \u003cp\u003eGrey relational analysis is a multi-attribute decision-making method proposed by Kuo et al.\u003csup\u003e25\u003c/sup\u003e This method judges the closeness of different sequences by the similarity of the geometric shape of the sequence curves. Through grey relational analysis, the key factors affecting the coupling coordination degree can be identified. The grey relational degree between the coupling coordination degree and the selected indicators of each city in the Golden Triangle of the Yellow River is calculated, with the coupling coordination degree selected as the reference sequence and each indicator as the comparison sequence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndicator System Construction\u003c/h2\u003e \u003cp\u003eBased on principles of scientificity, systematicness, comprehensiveness, and indicator accessibility\u003csup\u003e26\u003c/sup\u003e, this paper constructs three subsystems for the four cities in the Golden Triangle of the Yellow River region: water resources, socio-economic, and ecological systems. For the water resource system, eight indicators are selected from three aspects: water resource endowment, water usage structure, and water resource utilization degree. For the socio-economic system, nine indicators are chosen from two perspectives: regional economic structure and social development level. For the ecological environment system, seven indicators are picked from ecological conditions and environmental pressure. Altogether, 24 indicators are selected to construct the evaluation indicator system for the water resource-socioeconomic-ecological environment system in the Golden Triangle of the Yellow River, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe coupling evaluation index system of WSE system in the Yellow River Golden Triangle region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriterion layer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAttribute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eunit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCombined weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e \u003cp\u003eWater resources - social economy - ecological environment system evaluation index system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eWater resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita water resources /X\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003em\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of industrial water use / X\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of domestic water use /X\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita water consumption /X\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003em\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater production modulus /X\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,000 m\u0026sup3;/km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIrrigation water per mu of farmland /X\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003em\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater consumption per 10,000 yuan of GDP / X\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003em\u0026sup3;/\u003c/p\u003e \u003cp\u003e10,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of groundwater supply /X\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eSocial economy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita GDP /Y\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYuan/ person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of primary industry in GDP /Y\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of secondary industry in GDP /Y\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe proportion of tertiary industry in GDP /Y\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita net income of rural residents /Y\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita disposable income of urban residents /Y\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrbanization rate /Y\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation density /Y\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePerson/ km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal retail sales of consumer goods /Y\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003csup\u003e8\u003c/sup\u003e yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eEcological environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen coverage rate of built-up area /Z\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of water used for ecological environment /Z\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePer capita green park area /Z\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e㎡\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComprehensive utilization rate of solid waste /Z\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWaste water discharge per 10,000 yuan of GDP /Z\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTons/ 10,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSulfur dioxide emissions per 10,000 yuan of GDP /Z\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTons/ 10,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNitrogen oxide emissions per 10,000 yuan of GDP /Z\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTons/ 10,000 yuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive Evaluation of the WSE System in the Golden Triangle Region of the Yellow River\u003c/h2\u003e \u003cp\u003eThe comprehensive evaluation index reflects the integrated development level of water resources, socioeconomics, ecological environment, and their coupled system in the Golden Triangle Region of the Yellow River, as shown in Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;2(a), the development level of water resources in the Golden Triangle Region of the Yellow River is not high, generally below 0.5 from 2011 to 2019, but it has significantly improved from 2020 to 2021. The development of water resources in Sanmenxia City is relatively unstable, showing a fluctuating state overall. It reached the worst development level in 2017 (0.223) and then fluctuated upward, reaching the best development level (0.739) in 2021. The development of water resources in Weinan City showed a trend of decreasing first and then increasing. After reaching the worst development level in 2016 (0.152), it gradually improved to the best level (0.769), with an increase of 406%. The water resources in Yuncheng City showed a steady upward trend overall. In 2012, it reached the lowest water resources development index value of 0.122 among the four cities, and then gradually increased to the highest water resources development index value of 0.913 among the four cities, with an increase of 648%. The development of water resources in Linfen City was relatively stable before 2020, but the development level was not high. It began to increase significantly in 2020.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;2(b), the socioeconomic development in the Golden Triangle Region of the Yellow River shows a trend of gradual improvement overall. The socioeconomic subsystem development level of Sanmenxia City is the best among the four cities, with the socioeconomic evaluation index increasing from 0.289 in 2011 to 0.762 in 2021. Weinan City has the fastest socioeconomic development among the four cities, with the socioeconomic evaluation index increasing from 0.161 in 2011 to 0.651 in 2021, an increase of 304%. The socioeconomic development of Yuncheng City and Linfen City is similar, but Yuncheng City's development is more stable. From 2011 to 2021, the socioeconomic evaluation index never decreased, steadily increasing from 0.265 in 2011 to 0.732 in 2021.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;2(c), the ecological environment development in the Golden Triangle Region of the Yellow River shows an upward trend overall. The development level of the ecological environment subsystem in the four cities was not high before 2016, generally below 0.4. However, after 2016, the development level of the ecological environment in the four cities rapidly improved. Among them, the development level of the ecological environment in Sanmenxia City was the worst among the four cities before 2016, and its ecological environment evaluation index was all below 0.2. The comprehensive evaluation index of the ecological environment in Weinan City has grown steadily, showing a steady upward trend overall. The development level of the ecological environment in Yuncheng City was higher than that in Linfen City before 2014. After 2014, the ecological environment of Yuncheng City showed a gradual growth trend, while the ecological environment of Linfen City showed an upward trend overall, but the upward trend was unstable.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;2(d), the development of the water resources-socioeconomic-ecological environment system in the Golden Triangle Region of the Yellow River can be divided into two stages: the low-level development stage from 2011 to 2016 and the rapid development stage from 2016 to 2021. Before 2016, the comprehensive evaluation index of the WSE coupling system has been less than 0.4, which is at a relatively low development level. During this period, the comprehensive evaluation index of the coupling system in Linfen City and Yuncheng City fluctuated somewhat. After 2016, the comprehensive evaluation index of the coupling system in the four cities rose rapidly. Sanmenxia City has been in a leading position before 2020, but due to the impact of water resources, the comprehensive evaluation index has decreased, putting it in a backward position. Linfen City has developed the fastest, with an increase from 0.260 in 2011 to 0.836 in 2021, an increase of 221%.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of WSE Coupling Coordination Degree\u003c/h2\u003e \u003cp\u003eBased on the coupling coordination degree model, the coupling degree and coupling coordination degree of the WSE coupling system for various cities in the Golden Triangle of the Yellow River region from 2011 to 2021 are calculated, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e to 5.\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the WSE coupling degrees of the four cities in the Golden Triangle of the Yellow River region from 2011 to 2021 are all higher than 0.8, indicating a high-level coupling stage. This suggests that the water resources subsystem, socio-economic subsystem, and ecological environment subsystem in each city of the Golden Triangle of the Yellow River region are closely connected, and the subsystems strongly interact with each other. However, there are fluctuations in the coupling degree of each city during development. For example, the WSE coupling degree of Sanmenxia City dropped from 0.963 in 2019 to 0.894 in 2020. This indicates that although the WSE system in each city is well-coupled, the subsystem development is not stable, which affects the coordinated development of the WSE coupling to some extent.\u003c/p\u003e\u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the coupling coordination degree of the WSE system in the Golden Triangle of the Yellow River region shows a trend of steady development followed by a gradual increase. From 2011 to 2015, there was little change in coupling coordination degree, but it rose from a barely coordinated stage in 2016 to a well-coordinated stage in 2021.\u003c/p\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;5, before 2016, the subsystems of water resources, socio-economic, and ecological environment in the four cities of the Golden Triangle of the Yellow River were not highly developed, keeping the coupling coordination degree of the WSE system at the verge of imbalance or barely coordinated stage. However, since 2016, with the economic growth and improvement of the ecological environment in various cities, the coupling coordination degree of the WSE system in each city has begun to gradually increase. Among them, Yuncheng and Linfen have seen the largest increase in coupling coordination degree, rising from a barely coordinated stage to a high-quality coordinated stage. Although the coupling coordination degree of the WSE system in Sanmenxia has increased, it declined in 2020 due to the influence of the water resources subsystem, showing a fluctuating upward trend. In 2021, the development of water resources, socio-economic, and ecological environment in the four cities was relatively similar. The three subsystems interacted and promoted development together, resulting in a significant increase in the coupling coordination degree of the four cities.\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThe evaluation indices for water resources, socio-economic, ecological environment, and the comprehensive evaluation index of the coupling system in the Golden Triangle of the Yellow River region indicate that there were minimal differences among the systems before 2016. However, after 2016, the comprehensive evaluation indices for socio-economic and ecological environment increased. With the implementation of the 13th Five-Year Plan, the government has strengthened its supervision of high-polluting enterprises, significantly reducing wastewater and exhaust emissions, leading to a substantial increase in the ecological environment evaluation index. Simultaneously, as the industrial structure gradually shifts from \"secondary, tertiary, primary\" to \"tertiary, secondary, primary,\" with the tertiary industry surpassing the secondary industry in proportion, the service industry in the Golden Triangle of the Yellow River region has gradually become the leading industry driving economic growth, further promoting economic development. The water resources evaluation index remained at a relatively low level until 2020. The increase in the evaluation index from 2020 to 2021 was primarily attributed to abundant rainfall, with the average rainfall in 2021 increasing by more than 50% compared to the multi-year average. As economic growth and urbanization accelerate, the demand for water resources has further increased. However, due to the limitation of total water resources, the water resources evaluation index remains low, exacerbating the contradiction between socio-economic development, ecological environmental protection, and water resource development and utilization. The lack of water resources has hindered the development of the Golden Triangle of the Yellow River.\u003c/p\u003e \u003cp\u003eThe coupling coordination degree of the WSE system in the cities of the Golden Triangle of the Yellow River region generally shows a trend of steady development followed by a gradual increase. From 2011 to 2015, the coupling coordination degree of the four cities in the Golden Triangle of the Yellow River was in a barely coordinated stage. During this period, the economic development of the Golden Triangle of the Yellow River was backward, and the urbanization rate was low. To achieve rapid economic growth, protection of the ecological environment was ignored, resulting in massive emissions of wastewater and exhaust. Additionally, due to water scarcity, unreasonable water use structure, and insufficient supervision of water-intensive industries, water resource utilization was inefficient. From 2016 to 2021, the coupling coordination degree of the WSE system in the cities of the Golden Triangle of the Yellow River rose from a barely coordinated stage to a well-coordinated stage. During this period, with the implementation of the 13th Five-Year Plan, the economy developed rapidly, the urbanization rate continuously increased, and ecological environmental governance achieved remarkable results. The comprehensive evaluation indices of the socio-economic subsystem and ecological environment subsystem increased, elevating the coupling coordination degree of the WSE system. In 2021, abundant rainfall in the Golden Triangle of the Yellow River region, coupled with improvements in water equipment in water-intensive industries, led to improved water resource allocation. This resulted in a notable upward trend in the comprehensive evaluation index of water resources, which further promoted economic development and ecological environmental improvement, ultimately leading to a well-coordinated stage of coupling coordination.\u003c/p\u003e \u003cp\u003eThe influencing factors of the coupling coordination degree of the WSE system in the cities of the Golden Triangle of the Yellow River are shown in Fig.\u0026nbsp;6. Indicators with a gray correlation degree exceeding 0.9 are selected as the main influencing factors. The socio-economic subsystem is the most critical subsystem affecting the coupled and coordinated development of the WSE system in Sanmenxia City. The socio-economic subsystem has the largest proportion of indicators (42.9%), while the ecological environment subsystem has the smallest proportion (25.2%). Among them, the indicator with the highest correlation degree is Y1 (per capita GDP), followed by Y6 (per capita disposable income of urban residents). Most indicators of the socio-economic subsystem have a correlation degree of 0.9. The proportion of subsystem indicators in Weinan, Yuncheng, and Linfen is similar, with the water resources subsystem accounting for approximately 34%, the socio-economic subsystem accounting for approximately 41%, and the ecological environment subsystem accounting for approximately 25%. Among the factors influencing the coupling coordination degree of the WSE system in Weinan City, the indicator with the highest gray correlation degree is Y6 (per capita disposable income of urban residents), followed by Y1 (per capita GDP), Y7 (urbanization rate), and Z3 (per capita park green area). X3 (proportion of domestic water use), Y4 (proportion of tertiary industry in GDP), and Y5 (per capita net income of rural residents) also have a correlation degree of 0.9. Among the factors affecting the coupling coordination degree of the WSE system in Yuncheng City, the indicators with the highest gray correlation degree are Y6 (per capita disposable income of urban residents) and Y9 (total retail sales of social consumer goods), followed by Y1 (per capita GDP). For the coupling coordination degree of the WSE system in Linfen City, the indicator with the highest gray correlation degree is Y1 (per capita GDP), followed by Y6 (per capita disposable income of urban residents), Y9 (total retail sales of consumer goods), and X3 (proportion of domestic water use).\u003c/p\u003e \u003cp\u003eThe identification of impact factors indicates that the indicators of the socio-economic subsystem are the main factors affecting the coupled and coordinated development of the WSE system in the Golden Triangle of the Yellow River region, mainly including indicators such as GDP per capita, the proportion of the tertiary industry in GDP, disposable income per capita of urban residents, net income per rural resident, and total retail sales of social consumer goods. At the same time, indicators such as the proportion of domestic water consumption and green park area per capita also greatly affect the coupled and coordinated development of the WSE system in the Golden Triangle of the Yellow River. Therefore, while accelerating economic development, optimizing industrial structure, and increasing residents' income, the Golden Triangle of the Yellow River should also rationally develop and utilize water resources, optimize water resource allocation, and focus on the development of water resources and ecological environment while emphasizing economic development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on the coupling coordination degree model of water resources, social economy, and ecological environment in the Golden Triangle of the Yellow River, an evaluation index system for the coupled and coordinated development of the water resources, social economy, and ecological environment system in the Golden Triangle of the Yellow River was constructed. The comprehensive evaluation index, coordination degree, and coupling coordination degree trends of the water resources, social economy, and ecological environment systems of various cities from 2011 to 2021 were studied. Grey correlation analysis was used to study the influencing factors that affect the coupled and coordinated development of the water resources, social economy, and ecological environment system in the Golden Triangle of the Yellow River. The main conclusions are as follows:\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The comprehensive evaluation index of the Golden Triangle of the Yellow River indicates that the comprehensive evaluation index of the socio-economic subsystem, ecological environment subsystem, and coupling system shows a trend of steady development followed by a gradual increase. There are large fluctuations in the ecological environment subsystems of Yuncheng and Linfen during their development, and the water resources subsystem was poorly developed before 2020, restricting socio-economic development and ecological environmental protection and governance.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The coupling degree of the three subsystems in the Golden Triangle of the Yellow River has always been at a high level of coupling stage. The three subsystems are closely connected and strongly influence each other. The coupling coordination degree of the coupling system shows a trend of steady development followed by a gradual increase, rising from a barely coordinated stage to a well-coordinated stage. Among them, Yuncheng and Linfen have risen to a superior coordination stage, showing a good overall development trend.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) According to grey correlation analysis, the socio-economic subsystem has the greatest impact on the coupled and coordinated development of water resources, social economy, and ecological environment in the Golden Triangle of the Yellow River, followed by the water resources subsystem. Among them, indicators such as GDP per capita, the proportion of the tertiary industry in GDP, disposable income per capita of urban residents, net income per rural resident, and water consumption per capita are the most important factors affecting the coupled and coordinated development of water resources, social economy, and ecological environment in the Golden Triangle of the Yellow River.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the National Natural Science Foundation of China (No. 42041006), the Major Science and Technology Project of Henan Province (No. 231100320100), Henan Province Natural Science Foundation Project (No.222300420013, No.242300420039), the Significant Science and Technology Project of Ministry of Water Resources (No. SKS-2022011), the Excellent Young Talents Project of Yellow River Conservancy Commission (No. HQK-202309).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.K. methodology, software, data curation, writing-original draft. L.X. conceptualization, proofreading. Z.W. proofreading. X.G. data collection. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarston, L., Cai, X. M. An overview of water reallocation and the barriers to its implementation. WIREs Water. 3, 658\u0026ndash;677, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wat2.1159\u003c/span\u003e\u003cspan address=\"10.1002/wat2.1159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, Z. L., Zuo, Q. T. 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To explore the interactive relationship and driving factors between water resources, social economy, and ecological environment in the Yellow River Golden Triangle region, taking the Yellow River Golden Triangle region as the research object in this paper. By constructing a coupling coordination evaluation index system of water resources, social economy, and ecological environment system, the coupling coordination development of this region from 2011 to 2021 is studied using the coupling coordination degree model, and the influencing factors of coupling coordination development are identified by grey relational analysis. The results show that from 2011 to 2021, the comprehensive evaluation index of water resources, social economy, and ecological environment in the Yellow River Golden Triangle region shows a trend of steady development followed by a gradual increase. The water resources subsystem restricts the development of the coupling system. The coupling coordination degree increased from a barely coordinated stage in 2011 to a well-coordinated stage in 2021. The coupling and coordinated development of Yuncheng and Linfen cities is better than that of Sanmenxia and Weinan cities. The social economy subsystem and water resources subsystem are the main factors affecting the coordinated development of the coupling system.\u003c/p\u003e","manuscriptTitle":"Coupling Coordination Analysis of Water Resources-Social Economy-Ecological Environment in the Yellow River Golden Triangle Area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 07:55:47","doi":"10.21203/rs.3.rs-4573159/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e72b1c6-229a-4109-bd94-85bd5d14a8b5","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33915311,"name":"Earth and environmental sciences/Environmental sciences"},{"id":33915312,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2024-10-07T05:24:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-01 07:55:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4573159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4573159","identity":"rs-4573159","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}