Scenario Simulation and Comprehensive Evaluation of Coupling Coordination Relationship Between Regional Water Use and Water Environmental Protection: A Case Study of Tianjin, China

Abstract

Water use and water environmental protection exhibit highly complex interactions, and their coupling coordination is essential for long-term urban sustainability. This study analyzes the system structure of water utilization, and constructs a water resources–social economy–water environment (WR-SE-WE) system dynamics model for Tianjin and five policy scenarios (business as usual (BAU), water conservation prioritization (WCP), social–economic advancement (SEA), water environmental protection (WEP), and integrated balanced development (IBD)) are simulated. A coupling coordination degree (CCD) model is employed to evaluate scenario performance. The key results show that Tianjin’s WR-SE-WE CCD keeps increasing but differentiates for different scenarios: IBD consistently outperforms all scenarios, achieving an optimal coupling coordination degree of 0.926 by 2035, while the other scenarios rank SEA (0.920) > WEP (0.902) > BAU (0.880) > WCP (0.874). The indicators’ quantitative results exhibit single-policy scenario trade-offs: WCP maximizes water efficiency and pollution control, but severely constrains social economy, offering a temporary solution. WEP excels in water resources supply but limits GDP growth, serving as an effective interim measure. SEA drives rapid economic expansion but strains resources and delays pollution control, making it suitable for long-term planning. Combining the obstacle degree model, four recommendations are proposed, including implementing cross-sector water governance, accelerating the green industrial transition, prioritizing reclaimed water, and scaling agricultural efficiency. These results provide a scientific basis for promoting high-quality regional development in the future.

1. Introduction

Water resources constitute the lifeline for urban emergence and development, they not only sustain human survival but also serve as the cornerstone for socioeconomic development and ecological equilibrium. However, China’s rapid urbanization and industrialization have imposed severe challenges on this critical resource [1]. According to the 2024 China Water Resources Bulletin, the nation’s total water resources amount to approximately 3.11 trillion m³, accounting for merely 6% of the global total. The per capita water availability stands at 2130 m³, roughly one-quarter of the global average, placing China among countries with severe water scarcity. Currently, approximately 400 Chinese cities face water supply shortages, with 110 experiencing severe water stress. Wastewater discharge exceeded 60 billion metric tons in 2020, exhibiting a continuous upward trend. This escalating pollution burden has precipitated watershed-scale ecological degradation, manifested through diminished aquatic ecological functions and deteriorating water quality, which in turn constrains socioeconomic development within these regions [2].

Regional-scale contradictions reveal a more intricate dynamic: Amid socioeconomic activities, the escalating water demands for agricultural irrigation, industrial production, and domestic use are compounded by wastewater discharges that exacerbate water pollution, threatening ecosystem integrity and public health [3]. Concurrently, economic expansion, industrial restructuring, and urbanization processes are fundamentally reshaping water resource allocation patterns and pollution dynamics, creating a complex nexus between water consumption, economic growth, and environmental sustainability. The United Nations Environment Programme (UNEP) issued the concept of “The Greening of Water” as early as over a decade ago, aiming to promote the transformation of water resource management from an “engineering-oriented” approach to a “system-coordination” model [4]. Over the past decade, the Chinese government has implemented sustainable measures including the Strictest Water Resources Management System and the Water Pollution Prevention and Control Action Plan [5]. While these approaches have mitigated supply–demand imbalances to some extent, they lack both systemic perspective and long-term coordination mechanisms, making it difficult to achieve a genuine win–win outcome between economic growth and ecological conservation. Therefore, how to simultaneously ensure socioeconomic development demands while realizing efficient water resource utilization and the effective control of water pollution has become a pressing challenge that requires urgent resolution for sustainable urban development in China.

To reconcile the relationships of the efficient utilization of water resources, the effective control of aquatic environmental pollution, and sustainable socioeconomic development, significant attention has been paid to clarifying their complex interrelationships for urban sustainability. Recent advances have emphasized the quantitative analysis of water-based subsystem interactions, employing methodologies including multi-objective optimization [6], artificial neural networks (ANNs) [7], and system dynamics (SD) modeling. While multi-objective optimization addresses conflicting objectives simultaneously, solution complexity and computational costs remain challenges [8]. ANNs demonstrate strong data processing capacity through automated feature extraction, yet their opaque internal mechanisms create interpretability limitations as typical black box models [9]. In contrast, SD modeling excels in handling nonlinear systems with uncertainty, enabling both static characterization and temporal dynamic simulation [10]. This capability has established SD as a prominent approach for understanding the relationship among resources, the economy, and the environment. Recent applications of SD in water resources research demonstrate a clear progression from single-dimensional analyses to integrated multisystem modeling. At the foundational level, the water supply–demand balance has been studied [11] yet usually lacks environmental attention. Building upon this, the scope expands to assessments of environmental carrying capacity within river basins, capturing dynamic interactions between water scarcity and pollution levels [12]. Meanwhile, the research trajectory advances towards integrated water–socioeconomic–environment system management [13,14], designed to evaluate how subsystem-specific policies can mitigate cross-system pressures. Over time, the complexity of SD applications has increased through the incorporation of additional subsystems—such as population, water resources, ecology, and economic development—to simulate their interplay [15], with the aim of identifying pathways that balance supply–demand dynamics with ecological conservation. Collectively, these studies underscore SD’s value in simulating complex water-related systems and testing policy scenarios, but most frameworks still have limitations in exploring the dynamic synergistic mechanisms between subsystems, lacking aggregated analysis of environmental pollutant pressures. Furthermore, in scenario setting, they tend to focus on the intervention effects of isolated policies rather than exploring comprehensive strategies for multi-objective synergistic optimization.

As a simulation methodology, the SD model fails to generate optimal solutions for resolving the synergistic relationship between efficient water utilization and aquatic environmental protection, which leads to a widespread lack of quantitative coordination assessment of the system. To address this, the coupling coordination degree model (CCDM) serves as a principal analytical tool for assessing two and more than two subsystem interactions and coordination, providing essential metrics for sustainability assessment [16]. Dual-subsystem studies typically examine binary relationships, such as between urbanization and water environment [17], water resources and environmental carrying capacity [18], or socioeconomic development and water environment [19]. These studies effectively identify key pairwise contradictions through CCDM quantification but are constrained by their narrow focus on two domains, failing to capture the multi-dimensional complexity of real-world water systems. To reflect holistic interactions, multisystem decomposition studies have expanded the application of CCDM by integrating more subsystems. Sun et al. establish the evaluation index system for the water resource–agricultural–population–ecological–economic system of Geermu City, with the inclusion of agriculture and population as independent subsystems, capturing the relationships between water use efficiency in agricultural production and the demographic pressure on water systems [20]. Yang and Chen integrate the ecological subsystem alongside the social economy subsystem, revealing the interaction between the elements of water and employment structure and industrial development in the social economy of the Pearl River Delta’s water resources–ecology–social economy system [21]. Zhang and Dong construct a water resources–socioeconomic–ecological environment system for Hebei Province, incorporating indicators such as industrial water consumption and ecological water allocation into the economic subsystem, providing insights into the impact of human activities on water resources [22]. In general, current CCDM research has basically established a quantitative assessment of the interaction relationship between water utilization and aquatic environmental protection. However, the definitions and refinement degrees of elements within subsystems are inconsistent, and the internal correlations among various elements require further exploration. In addition, as a supplement to the coupling coordination model, the obstacle degree model plays an essential role in diagnosing the key factors restricting coordinated development and identifying specific regulatory targets [23], which are generally absent in these studies. Additionally, few studies have combined CCDM methods with SD, which has led to the dynamic changes within the system being ignored and a lack of comprehensive insights into the synergistic mechanisms.

Based on the above analysis, this study takes Tianjin as the research object to address these gaps. The Haihe Basin’s location exemplifies this dilemma as China’s most intensively developed water-scarce region [24]. Within this basin, the Tianjin metropolis cluster epitomizes the dual pressures of surging urban water demands and environmental degradation, including water quality deterioration, thereby serving as a paradigmatic case for investigating the coupled relationship between urban water utilization and aquatic ecosystem protection. For this, a conceptual framework for water resources–social economy–water environment (WR-SE-WE) system interactions is established, and a system dynamics-based predictive model is developed. The evolutionary trajectories of Tianjin’s WR-SE-WE system are simulated through a scenario analysis encompassing five distinct development pathways. The CCDM is subsequently employed to quantitatively assess coordination levels across scenarios, using an obstacle degree model as a supplement, yielding targeted policy recommendations for exploring the coordinated path of regional water use and water environmental protection and providing scientific references for resolving the contradiction of “development–water use–ecology” in resource-based water-deficient areas. This research makes dual contributions: (1) Methodologically, it proposes an integrated framework combining a system dynamics forecasting model with a coupling coordination degree evaluation model. For the system dynamics forecasting model, this study emphasizes the element flows and interactions among subsystems, constructing a closed-loop feedback mechanism of “industrial structure–water use efficiency–wastewater discharge–recycled water reuse”. Regarding the evaluation model, it highlights the origins and stages of pollutants, while ensuring the transparency and interpretability of results through factor analysis and system clustering. (2) Empirically, Tianjin, a representative Chinese megacity with fragile water resources and a prominent contradiction between water environment and social economy is selected for analysis, providing theoretical insights and practical strategies applicable to similar regions globally.

2. Materials and Methods

2.1. The Study Area

This study focuses on Tianjin (as shown in Figure 1), a water-stressed direct-controlled municipality in northern China. As one of China’s four provincial-level municipalities and the economic hub of the Bohai Rim region, Tianjin faces dual challenges of chronic water scarcity and water environmental problems. The city’s per capita water resources persistently remain at 20% of the Chinese average, while its location within the Haihe River Basin exacerbates water quality concerns [25]. The Ecological and Environmental Bulletin of Tianjin (2022) revealed slight pollution levels in surface waters, with chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) concentrations exceeding the Grade III standards in China’s Environmental Quality Standards for Surface Water.

2.2. Simulation Model Based on SD

2.2.1. Analysis of the System Structure

The coordination between regional water use and water environmental protection is further decomposed into an interconnected triad of water resources, social economy, and water environment in this study. The synergy between regional water usage and water environmental protection is essentially manifested as a dynamic restraint among water resource supply, socioeconomic demand, and water environment carrying capacity, effectively analyzed by using a Pressure–State–Response (PSR) framework, as shown in Figure 2.

In this system, water resources act as one of the key limiting factors for sustainable development at both nationwide and regional levels [26], while social economy activities including agricultural irrigation, industrial production, and residential consumption require substantial water resources, which constitute the primary source of pressure. This pressure is manifested as high water demand, reflected in the availability of water resources and the quality of the water environment, which may strain the supply–demand balance of the water resources subsystem, and wastewater discharge, which imposes a pollution burden on the water environment subsystem, adversely affecting human health and ecological integrity. These pressures alter the state of the entire system, and an altered state subsequently triggers a feedback response regarding development scale, structural adjustments within the social economy subsystem, and the utilization patterns of the water resources subsystem [27]. Regarding the specific situation in Tianjin, three subsystems are further elaborated below.

(1)

Water resources subsystem (WR)

This subsystem addresses the interplay between water supply capacity and water use structure, forming the foundational resource framework that supports both population needs and economic growth in Tianjin. The selection of variables related to water supply—such as surface water (differentiated into local and incoming sources to highlight dependency on external transfers), groundwater, and non-conventional sources like reclaimed water, desalinated water, and stormwater—is guided by the need to accurately represent Tianjin’s diverse and multi-source supply portfolio, which is crucial for assessing resource security and sustainability. The total water supply is calculated as the sum of these key sources, as in Formula (1). Variables depicting the proportion of each supply source are included to analyze the evolving supply structure. On the demand side, water is allocated for ecological water use alongside socioeconomic activities, which span the domestic (split into urban and rural per capita and total use to reflect demographic differences), industrial, agricultural, and tertiary industries, and construction water use. These variables are chosen to capture the complete allocation of water across competing sectors, represented by the total water use calculated in Formula (2), and to evaluate the efficiency of water use within the socioeconomic framework. Variables like wastewater reuse (a function of treated wastewater and the reuse rate, as in Formula (3)) are critical for representing closed-loop water management efforts.

total water supply = surface water supply + groundwater supply + reclaimed water supply

(1)

total water use = industrial water use + agricultural water use + urban domestic water use + rural domestic water use + tertiary industry water use + construction industry water use + ecological water use

(2)

wastewater reuse = total wastewater collection and treatment × wastewater reuse rate

(3)

(2)

Social economy subsystem (SE)

This subsystem primarily examines the level of economic development and demographic dynamics, subsequently investigating their impacts on water consumption for socioeconomic activities and pollutant emissions across various sources. The economic development level is quantified by the Gross Domestic Product (GDP), where the increment of GDP is driven by the total GDP and its growth rate, as shown in Formula (4). It is categorized into three major sectors: the primary industry (agriculture), secondary industry (encompassing manufacturing and construction, with value added and proportion indicators for each), and tertiary industry (value added and proportion). The value added by the tertiary industry, for instance, is derived from the overall GDP and the proportions of the other sectors, as in Formula (5). These variables are selected to represent the economic scale and growth and, crucially, the structural composition of the economy, as different sectors exert distinct pressures on water resources and the environment. Demographic variables include total population, whose growth is modeled by the increment of population (Formula (6)), and structural composition (urban population, rural population, and urbanization rate) [28], with their fluctuations influenced predominantly by population growth rates and the scale of non-agricultural industries. These variables are essential for estimating domestic water demand and wastewater generation. Furthermore, water use intensity metrics (water use per 10,000 CNY of industrial/agricultural value added) directly link economic output to resource consumption in sectors like industry (Formula (7)), reflecting technological efficiency and informing the pressure on the WR subsystem.

increment of GDP = total GDP × change rate of GDP

(4)

value added of tertiary industry = GDP × (1 − proportion of the value added of the primary industry − proportion of value added of secondary industry)

(5)

increment of population = total population × change rate of population change

(6)

industrial water use = water use per 10,000 CNY of industrial added value × industrial value added/10,000

(7)

(3)

Water environment subsystem (WE)

This subsystem monitors water pollution from three discharge sources: residential (including domestic, tertiary industry, and construction), agricultural, and industrial [29]. Some variables are selected to characterize the pollution burden from generation to ultimate discharge, following the pollution pathway. In accordance with Tianjin’s Regulations on the Total Discharge Control of Key Pollutants, COD and NH₃-N have been prioritized as core water quality indicators for pollution control. Consequently, taking COD as an example, the COD generation from all key sources is calculated as in Formula (8). Variables representing the treatment and removal processes are essential for evaluating the effectiveness of pollution control infrastructure. The total COD removal is modeled based on the treatment capacity and efficiency, as in Formula (9), which directly determines the final total COD discharge (Formula (10)) into the environment. Furthermore, ecological water use has been identified as an effective strategy for enhancing aquatic environmental quality through natural purification processes and habitat restoration [30]; its consideration, though primarily in the WR subsystem, is relevant for the WE subsystem’s response.

total COD generation = agricultural COD generation + residential COD generation + industrial COD generation

(8)

total COD removal = total COD generation − total wastewater generation × wastewater collection rate × wastewater treatment rate/10,000 × COD concentration in wastewater treatment plant effluent

(9)

total COD discharge = total COD generation − total COD removal

(10)

2.2.2. Design of the System Dynamics Model

Following the system structure analysis, the system dynamics model is developed using AnyLogic 8.8.6 Personal Learning Edition software. The model parameters are calibrated using historical datasets from 2012 to 2022. Data sources include the Tianjin Statistical Yearbook, the Tianjin Environmental Bulletin, the Tianjin Water Resources Bulletin, the Tianjin Statistical Bulletin on National Economic and Social Development, and government documents. The model’s visual representation is shown in Figure 3, and the complete formulas and parameters are shown in Tables S1–S3.

2.3. Design of Scenarios

Assuming stable natural conditions (e.g., precipitation, total water resources) and current capacities for water supply, economic growth, and pollution control, five scenarios are designed:

(1)

Business as usual (BAU)

This scenario maintains existing conditions—water supply capacity, population growth rate, urban development patterns, and pollution control measures—to serve as a baseline and for assessing changes under alternative policies. No new water-saving, economic restructuring, or environmental interventions are implemented.

(2)

Water conservation prioritization (WCP)

Aligned with China’s National Water Conservation Action Plan and the 20th National Congress of the Communist Party of China, this scenario prioritizes reducing total water consumption through the dual control of water use intensity and efficiency improvements. Key strategies include moderating economic expansion and population growth, accelerating reductions in agricultural/industrial water use per 10,000 CNY of value added, and adhering to the principle of water conservation prioritization. These measures aim to alleviate water resource constraints while reducing wastewater generation and environmental pressure.

(3)

Social–economic advancement (SEA)

Reflecting Tianjin’s “14th Five-Year Plan”(The “14th Five-Year Plan for High-Quality Development of Tianjin’s Manufacturing Industry” and the “Outline of the 14th Five-Year Plan and Long-Range Objectives Through the Year 2035 for National Economic and Social Development of Tianjin Municipality”) objectives for industrial green transformation, this scenario emphasizes economic growth coupled with structural optimization. Three core strategies are used to represent “advancement”: prioritizing tertiary industry development to promote economic growth, phasing out high-pollution industries to reduce pollutants; and accelerating migration to non-agricultural sectors to support industrial upgrading.

(4)

Water environmental protection (WEP)

Guided by Tianjin’s water quality targets (The “14th Five-Year Plan for Ecological and Environmental Protection in Tianjin”), this scenario focuses on pollution reduction while maintaining economic output. Systematic efforts are implemented: enforcing stricter pollutant discharge standards (COD/NH₃-N), optimizing wastewater collection infrastructure and treatment facility efficiency, and increasing ecological water use to improve the stability of the water environment.

(5)

Integrated balanced development (IBD)

To achieve comprehensive development, this scenario establishes synergistic integration and balance between water conservation, economic growth, and environmental protection.

Scenario design rationale: all parameters in the BAU scenario remain unchanged (regarded as 100%) to serve as a baseline. WCP, SEA, and WEP represent radical adjustments to single subsystems, while IBD adopts moderate multisystem parameter optimization. To be specific, WCP mainly adjusts the indicators related to water usage intensity and economic and demographic growth, with a magnitude of increase or decrease mainly in the range of approximately 30–40%, reflecting a strategy of curbing demand and intensifying conservation efforts in line with national water dual-control policies; SEA primarily focuses on parameters governing economic structure and the intensity of pollutant generation, with a magnitude of increase or decrease mainly in the range of approximately 25–40%, embodying the city’s focus on high-quality, greener economic advancement; WEP alters parameters associated with environmental infrastructure and pollution control, with a magnitude of increase or decrease mainly in the range of approximately 30–40%, supporting municipal goals for water quality compliance and ecological sustainability; and IBD applies moderate, balanced adjustments (typically 10–20%). The complete parameter values are provided in Table S4. This framework enables the systematic exploration of high-quality water use development pathways for Tianjin.

2.4. Selection of Evaluation Indicators

The selection of evaluation indicators is guided by two critical criteria: the comprehensive representation of subsystem characteristics and the rigorous assessment of data availability. Based on the principles of scientificity, systematicness, operability, and regional specificity in indicator selection, an initial system comprising 68 indicators is established (as shown in Table S5).

Due to the excessive number of system indicators and their varying degrees of relevance to the system, a comprehensive evaluation of all indicators is impractical. To ensure the rationality of evaluation results while adequately representing the water resources, socioeconomic, and ecological environment systems, a combination of factor analysis and cluster analysis is applied for indicator screening.

2.4.1. Factor Analysis

Factor analysis is conducted using SPSS Statics 27 to screen indicators. In the “Factor Analysis: Extraction” dialog box, Principal Component Analysis (PCA) is selected as the extraction method—a widely used dimensionality reduction technique that captures components with maximum variance. Considering potential differences in variable scales, the correlation matrix (rather than the covariance matrix) is adopted for analysis. The Kaiser criterion is applied for extraction, retaining factors with eigenvalues greater than 1 to balance interpretability and information retention. The factor extraction results are presented in Table S6.

Additionally, in the “Factor Analysis: Rotation” dialog box, Varimax rotation—a commonly used orthogonal rotation method—is applied to maximize the variance of factor loadings among variables, enhancing interpretability. By selecting the “Rotated solution” option, rotated factor loadings are displayed to clarify the associations between variables and factors. The factor analysis in SPSS effectively reduces dimensionality, identifies key factors, and optimizes the factor structure, ensuring that the extracted factors accurately represent and screen the original indicators. The results of the rotated loading matrix analysis are shown in Table S7.

2.4.2. Cluster Analysis

To avoid redundancy among indicators, hierarchical clustering in SPSS Statics 27 is applied to classify all system indicators. The “Between-groups linkage” method is selected for clustering, which calculates the average distance between all pairs of cases in different clusters, balancing sensitivity to cluster differences and computational stability. The measurement standard used is the Squared Euclidean Distance, quantifying the squared straight-line distance between data points in multidimensional space. This emphasizes significant differences and aligns with the requirement to capture distinct variations among indicators. The cluster analysis outputs include an “Agglomeration Schedule” table, results across different cluster numbers, and a dendrogram. The meaningful clustering outcomes are summarized in Table S8.

2.4.3. Final Indicator Selection

The final indicators should be selected through a process that combines the statistical results from factor and cluster analysis with theoretical justification based on Tianjin’s practical conditions. Some indicators are excluded from the final system due to insufficient statistical strength (loadings < 0.5 on any factor), significant cross-loadings (|>0.4| on multiple factors), or redundancy within clusters. Cross-loadings compromised discriminant validity, while redundancy led to the removal of less representative indicators to maintain conceptual clarity. In rare instances, theoretically or contextually irrelevant indicators (despite meeting statistical thresholds) are replaced by more suitable alternatives from the same cluster to align with the study’s focus on Tianjin’s specific conditions. All of the excluded indicators and the reasons for exclusion are presented in Table S9. The final coupling coordination evaluation indicator system for Tianjin’s WR-SE-WE system is established, comprising 18 indicators, and presented in

Table 1. The screening results of the indicators.

Subsystem	Indicator Category	Indicator	Specific Meaning of the Indicator
Water resources	Effect of water resources protection	Ecological water use	During a certain period, the amount of water resources that need to be guaranteed to maintain the structure and function of ecosystems.
		Proportion of groundwater supply	The proportion of groundwater supply in the total regional water supply, reflecting the degree of dependence on groundwater in water resources development.
	Water use scale	Tertiary industry water use	The amount of water consumed in the production and operation processes of service industries.
		Domestic water use	The total amount of water resources consumed in the daily life of urban and rural residents.
	Water supply capacity and structure	Total water supply	The total amount of water resources provided by various water supply projects such as surface water, groundwater, and reclaimed water in the region, reflecting the water supply guarantee capacity.
		Proportion of reclaimed water supply	The proportion of reclaimed water supply in the total water supply, reflecting the level of wastewater recycling.
Social economy	Economic water use efficiency	Water use per 10,000 CNY of agricultural value added	The amount of water resources consumed per 10,000 CNY of added value created in agriculture, measuring the level of water conservation and water use efficiency in agriculture.
		Water use per 10,000 CNY of industrial value added	The amount of water resources consumed per 10,000 CNY of added value created in industry, reflecting the water-saving capacity of industrial production.
	Achievements of structural transfer	Urbanization rate	The proportion of urban permanent population in the total regional population, reflecting the degree of urban-rural population structure transformation.
		Proportion of the value added of the primary industry	The proportion of the added value of the primary industry in GDP, reflecting the status of agriculture in the industrial structure and its transformation trend.
	Scale of economic development	Per capita GDP	Regional GDP divided by the total population, measuring the level of economic development and per capita production capacity.
		Tertiary industry value added	The total output value of the service industry, reflecting the development scale of the largest service industry in the economic structure.
Water environment	Level of wastewater generation	Agricultural wastewater generation	The total amount of wastewater generated from agricultural production, reflecting the pollution load of agricultural activities.
		Industrial wastewater generation	The total amount of wastewater discharged during industrial production, reflecting the intensity of industrial pollution generation.
	Degree of water pollutants	Industrial COD generation	The content of chemical oxygen demand in industrial wastewater, measuring the degree of organic pollution in industrial wastewater.
		Residential NH3-N generation	The content of ammonia nitrogen in domestic sewage, reflecting the nitrogen pollution level of domestic sewage.
	Efficiency of pollution treatment	Wastewater collection and treatment rate	The proportion of the total amount of wastewater collected and treated to the total amount of wastewater generated, reflecting the coverage capacity of the wastewater collection system.
		COD concentration in wastewater treatment plant effluent	The concentration of chemical oxygen demand in the effluent of wastewater treatment plants, reflecting the pollutant removal effect of wastewater treatment facilities.

.

2.5. Coupling Coordination Degree Evaluation Model

The coupling coordination degree (CCD) model quantifies the interactions, dependencies, and synergistic states between two or more systems. Elevated CCD values signify robust intersystem relationships that foster positive feedback mechanisms and enhance systemic efficiency [31]. Applying CCD metrics to the WR-SE-WE system evaluates the impacts of different policies.

2.5.1. Entropy Weight Method

Indicator weights reflect the relative importance under unified evaluation objectives, critically influencing multi-criteria assessments. In terms of the method, AHP relies on expert judgment, which may introduce subjectivity; CRITIC considers the correlation between indicators but might overlook the intrinsic information distribution of the data itself; the entropy weight method, an objective weighting technique, determines weights based on the degree of variation in the values of each indicator, effectively mitigating subjective bias, and highlights the intrinsic information within the data itself, making it particularly valuable for improving the robustness of coordination assessments [32]. This study prioritizes and implements the entropy weight method, executed through the following five steps:

Step 1: Data standardization.

The raw data are normalized to eliminate the effects of dimensional heterogeneity and polarity. For this purpose, the extreme value method—the most prevalent approach in related studies using the CCD model—is adopted. The calculation formulas are as follows:

For positive indicators (higher values desirable)

$${x^{\prime }}_{ij}={\displaystyle \frac{x_{ij}-\mathit{min}\left(x_i\right)}{\mathit{max}(x_i)-\mathit{min}(x_i)}}$$

(11)

For negative indicators (lower values desirable)

$${x^{\prime }}_{ij}={\displaystyle \frac{max(x_i)-x_{ij}}{\mathit{max}(x_i)-\mathit{min}(x_i)}}$$

(12)

where $x_{ij}$ is the original value of indicator i in year j; ${x^{\prime }}_{ij}$ is the standardized value of indicator i in year j; $\mathit{max}(x_j)$ is the maximum value of indicator i in all years; and $\mathit{min} \left(x_j\right)$ is the minimum value of indicator i in all years.

Step 2: Proportion calculation.

$$y_{ij}={\displaystyle \frac{{x^{\prime }}_{ij}}{{\sum }_{j=1}^m{x^{\prime }}_{ij}}}$$

(13)

where $y_{ij}$ is the proportion of indicator i in year j; m is evaluation years.

Step 3: Entropy calculation.

$$e_i=-{\displaystyle \frac{1}{\mathit{ln} (m\times n)}}\times \sum y_{ij}\mathit{ln}{y}_{ij}$$

(14)

where e_i is entropy of indicator i and states that when ${y^{\prime }}_{ij}=0$, there is ${y^{\prime }}_{ij}\times \mathit{ln}{y^{\prime }}_{ij}=0$; n is scenario count.

Step 4: Redundancy calculation.

$$g_i=1-e_i$$

(15)

where $g_i$ is the redundancy of indicator i.

Step 5: Weight assignment.

$$w_i={\displaystyle \frac{g_i}{{\sum }_{j=1}^n{g}_i}}$$

(16)

where $w_i$ is the weight of indicator i.

2.5.2. Coupling Coordination Calculation

This is achieved through four key steps:

Step 1: Calculation of comprehensive evaluation index (CEI).

$$Y_i=\sum _{i=1}^n{w}_i{x^{\prime }}_{ij}$$

(17)

where $Y_i$ is the comprehensive evaluation index of indicator i, considered as its score.

Step 2: Calculation of coupling degree.

$$C_j=\sqrt[k]{{\displaystyle \frac{{\prod }_{i=1}^k{Y}_i}{(\frac{{\sum }_{i=1}^k{Y}_i}{n}{)}^k}}}$$

(18)

where $C_j$ is coupling degree; k is subsystem count.

Step 3: Calculation of comprehensive development index (CDI).

$$T_j={\mathsf{\partial }}_i{Y}_i$$

(19)

where $T_i$∈[0, 1], is the comprehensive development index of each subsystem, with higher values indicating the superior performance of the subsystem; ${\mathsf{\partial }}_i$ is the weight of each subsystem, taking 1/3 for the WR, the SE, and the WE.

Step 4: Calculation of coupling coordination degree.

$$D_j=\sqrt{C_j\times T_j}$$

(20)

where $D_j$∈[0, 1], is the coupling coordination degree, with higher values indicating the superior performance of the entire system.

2.5.3. Obstacle Degree Model

To diagnose the key factors restricting the coordinated development and identify specific regulatory targets, an obstacle degree model is introduced. This model calculates the contribution of each indicator’s deficiency to the overall deficiency of its parent subsystem. This is achieved through two key steps:

Step 1: deviation calculated calculation.

$$V_i=1-{x^{\prime }}_{ij}$$

(21)

where V_i is the deviation of indicator i, representing the gap between the indicator’s standardized value and the optimal value.

Step 2: obstacle degree calculation.

$$O_i={\displaystyle \frac{w_i\cdot V_i}{{\sum }_{i=1}^l{w}_i\cdot V_i}}$$

(22)

where O_i is the obstacle degree of indicator i, with higher values indicating a greater obstacle to the coordination; l is the number of indicators.

3. Results

3.1. Validation of System Dynamics Model

The system dynamics model underwent four validation protocols to ensure theoretical soundness and predictive capability.

3.1.1. Structural Verification

During model development, an extensive literature review and data consultation are conducted to ensure appropriate system boundaries and causal relationships. Parameter estimation and equation formulation are iteratively refined to maintain theoretical consistency with the observed data.

3.1.2. Behavioral Adequacy

Model stability is confirmed through repeated executions in AnyLogic software, with outputs demonstrating close alignment with expectations. No computational instabilities or anomalous oscillations are observed during multi-year simulations.

3.1.3. Sensitivity Analysis

During the development and validation of the system dynamics model, given the presence of multiple variables and parameters with varying degrees of uncertainty or ranges of variation, this analysis helps identify parameters that disproportionately influence model behavior. In this study, six parameters covering the important aspects of three subsystems are selected for sensitivity testing to examine their ±10% variations within plausible ranges under baseline conditions.

The sensitivity analysis results (as shown in Figure 4) demonstrate that perturbations in parameters produce slight deviations, with no evidence of disproportionate amplification or non-physical oscillations. It suggests relative model robustness to parameter uncertainties within the tested ranges. The effective sensitivity validation confirms that the model outputs remain stable without requiring exceptional parameter precision.

3.1.4. Historical Validation

Given the multi-parameter nature of the integrated model, key variables are selected from three subsystems for historical validation. The historical validation period (2012–2022) is selected based on data availability, with 2012 designated as the baseline year. Model performance is evaluated through two metrics: (1) mean absolute error (MAE) for the 2012–2022 period; (2) year 2022 prediction error. The validated variables and corresponding results are presented in

Table 2. Historical verification result.

Subsystem	Selected Variable	Error in 2022 (%)	Average Error from 2012 to 2022 (%)	Annotation
Social Economy	Total GDP	0.32	2.43	The average error is only calculated from 2016 to 2022.
	Value added of industry	3.95	2.92
	Value added of tertiary industry	1.60	5.16
	Urbanization rate	0.02	0.10
	Population	0.09	2.10
	Urban population	0.11	2.00
Water Resources	Groundwater supply	2.42	7.62
	Surface water supply	7.35	0.18
	Industrial water use	2.59	8.16
	Socioeconomic water use	2.54	3.80
	Domestic water use	3.51	0.60
Water Environment	Wastewater treatment rate	0.71	0.40	The average error is only calculated for the years 2020 to 2022.
	Wastewater reuse	5.34	5.42
	Total wastewater generation	1.35	1.75
	Total COD generation	3.90	0.82
	Total NH3-N generation	4.25	2.02

.

The historical validation demonstrates that all subsystem variables maintained simulation errors below 10% relative to the observed values, indicating strong temporal agreement. Notably, adjustments to the statistical caliber by the local statistics bureau for select variables in the water resources subsystem (revised in 2016) and the water environment subsystem (revised in 2020) have rendered interannual comparability constraints. Nevertheless, the simulated trajectories preserved an appropriate directional alignment with historical trends throughout the validation window, confirming the model’s effectiveness in historical reproduction.

3.2. Simulation Results Under Different Scenarios

The temporal changes in the WR, SE, and WE indicators under the five scenarios are simulated and presented in Figure 5, Figure 6 and Figure 7, respectively.

3.2.1. Simulation Results of the Water Resources Subsystem

The total water supply grows annually across all scenarios, with WEP consistently highest and WCP lowest. Reclaimed water proportions rise significantly, led by WEP increasing to 29.35%, followed by IBD. Ecological water use increases linearly, being highest under WEP. The proportion of groundwater supply declines substantially in all cases, though WCP maintains the highest share. Domestic water use shows gradual growth and is consistently the lowest in WCP. Tertiary industry water use expands exponentially, with SEA demonstrating the sharpest increase and WCP remaining the lowest throughout the period.

3.2.2. Simulation Results of the Social Economy Subsystem

Water use per 10,000 CNY of agricultural value added declines substantially across all scenarios, with WCP achieving the most rapid reduction to 99.88 m³ per 10,000 CNY by 2035. Water use per 10,000 CNY of industrial value added improves most significantly under WCP, reaching 3.38 m³ per 10,000 CNY. Tertiary industry value added grows exponentially, led by SEA surging to 3.37 × 10¹² CNY. Per capita GDP increases most dramatically under SEA, rising to 3.20 × 10⁵ CNY, significantly outpacing other scenarios. Urbanization rates show nearly identical gradual growth across all contexts. The proportion of the value added of the primary industry experiences marginal decline in every scenario, with BAU decreasing to 1.58%.

3.2.3. Simulation Results of the Water Environment Subsystem

Agricultural wastewater generation declines steadily across all scenarios, with WCP achieving the most rapid reduction to 2.88 × 10⁶ m³ by 2035. Industrial wastewater generation decreases significantly, particularly under WCP which reaches the lowest level of 2.6 × 10³ m³. Industrial COD generation shows substantial reductions, led by WCP dropping to 605.56 tons. Residential NH₃-N generation plummets most dramatically under WEP, falling to 1.03 tons. Wastewater collection and treatment rate rise consistently, reaching 99% in all scenarios by 2032. COD concentration in wastewater treatment plant effluent decreases sharply across scenarios, with WEP achieving the most drastic reduction.

3.3. Results of Entropy Weight Quantification of Indicators

The observed data and simulation results are uniformly quantified by the entropy weight method to form the scores of each indicator, with corresponding calculated metrics including information entropy, redundancy, weighting coefficients, and indicator properties (as shown in

Table 3. Indicator information under the entropy weight method.

Subsystem	Indicator	Entropy	Redundancy	Weight	Orientation
WR	Ecological water use	0.9512	0.0488	0.2073	Positive
	Proportion of groundwater supply	0.9646	0.0354	0.1502	Negative
	Tertiary industry water use	0.9853	0.0147	0.0626	Negative
	Domestic water use	0.9578	0.0422	0.1793	Negative
	Total water supply	0.9371	0.0629	0.2670	Positive
	Proportion of reclaimed water supply	0.9685	0.0315	0.1336	Positive
SE	Water use per 10,000 CNY of agricultural value added	0.9687	0.0313	0.1059	Negative
	Water use per 10,000 CNY of industrial value added	0.9589	0.0411	0.1389	Negative
	Urbanization rate	0.9425	0.0575	0.1943	Positive
	Proportion of the value added of the primary industry	0.9687	0.0313	0.1059	Negative
	Per capita GDP	0.9354	0.0646	0.2184	Positive
	Tertiary industry value added	0.9300	0.0700	0.2366	Positive
WE	Agricultural wastewater generation	0.9995	0.0005	0.0029	Negative
	Industrial wastewater generation	0.9516	0.0484	0.3115	Negative
	Industrial COD generation	0.9571	0.0429	0.2761	Negative
	Residential NH3-N generation	0.9853	0.0147	0.0949	Negative
	Wastewater collection and treatment rate	0.9995	0.0005	0.0029	Positive
	COD concentration in wastewater treatment plant effluent	0.9516	0.0484	0.3115	Negative

). To facilitate comparative analysis, historical observed data after 2015 are incorporated, with missing values in the dataset being imputed using linear interpolation techniques. During the historical period, the performance of 18 indicators in 2015 and 2020 is compared and shown in Figure 8. Most indicators show improvement, such as total water supply, the proportion of reclaimed water supply, industrial COD generation, and the value added of tertiary industry.

The values of the three key years during the simulation period are plotted as a heatmap, shown in Figure 9, which will be conducive to the direct comparison of scenario advantages. The heatmap illustrates the score of each year under different scenarios. The values of indicator score are represented by a gradient color scale, with blue indicating the lowest values and red indicating the highest values. Overall, most indicators show gradual score increases from 2025 to 2035. The SEA scenario consistently excels in economic indicators like per capita GDP and tertiary industry value added, particularly in 2035, where it achieves the highest economic scores. For WR, WEP leads in metrics such as total water supply and reclaimed water usage throughout all years. The IBD scenario demonstrates balanced strength, frequently ranking second or first across multiple categories. Conversely, WCP trails in most indicators, especially in water supply and economic performance by 2035. For WE, industrial COD generation improves steadily in all scenarios, yet agricultural wastewater scores remain persistently low. WEP and IBD show superior results in wastewater treatment efficiency, with WEP achieving notably high COD concentration scores. The proportion of primary industry value added consistently scores lowest across all scenarios and years. Economic and urbanization indicators exhibit the most significant growth, while ecological water use advances moderately.

3.4. Analysis of CDI and CCD Evolution of the System Under Different Scenarios

All three subsystems show consistent score increases across all five scenarios from 2015 to 2035, as shown in Figure 10a, as quantified by the CDI values. In the WR, WEP consistently achieves the highest scores, reaching 0.777 in 2035, followed closely by IBD and SEA. BAU and WCP trail notably, with WCP being the lowest performer. For the SE, SEA dominates throughout, reaching 0.918 by 2035, while IBD ranks second. BAU and WEP show moderate gains, but WCP consistently lags behind all others. In the WE, IBD emerges as the top performer by 2035 with 0.965, slightly ahead of WCP and SEA. WEP starts strong but is overtaken by IBD after 2028 and by WCP after 2033, while BAU remains the weakest across all years.

The evolution of the coupling coordination, quantified by the CCD values, exhibits distinct period characteristics, as shown in Figure 10b. During the simulation period, the simulated scores under different scenarios begin to diverge while generally maintaining an increasing trend. Among the five scenarios, IBD emerges as the dominant performer, rising robustly to 0.926 by 2035, overtaking all other scenarios and becoming the top scenario. WEP initially leads in the earlier years but grows moderately to 0.902 by 2035, falling to third place after being surpassed by SEA in 2030. SEA demonstrates the strongest sustained growth, accelerating to 0.920 by 2035, securing second place after exceeding WEP in 2028. Meanwhile, WCP and BAU consistently trail: WCP starts slightly stronger than BAU but grows marginally, peaking at 0.874; it is overtaken by BAU by about 2032 and finishes last.

3.5. Comparative Analysis of Subsystems Under Different Scenarios in 2035

The comparative comparison in 2035 is shown in Figure 11. The WR scores range from 0.678 under WCP to 0.777 under WEP. SE performance varies more significantly, reaching its lowest point at 0.691 for WCP and its highest at 0.918 for SEA. The WE consistently achieves the strongest results across all scenarios, from 0.869 for BAU to 0.965 for IBD. Despite variations in subsystem performance, the CCD remains uniformly high, ranging from 0.874 for WCP to 0.926 for IBD.

Among the scenarios, IBD demonstrates the strongest overall performance, leading in both WE at 0.965 and CCD at 0.926 while also achieving robust SE and WR scores. SEA follows closely with exceptional SE achievement at 0.918 and high CCD at 0.920. WEP shows notable strength in WR at 0.777 and WE at 0.937, though its SE score is relatively lower at 0.740. Meanwhile, BAU and WCP exhibit specific subsystem weaknesses; BAU has a moderate SE performance at 0.757 while WCP registers lower scores in WR at 0.678 and SE at 0.691. Nevertheless, both maintain coordination levels above 0.87.

3.6. Obstacle Factor Diagnosis of WR-SE-WE System Coupling Coordination

The obstacle degree diagnosis for the WR-SE-WE system from 2015 to 2035 reveals a clear evolution of primary limiting factors, as shown in

Table 4. Top 3 indicators by ranking and their obstacle degrees in each subsystem for obstacles to WR-SE-WE coupling coordination degree under different scenarios (2015–2035). (TWS: Total water supply; RWS%: Proportion of reclaimed water supply; Eco-WU: Ecological water use; GWS%: Proportion of groundwater supply; DWU: Domestic water use; TIWU: Tertiary industry water use; Agri-WU/10k: Water use per 10,000 CNY of agricultural value added; Ind-WU/10k: Water use per 10,000 CNY of industrial value added; GDP/cap: Per capita GDP; TI-VA: Value added of tertiary industry; Urban%: Urbanization rate; PIVA%: Value added of primary industry; Agri-WWG: Agricultural wastewater generation; Ind-WWG: Industrial wastewater generation; COD-G (Ind): Industrial COD generation; NH₃-G (Res): Residential NH₃-N generation; WW-Treat%: Wastewater collection and treatment rate; COD-Conc (Eff): COD concentration in wastewater treatment plant effluent).

Year	Scenario	WR Subsystem			SE Subsystem			WE Subsystem
		Indicator 1 Obstacle Degree (%)	Indicator 2 Obstacle Degree (%)	Indicator 3 Obstacle Degree (%)	Indicator 1 Obstacle Degree (%)	Indicator 2 Obstacle Degree (%)	Indicator 3 Obstacle Degree (%)	Indicator 1 Obstacle Degree (%)	Indicator 2 Obstacle Degree (%)	Indicator 3 Obstacle Degree (%)
2015		TWS	Eco-WU	GWS%	TI-VA	GDP/cap	Urban%	COD-Conc (Eff)	Agri-WWG	COD-G (Ind)
		10.21	7.92	5.74	9.05	8.35	7.43	7.14	6.87	6.80
2020		TWS	Eco-WU	GWS%	TI-VA	GDP/cap	Urban%	Agri-WWG	Ind-WWG	COD-G (Ind)
		12.12	8.54	3.87	10.17	9.29	7.01	7.39	6.56	6.22
2025	BAU	TWS	DWU	Eco-WU	TI-VA	GDP/cap	Urban%	Agri-WWG	Ind-WWG	COD-G (Ind)
		9.64	7.36	6.31	11.71	10.14	7.33	8.87	5.96	5.09
	WCP	TWS	Eco-WU	DWU	TI-VA	GDP/cap	Urban%	Agri-WWG	Ind-WWG	COD-Conc (Eff)
		10.32	6.95	6.90	12.49	10.93	7.42	7.04	5.20	5.01
	SEA	TWS	DWU	Eco-WU	TI-VA	GDP/cap	Urban%	Agri-WWG	Ind-WWG	COD-G (Ind)
		9.75	7.49	6.44	11.52	9.96	6.99	9.54	5.82	4.97
	WEP	TWS	DWU	Eco-WU	TI-VA	GDP/cap	Urban%	Agri-WWG	Ind-WWG	COD-G (Ind)
		9.98	7.82	6.28	12.51	10.85	7.79	9.27	6.21	5.30
	IBD	TWS	DWU	Eco-WU	TI-VA	GDP/cap	Urban%	Agri-WWG	Ind-WWG	COD-G (Ind)
		10.21	7.50	6.52	12.25	10.59	7.49	8.92	6.02	5.14
2030	BAU	DWU	TWS	Eco-WU	TI-VA	GDP/cap	PIVA%	Agri-WWG	Ind-WWG	COD-G (Ind)
		13.36	7.61	5.62	11.91	10.01	7.96	10.27	5.21	4.45
	WCP	DWU	TWS	Eco-WU	TI-VA	GDP/cap	PIVA%	COD-Conc (Eff)	Agri-WWG	Ind-WWG
		11.77	9.69	7.69	14.86	13.00	8.58	4.60	4.31	3.07
	SEA	DWU	TWS	Eco-WU	TI-VA	GDP/cap	PIVA%	Agri-WWG	COD-Conc (Eff)	Ind-WWG
		14.36	7.81	6.12	10.42	8.77	8.01	12.98	4.64	4.59
	WEP	DWU	TWS	Eco-WU	TI-VA	GDP/cap	PIVA%	Agri-WWG	Ind-WWG	COD-G (Ind)
		14.87	7.34	4.57	13.64	11.56	8.87	10.89	5.40	4.61
	IBD	DWU	TWS	Eco-WU	TI-VA	GDP/cap	PIVA%	Agri-WWG	Ind-WWG	COD-G (Ind)
		14.51	8.51	5.83	12.70	10.62	9.09	10.24	5.00	4.27
2035	BAU	DWU	TIWU	Eco-WU	PIVA%	TI-VA	GDP/cap	Agri-WWG	COD-Conc (Eff)	Ind-WWG
		25.08	6.50	3.85	10.99	8.84	6.98	13.10	4.22	3.77
	WCP	DWU	Eco-WU	RWS%	TI-VA	GDP/cap	PIVA%	COD-Conc (Eff)	NH3-G (Res)	Agri-WWG
		20.67	8.29	6.82	16.64	14.82	11.82	4.54	0.51	0.00
	SEA	DWU	TIWU	Eco-WU	PIVA%	Ind-WU/10k	Agri-WU/10k	Agri-WWG	COD-Conc (Eff)	Ind-WWG
		32.51	11.65	5.13	12.58	1.69	1.00	22.64	5.62	1.74
	WEP	DWU	TIWU	TWS	PIVA%	TI-VA	GDP/cap	Agri-WWG	Ind-WWG	COD-G (Ind)
		29.38	7.27	0.00	12.89	11.78	9.63	14.04	3.59	3.06
	IBD	DWU	TIWU	Eco-WU	PIVA%	TI-VA	GDP/cap	Agri-WWG	Ind-WWG	COD-G (Ind)
		33.52	11.83	2.94	15.31	6.04	4.45	14.05	2.42	2.06

. For 2015 and 2020, total water supply and industrial COD generation are common key obstacles. To be specific, WR’s top obstacles centered on water supply and ecological water use indicators, SE centered on tertiary industry value added, per capita GDP, and urbanization rate, and WE centered on wastewater generation and pollutant concentration or generation. From 2025 to 2035, total water supply is the main obstacle for WR in 2025, while domestic water use becomes the top obstacle for the subsystem in 2030 and 2035; ecological water use or tertiary industry water use serve as secondary obstacles for WR. For SE, tertiary industry value added and per capita GDP are leading obstacles in 2025 and 2030, and primary industry value added becomes prominent in some 2035 scenarios. Agricultural wastewater generation is consistently a key obstacle for WE, while the other top obstacles for the subsystem—such as wastewater treatment effluent COD concentration, industrial wastewater generation, or industrial COD generation—vary by scenario. Overall, the primary obstacles are total water supply and domestic water use for WR, tertiary industry value added for SE, and agricultural wastewater generation for WE.

From the perspective of contrast in the scenarios, WCP notably reduces the obstacle degree of domestic water use in WR and drastically lowers that of agricultural wastewater generation in SE; SEA mainly reduces the obstacle degrees of tertiary industry value added, per capita GDP, and, later, industrial/agricultural water use per 10,000 CNY in SE; and the WEP scenario reduces the obstacle degrees of total water supply and ecological water use in WR. IBD also reduces the obstacle degrees of some indicators in each subsystem. These results are basically consistent with the coupling coordination evaluation results.

4. Discussion

4.1. Analysis of the Coupling Coordination for Regional Water Use and Water Environmental Protection

The scenario simulation and comprehensive evaluation reveal profound insights into the coupling coordination mechanisms governing Tianjin’s WR-SE-WE systems. IBD emerges as the optimal pathway, achieving a CCD of 0.926 by 2035, significantly outperforming single-focus scenarios. This superiority stems from its capacity to resolve critical subsystem trade-offs: While SEA maximizes economic growth with tertiary industry value added surging, it neglects water supply constraints, causing the water resources subsystem to lag behind WEP—consistent with Yang et al.’s research [33]. However, WEP excels in ecological water use and pollution control, but suppresses per capita GDP growth, falling below SEA. IBD navigates these tensions by synchronizing water efficiency improvements without stifling output—with reclaimed water expansion and stringent COD control, thereby avoiding the coordination ceilings encountered by radical scenarios [34].

The obstacle degree analysis provides a mechanistic explanation for these outcomes, revealing that the key limiting factors evolve over time and differ by policy focus. The consistent rise in domestic water use as the primary obstacle across all scenarios by 2035 underscores a universal challenge of managing urban residential demand. The persistent role of economic indicators like tertiary industry value added as top obstacles in WCP and WEP quantitatively confirms that these scenarios incur significant economic costs. Conversely, the emergence of agricultural wastewater generation as a top obstacle in the high-growth SEA and balanced IBD scenarios highlights a new critical bottleneck: effective non-point source pollution control becomes a prerequisite for sustainable development under rapid economic expansion. This diagnostic approach moves beyond identifying which scenario is best to pinpointing exactly where each policy succeeds or fails, offering specific regulatory targets. WCP, despite achieving the sharpest reductions in agricultural water use and COD generation, inadvertently triggers a negative spiral: stringent conservation weakens the ability of wastewater reuse and reduces the economic scale. This positions WCP as a short-term crisis response—effective for rapid water stress alleviation but economically unsustainable beyond 2030 as evidenced by its last-place CCD by 2035. Meanwhile, WEP’s end-of-pipe treatment focus enhances the WR and WE scores, validating the necessity of unconventional water utilization in water-stressed cities [35,36] but requiring advanced infrastructure often constrained in smaller plants, echoing findings on the technical limitations of pollution control. Such mid-term environmental remediation delivers timely water quality gains (WEP leads CCD until 2028) yet struggles to sustain potential for reducing pollutants without economic vitality, similar to prior studies that found limited effectiveness in industrial NH₃-N mitigation. In contrast, IBD activates a virtuous cycle: reclaimed water investment alleviates groundwater stress while boosting treatment rates, freeing resources for economic development—validating the multi-stage interventions demonstrated in water-scarce regions [37].

Tianjin’s industrial and agricultural legacy fundamentally shapes these mechanisms. The South-to-North Water Diversion Project’s post-2015 implementation enhanced surface water supply by over 30% [38], while stringent Water Pollution Prevention Regulations drastically reduced point-source pollutants [39]. The obstacle diagnosis, however, reveals that despite these improvements, agricultural non-point source pollution remains a deeply entrenched structural challenge, becoming a top obstacle in future-oriented scenarios. Persistently low agricultural wastewater scores across all scenarios, however, reveal deeper structural challenges. Despite generating only 40% of total wastewater in Tianjin, agriculture contributes disproportionately to COD burdens [40], necessitating targeted source interventions that remain under-prioritized [41]. The high weight of industrial COD generation further underscores how economic restructuring mediates water–pollution linkages. SEA’s phased industrial transition initially elevates COD output but later reduces it through sectoral shifts, whereas WCP suppresses pollution via economic contraction. The delayed but accelerating CCD rise in SEA—surpassing WEP after 2028 and reaching near-optimal coordination by 2035—reveals its strength as a long-term transformation strategy, where initial economic restructuring costs gradually yield systemic dividends. Both approaches prove inferior to IBD’s balanced integration of efficiency gains and structural change, aligning with strategies resolving water–economy conflicts. Critically, ecological water use is a high-weight WR indicator: its expansion under WEP and IBD correlates with enhanced water environment resilience, whereas its neglect in BAU and WCP exacerbates subsystem imbalances. This mirrors Tianjin’s post-2015 improvements from ecological management policies, though trans-regional water transfers remain essential to overcome inherent scarcity [42].

4.2. Recommendations

Given that different scenarios and factors have varying degrees of impact on the improvement of the coordination of water resources, social economy, and the water environment in Tianjin, strategic priorities must be refined to target these key constraints directly as follows:

First, addressing the universal obstacle of domestic water use requires a dual strategy. The low score of domestic water use across all scenarios, especially becoming the primary obstacle in WR by 2035, demands immediate and innovative urban water governance. Policymakers should institutionalize a cross-sector governance framework that integrates the functions of water affairs, urban planning, and pricing authorities. A paradigm of “efficiency enhancement–pollution reduction–water conservation” should be established to mitigate policy conflicts.

Second, overcoming the critical economic obstacles prevalent in the WCP and WEP scenarios necessitates accelerating the industrial green transition. Tianjin’s dual industrial–service economic drivers necessitate phased transformation strategies [43]. The persistent high obstacle degree of per capita GDP and tertiary industry value added in these scenarios confirms that simply constraining water or focusing on end-of-pipe treatment stifles economic vitality. Outdated manufacturing sectors with excessive water intensity per GDP unit and low value added output should be gradually phased out. Complementing this, modern service sectors require targeted development to elevate the tertiary industry’s contribution beyond 75%, ultimately forging a “green industrial base with high-end service leadership” model.

Third, structural supply reforms must prioritize the obstacles identified in the water resources subsystem. To combat the persistent obstacle of groundwater dependency and boost reclaimed water use, large-scale wastewater reclamation must be prioritized to achieve a 30% share, embedding this mandate in urban renewal planning for high-consumption industries. Simultaneously, ecological water allocations should be optimized using dynamic regulation—ensuring sufficient flows for environmental resilience while preventing disproportionate diversion from socioeconomic needs. Critically, groundwater dependency should be reduced by institutionalizing unconventional water sources in supply portfolios [44], which is confirmed by a high obstacle degree of proportion of groundwater supply.

Fourth, the emergence of agricultural wastewater generation as the top obstacle in the WE subsystem for the SEA and IBD scenarios reveals an urgent issue, and dual efforts should focus on agricultural non-point source pollution control and efficiency upgrades. The proven effectiveness of water conservation in agriculture should be capitalized on—exemplified by WCP’s achievement in reducing water use per 10,000 CNY of agricultural value added. The large-scale adoption of high-efficiency irrigation in key production zones to curb agricultural wastewater generation at the source should be prioritized. This is no longer just an efficiency issue but a primary pollution control strategy for sustaining coordination under high-growth scenarios. This should be complemented with nutrient management innovations to mitigate associated pollution risks, transforming farming into a low-water, low-waste sector.

4.3. Limitations

The findings of this study are derived from model simulation and calculation, and their practical application value needs to be further validated against real-world conditions. Moreover, this study excludes external perturbations including extreme events and climate variability, potentially affecting long-term prediction accuracy. While the framework of this study focuses on Tianjin, application to other regions requires local parameter calibration (e.g., water use factors) and validation against regional historical data.

5. Conclusions

This study takes Tianjin as a case study to explore the coupling coordination between regional water use and water environment protection based on the water resources–social economy–water environment (WR-SE-WE) framework. Five scenarios are simulated through system dynamics modeling and evaluated based on coupling coordination degree. The key results indicate that the coupling coordination degree continuously increases for all scenarios but specific situations vary: the integrated balanced development scenario achieves optimal coordination, consistently outperforming all scenarios, rising to 0.926 by 2035, which is greater than the social-economic advancement scenario (0.920), water environmental protection scenario (0.902), business-as-usual scenario (0.880), and water conservation prioritization scenario (0.874). Based on the quantitative results, the scenarios have different focuses: The water conservation prioritization scenario achieves minimum water usage and pollutant generation, but only yields a low social economy score, offering a temporary solution. The water environmental protection scenario excels in total and reclaimed water supply, but it constrains GDP growth, serving as an effective interim measure. Conversely, the social–economic advancement scenario drives a rapid economic increase in per capita GDP and value added of the tertiary industry, suitable for long-term planning, but increases water consumption and early pollutant pressure. Obstacle degree diagnosis further identifies domestic water use, total water supply, tertiary industry value added, and agricultural wastewater generation as the primary future bottlenecks to coordination, with varying degrees of reduction under different scenarios, confirming the quantitative results. Consequently, corresponding targeted recommendations are proposed including implementing cross-sector water governance, accelerating the green industrial transition, prioritizing reclaimed water, and scaling agricultural efficiency. This study provides a modeling framework and policy insights for supporting sustainable water management, subject to the inherent limitations of simulation models, and these findings could be validated through empirical studies in the future work.