Coupling Patterns Between Urbanization and the Water Environment: A Case Study of Neijiang City, Sichuan Province, China

Abstract

The ongoing advancement of urbanization has significantly amplified its impacts on the water environment. Understanding the coupling relationships between urbanization and the water environment (UAWE) is crucial for Chinese policymakers aiming to promote sustainable urban development. In this study, a comprehensive UAWE evaluation model was developed to examine the development trajectories in Neijiang City from 2012 to 2022. Methodologically, a comprehensive evaluation approach was applied to assess urbanization and water resource trends over this period, followed by the development of a Coupling Coordination Degree Model (CCDM) to quantify their synergistic relationship. The results showed that the coupling between the comprehensive urbanization index and the water environment system evolved over time, as reflected in the following key findings: (1) Neijiang underwent three distinct stages from 2012 to 2022 in terms of coupling and coordination between urbanization and the water environment: Basic Coordination (2012–2015), Good Coordination (2016–2020), and Excellent Coordination (2020–2022). (2) Urbanization exerted varying impacts on subsystems of the water environment, with the pressure-response subsystems exhibiting marked volatility from 2012 to 2022. The impact intensity followed the order spatial urbanization > economic urbanization > social urbanization > population urbanization. These findings offer valuable theoretical and practical insights for aligning urban sustainability goals with effective water environment protection measures. This study provides essential guidance for policymakers in Neijiang and similar regions, enabling the development of tailored strategies for sustainable urbanization and enhanced water management.

1. Introduction

Urbanization, as a composite developmental process, has evolved under the combined influences of economic growth, stages of industrialization, and resource distribution patterns. Since the reform era, China has experienced rapid urbanization [1], with the urbanization rate increasing from 19.39% in 1980 to 53.73% in 2013, as reported by the China Statistical Yearbook [2]. Sustainable urbanization relies on the support of water-related environmental elements, while the protection and improvement of the water environment are significantly influenced by the level of urban development [3]. Rapid urbanization exerts profound impacts on water environments through increased domestic and industrial water consumption, altered land use patterns, and intensified wastewater discharge. These pressures can lead to water scarcity, pollution, and ecological degradation. Understanding the urbanization–water environment nexus is therefore critical for developing integrated management strategies, especially in developing regions or inland cities where water resources are often limited and unevenly distributed. The rapid pace of urbanization and economic expansion in China has intensified challenges related to water environments. Simultaneously, water pollution and ecological degradation have exacerbated water scarcity, thereby undermining the region’s capacity for sustainable development [4,5]. Water-related environmental issues resulting from rapid urbanization have garnered growing attention from both the public and academic communities [6].

The concept of coupling, which originates from physics [6,7], refers to the interactive dynamics between two or more systems through various mechanisms. The relationship between urbanization and the water environment is characterized by urbanization-induced stress on water systems and, conversely, constraints imposed by the water environment on urban development. Existing studies have confirmed measurable coupling relationships between urbanization and the water environment [8]. Researchers have employed coupled models, coordination degree assessments [9,10], and integrated system dynamics approaches [11] to explore these interactions. However, most research has focused on prefecture-level cities or provinces in the economically developed eastern regions, while studies concerning underdeveloped areas or watershed-scale coordination between urbanization and water resources remain limited. In addition, inconsistent application of coupling coordination models in urban development and water environment research has led to methodological shortcomings, including subjective indicator selection and weighting procedures, thereby reducing model validity and result robustness [12,13].

The Tuo River, a major tributary of the upper Yangtze River, serves as a core water conservation area and agricultural base within the Sichuan Basin and the Chengdu–Chongqing economic circle [14]. Neijiang City, situated in the midstream section of the Tuo River Basin, functions as an industrial hub supporting approximately 3.14 million residents. The river’s 143 km main stream traverses five administrative divisions—Shizhong District, Dongxing District, Longchang City, Zizhong County, and Weiyuan County—while meeting both ecological and socio-economic demands. As a key node in the Chengdu–Chongqing economic zone, Neijiang specializes in food and beverage production, equipment manufacturing, and new materials industries. In 2022, the secondary sector contributed 41.2% to the local economy, with industrial wastewater accounting for 65% of the total pollutant discharge. The city’s urbanization rate rose from 42.3% in 2010 to 55.8% in 2022, with the urban population exceeding one million. This rapid urbanization has intensified pressure on the water environment: while 83.3% of the Tuo River’s water quality met standards in 2023, tributaries such as the Qiu and Daqing Rivers failed to achieve Class III standards due to elevated total phosphorus and ammonia nitrogen concentrations. Although municipal wastewater treatment coverage reached 92%, approximately 35% of pipelines remain misconnected, and industrial zones continue to be subjected to stress from heavy metal and organic pollutants.

Under the strategy outlined in the 14th Five-Year Plan—emphasizing the construction of an industrial powerhouse and a regional logistics hub—Neijiang’s key development zones, including the Neijiang Hi-tech Electronics Park and Longchang Photovoltaic Park, are projected to generate 12 million tons of industrial wastewater annually. The urbanization rate is targeted to reach 58%, further increasing the pressure on the water environment’s carrying capacity. In this context, it is imperative to conduct targeted investigations into the coupling relationship between urbanization and the water environment (UAWE) in Neijiang. Accordingly, this study aims to (1) construct a 15-indicator evaluation framework spanning urbanization and water environment dimensions to compute UAWE development indices across five counties (2012–2022), thereby revealing the spatial differentiation of subsystems among functional zones; and (2) apply the Coupling Coordination Degree Model (CCDM) to assess UAWE coordination, providing a scientific foundation for implementing the water-centric planning strategies stipulated in the Tuo River Basin Water Ecological Conservation Plan and advancing the “Sweet City Lake—Upper Yangtze Ecological Barrier” initiative.

Although the CCDM framework has been widely applied in urbanization–environment coupling studies, this research contributes novel insights by integrating fine-grained water-related indicators specific to Neijiang’s regional context, and by systematically assessing subsystem interactions (e.g., irrigation, wastewater discharge, industrial water use) within a unified analytical structure. Furthermore, the study offers targeted policy recommendations based on indicator trends, which can provide practical guidance for water environment management in mid-sized inland cities. These aspects distinguish this work from previous studies and enhance its applicability to real-world planning and governance.

2. Profile of the Study Area

Located in the central Sichuan Province within the Tuo River and Min River basins of the upper Yangtze River (Figure 1), Neijiang City is endowed with abundant water resources. With an average annual surface water availability of 1.521 billion cubic meters and groundwater reserves of 151 million cubic meters, the city is capable of meeting the water demands associated with residential consumption, industrial activities, and infrastructure development. Neijiang is characterized by a subtropical monsoon climate, resulting in mild and humid conditions accompanied by substantial precipitation. Annual rainfall ranges from 1000 to 2000 mm, with 60–80% occurring during the flood season (May–September). As an important agricultural region in Sichuan, the city’s river systems serve as reliable sources of irrigation and support ecological stability via rich hydrological resources. The river network features a density of 1.18 km/km², and this dense hydrological structure ensures efficient water utilization and soil-water conservation. By leveraging the dual advantages provided by the Tuo River and Min River systems, Neijiang has facilitated rational water allocation, thus supporting long-term urban sustainability.

3. Materials and Methods

3.1. Indicator System Construction

The construction of an indicator system is a fundamental step in evaluating the level of coordinated development. This study adopts objective, comprehensive, and scientific methods to assess the status of the water environment amid ongoing urbanization. In constructing the indicator system, the core connotations and characteristic requirements of the Urbanization–Water Environment (UAWE) framework are carefully considered. While a large number of potential indicators can reflect various aspects of these subsystems, including population dynamics, economic activity, land use, water quality, and resource availability, incorporating all relevant indicators would result in redundancy, increased complexity, and potential overlap among variables. This would not only dilute the interpretability of the results but also weaken the efficiency of the model [2].

Urbanization imposes considerable pressure through population concentration, economic expansion, spatial development, and societal transformation [15], which in turn threatens the sustainability of the water environment. Following the principles of comprehensiveness, scientific rigor, accessibility, and representativeness [16], this study establishes a 15-indicator urbanization evaluation system for Neijiang City covering the period from 2012 to 2022. The system is structured around four dimensions: demographic, economic, social, and spatial urbanization.

In the process of indicator selection, this study adhered to three fundamental principles: representativeness, data availability, and analytical efficiency, ensuring that each indicator has clear relevance and independence. Considering the close relationship between urbanization and the water environment, the 15 selected indicators comprehensively reflect the impacts of urbanization on both the quantity and quality of water resources and meet the requirements for evaluating urbanization development and water environment sustainability. To avoid redundancy and improve model accuracy, we excluded indicators with incomplete time series, unstable data quality, or high correlations with other variables based on preliminary correlation analysis. Through this scientific selection process, the final indicator system ensures both the integrity and representativeness of the model while maintaining its practicality and operability, thereby enhancing the reliability and applicability of the UAWE assessment.

To guide the selection of water environment indicators, the Pressure–State–Efficiency–Response (PSER) framework (Figure 2) is adopted. It is applicable to describing the complete chain of effects of “urbanization pressure—water environment state—utilization efficiency—social response.” The innovative “efficiency” dimension compensates for the shortcomings of the traditional PSR framework, which lacked sufficient consideration of efficiency factors in the process of resource utilization and pollution reduction. Pressure indicators represent internal and external stresses, such as resource overuse, pollutant discharge, and overexploitation. State indicators describe the current condition or health status of water systems under stress, including water resources per capita and water resource utilization rate. The water popularization rate of the urban population is the result of stress on water supply services and is not a proactive measure, so they are therefore classified as such. Efficiency indicators quantify the system’s ability to utilize resources and technologies efficiently, often expressed through resource utilization efficiency and pollutant reduction rates. Response indicators capture the actions of societal, governmental, and corporate actors in response to system pressures and changes in environmental conditions.

Table 1. The urbanization evaluation index system and water environment evaluation index system of Neijiang.

Target Layer	Standardized Layer	Indicator Layer	Units	Indicator Types
Urbanization	Population Urbanization	Non-agriculturalization rate	%	+
		The proportion of the population of the tertiary industry to the total employment population	%	+
		Urban population density	people/km2	+
	Economic Urbanization	GDP per capita	CNY/people	+
		Value added of the tertiary sector as a share of GDP	%	+
		Per capita disposable income of urban residents	CNY/people	+
		Total retail sales of consumer goods per capita	CNY/people	+
	Social Urbanization	Doctors per ten thousand people	people/10,000 people	+
		Public transportation per ten thousand people	vehicles/10,000 people	+
		Number of university students per ten thousand people	people/10,000 people	+
	Spatial Urbanization	Road area per capita	m2/people	+
		Per capita urban living space	m2/people	+
		Public green space per capita in cities	m2/people	+
		Green space coverage in built-up areas	%	+
		Proportion of urban built-up area to national land area	%	+
Water Environment	Pressure	Total urban water consumption	10,000 m3	-
		Chemical Oxygen Demand (COD) emissions	10,000 t	-
		Total sewage discharge	10,000 t	-
		Daily domestic water consumption per capita	L	-
	State	Water popularization rate of the urban population	%	+
		Water resources per capita	m3/people	+
		Water resource utilization rate	%	-
		Centralized—drinking water sources—water quality standard rate	%	+
		Qualified rate of water quality in functional areas of the urban water environment	%	+
	Efficiency	Water use per CNY 10,000 of GDP	m3/CNY 10,000	-
		Water consumption per CNY 10,000 of industrial added value	m3/CNY 10,000	-
		Wastewater discharge per CNY 10,000 of GDP	t/CNY 10,000	-
		Chemical Oxygen Demand (COD) emissions per CNY 10,000 of GDP	kg/CNY 10,000	-
	Response	Centralized sewage treatment rate	%	+
		Industrial wastewater discharge compliance rate	%	+
		Recovery rate for industrial use	%	+
		Proportion of effective irrigated area	%	+

presents the final UAWE relationship evaluation indicator system.

3.2. Data Sources and Preprocessing

This study draws primarily on statistical data from the Neijiang Statistical Yearbook [17], Neijiang Water Resources Bulletin [18], Neijiang Ecological Environment Statistical Bulletin [19], and Sichuan Provincial Water Resources Bulletin [20], covering the period from 2012 to 2022. To address the incommensurability among various evaluation indicators, the range standardization approach is employed to convert raw data into dimensionless values, thereby enabling cross-comparison among indicators with differing units and scales.

The polarity of each indicator is also considered, as higher values may reflect either adverse effects (e.g., elevated pollutant concentrations) or positive outcomes (e.g., increased treatment efficiency), depending on the semantic meaning of the indicator. Accordingly, the indicator system incorporates both positive indicators (where higher values are preferable) and negative indicators (where lower values are desirable). The standardization formulae for positive and negative indicators are defined as follows:

$$X_j=\left\{{\displaystyle \frac{\frac{x_j-x_{j\min }}{x_{j\max }-x_{j\min }}(Positive)}{\frac{x_{j\max }-x_j}{x_{j\max }-x_{j\min }}(Negative)}}\right.$$

(1)

In the formula, $x_{j\min }$ and $x_{j\max }$ represent the initial minimum and maximum values of indicator $j$ during the study period, while $x_j$ and $X_j$ denote the initial and standardized values of the indicator, respectively.

3.3. Research Methods

3.3.1. Calculation of Indicator Weights

This study employs an enhanced entropy method to determine weights for element layers and indicator layers in the urbanization system and water environment security system. The entropy method is a weighting technique that calculates index weights by evaluating the dispersion degree of indicator data [10]. Entropy serves as a measure of system disorder degree or information uncertainty [21]. A higher entropy value indicates lower data variability and reduced information content of the indicator, thus warranting smaller weights in comprehensive evaluations. The entropy method has been validated as a potentially useful approach for measuring objective weights [22]. The specific steps are as follows:

(1)

The data were normalized. The normalization formula for determining the proportion of the $i$th sample value under the $j$th indicator in each system was adopted. The expression is:

$$P_{ij}={\displaystyle \frac{X_{ij}}{{\displaystyle {\sum }_{i=1}^n{X}_{ij}}}}$$

(2)

In the formula, $n$ is the number of statistical years.

(2)

Entropy value calculation. The entropy value of the $j$th indicator was calculated using the following formula:

$$E_j=-{\displaystyle \frac{1}{\ln (n)}}\ast {\displaystyle \sum _{i=1}^n({Pi}_j\ast \ln ({Pi}_j))}$$

(3)

(3)

Coefficient of variation. The coefficient of variation for the $j$th indicator was computed as follows:

$$d_j=1-e_j$$

(4)

(4)

Indicator weight determination. The weight of the $j$th indicator was determined based on the following formula:

$$w_j={\displaystyle \frac{d_j}{{\displaystyle {\sum }_{j=1}^m{d}_j}}}$$

(5)

In the formula, $n$ is the number of years, and $m$ is the number of indicators in the subsystem.

3.3.2. Coupling Coordination Degree Model

The Coupling Coordination Degree Model (CCDM) is considered to have significant implications for future urban development planning [23]. In this study, the CCDM is employed to quantify the degree of coordinated development between the urbanization and water environment systems. The Coupling Degree (CD) characterizes the intensity of mutual influence among systems or their constituent elements, whereas the Coupling Coordination Degree (CCD) reflects the quality of coordination between systems or internal components. While the methodological framework builds upon established models, this study’s contribution lies in its practical applicability. By applying the CCDM approach to a mid-sized inland city, this research provides localized insights and actionable recommendations that can inform sustainable water environment governance in similar urban contexts facing rapid urbanization and ecological challenges.

(1)

The coupling function of the Urbanization and Water Environment (UAWE) system is expressed as follows:

$$CD=\left\{(u_1\cdot u_2)/{\left[(u_1+u_2)/2\right]}^2\right\}$$

(6)

In the formula, $u_1$ represents the comprehensive index value of the urbanization system, while $u_2$ denotes the comprehensive index value of the water environment system.

(2)

The UAWE comprehensive index is calculated using an arithmetic weighting method to reflect the overall synergistic interactions within Neijiang City’s UAWE framework. The corresponding formula is provided as follows:

$$T=\alpha u_1\times \beta u_2$$

(7)

In the formula: $\alpha$ and $\beta$ denote undetermined coefficients. Given equal importance of urban and water environment subsystems, this study adopts $\alpha =\beta =0.5$ [24].

(3)

The formula for CCD is:

$$CCD=\sqrt{CD\times T}$$

(8)

The classification of coupling coordination levels typically relies on subjective judgment within the academic literature [7]. Drawing on research findings from both domestic and international scholars [25,26], a set of evaluation grades and classification criteria for the coordination between urbanization and the water environment system has been developed, which are systematically presented in

Table 2. Classification criteria for the degree of coupling coordination.

Composite Class	Coordination Degree	Classification
Coordination development	0.8 < D ≤ 1	Excellent coordination
Transformation development	0.6 < D ≤ 0.8	Good coordination
	0.4 < D ≤ 0.6	Basic coordination
Uncoordinated development	0.2 < D ≤ 0.4	Low coordination
	0 < D ≤ 0.2	Serious coordination

.

The overall research framework has been constructed based on the components illustrated in Figure 3. Figure 3 illustrates the conceptual framework of the Urbanization–Water Environment (UAWE) with a distinct structural arrangement proposed in this study.

4. Results and Analysis

4.1. Calculation Results of Indicator Weights

The entropy method was applied to determine the indicator weights for Neijiang’s urbanization and water environment systems from 2012 to 2022, and the corresponding computational results are presented in

Table 3. Weight of urbanization system indicators in Neijiang City from 2012 to 2022.

Target Layer	Standardized Layer	Indicator Layer	Units	The Weight of Each Index of the Entropy Method
Urbanization	Population Urbanization	Non-agriculturalization rate	%	0.0506
		The proportion of the population of the tertiary industry to the total employment population	%	0.1248
		Urban population density	people/km2	0.0002
	Economic Urbanization	GDP per capita	CNY/people	0.0752
		Value added of the tertiary sector as a share of GDP	%	0.0583
		Per capita disposable income of urban residents	CNY/people	0.0731
		Total retail sales of consumer goods per capita	CNY/people	0.0723
	Social Urbanization	Doctors per ten thousand people	people/10,000 people	0.0587
		Public transportation per ten thousand people	vehicles/10,000 people	0.075
		Number of university students per ten thousand people	people/10,000 people	0.0775
	Spatial Urbanization	Road area per capita	m2/people	0.0816
		Per capita urban living space	m2/people	0.0757
		Public green space per capita in cities	m2/people	0.0909
		Green space coverage in built-up areas	%	0.0417
		Proportion of urban built-up area to national land area	%	0.0444

and

Table 4. Weights of water environment system indicators in Neijiang City from 2012 to 2022.

Target Layer	Standardized Layer	Indicator Layer	Units	The Weight of Each Index of the Entropy Method
Water Environment	Pressure	Total urban water consumption	10,000 m3	0.0809
		Chemical Oxygen Demand (COD) emissions	10,000 t	0.0974
		Total sewage discharge	10,000 t	0.141
		Daily domestic water consumption per capita	L	0.0475
	State	Water resources per capita	m3/people	0.0592
		Water resource utilization rate	%	0.0386
		Water popularization rate of the urban population	%	0.057
	Efficiency	Water use per CNY 10,000 of GDP	m3/CNY 10,000	0.0294
		Water consumption per CNY 10,000 of industrial added value	m3/CNY 10,000	0.0831
		Wastewater discharge per CNY 10,000 of GDP	t/CNY 10,000	0.0932
		Chemical Oxygen Demand (COD) Emissions per CNY 10,000 of GDP	kg/CNY 10,000	0.0606
	Response	Centralized sewage treatment rate	%	0.032
		Recovery rate for industrial use	%	0.0738
		Proportion of effective irrigated area	%	0.1063

, respectively.

4.2. Analysis of Comprehensive Level Variations in the Urbanization–Water Environment

4.2.1. Urbanization Comprehensive Level Dynamics

Using entropy-derived weights for urbanization indicators from 2012 to 2022, the system was categorized into four subsystems: population, economic, social, and spatial urbanization. As shown in Table 3, spatial urbanization was assigned the highest weight (0.3343), followed by economic (0.2790), social (0.2112), and population urbanization (0.1755). Higher indicator weights indicate a higher level of systemic importance [27]. Six indicators—tertiary sector employment ratio, per capita urban green space, road area per capita, college students per 10,000 population, residential area per capita, and GDP per capita—account for 52.57% of the total urbanization weights, which constitute the primary drivers of urbanization.

Neijiang’s urbanization has been primarily driven by transformations in labor structure, urban population density, economic performance, educational attainment, and healthcare accessibility [28]. The notable weight of the non-agricultural employment rate further highlights the critical role of the economic transition from agriculture to industry [29]. This analysis clarifies the contribution of specific factors to urbanization, thereby providing a scientific basis for urban development strategies and policymaking.

As demonstrated in Figure 4, Neijiang’s urbanization comprehensive index has shown significant growth, rising from 0.1788 in 2012 to 0.7189 in 2022, with an average annual growth rate of approximately 5.40%. Notably, spatial and economic urbanization registered annual growth rates of 2.70% and 1.85%, respectively. This trend underscores marked progress in infrastructure development, spatial planning, and economic restructuring, with spatial urbanization having the most significant influence.

Figure 4 also illustrates that the most substantial improvements occurred in the spatial urbanization subsystem, reflected in increased per capita road area, residential space, and public green space. These changes are indicative of improvements in urban infrastructure and living conditions, thereby accelerating the urbanization process. The social urbanization subsystem has experienced steady growth, reflecting gradual improvement in residents’ living standards and the availability of public services, especially in healthcare and education.

In contrast, population urbanization follows a growth-then-decline pattern, peaking at 0.1494 in 2018 during the early to mid-industrialization phase, when population migration was more pronounced. However, as regional economic development became more balanced, growth momentum slowed, leading to a slight decline. This pattern, depicted in the radar charts, indicates a state of developmental equilibrium accompanied by emerging bottlenecks in population urbanization.

Overall, Neijiang’s urbanization can be characterized by a spatial-economic driven growth model, particularly reflected in infrastructure and spatial planning advancements. By systematically analyzing subsystem weights and development trajectories, this study identifies the primary driving forces of urbanization and provides scientific support for future urban planning and policy formulation.

4.2.2. Water Environment Comprehensive Level Dynamics

Using entropy-derived weights for water environment indicators from 2012 to 2022, the system was divided into four subsystems: pressure, state, efficiency, and response. As shown in Table 4, the pressure subsystem was assigned the highest weight (0.3668), followed by efficiency (0.2663), response (0.2121), and state (0.1548). These weights indicate the relative significance of each subsystem in water resource management, emphasizing the leading role played by the pressure subsystem in determining water environment quality. Five indicators—total wastewater discharge, effective irrigation area, COD emissions, wastewater discharge per CNY 10,000 GDP, and water consumption per CNY 10,000 industrial added value—which represent pivotal drivers of water system evolution, collectively account for 52.10% of the total weight.

Figure 5 illustrates the fluctuating growth of Neijiang’s comprehensive water environment index from 2012 to 2022, reaching a peak of 0.7940 in 2022. The subsystem trends show distinct variation patterns: pressure and response subsystems experienced substantial volatility between 2013 and 2019, largely due to fluctuations in pollutant discharge and variations in mitigation efforts. Oscillations in the pressure subsystem are indicative of ongoing stress from wastewater emissions and industrial water use, while the response subsystem reflects adjustments in governance strategies and policy responses.

Overall, water environment quality has remained at a moderate level. The efficiency subsystem has followed a steady upward trend, which is strongly associated with Neijiang’s rapid economic expansion. While this economic growth has enhanced water use efficiency, effective management strategies are still necessary to mitigate the resulting environmental pressures. Meanwhile, the state subsystem has remained relatively stable, suggesting a general balance in water resource availability.

To enhance water environment conservation, Neijiang should develop evidence-based policies for wastewater treatment and water resource management that align with observed indicator trends. Priority should be given to reducing wastewater discharge, optimizing irrigation areas through practical measures such as adopting efficient irrigation technologies, reallocating farmland based on regional water availability, and encouraging water-efficient agricultural practices. These measures help optimize irrigation practices and relieve systemic pressure, alongside responsive strategies that address water scarcity and pollution. Strengthening the management of each subsystem is essential to ensure the continued improvement of water quality and the overall advancement of the water environment system.

4.3. Coupling Coordination Analysis Between Urbanization and the Water Environment

4.3.1. Temporal Dynamics of Urbanization–Water Environment Coupling

As demonstrated in Figure 6, the coupling coordination degree (CCD) between urbanization and the water environment in Neijiang was observed to exhibit sustained growth from 2012 to 2022, as indicated by the fitted equation y = 0.0428x − 85.6382 (R² = 0.9812). The CCD increased from 0.4 in 2012 to 0.9 in 2022, suggesting a progressive optimization of interactions between the systems. This trajectory corresponds to threshold transition characteristics in the Coupling Coordination Degree Model (CCDM), where exceeding a CCD of 0.6 indicates a transition from an antagonistic phase to a phase of coordinated development. The observed CCD progression has been associated with ecological restoration policies in Neijiang, particularly those enhancing water governance and enforcing centralized industrial pollution control. Technological advancements and industrial restructuring—evidenced by a 12% annual increase in the service sector’s share after 2018—have alleviated pressures exerted by urbanization on the water environment. The high R² value (0.9812) confirms a strong linear correlation in CCD, thereby validating the applicability of linear regression for analyzing environmental systems [28]. Nevertheless, the CCD remains below the optimal threshold (>0.95), indicating the necessity for enhanced water recycling mechanisms to support the achievement of sustainable development goals.

An integrated analysis of Table 3 and Figure 6 identifies three evolutionary phases in the coupling coordination between urbanization and water environment systems in Neijiang City. During the initial phase (2012–2015), low-level coordination was exhibited by the systems amid nascent urbanization characterized by spatial expansion and industrial agglomeration. Inadequate infrastructure, particularly the limited wastewater treatment capacity (only 120,000 tons/day in 2012), led to the substantial discharge of domestic and industrial effluents into the Tuo River, thereby exacerbating water quality deterioration. Key water quality indicators, including COD (28 mg/L), NH₃-N (2.1 mg/L), and total phosphorus (0.38 mg/L), consistently exceeded Grade III standards (Figure 7), thereby posing substantial environmental security risks. This period was characterized by pronounced antagonistic interactions, in which rapid urban growth imposed severe hydrological pressures, while environmental constraints hindered sustainable development.

The transitional phase (2016–2020) was characterized by moderate coordination following the implementation of the Tuo River Basin Comprehensive Management Initiative and the Green Ecosystem Construction “Centennial Project.” Strategic interventions included (1) achieving 100% coverage of wastewater treatment facilities across 83 administrative towns, thereby increasing treatment capacity to 320,000 tons/day (a 167% increase), and (2) the systematic remediation of 156 industrial discharge outlets along the main river channel. These measures resulted in an improvement in water quality compliance rates from 62% in 2015 to 89% in 2020, with COD and NH₄-N concentrations decreasing by 37% and 52%, respectively. The systems progressively transitioned from antagonistic interactions to synergistic ones, demonstrating reciprocal enhancement.

The advanced phase (2020–2022) was marked by high-level coupling coordination, accompanied by matured urbanization (with a 55.8% urbanization rate) and an optimized industrial structure (with the tertiary sector accounting for 43.6% of GDP). Concurrent ecological revitalization efforts, including the implementation of the River Chief System and the rehabilitation of 1200 hectares of mining wastelands, contributed to maintaining water quality at Grade III+ standards (92% compliance). The green industrial transition led to reductions in energy intensity by 18.7% and emission intensity by 25.3%, thereby decoupling economic growth from environmental pressures. This phase exemplifies a virtuous cycle in which urban development and ecological conservation mutually reinforce one another, thereby establishing a paradigm for high-quality regional development.

4.3.2. Coupling Coordination Analysis Between Urbanization System and Water Environment Subsystem

As evidenced by Figure 8, the benefit subsystem demonstrates the highest degree of coupling correlation with the urbanization system within the water environment subsystem, indicating a statistically significant positive relationship and optimal coordinated development. The coupling correlation indices gradually decrease from the response to the state and pressure subsystems in relation to the urbanization system. Throughout the urbanization process, fluctuations in urban population density and non-agriculturalization rates have led to significant variability in Neijiang City’s per capita resource availability over the past decade. Although high rates of water resource development and utilization initially supported Neijiang’s early-stage urbanization by ensuring water supply, excessive exploitation may lead to future water scarcity, thereby hindering continued urban development. Consequently, the advancement of urbanization has been accompanied by a gradual decline in water resource exploitation rates. These dual factors have continuously influenced the coupling correlation between the state subsystem and the urbanization system. The pressure subsystem exhibits the coexisting characteristics of a decline in total urban water consumption and an increase in per capita domestic water consumption. In parallel, it is affected by elements such as improved industrial efficiency offsetting emissions growth, policy delays, and data interference from extreme factors, resulting in a weak degree of coupling correlation with the urbanization system.

4.3.3. Coupling Coordination Analysis Between Urbanization Subsystems and the Water Environment System

Figure 9 presents the coupling relationship between urbanization subsystems and the water environment system in Neijiang City from 2012 to 2022, based on regression analysis used to derive the corresponding curve parameters. The results indicate that among the urbanization subsystems, economic and spatial urbanization exhibit relatively strong coupling correlations with the water environment system, showing a significant upward trend in conjunction with improved water resource availability. Population urbanization shows a minimal degree of coupling correlation with the water environment system. A decline in the number of doctors and a sharp reduction in college students per 10,000 people during the later stages resulted in a low coupling correlation between social urbanization and the water environment system. During the process of urbanization, the four subsystems—population, economic, social, and spatial urbanization—interact dynamically and exert a collective influence on the water environment system. To promote coordinated and sustainable development between urbanization and the water environment, government agencies should enhance the integration of public services and water resource governance, promote technological innovation, and facilitate a qualitative transformation from “water-constrained cities” to “water-enriched cities.”

5. Discussion

5.1. Sensitivity Analysis

To test the robustness of the results, a sensitivity analysis was conducted by adjusting the weights of the two subsystems within a reasonable range, taking into account potential influences of regional characteristics and industrial structures on the model outcomes.

Specifically, Neijiang is a city with a notable level of industrialization, where the secondary industry accounts for 41.2% of its GDP and industrial wastewater contributes to 65% of the total pollutant discharge. As a result, the pressure on water resources during the process of economic urbanization may vary disproportionately, particularly in terms of the water environment burden.

To examine the model’s robustness under different weight scenarios, we varied the values of α and β within the range of [0.3, 0.7], while keeping their sum equal to 1. As shown in Figure 10, the analysis reveals that the overall trend of the coordination degree (D) remains consistent across different weight settings, with only minor fluctuations observed in specific years. This indicates that the core conclusions of the model are robust, even when α and β deviate from the baseline.

In addition, previous studies on similar industrial cities were referenced to further validate the potential impact of economic urbanization on water environment systems under industrialization contexts. Therefore, the assumption of α = β = 0.5 remains reasonable within the current analytical framework. Nevertheless, this assumption can be adjusted in future studies depending on specific regional or policy-driven contexts.

This sensitivity analysis provides a theoretical basis for adjusting weight parameters in more refined, region-specific models and offers insights for future research—particularly in exploring the sensitivity of water environments under varying industrial structures and urbanization trajectories.

5.2. Practical and Feasible Regulatory Measures to Improve the Coupling of UAWE of Neijiang City

The coupling relationship between urbanization and water environment security in Neijiang is dynamic and evolves over time. As urbanization accelerates, inevitable changes occur in the watershed’s water environment—typically marked by deterioration in the early stages of urban growth. However, in the middle and later stages, water quality tends to improve due to the implementation of environmental governance and policy interventions.

To address these challenges, Neijiang should adopt targeted regulatory measures across three key dimensions—technology, management, and economics—with the goal of enhancing the coordination between urbanization and water environment security and promoting sustainable watershed development.

(1)

Technological Measures:

Heavy Metal Pollution Control in Electronics Parks: Promote the adoption of advanced heavy metal wastewater treatment technologies (e.g., precipitation, electrolysis), strengthen wastewater discharge monitoring, and ensure that discharge standards are consistently met.

Water Recycling in the Photovoltaic Industry: Introduce wastewater recovery and treatment systems in photovoltaic manufacturing to achieve zero or low water discharge, improving resource efficiency.

(2)

Managerial Measures:

Environmental Regulation and Enforcement: Strengthen environmental oversight and enforcement for high-pollution industries such as electronics parks and photovoltaic production to ensure strict compliance with water protection standards.

Optimized Water Resource Management: Implement rational water allocation mechanisms and water quota systems and encourage enterprises to improve water-use efficiency and reduce waste.

(3)

Economic Measures:

Incentive Policies for Green Industries: Provide tax incentives and financial subsidies to enterprises that adopt water-saving and environmentally friendly technologies, thereby promoting the research, development, and deployment of green innovations.

Industrial Structure Optimization: Facilitate the green transformation of water-intensive industries by enhancing water-use efficiency and improving wastewater treatment capabilities, ultimately reducing overall pollution.

By implementing these integrated measures across the technological, managerial, and economic domains, Neijiang can effectively enhance the coupling between urbanization and water environment security, thereby promoting sustainable watershed development. In particular, industry-specific strategies targeting key local sectors—such as electronics parks and the photovoltaic industry—can reduce water pollution and resource consumption, improve water quality, and provide sustainable momentum for the urbanization process. This approach offers a viable path toward achieving a win–win outcome for both economic development and environmental protection.

5.3. Comparative Analysis and Research Limitations

Compared to existing research, this study addresses several key gaps: (1) Although prior studies have explored resource and environmental issues, most have focused on ecological protection at a macro level, with relatively few examining the specific relationship between water environment dynamics and urbanization [30,31]. This study fills that gap by analyzing the development of urbanization and water environment coordination in Neijiang City from an integrated management perspective. (2) Targeting Neijiang—a representative medium-sized basin city in the upper reaches of the Yangtze River—this study proposes an analytical framework with strong regional adaptability, offering a replicable and practical pathway for similar cities undergoing green transformation. (3) Through the construction of a coupling coordination model and sensitivity analysis, this study clarifies the influence pathways of different urbanization dimensions (e.g., population, economy, spatial expansion) on various subsystems of the water environment (pressure, state, response), thereby addressing previous limitations related to insufficient quantification and vague causality.

Despite its contributions, this study has several limitations: (1) The data used are primarily derived from official statistical yearbooks and environmental monitoring reports. While authoritative and representative, these data sources have limited temporal resolution, typically offering only annual observations. This makes it difficult to capture real-time environmental variations such as seasonal fluctuations, pollution incidents, or short-term extreme climate events, potentially leading to information gaps in the dynamic behavior of the coordination degree (D). Moreover, the study did not incorporate third-party monitoring data or remote sensing sources for cross-validation, which may limit the external reliability and generalizability of the model results. Future research could address this by integrating high-frequency environmental monitoring data, satellite imagery, or open-access third-party datasets to better reflect water environment dynamics and strengthen empirical support. (2) Due to limited data availability, the current indicator system remains incomplete, which may affect the accuracy of the results. As environmental data become more accessible, the indicator system should be expanded and refined to improve analytical rigor. (3) The existing coupling coordination model is not yet equipped to fully capture the complex interactions between socioeconomic and policy factors, such as population mobility, industrial transformation, and the dynamic impacts of water-related policy interventions. (4) From a methodological perspective, the use of min–max normalization (Formula (1)) may distort data distributions. Future studies could explore more robust standardization methods, such as Z-score normalization, to enhance result reliability. Additionally, Table 3 shows that the weight of “urban population density” in the urbanization subsystem is only 0.0002, which appears inconsistent with its theoretical importance. This suggests that the entropy method may be ineffective for variables with low variability. Future research could further investigate the behavior and limitations of the entropy method as a weighting tool.

6. Conclusions

This study establishes a theoretical framework for the coupling relationship between urbanization and the water environment, employing comprehensive evaluation methods and a coupling coordination model to analyze the urbanization–water environment (UAWE) interaction in Neijiang City, Sichuan Province, China, from 2012 to 2022. The main findings are summarized as follows:

(1)

The evaluation index system for Neijiang’s urbanization subsystem was constructed using population density and the proportion of the urban population to measure population urbanization; regional GDP and industrial output to reflect economic urbanization; per capita retail sales of consumer goods, number of hospital beds per 10,000 people, and per capita public green space to represent social urbanization; and built-up area and paved road area to characterize spatial urbanization. The results reveal that economic urbanization exhibited robust growth, driven by rising regional GDP associated with industrial park development and industrial restructuring. Spatial urbanization accelerated significantly, characterized by continuous expansion of the built-up area and urban spatial layout. In contrast, population urbanization progressed at a slower pace, while social urbanization, though improved, still requires optimization in public service resource allocation. Overall, urbanization in Neijiang has gradually stabilized at a relatively high level.

(2)

An evaluation framework for the coupling between urbanization and water environment security was established, incorporating multidimensional indicators from the urbanization subsystem and water environment security measures such as water quality compliance rate, sewage treatment rate, water resource utilization rate, and functional zone compliance rate. The entropy method was applied to assign objective weights and enable in-depth trend analysis. From 2012 to 2022, the overall level of urbanization in Neijiang increased in a stepwise manner, with rapid acceleration driven by strong momentum in economic and spatial urbanization. Conversely, the water environment security system exhibited frequent fluctuations due to factors such as the Tuo River Basin remediation project and inconsistent industrial pollution control efforts, resulting in variability in water quality and wastewater treatment performance.

(3)

The calculated coupling coordination degree (CCD) between urbanization and water environment security in Neijiang showed a nonlinear, fluctuating upward trend over the study period. In the early years, rapid urban expansion exerted considerable pressure on water resources, hindering CCD improvement. However, the implementation of ecological restoration initiatives such as the “Generation Project” accelerated CCD growth. The coupling correlation coefficient (r > 0.85) confirms a strong coupling effect, indicating a progressively synergistic relationship between urbanization and water environment security.

(4)

Further analysis reveals significant variability in the pressure and state subsystems of Neijiang’s water environment security system. During periods of rapid urbanization, increased industrial effluent and domestic sewage imposed substantial stress on the pressure subsystem. Although the state subsystem improved under strengthened environmental regulations and policy interventions, instability remains. Overall, the system maintains a high correlation with urbanization—initially constrained but later rebounding—eventually achieving coordinated development. Among the urbanization subsystems, economic urbanization exhibits the strongest coupling correlation with the water environment, underscoring the profound impact of industrial and economic activity. This finding highlights the urgent need for a green transition and sustainable development practices to foster a positive and resilient coupling between urbanization and water environment security.