Research on Ecological Protection and High-Quality Development of the Lower Yellow River Based on System Dynamics

Abstract

The harmonious development between water resources, the economy, and the environment is the foundation of regional high-quality development, and the three are closely related. Based on a full analysis of the current situation of the lower Yellow River, this paper combines domestic and foreign studies, compares the framework of the relationship between water resources, the economy, and environment systems, selects 30 indicators such as average rainfall, natural population growth rate, PM2.5, etc., and establishes an evaluation model of the coupled coordination of the water resources–economy–environment system of the lower Yellow River, and then uses system dynamics to simulate and predict the degree of coupled coordination in the sub-scenarios. The conclusions are as follows: (1) The level of coordinated development in the water resources–environment system outperforms that of the water resources–economy and economy–environment systems in the cities along the lower Yellow River. (2) The coupling coordination degree of the lower reaches of the Yellow River increases from 0.5849 to 0.8209 during the period of 2006–2020, with an average annual increase of 2.62%. (3) A sub-scenario simulation for the 2006–2020 period indicated that the coordination development of the WEE system is optimal under the scenario 4 model, with the coupling coordination degree expected to reach 0.8928 in 2035, marking the advent of high-quality coordinated development. This study can provide a theoretical basis for the ecological protection and high-quality development of the Yellow River Basin.

1. Introduction

Water resources, economy, and environment are intricately interconnected. Water resources serve as the bedrock for environmental conservation and economic growth. In turn, environmental protection underpins economic development, while economic expansion drives environmental preservation [1]. Since the 18th Party Congress, collective efforts from relevant regions and departments have intensified ecological protection and environmental management. They have actively promoted the economical and intensive use of water resources, markedly enhancing the ecological environment of the Yellow River Basin.

The precondition for high-quality growth in the basin is the coordinated development of these systems––a vital requirement for social progress. Scholars’ investigations into coordinated development have evolved from qualitative studies into a blend of qualitative and quantitative research. The advent of econometric modeling has spearheaded the robust development and application of these combined research methodologies. Techniques such as hierarchical analysis [2], the entropy method [3], and gray correlation analysis [4,5] have found wide-ranging applications in resource and environment studies. Building on this research, some scholars have incorporated water and carbon footprints to examine the harmony between resources and the environment [6]. In recent times, the broad adoption of remote sensing (RS) and geographic information system (GIS), along with “3S” spatial analysis, has propelled the study of coordinated development to a new plateau [7,8].

Water resources serve as invaluable natural and economic assets and are a fundamental assurance for the positive development of environmental systems. Research into the coupling and coordination of water resources–economy–environment (WEE) systems has increasingly become a focal point for regional high-quality development studies. Many scholars at home and abroad have performed a series of studies on system coupling.

The concept of “system coupling” was first introduced by Harken in the 1970s, who suggested that the natural world consists of numerous systems with varying properties. Despite their different compositions, these systems may share tight connections with each other [9]. In 1993, the United Nations Statistics Division published the first draft of the System of Integrated Environmental and Economic Accounting (SEEA), constructing a framework for capital flows and accounting for environmental resource stocks. Subsequently, in 2003, the United Nations Statistics Division introduced the latest version of the SEEA, encouraging conceptual shifts in the direct linkages between the economy, resources (including water resources), and the environment [10]. This formed the bedrock for the coupled WEE theory, triggering an upsurge in scholarly interest in this subject area.

Several researchers, such as Juniah et al., used South Sumatra, Indonesia as their study area and argued for the potential economic value of water resources and environmental sustainability [11]. Costanza emphasized that the pursuit of sustainable development involves not only rapid economic growth and enhanced environmental protection but also rational resource management within a social framework, maximizing resource utilization [12]. Mutisya and Yarime used Zambia as their study area to examine the coupling and coordination of resource, economic, and environmental systems, suggesting a need for reinforced social management to improve coupling degrees [13].

In October 2021, the State Council of the Central Committee of the Communist Party of China unveiled the Outline of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin. It highlighted that the core challenges obstructing the high-quality development of the Yellow River Basin included sluggish economic growth, water resource scarcity, localized ecological pollution, and an elevated potential for ecological crises. Accordingly, many scholars have researched the coupled water resources, economic, and environmental systems in the Yellow River Basin.

Gao et al. [14] investigated the level and driving factors of the coupled and coordinated development of the WEE system in the Yellow River Basin. Their findings showed that the integrated development of the three systems in the Yellow River Basin was still at an early stage, displaying significant disparities and a clear east–west imbalance. Meanwhile, Jiang et al. [15] developed a coupled coordination system between the WEE system, studying its spatiotemporal evolution. They found that the water resources subsystem lagged considerably, and while the level of coupling coordination between the three systems had gradually improved over time, progress was slow. Zhang et al. [16] incorporated the extreme difference change method into the evaluation of the coupled coordination level of the resource–environment–economy complex system in the Yellow River Basin. They concluded that the comprehensive evaluation value of the resource subsystem was higher than that of the economic subsystem, which in turn was greater than that of the environmental subsystem. Although these subsystems underwent a favorable transition in 2011, considerable mutual constraints among them still persisted. Zhao et al. [17] established a binary and quadratic coupled coordination system, which was grounded in the development status of each dimension of energy, economy, environment, and ecology in the Yellow River Basin. They then provided strategic suggestions for the high-quality development of the Yellow River Basin, based on varying regional coupling and coordination levels.

System modeling and simulation constitute a major method for studying the degree of coordination of coupled systems. Research into coordinated development model instances dates back to the evolution of traditional linear optimization methods into modern nonlinear techniques, and subsequently to the combination of multiple methods. Morshed et al. [18] provided an overview of the progress of genetic algorithms in handling nonlinear, nonconvex, and discontinuous aspects, suggesting areas for future improvements. Madani et al. [19] established a multi-objective water allocation model under uncertainty, solving it using genetic algorithms. Prodanovic et al. [20] constructed a coupled hydrological simulation and outlined two modules of socioeconomic processes, using a systems approach to assess the risk and vulnerability of the Upper Thames watershed in Canada under coupled scenarios of climate pattern changes and socioeconomic development.

Artificial neural networks (ANNs) and MATLAB, as commonly used methods and tools for modeling control systems’ coupling processes, are primarily employed for computational modeling and the prediction of coupling coordination during the analysis of coupled systems [21]. This includes neural network predictive modeling of multivariate coupled systems [22], and controlling the spatiotemporal chaotic behavior of such systems [23]. Wu et al. [24] used a coupled ANN and GIS to predict and evaluate the oil production potential of the 30 Submerged Hills reservoir in Chengbei, Shandong Province. Applying a sensitivity analysis, they systematically evaluated the sensitivity of each primary control factor in the area, effectively addressing the difficulty of determining the influence degree of each factor through the weight coefficient matrix in ANNs. Moreover, gray correlation degree models [25] can be applied to research on the coupled coordination relationship. Liu et al. [26] uncovered the key factors of coupled coordination between urbanization and ecological environment systems in Chinese provinces and regions. They analyzed the spatial distribution and evolution patterns of regional coupling coordination degrees from spatial and temporal viewpoints. Xie et al. [27] proposed a novel probabilistic statistics-based coupling coordination analysis method. Simulation experiments indicated that such an analysis method has wide-ranging application potential. Furthermore, issues such as coupled system coordination optimization invariably come into play during system modeling. Methods for system optimization, such as the coupled system co-evolutionary multidisciplinary design optimization algorithm (MDO) [28] and parallel global sensitivity equation method (GSE) [29], can decompose complex systems into simpler subsystems, significantly enhancing the solution efficiency.

The system dynamics approach, a prominent method for simulating and predicting the interactions and dynamic evolution within a system’s coordinated development, can address complex problems with multiple variables, nonlinear relationships, and numerous feedback loops within the system’s structure [30,31,32,33,34]. This method has been employed in China for over two decades, with significant progress made in its application to sustainable development studies. These studies have greatly influenced sustainable development strategies, with examples including population sustainable development models [35] and energy sustainable development models [36] in strategic decision making for sustainable development. Regarding regional environmental carrying capacity, Chinese scholars have established modules for water resources, land resources, and forest resources [37,38,39]. Tang et al. [40] simulated the dynamic complexity of forest resources based on the evolutionary process of forest resources, using data from Da’an City, Jilin Province.

Nevertheless, studies on the coordinated evaluation of regional WEE systems have some limitations. First, as the mouth of the Yellow River, the lower Yellow River, provide important external water resources for the economic and social development of the regions along the Yellow River and the recharge of groundwater, and the delta in the lower reaches of the Yellow River is the most complete wetland ecosystem in the warm temperate zone of China, its coordinated development or not has a large impact on the high-quality development and ecological protection of the Yellow River Basin. However, current research has not adequately addressed this, with most studies focusing on national or provincial subjects, and few articles based on the lower reaches of the Yellow River. Second, while prediction is a popular research topic, scholars predominantly apply prediction models to forecast the coupling coordination degree of composite systems. Many rely on the GM(1,1) [25] prediction model, while others use time series prediction models such as ARIMA [41] and the BP [42] neural network to analyze and predict the coupling coordination degree. However, these models heavily depend on time series, making predictions of coupling coordination less scientifically robust. Based on the above deficiencies, this paper innovatively puts forward a comprehensive evaluation index system of the downstream Yellow River water resources–economy–environment system, and utilizes the improved combination weight method, modified combined weighting method, and other methods to analyze and evaluate the high-quality development of the cities along the lower Yellow River, and, for the first time, utilizes system dynamics to simulate the prediction of sub-scenarios on the coupled and coordinated development of the downstream Yellow River water resources–economy–environment system.

The structure of this paper is as follows: first, we propose an evaluation index system for the WEE system. The weights of each index are then determined using an improved combined weighting method, and a coupled coordination evaluation model of the lower Yellow River’s WEE system was constructed to investigate the development level of the water resources, economy, and environment subsystems. Ultimately, the paper utilizes system dynamics to predict the development trend of the coupling coordination degree of the downstream of the Yellow River in the next 15 years based on the data of the historical validation period, adjusting the parameter changes and setting up different scenarios according to the prediction results combined with relevant planning, seeking for a better-quality development of the Yellow River Basin.

2. Overview of the Study Area and Research Methods

2.1. Overview of the Study Area

The Yellow River section below Taohua Valley in Zhengzhou, Henan Province is the lower Yellow River, with a river length of 786 km and a basin area of only 23,000 square kilometers, accounting for 3% of the whole basin area; the downstream section has a total drop of 93.6 m, and an average drop of 0.12‰; the interval of the increase in the amount of water accounted for 3.5 percent of the amount of water of the Yellow River. Due to the large amount of sediment in the Yellow River, the downstream section has been silted up for a long time to form the world-famous “hanging river on the ground”. Henan Province is an important agricultural, grain production, and animal husbandry province. Since 1978, the effective irrigation area of the Yellow River has increased continuously, from only 1.45 million mu to 13.6 million mu today. Shandong Province is located in the lower reaches of the Yellow River, which is the only big river in Shandong Province, and most of the urban areas in Shandong Province rely on the Yellow River for agricultural irrigation and industrial development. Therefore, this study selects 13 municipalities along the lower reaches of the Yellow River in Henan and Shandong provinces as the study object to comprehensively analyze and evaluate the current status of water resources, economy, and environmental system of the lower Yellow River. The study area is shown in Figure 1.

2.2. Research Methods

2.2.1. WEE Indicator System Construction

In this paper, we incorporate the realistic characteristics of the water resources, economy, and environmental subsystems when constructing the index system, providing a comprehensive view of the lower Yellow River’s WEE system. The whole system consists of 30 indicators encompassing the atmospheric environment, water environment, ecological environment, and social environment. The details of each indicator are presented in

Table 1. Comprehensive evaluation index system of the WEE system in the lower Yellow River.

Serial Number	System	Tier 1 Indicators	Tier 2 Indicators	Indicator Calculation	Unit
1	Water	Water supply capacity	Average annual rainfall	Multi-year rainfall/year	mm
2			Water resources per capita	Total water resources/total population	m3/person
3			Number of water production systems	Total water resources/total annual rainfall	-
4			Modulus of water supply	Water supply/land area	106 m3/km2
5			Water resources modulus	Total water resources/land area	106 m3/km2
6			Comprehensive production capacity of urban public water supply	Statistics	million m3/d
7		Water consumption	Total water consumption	Total annual water consumption	m3
8			Water resources development and utilization	Water consumption/total water resources	%
9			Water demand per capita	Water requirement/total population	m3/person
10			Water consumption of CNY 10,000 GDP	Water consumption/GDP	m3/million
11	Economy	Population development	Natural population growth rate	Birth rate-death rate	‰
12		Economic development	GDP per capita	GDP/total population	yuan/person
13			GDP growth rate	(Current GDP-base year GDP)/base year GDP	%
14			Proportion of primary production	Ratio of primary industry output to GDP	%
15			Proportion of secondary production	Ratio of secondary industry output to GDP	%
16			Ratio of tertiary production	Ratio of tertiary sector output to GDP	%
17			General budget revenue	Statistics	Billion
18		Social development	City municipal utilities construction Fixed asset investment completion	Statistics	Billion
19			Total retail sales of social consumer goods	Statistics	1010 yuan
20			Balance of deposits in financial institutions at the end of the year	Statistics	1010 yuan
21			Urbanization rate	Urban population/total population	%
22	Environment	Atmospheric environment	PM2.5	Statistics	µg/m3
23			Carbon dioxide emissions	Total gas supply, total electricity consumption, and total heat supply are summed by multiplying the carbon emission factors	million tons
24		Water environment	Wastewater discharge	Statistics	million m3
25			Sulfur dioxide emissions	Statistics	tons/year
26		Ecological environment	Ecological water use rate	Ecological water consumption/total water consumption	%
27			Ecological attention	Government documents word frequency search	times
28		Social environment	Greening coverage of built-up areas	Greening coverage area/total area	%
29			Harmless disposal rate of domestic waste	Amount of harmless disposal of domestic waste/total domestic waste	%
30			Sewage treatment rate	Wastewater treatment capacity/total sewage discharge	%

.

2.2.2. Modified AHP—CRITIC Combined Weighting Method

Two methods exist for determining attribute weighting: the subjective and objective assignment methods. The subjective assignment method consists of determining attribute weights according to a decision maker’s subjective emphasis on each attribute, whose raw data are obtained by the decision maker’s subjective judgment based on experience; the subjective assignment method used in this paper is AHP [43] (analytic hierarchy process). The objective assignment method uses the relationship between the original data through certain mathematical methods to determine the weights, the results of its judgment does not depend on the subjective judgment of people; there is a strong mathematical theory behind it; the objective assignment method used in this paper is CRITIC [44] (criteria importance though intercriteria correlation). Both methods possess their own advantages and limitations and complement each other to some extent. A scientific approach involves the integration of subjective and objective weighting, thus employing a combined weighting method. Although this method is widely used in comprehensive evaluations, there is no standard procedure for determining weight coefficients during the combination of different weights.

In this paper, we employ a combined approach, using the analytic hierarchy process (AHP), a subjective assignment method introduced by Tomas L. Saaty in the 1970s, and the criteria importance through intercriteria correlation (CRITIC), an objective assignment method proposed by Diakoulaki. A mathematical model aimed at ensuring consistent subjective and objective information is established to find the combined coefficients of the subjective and objective weights. This method allows us to obtain attribute weights reflecting the degree of both subjectivity and objectivity [43,44,45].

In this paper, the subjective weight vector calculated by the AHP method is expressed as $w^{\prime }={(w_1^{\prime },w_2^{\prime },\cdots ,w_n^{\prime })}^T$, satisfying $0\le w_j^{\prime }\le 1,{\displaystyle \sum _{j=1}^n{w}_j^{\prime }=1}$; the objective weight vector calculated by the CRITIC method is $w^{″}={(w_1^{″},w_2^{″},\cdots ,w_n^{″})}^T$, satisfying $0\le w_j^{″}\le 1,{\displaystyle \sum _{j=1}^n{w}_j^{″}=1}$. By fusing the subjective and objective weights, the final weight vector is:

$$w=\alpha w^{\prime }+\beta w^{″}$$

(1)

where $\alpha ,\beta$ satisfy:

$$\alpha ,\beta \ge 0\mathrm{and}\alpha +\beta =1$$

(2)

With the aim of aligning the values of the weighted attributes determined using the subjective and objective weights, we develop an optimization model for determining the coefficients in the combined weights:

$$\min Z={\displaystyle \sum _{i=1}^m{d}_i}\min Z={\displaystyle \sum _{i=1}^m{d}_i}={{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n(\alpha r_i{}_j{w}_j^{\prime }-\beta r_i{}_j{w}_j^{″})}}}^2$$

(3)

Solving this model yields:

$$\alpha =\frac{{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{r}_{ij}^2{w}_j^{″}(w_j^{\prime }+w_j^{″})}}}{{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{r}_{ij}^2(w_j^{\prime }+w_j^{″}{)}^2}}}$$

(4)

$$\beta =\frac{{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{r}_{ij}^2{w}_j^{\prime }(w_j^{\prime }+w_j^{″})}}}{{\displaystyle \sum _{i=1}^m{\displaystyle \sum _{j=1}^n{r}_{ij}^2(w_j^{\prime }+w_j^{″}{)}^2}}}$$

(5)

The combined weight w can be determined by substituting Equations (4) and (5) into Equation (1).

The combined weighting method exploits the complementarity of subjective and objective weighting, making the process of assigning indicators more scientific and reasonable. Although this method is frequently used, no uniform standard exists for weighting subjective and objective methods during combination weighting. In this study, the combined weight calculation formula is utilized to establish a mathematical model for consistent objective and subjective information. We then calculate the combined weight coefficients for each subsystem of the lower Yellow River and cities along its course. The results are presented in Figure 2 (combined weight coefficients of the lower Yellow River and cities along the route obtained based on Formulas (1)–(5) and Figure 3 (Combined weights of 30 indicators obtained based on Formulas (1)–(5)).

2.2.3. Coupled Coordination Evaluation Method

Combining this with the weight calculation method, we obtain the comprehensive evaluation function.

$$W_i={\displaystyle \sum _{j=1}^m{w}_j{X}^{\prime }{}_{ij}}$$

(6)

$$E_i={\displaystyle \sum _{j=1}^m{w}_j{Y}^{\prime }{}_{ij}}$$

(7)

$$N_i={\displaystyle \sum _{j=1}^m{w}_j{Z}^{\prime }{}_{ij}}$$

(8)

where W_i, E_i, and N_i denote the comprehensive evaluation indices of water resources, economic, and environmental subsystems, respectively, $w_j$ represents the combined weight value of each subsystem indicator j, and $X^{\prime }{}_{ij}$, $Y^{\prime }{}_{ij}$, and $Z^{\prime }{}_{ij}$ symbolize the standardized data of indicator j in year i.

The coupled coordination evaluation model is widely used across various fields. In this paper, we based on prior research [46,47,48,49], refer to the results of three modified coupling formulas after correction [50]. The calculation formula is as follows:

$$C=\frac{3\sqrt[3]{W_i{E}_i{N}_i}}{W_i+E_i+N_i}$$

(9)

The coupling degree indicates the interaction level between the water resources, economy, and environmental subsystems, where 0 ≤ C ≤ 1. A larger C value signifies a more coordinated system. The classification results of the coupling degree are presented in

Table 2. Coupling coordination degree.

Degree of Coupling Category	Degree of Coupling Descriptor	Degree of Coupling Coordination Category	Type of Coupling Coordination
[0.0–0.3]	Low-level coupling	[0.0–0.1]	Extremely dysfunctional recession
		(0.1–0.2]	Severe dysregulation recession
(0.3–0.5]	Low-level coupling	(0.2–0.3]	Moderate dysregulation recession
		(0.3–0.4]	Mild dysregulation recession
(0.5–0.8]	Breaking-in coupling Breaking-in coupling	(0.4–0.5]	On the verge of dysfunctional recession
		(0.5–0.6]	Barely coordinated development
		(0.6–0.7]	Primary coordination development
(0.8–1.0]	High-level coupling	(0.7–0.8]	Intermediate coordination development
		(0.8–0.9]	Good coordination development
		(0.9–1.0]	High-quality coordinated development

.

To better reflect the level of coupled and coordinated development of the WEE system in the lower Yellow River, we apply the coupling coordination degree model for calculations. The formula is:

$$D=\sqrt{C\times T}$$

(10)

$$T=\alpha W_i+\beta E_i+\gamma N_i$$

(11)

where $\alpha$, $\beta$, and $\gamma$ represent the weight of the importance of each subsystem, D symbolizes the coupling coordination degree, and T denotes the system comprehensive evaluation index. It is assumed that the degree of influence of the water resources, economy, and environmental systems on the social development of the lower Yellow River is the same, so it is set as $\alpha =\beta =\gamma =1/3$.

This paper examines the degree of coupled and coordinated development of the WEE system in the lower Yellow River. Reference is made to existing research when grading the degree of coupled coordination [16,17,49], as tabulated in Table 2.

2.2.4. System Dynamics

System dynamics is a multidisciplinary field rooted in systems science. It integrates feedback and information theory, utilizing computer simulations and integration technology. The crux of this discipline is the quantitative depiction of the unique connections between various system variables and their feedback mechanisms. This is achieved through a system of first-order differential equations. From there, the causal links between different subsystems within the system are analyzed. The Vensim analysis software is utilized to draft system flow diagrams and construct a system dynamics model of the water environment [51].

Four primary types of equations are used in the system dynamics model to describe relationships between different variables: state equations, rate equations, auxiliary equations, and table functions [52].

State equations are used to represent the system’s state at a specific time point, as the system is in constant flux. State equations are primarily applied to system variables that accumulate effects over time. The state value of such a variable is the sum of its prior state’s original value and the amount of change. The equations used to calculate the values of these variables in system dynamics are collectively referred to as state equations and are represented by the following equations:

$$LK=LJ+DT(IR.JK-OR.JK)$$

(12)

where LK and LJ denote the states of variable L at moments K and J, JK is the time difference between moments K and J, DT represents the simulation time, whose magnitude is equal to JK, and IR.JK and OR.JK signify velocity variables.

Rate equations are used in system dynamics to represent the amount of change in the system per unit time. This equation can be derived from the transformation of Equation (12):

$$IR.JK-OR.JK=\frac{L.K-L.J}{DT}$$

(13)

The rate equation differs from the state equation, offering more flexibility in its format.

Auxiliary equations assist in establishing the productivity equation in the system dynamics model. They can take various formats, have flexible usage, and are often denoted by the letter A.

Table functions are employed when the relationship between the model’s variables of interest is not strictly linear, and some display a nonlinear relationship. Consequently, such variables cannot be represented through algebraic combinations of other variables. They need to be defined through graphs created from nonlinear data, which is the role of table functions. These are often denoted by the letter T.

The simulation of the coordinated development of the coupled WEE system of the lower Yellow River, using system dynamics, comprises five main steps [53]:

(1)

Determining the system boundary of the study area, and defining the base year for the study and the simulation time.

(2)

Outlining the subsystems of the WEE coupled system, identifying the causal relationships among the variables, subsystems, and their associated equation groups.

(3)

Utilizing the Vensim-PLE 9.3.5 software to create a flow diagram of the WEE coupled system, based on the defined subsystems, and establishing the system dynamics model.

(4)

Conducting a simulation analysis according to the established system dynamics model, verifying the simulation accuracy with reference year variables, and determining parameter rates based on the verification results.

(5)

Short-term simulation predictions of coupled WEE systems under different development scenarios using the debugged system dynamics model.

2.3. Data Source

The study of the degree of coupled and coordinated development of the WEE system in the lower reaches of the Yellow River utilized data from sources including the 2006–2020 China Statistical Yearbook, Shandong and Henan Provincial Statistical Yearbooks, Water Resources Bulletins of Shandong and Henan Provinces, as well as the 2006–2020 Municipal Statistical Yearbooks and Water Resources Bulletins of the lower reaches of the Yellow River.

3. Result and Analysis

3.1. Coupled Coordination Analysis and Evaluation

a.

Water resources–economy coupling coordination analysis

Figure 4 shows the trend of the evolution of the coupled water resources–economy system coordination degree of each city along the lower Yellow River during the period of 2006–2020. Figure 4 demonstrates that the coupling coordination degree of the water resources–economy of cities along the lower reaches of the Yellow River undergoes fluctuations and growth. The average coupling coordination degree rose from 0.5711 in 2006 to 0.7850 in 2020, evolving from barely coordinated to intermediate coordination. Specifically, Dongying and Jining, cities along the Shandong section of the Yellow River Basin, were at the primary coordination stage in 2006. Concurrently, Zhengzhou, Kaifeng, and Puyang––cities along the Henan section of the Yellow River––reached the primary coordination stage.

In Figure 4, the water resources–economy coupling coordination degree of some cities shows a faster decline in certain years. For instance, Jinan’s coupling coordination degree decreased by 15.63% in 2014. This is primarily associated with the drop in rainfall in Jinan that year (the average precipitation of Jinan in 2014 was 444.0 mm, 40.77% less than the 749.6 mm in 2013, which is lower than the multi-year average rainfall of 647.9 mm by 31.48%). Puyang’s water resources–economy coupling coordination declined by 17.45% in 2019, which is primarily due to a significant decrease in rainfall and water resources. As of 2020, among the 13 cities along the lower Yellow River, only Jinan, Zibo, Jining, Tai’an, Heze, and Zhengzhou, have reached a good coordination stage of water resources–economy coupling coordination. The remaining cities are still in the primary or intermediate coordination stage.

Figure 5 illustrates the evolution of the water resources–economy system’s coupling coordination in the lower Yellow River from 2006 to 2020. The system developed from barely coordinated in 2006 to well coordinated in 2020, with an intermediate average coupling coordination of 0.7083. The coupling coordination degree of this system experienced fluctuations in some years. For instance, in 2016, the commencement year of the 13th Five-Year Plan, there was a positive overall economic trend, and abundant rainfall in the lower Yellow River. These conditions led to an 11.42% increase in the coupling coordination degree of the water resources–economy compared to 2015.

However, a decrease of 7.09% was observed in 2017 compared to 2016. This decline was attributed to the severe overdraft of groundwater in the Henan section of the Yellow River Basin and the large discharge of industrial wastewater in the basin during that year. A more noticeable decline of 16.68% occurred in the water resources–economy coupling coordination in 2019, followed by a rebound of 16.06% in 2020. Overall, the 13th Five-Year Plan period experiences “W”-shaped fluctuations in the water resources–economy coupling coordination degree of the lower Yellow River. The overall degree of coupling coordination is significantly influenced by the water resources subsystem.

b.

Water resources–environmental coupling coordination analysis

Figure 6 presents the trend of the water resources–environment system coupling coordination in cities along the lower Yellow River. In 2006, only Zibo, Dezhou, Liaocheng, and Binzhou cities were at the barely coordinated stage of water resources–environment system coupling coordination. The other cities were at the primary or intermediate coordination stage, with an average coupling coordination degree of 0.6362 across 13 cities, representing the primary coordination stage.

By 2020, some cities along the lower Yellow River still remained at the intermediate coordination stage. These cities lagged slightly in the development of their water resources and environmental subsystems compared to other cities, indicating a need to bolster water resources conservation and ecological environmental protection. In the same year, Jinan, Zibo, Jining, Tai’an, Heze, Zhengzhou, and Xinxiang achieved good coupling coordination. Notably, the water resources–environment coupling coordination degree of Jining stood at 0.8997, almost reaching the stage of high-quality coordination.

Figure 7 depicts the coupling coordination degree of the water resources–environment system in the lower Yellow River from 2006 to 2020. The overall fluctuating growth trend shows improvement, with the coupling coordination degree ranging between 0.5992 and 0.8423. The system evolved from a barely coordinated stage to a good coordination stage, achieving an intermediate coupling coordination stage with a multi-year average value of 0.7341.

Significant fluctuations were seen in individual years. In particular, large increases occurred in 2007, 2009, 2016, and 2020 with rates of 11.91%, 11.42%, 11.00%, and 13.22%, respectively. More considerable decreases can be noted in 2017 and 2019, of 8.69% and 8.54%, respectively. Upon comparative analysis, it was found that years with large fluctuations in the coupling coordination of both the water resources–environment and water resources–economy systems are similar. Both systems are notably influenced by the water resources subsystem.

c.

Coupled economy–environmental coordination analysis

Figure 8 illustrates the development trend of the economy–environment system’s coupling coordination among cities along the lower Yellow River. From 2006 to 2010, the disparities in the economy–environment coupling coordination development among the cities were relatively small. However, from 2011 to 2019, these disparities widened. The average degree of economy–environment coupling coordination for the city group along the lower Yellow River in 2020 was 0.7939, marking an intermediate coordination stage on the verge of transitioning to a good coordination stage.

The coupling coordination degree between economy and environment for each city consistently exhibits an upward trend. However, substantial fluctuations in this value can be observed in individual years for certain cities. For instance, in 2015, the coupling coordination degree for Xinxiang experienced a significant decline, dropping by 14.55%. Similarly, in 2019, Dezhou noted a marked decrease in its coupling coordination degree, a drop of 11.83%. Meanwhile, the economy–environmental system coupling coordination degree of other cities revealed a different pattern. In 2014, Zhengzhou’s coupling coordination degree surged, posting an increase of 15.38%. The most substantial surge occurred in Dezhou in 2020, with an impressive increase of 15.01%. In 2016, Xinxiang’s coupling coordination degree rose considerably, up by 13.58%. Moreover, a substantial increase was observed in Jinan’s coupling coordination degree in 2014, increasing by 11.90%.

Considering the cities along the lower Yellow River from 2006 to 2020, the average annual economy–environmental system coupling coordination was calculated. Tai’an ranked highest, possessing an average annual value of 0.7488. Despite being in an intermediate coordination stage, it is still some distance from reaching an optimal coordination stage. On the other hand, Heze, Zhengzhou, Kaifeng, Puyang, and Xinxiang maintain average annual economy–environment system coupling coordination values below 0.7. These cities are still in the initial coordination stage.

The economy–environmental system’s coupling and coordination development trend along the lower Yellow River is displayed in Figure 9. This graph shows a more gradual growth trend with lesser overall fluctuations compared to the water resources–economy and water resources–environment coupling coordinations. Over the 2006–2020 period, the economy–environmental system coupling coordination ascended from 0.5962 to 0.8139, advancing from barely coordinated to well coordinated. The average annual growth rate during this period was 2.31%.

d.

Water resources–economy–environmental coupling coordination degree analysis

Figure 10 depicts the development level of the lower Yellow River’s WEE system’s coupling coordination. This metric grew from 0.5849 in 2006 to 0.8209 in 2020, a trend punctuated by periods of fluctuation. During the 11th and 12th Five-Year Plan periods, the trend was generally upward, with a slight decline in 2012 and significant fluctuations during the 13th Five-Year Plan.

In 2006, the WEE system barely reached coordination. From 2007 to 2010, a period coinciding with the 11th Five-Year Plan, primary coordination was achieved. During this time, the country focused on economic development alongside water conservation and environmental protection, with a significant push for sewage recycling. The Ministry of Construction organized the drafting of the “Yellow River Basin Urban Sewage Treatment Project Construction Eleventh Five-Year Plan” in 2006. The focus was on water quality, the state of urban water supply and drainage facilities, water pricing, and related issues. However, this period also witnessed a weak ecological environment in the Yellow River Basin, largely due to the most significant flood since 1986. The flood resulted in a peak flow rate of about 3200 cubic meters per second at some hydrological stations, causing severe siltation downstream.

During the 13th Five-Year Plan, the WEE system’s coupling coordination fluctuated between good and intermediate coordination. It achieved a level of 0.8107 in 2016, decreased to 0.7573 in 2017, increased to 0.8019 in 2018, maintained the same level in 2019, dropped to 0.7414 in 2019, and finally, rose to 0.8209 in 2020. The decrease in WEE coupling coordination in 2017 and 2019 was mainly due to a decrease in rainfall in several downstream cities.

From 2006 to 2020, the coupling coordination of WEE systems in cities along the Yellow River’s lower reaches transitioned from barely coordinated to well coordinated. In 2006, Jinan, Zibo, Dezhou, Liaocheng, Kaifeng, and Xinxiang barely achieved coordination, while the remaining cities were in primary coordination. By 2012, most cities along the Yellow River’s lower reaches had achieved primary coordination, with some even reaching intermediate coordination. In 2020, Jinan, Zibo, Jining, Tai’an, Zhengzhou, and Xinxiang achieved good coordination, while Dongying, Dezhou, Liaocheng, Binzhou, Heze, Kaifeng, and Puyang were still at intermediate coordination. However, Dezhou, Binzhou, and Heze were on the cusp of good coordination, with coupling coordination degrees of 0.7726, 0.7997, and 0.7877, respectively. These trends indicate that, amid rapid economic development, each city along the Yellow River’s lower reaches is continually adjusting its resource allocation and enhancing ecological protection. Consequently, the coupling coordination degree of the water resources–economy–environment system along the Yellow River’s lower reaches is steadily evolving toward good overall coordination.

3.2. Coupling Coordination Prediction

In this paper, we analyze a complex WEE system that interconnects multiple elements. To examine this complexity, a dynamic model of the lower Yellow River’s three subsystems is constructed. This model encapsulates 13 cities along the lower Yellow River and spans a simulation period from 2006 to 2035. The years 2006–2020 provide a historical validation period, and the years 2021–2035 project future trends, with a one-year time step.

a.

Flowchart creation

To formulate the model, we categorize the indicators from the manuscript into state variables, auxiliary variables, and velocity variables. Each system factor’s attributes dictate its classification, and we express their interrelationships quantitatively using numerical equations.

The water resources system’s state variable is total water consumption, with water supply acting as the velocity variable. Other key components of this subsystem, including average annual rainfall, water resources per capita, water supply modulus, and water consumption of CNY 10,000 GDP, serve as constant and auxiliary variables.

In the economy and social system, we consider GDP growth, total retail sales of social consumer goods, and year-end financial institution deposit balance as primary velocity variables. Additionally, we use general budget income, GDP per capita, and the ratios of primary, secondary, and tertiary production to illustrate the socioeconomic development in the lower reaches of the Yellow River, treating the remaining variables as auxiliary.

The eco-environmental system uses the urban green area and ecological water demand as the main variables, complemented by PM_2.5, sulfur dioxide emissions, sewage treatment rate, and wastewater emissions. These additional factors quantitatively represent the urban environmental condition. The urban green area serves as the main state variable, with its increment defined as the velocity variable, with all remaining variables considered auxiliary.

The flow map of the water resources, economy, and environment systems in the lower Yellow River is shown in Figure 11.

b.

Model accuracy verification

After establishing the model, we conduct a historical data accuracy check to ensure its reliability and validity, further analyzing the simulation results for any deviations from the actual values. If the absolute value of the relative error falls between 0 and 10%, we deem the model simulation satisfactory. If it lies within the 10% to 20% range, we can refine the model through parameter adjustments and rate determinations. However, if the relative error exceeds 20%, the model may have inherent issues. In this case, we will review the model, modify the feedback structure, and reset the correlation equations among the variables.

This paper leverages data collected from 2006 to 2020, treating them as known variable values. The year 2006 serves as the base year for the system simulation. A comparative analysis of the simulated variable values and the actual data provides a measure of the model’s simulation accuracy, the simulation accuracy check of some parameters of water resources subsystem are shown in

Table 3. Simulation accuracy check of some parameters of water resources.

Year	Total Water Consumption (Billion m3)			Water Resources per Capita (m3)
	Actual Value	Simulated Value	Relative Error %	Actual Value	Simulated Value	Relative Error %
2013	214.71	216.51	0.84	230.17	229.27	−0.39
2014	206.84	211.62	2.31	134.95	133.87	−0.80
2015	208.10	212.45	2.09	170.27	168.11	−1.27
2016	210.37	215.58	2.48	218.90	214.71	−1.91
2017	212.32	218.12	2.73	159.04	155.21	−2.41
2018	217.51	223.7	2.85	252.46	245	−2.95
2019	234.48	237.24	1.18	147.26	143.83	−2.33
2020	225.19	229.68	1.99	225.59	226.3	0.31

, the simulation accuracy check of some parameters of the economic subsystem subsystem are shown in

Table 4. Simulation accuracy check of some parameters of the economic subsystem.

Year	GDP per Capita (CNY)			Total Retail Sales of Social Consumer Goods (1010 CNY)
	Actual Value	Simulated Value	Relative Error %	Actual Value	Simulated Value	Relative Error %
2013	50,302.95	50,103.7	−0.40	11.60	12.075	4.07
2014	53,820.22	53,389.1	−0.80	13.11	13.27	1.20
2015	56,202.34	55,482.8	−1.28	14.70	14.465	−1.57
2016	59,807.59	58,660.9	−1.92	16.17	15.66	−3.15
2017	64,860.85	63,300.8	−2.41	17.88	16.855	−5.75
2018	69,649.26	67,589.1	−2.96	18.32	18.05	−1.48
2019	65,662.32	64,134.4	−2.33	19.45	19.245	−1.03
2020	65,529.73	65,528.9	0.00	20.57	20.43	−0.70

, the simulation accuracy check of some parameters of the environmental subsystem subsystem are shown in

Table 5. Simulation accuracy check of some parameters of the environmental subsystem.

Year	Ecological Attention (Times)			Wastewater Discharge (Million m3)
	Actual Value	Simulated Value	Relative Error %	Actual Value	Simulated Value	Relative Error %
2013	64,578.00	72,866.3	12.83	25,590.26	26,387.944	3.12
2014	68,540.00	73,443.6	7.15	26,979.38	27,412.683	1.61
2015	67,630.00	74,020.9	9.45	29,282.15	28,048.6	−4.21
2016	77,231.00	74,598.2	−3.41	26,245.46	28,205.257	7.47
2017	75,641.00	75,175.5	−0.62	27,654.38	27,792.216	0.50
2018	74,721.00	75,752.8	1.38	28,460.92	26,719.039	−6.12
2019	82,181.00	76,330.1	−7.12	25,289.31	24,895.288	−1.56
2020	80,556.00	76,907.4	−4.53	21,410.23	22,230.525	3.83

.

We tested the accuracy of six representative variables. Except for some years where the relative error exceeded 10%, the accuracy test results met the simulation requirements. Following the accuracy test, it was determined that the model, upon parameter rate determination, satisfies the reliability requirements. Thus, it is suitable for the next step of scenario prediction.

c.

Scenario assumptions and projections

The trends observed from 2006 to 2020, combined with the assumption of no extremes in natural variables such as rainfall, total water resources, and population growth rate during the forecast period, inform the following four scenarios:

Scenario 1: Continuation of the status quo. This scenario assumes that the current state of indicator development, population growth, economic expansion, and urban development remains unchanged.

Scenario 2: Cost reduction and slowdown. While maintaining the status quo, this scenario suggests slowing down population growth and urban social development to cut costs.

Scenario 3: Environmental protection. This scenario focuses on maintaining the status quo while slowing GDP growth and reducing total water consumption. It aims to increase the proportion of primary production and reduce secondary production, thereby strengthening air quality control and further reducing PM_2.5, sulfur dioxide, and carbon dioxide emissions.

Scenario 4: Integrated development. This scenario considers a comprehensive approach that integrates water resources, economic and social development, and ecological protection, balancing the indicators from the above scenarios.

Using the Vensim-PLE 9.3.5 software, we conducted simulation predictions for these four scenarios. We calculated the predicted values for each index within the coupled evaluation index system for the lower Yellow River’s WEE system, spanning 15 years from 2021 to 2035. Due to the volume of data, we only display the simulation prediction results for the years 2025, 2030, and 2035 in Figure 12.

As Figure 12 illustrates, under scenario 1 (maintaining existing development levels), the coupled and coordinated development of the lower Yellow River increases from 0.6816 to 0.8298 between 2025 and 2035, reaching a satisfactory coordination stage by 2035. However, the growth rate of the overall coordination degree is slow and yields limited impact.

For scenario 2, which involves maintaining the status quo and suitably slowing population growth and urban social development, the WEE coupling system coordination in the lower Yellow River displays a favorable trend from 2025 to 2035. The coupling coordination degree rises from 0.6831 to 0.8450, evolving from a primary to a good coordination state.

Regarding scenario 3, it involves maintaining the status quo, enhancing primary production, decreasing secondary production, and reducing GDP growth and total water consumption. By strengthening atmospheric environmental control measures, we can further reduce PM_2.5, sulfur dioxide, and carbon dioxide emissions compared to the status quo. Consequently, the WEE coupling system coordination within the lower Yellow River displays a positive trend from 2025 to 2035. The degree of coordination increases from 0.7017 to 0.8591, evolving from an intermediate coordination state to a satisfactory one.

As for scenario 4, it integrates water resource management, socioeconomic development, and ecological environmental protection, achieving a balance among the indicators from the previous scenarios. This scenario results in the most beneficial trend for the WEE coupling system in the lower Yellow River. Between 2025 and 2030, the degree of coupled and coordinated development rises from 0.7087 to 0.8097, approaching a satisfactory state of coordination. By 2035, the coordination degree reaches 0.8928, illustrating a commendable state of coordinated development, even though it still resides within the satisfactory range.

4. Discussion

This paper thoroughly assesses the coupling coordination of water resources–economy, water resources–environment, and economy–environment systems in the cities along the lower Yellow River. Referring to relevant studies [54], the overall coordination level of water resources–economy between Henan and Shandong has gradually improved in the same study years, which is the same as the conclusion of this paper. In another study, the level of economy–environment coupling coordination in Shandong Province showed an upward trend in the same study years, which was similar to the conclusion of this study [55].

The national strategy now includes the high-quality development and ecological protection of the Yellow River Basin, and the lower Yellow River plays a crucial role within this basin. Future city development along the lower Yellow River should emphasize water resource protection, including projects for water diversion, transmission, and storage. It is essential to enhance water resource usage efficiency, fully utilize surface water, improve water resource management and application measures, and implement effective water resource protection strategies. In terms of economic development, it is vital to emphasize ecological economic growth, focus on energy conservation, emission reduction, and the expansion of the circular economy, and enhance the resource recycling system. From an environmental perspective, we must bolster comprehensive environmental pollution management, regulate the total emission of pollutants, undertake comprehensive improvements of the water environment, clarify responsibilities for water environment protection, and speed up the ecological construction of water source protection zones.

The coupled and coordinated development among complex systems is intricate and dynamic. This paper has established an evaluation index system for the coordinated development of the water resources–economy–environment system in the lower Yellow River. Despite obtaining some results, there is still room for improvement, given the influence of numerous factors. Due to data collection and time problems, the research cycle of this paper is 15 years, which can be further extended to 20 years in subsequent in-depth research to improve the scientific validity and accuracy of the conclusions. Meanwhile, with reference to relevant studies, spatial metrology models can be used to explore the driving factors of the coupled and coordinated development level of the lower Yellow River. This study is only a beginning. Future research should start by establishing an index system, considering the Yellow River Basin as a life community, incorporating elements of mountains, water, forests, lakes, grasslands, and sand. Information should be gathered from various sources to create a comprehensive index system that includes resources, energy, land, economy, and environment.

5. Conclusions

The coordinated development between water resources, the economy, and the environment is fundamental for high-quality regional development. With the rapid economic growth in the Yellow River Basin, challenges such as increased water consumption, uneven water resource distribution, low water resource utilization efficiency, excessive groundwater extraction, and environmental degradation are progressively becoming significant constraints on economic development. Consequently, determining the optimal coordinated development between water resources, economy, and environment has become a prominent research area. This paper presents the following findings after a series of investigations.

The coupling coordination analysis of the two subsystems within the cities along the lower Yellow River reveals a general trend of increasing coupling coordination degrees. By 2020, the average water resources–economy system’s coupling coordination degree reached an intermediate coordination stage at 0.7950. The average coupling coordination degree of the water resources–environmental system was 0.8016, indicating good coordination. The economy–environmental system had an average coupling coordination degree of 0.7939, reflecting an intermediate coordination stage.

The time series analysis of the WEE system’s coupling coordination degree in the lower Yellow River from 2006 to 2020 displays fluctuations but overall increases. By 2020, the coupling coordination degree reached a good coordination stage, with a value of 0.8209.

Simulation predictions using a system dynamics model suggest that under different development scenarios the coupled WEE system in the lower Yellow River will see varying degrees of improvement over the next 15 years. Among the scenarios, scenario 4 yielded the most substantial relative improvement and noticeable impact. The coupling coordination degree of the WEE system in the lower Yellow River is projected to reach 0.8928 in 2035, signifying a quasi-high-quality coordination state.