Coupling coordination evaluation and system regulation of water environmental carrying capacity from a green development perspective Open Access

Water environment carrying capacity (WECC) is a critical indicator of water resource sustainability, particularly amidst rapid economic development and urbanization. In its narrow sense, WECC refers to the capacity of the water environment to accommodate pollutants, while in a broader sense, it represents the maximum ecological pressure the environment can endure without compromising its quality and functions (Zhang et al. 2021). This broader definition emphasizes the interaction between human activities and natural processes, and is frequently used to plan and regulate the long-term impacts of human activity on water resources and ecosystems (Chen et al. 2022). With ongoing industrialization and urbanization, water scarcity and environmental degradation have become pressing global challenges. Increasing water demand and human-induced pressure further exacerbate these issues (Meng et al. 2018). Therefore, accurately assessing and managing WECC is essential for achieving regional sustainable development (Chen & Tang 2023).

In recent years, the concept of green development has gradually emerged as a key approach to balancing economic growth with environmental protection, serving as a sustainable development pathway (Weng et al. 2018). Although many countries have developed and implemented green development strategies, such as Germany's ‘Energiewende’ (Renn & Marshall 2016), the European Union's ‘European Green Deal’ (Samper et al. 2021) and Brazil's rainforest protection policies (Vieira & Panagopoulos 2024), the synergistic effects between water resource management and green development remain underexplored. Particularly in developing countries, achieving efficient water resource utilization (WRU) alongside environmental protection amid rapid economic growth remains a pressing scientific challenge. While existing studies have extensively explored green development (Feng et al. 2017) and water resource management (Sheffield et al. 2018; Xiang et al. 2021), most research remains confined to single domains, lacking a systematic analysis of the interplay between the two. Moreover, current studies predominantly rely on static analytical methods, making it difficult to capture the dynamic changes of WECC within the human–water interaction system (Wang et al. 2018; Zhao et al. 2021).

As one of the key drivers of global green development, China has widely applied the concept of green development in the development of the Yangtze River Economic Belt (YREB), particularly in improving WRU efficiency and controlling pollution emissions (Kong et al. 2022). Taking Wuhan City in Hubei Province (part of YREB) as an example, local authorities have implemented strict emission standards and closed several high-pollution, high-water-consuming factories along the Yangtze River. Additionally, multiple wastewater treatment plants have been newly constructed or upgraded, and advanced wastewater treatment technologies have been introduced. Since 2017, the water quality compliance rate of major water bodies in the Yangtze River Basin in Hubei Province has been increasing annually, reaching 93.2% in June 2024 (Xu et al. 2023). These green development measures have effectively enhanced regional WECC. However, the sustainability of water environments under socio-economic pressures requires further investigation, with the core focus on assessing WECC's performance under these pressures (Hu et al. 2021). Green development, by emphasizing resource efficiency, pollution control, and ecological protection (Feng et al. 2017; Zhang & Li 2020), offers a novel perspective for this research. Therefore, it is necessary to integrate the concept of green development into the WECC assessment framework to reveal the coordination mechanisms between the two within complex socio-economic systems. This integration will also provide theoretical support and policy recommendations for the WECC improvement.

To achieve this objective, dynamic analytical methods are essential for overcoming the limitations of static assessments (Wang & Xu 2015). WECC represents a complex human–water interaction system involving multiple factors, including water resources, economic development, population growth, and pollution emissions (Zhang et al. 2019; Kong et al. 2022). System dynamics (SD), due to its strengths in dynamic simulation and feedback analysis (Li et al. 2020), serves as an ideal tool for modeling and predicting WECC (Jin et al. 2009; Zhou et al. 2017; Dai et al. 2022). This study integrates the comprehensive index method (Wang et al. 2018) with SD to develop a targeted assessment framework, which was applied in Hubei Province, a core region of the YREB. First, an evaluation index system for WECC and green development was established to quantitatively analyze the driving factors of their coupling coordination relationship. Subsequently, the SD model was employed to simulate WECC trends in Hubei Province under five development scenarios. The study reveals the coordination mechanisms between the two within complex socio-economic systems and elucidates the strategic advantages of green development. The novelty of this research lies in its dynamic integration of green development into WECC assessment, addressing the gaps in traditional studies.

The data used in this study cover various aspects of social development and the natural environment in Hubei Province and its subordinate regions. Social data, including population, economy, and energy, are primarily sourced from the National Bureau of Statistics, Hubei Provincial Statistics Yearbook, and municipal statistical reports. Urban development data are mainly obtained from the Ministry of Housing and Urban-Rural Development website (https://zjt.hubei.gov.cn/). Water resource data are sourced from Hubei's municipal water resource bulletins and environmental reports. For cases where data for certain indicators are missing, values are derived through interpolation using data from adjacent years.

The green development index system comprises four criteria layers: green growth efficiency (GGE), green carrying potential (GCP), green living quality (GLQ), and green protection efforts (GPE). These dimensions collectively reflect the regional green development level (GDL) from the perspectives of economic performance, resource utilization, living standards, and policy implementation.

The WECC index system incorporates three core criteria: WRU, water environment quality (WEQ), and socio-economic development (SED). These criteria are interrelated and collectively evaluate the sustainability of the water environment from distinct perspectives. The WRU carrying capacity emphasizes effective water usage management and resource efficiency to ensure the rational development and sustainable utilization of water resources. The WEQ carrying capacity prioritizes the preservation of aquatic ecology and water quality, evaluating the extent to which pollution from economic activities exceeds the water environment's self-purification capacity. The SED carrying capacity assesses the ability of water resources to support economic growth and population expansion, particularly under conditions of high-intensity economic activity and increasing water demand. The index systems for GDL and WECC are detailed in Tables 1 and 2.

This study employed both subjective and objective methods to determine the weights of each indicator. Specifically, it combined the Analytic Hierarchy Process (AHP) with the entropy weight method to leverage the strengths of expert judgment and objective data, respectively. Detailed steps for these methods are available in Wu et al. (2022). The resulting weight values are presented in Tables S1 and S2 in the Supplementary information.

The GDL and WECC were calculated using the SMI-P method, which integrates single indicator quantification, multiple indicator synthesis, and multiple criteria integration. This approach is widely applied in fields such as water quality evaluation, water resource management, and water security (Zuo et al. 2020; Qiu et al. 2022; Wang et al. 2022a). The specific calculation steps are as follows:

Based on the distribution characteristics of the CCD, and drawing on the classification standards for coupling coordination types from similar regions and related studies (Wang et al. 2022b; Cheng et al. 2023; Shi et al. 2023; Li et al. 2024), this study divided the CCD into three stages and five types. This classification is designed to ensure the comparability of the CCD over time, as shown in Table S6, Supplementary material.

After that, the GeoDetector was used to quantitatively identify the factors that significantly affect the coupling coordination between green development and WECC. This analysis provides a basis for determining the key regulatory variables under different green development scenarios for subsequent SD modeling. The calculation principle and method of the GeoDetector are detailed in Supplementary Information, S1.

In the model flowchart, some variables exhibit simple linear relationships that can be addressed through basic algebraic equations, such as the relationship between GDP, per capita GDP, and total resident population. However, certain variables display more complex interactions; for instance, the relationships among forest coverage, total energy consumption, and CO₂ emissions are not merely linear but influenced by multiple factors. This study conducted regression analysis on historical data from 2012 to 2022 to examine the dynamic relationships among these three variables, establishing complex regression equations accordingly. These equations account not only for the direct impact of energy consumption on CO₂ emissions but also incorporate forest coverage as a key factor in carbon sequestration to simulate its regulatory effect on CO₂ balance. This approach enables the model to more accurately reflect the interactive relationships between variables.

To verify the accuracy of the simulated behavior of the constructed SD model compared to the behavior observed in the real system, two important tests were conducted: model validation and sensitivity testing. The detailed procedure can be found in Xing et al. (2019). The test results are presented in Section 3. Additionally, the methods for determining each parameter in the SD model flowchart, along with the model calibration method, are detailed in Supplementary Information, S2 and S3.

Five scenarios were designed to simulate the WECC of Hubei Province. Each scenario corresponds to a different combination of variables, and simulations were conducted to compare the performance of the WECC under varying variable settings. The specific descriptions of each scenario are listed in Table 3. The system boundary of the SD model was set to the administrative boundary of Hubei Province. The period from 2012 to 2022 serves as the model validation interval, while the period from 2023 to 2030 is used for model prediction and regulation. The simulation step length is 1 year. Table 3 lists the target levels that each regulatory variable should reach by the end of the regulation period in 2030 under different scenarios.

Table S7, Supplementary material presents the GDL of 16 cities in Hubei Province from 2012 to 2022. Overall, the GDL of Hubei Province showed a consistent upward trend, rising steadily from 0.495 (intermediate level) in 2012 to 0.719 (mid-high level) in 2022. This growth was generally stable, though a brief decline occurred in 2020 due to the COVID-19 pandemic, followed by a rapid recovery and continued improvement, reflecting strong resilience in green development. Drawing on Wang et al. (2024), who utilized nighttime light remote sensing to assess economic resilience, this recovery aligns with Hubei's economic resilience, particularly evident in Wuhan – the pandemic epicenter – and its surrounding cities, which supported a robust rebound in green development. In addition, Xiao et al. (2022) found that the GDL in Hubei Province significantly increased after 2014, attributing this to sustained policy efforts in pollution control and ecological protection, which likely laid the foundation for the continued growth of GDL in Hubei Province.

Across the 16 cities, GDL exhibited an upward trend, though the rates of increase varied significantly. Jingzhou recorded the largest increase at 73.38%, while Wuhan saw the smallest at 12.92%. Nevertheless, Wuhan maintained the highest GDL in the province for most years, only surpassed by Yichang in 2021 and 2022. Wuhan, Yichang, and Xiangyang, as core cities of the three metropolitan clusters, consistently led in GDL. In 2020, the COVID-19 pandemic caused negative growth in GDL for 11 cities across the province (Jiang & Luo 2020). Although lockdown measures reduced certain pollutant emissions (e.g., lower wastewater discharges due to industrial cutbacks), the economic pressures and developmental stagnation outweighed these short-term environmental benefits, failing to reverse the overall downward trend that year.

Table S8, Supplementary material presents the WECC scores for 16 cities in Hubei Province from 2012 to 2022. Overall, the WECC of Hubei Province exhibits a fluctuating but generally upward trend, increasing from 0.479 in 2012 to 0.657 in 2022. This trend is consistent with Yue et al. (2024)'s study on Hubei Province, which found that the Urban Water Ecological Carrying Capacity (UWECC) generally increased from 2010 to 2020. They noted a significant rise in 2020 due to the COVID-19 pandemic, attributing it to changes in population mobility and WRU. Notably, the WECC improved from an intermediate to a mid-high level in 2016. This change is closely linked to the Chinese government's issuance of the ‘Water Pollution Prevention and Control Action Plan’ (commonly known as the ‘Ten Measures for Water’) in 2015, which is a critical policy document aimed at addressing water pollution. The plan focuses on improving water quality and curbing the trend of water pollution through measures such as strict control of industrial pollution, enhancing urban sewage treatment capacity, and reducing agricultural non-point source pollution (Zhou et al. 2021). These measures have been gradually implemented since 2016, leading to significant improvements in national water quality (Chen 2024). By 2022, Yichang City was the only city with a high-level WECC, likely benefiting from the ecological restoration efforts of the ‘Yangtze River Protection’ initiative (Kong et al. 2022). In contrast, Xiantao City was the only one at a low-mid level. Among the remaining 14 cities, 10 reached a mid-high level, and four remained at an intermediate level.

By 2022, most cities in Hubei Province had reached the advanced coordination type. This trend not only validates the long-term effectiveness of policy implementation but also suggests that the gap in coordinated development between regions is narrowing. However, while the differences in CCD among the three metropolitan clusters are minimal, regional disparities within each cluster have not been fully eliminated. As of 2022, each metropolitan cluster still contains cities at the primary coordination level: Suizhou in the Xiangyang cluster, Enshi in the Yichang-Jingzhou cluster, and Xiantao, Tianmen, and Qianjiang in the Wuhan cluster. The lag in these cities may be attributed to their remote geographical locations, weak economic foundations, or insufficient policy enforcement, indicating that Hubei Province needs to further address the coordination shortcomings in peripheral areas to achieve balanced development across the province.

Table S10, Supplementary material presents the top ten driving factors influencing the CCD between GDL and WECC in Hubei Province. From 2012 to 2022, significant changes occurred in the driving factors. Combined with the evaluation index systems of GDL and WECC (Tables 1 and 2), the following analysis examines how driving factors promote the CCD through indicator interactions.

In 2012, the reuse rate of industrial water (q= 0.746, ranking first, corresponding to GDL's DE4) was the most critical driving factor, highlighting the pivotal role of industrial water resource management in coupling coordination. However, by 2022, the reuse rate of water resources (q= 0.904, ranking first, corresponding to WECC's WE4) emerged as the primary factor, indicating a policy shift from industrial water management to comprehensive water resource recycling. This shift, by enhancing WE4, not only reduced ‘water consumption per 10,000 yuan of GDP’ (WE5) but also formed a synergistic effect with GDL's ‘energy consumption per unit of GDP’ (BR1), further strengthening the coordination between GDL's GCP and WECC's WRU. Meanwhile, the reuse rate of industrial water (q= 0.883) retained its importance (ranking third), demonstrating that efficient water use in the industrial sector remains a foundational support for coordinated development.

The focus of pollution control shifted from COD emission per unit of GDP in 2012 (q= 0.687, ranking second, corresponding to WECC's WF1) to CO₂ emission per unit of GDP in 2022 (q= 0.890, ranking second, corresponding to GDL's BE4), reflecting Hubei's gradual transition from water pollution control to addressing carbon emissions and climate change. Carbon emission reduction enhanced GDL's GCP by reducing ‘CO₂ emission per unit of GDP’ (BE4), which not only lowered air pollution but also indirectly improved WECC's WEQ (e.g., WF1 and WF2), thereby strengthening the synergy between GDL and WECC.

Green space expansion marked another notable change in 2022. In 2012, green space-related indicators did not rank among the top ten driving factors, but by 2022, the green coverage rate of built-up areas (q= 0.838, ranking fourth, corresponding to GDL's DP2) and per capita green space (q= 0.670, ranking fifth, corresponding to GDL's BR2) became significant factors, reflecting Hubei's growing emphasis on ecological environment and urban greening in promoting green development. Green spaces enhanced GDL's GCP (e.g., BR2) and GPE (e.g., DP2) while simultaneously improving WECC's ecological restoration capacity (e.g., reducing non-point source pollution from WF4), thus promoting coordination between the two.

The driving force of technological innovation also shifted. In 2012, the number of invention patents per 10,000 people (q= 0.585, ranking sixth, corresponding to GDL's CR1) was the primary indicator of technological innovation, whereas by 2022, the proportion of high-tech industries (q= 0.650, ranking sixth, corresponding to GDL's AI2) replaced patents as the key indicator, indicating a transition from quantitative accumulation to the industrialization of high-tech sectors. This shift, by enhancing GDL's industrial structure (e.g., AI2), promoted green economic transformation, more closely coupling GDL's GGE (e.g., AE1) with WECC's WEQ (e.g., WF1) and WRU (e.g., WE6), reflecting technology's profound impact on coordinated development.

The balance between economy and environment varied across the two time points. In 2012, per capita urban disposable income (q= 0.567, ranking seventh, corresponding to GDL's AE2) and per capita GDP (q= 0.562, ranking eighth, corresponding to GDL's AE1) were the main economic driving factors, demonstrating the significant influence of economic growth. However, by 2022, infrastructure factors such as the density of water supply pipelines in built-up areas (q= 0.520, ranking ninth, corresponding to GDL's CI1) gained prominence, while the influence of economic factors weakened (per capita urban disposable income dropped to q= 0.459, ranking tenth), indicating Hubei's gradual shift from rapid economic growth to resource conservation and infrastructure optimization. The improvement in CI1 directly supported WECC's SED (e.g., WG3) and GDL's GLQ, reflecting infrastructure's critical role in coordination.

Guided by the concept of green development, Hubei Province achieved deep coupling coordination between GDL and WECC by optimizing WRU, controlling carbon emissions, expanding green spaces, and promoting technological innovation. These driving factors do not act solely on WECC but rather enhance the synergy between GDL and WECC across the dimensions of resources, environment, and economy, thereby facilitating the comprehensive coordinated development of economy, environment, and society.

To assess the impact of different parameter variations on the model output, seven key parameters were selected for sensitivity testing. Based on the possible value ranges of the parameters, the specific variation ranges were from −95% to +150%. Table S11, Supplementary material lists the positive and negative variation ranges of each parameter along with their corresponding sensitivity levels. Overall, most parameters exhibit low sensitivity, with sensitivity levels ranging from 0.0001 to 7.3953%. In contrast, the sensitivity of industrial wastewater discharge intensity is relatively high, indicating that this parameter plays a particularly critical role in the SD model.

Based on the increase in WECC, the optimal regulatory strategies for Hubei Province are ranked as follows: green development scenario > resource conservation scenario > environmental protection scenario > status quo maintenance scenario > economic priority scenario. Under the status quo maintenance scenario, WECC shows slow and steady growth (an increase of 4.83%), indicating that without new measures, the water environmental carrying capacity improves, but the progress is relatively slow. In the economic priority scenario, despite rapid economic development, the WECC shows a downward trend throughout the regulation period (a decrease of 1.61%). This result suggests that pursuing economic growth without considering environmental protection will put enormous pressure on water resources, leading to a decline in carrying capacity.

In contrast, both the Environmental Protection and Resource Conservation scenarios show significant improvement trends. Under the Resource Conservation scenario, WECC steadily increases (an increase of 10.33%), emphasizing the importance of prioritizing the reduction of water resource consumption and improving efficiency. The Environmental Protection scenario initially grows at the same rate as the Resource Conservation scenario but experiences slower growth in the later period. Through investments in environmental protection and enhanced water pollution control measures, WECC increases by 8.46% by 2030.

The most ideal scenario is the Green Development model, which achieves the maximum increase in WECC, with a growth of 12.08% by 2030. This demonstrates that green development can effectively balance economic and environmental needs, maximizing the water environmental carrying capacity, and is the best path to achieving sustainable development.

Based on the index system presented in Table 2, the changes in the three dimensions of WECC under different scenarios were simulated (Figure 8(b)–8(d)). First, among the eight evaluation indicators of the WRU dimension, WE5–WE8 (Table 2), which represent the efficiency of industrial, agricultural, and residential water use, appear in the constructed SD model flowchart. Therefore, the differences in WRU capacity across different scenarios, as shown in Figure 8(b), arise from variations in water use efficiency. The green development and resource conservation scenarios stand out for their growth advantages in WRU capacity throughout the forecast period, driven by improvements in water use efficiency and reductions in water waste, especially through the promotion of water-saving technologies and recycling, which alleviated water resource pressure. In contrast, the status quo maintenance scenario shows almost negligible changes. Due to a lack of effective management and technological improvements, water use efficiency stagnates, and the carrying capacity is difficult to enhance, potentially leading to long-term water resource depletion risks.

Secondly, among the four indicators of the WEQ dimension, three of them (WF1–WF3), which represent pollutant and wastewater emissions, also appear in the SD model flowchart. Therefore, the differences in WEQ carrying capacity, as shown in Figure 8(c), mainly result from the water pollution intensity of economic activities. Except for the economic priority scenario, all other scenarios show an increasing trend over the next eight years. Among them, the green development and environmental protection scenarios exhibit the largest growth, with the former increasing steadily and the latter initially increasing rapidly before slowing down. Under the economic priority scenario, the WEQ carrying capacity shows a clear decline. In this scenario, total sewage discharge and COD emissions per unit GDP increase year by year, with total sewage discharge expected to reach 84.66 tons by 2030, and COD emissions per unit GDP reaching 20.67 tons per 100 million yuan. This is primarily due to the increase in emissions caused by the rapid expansion of economic activities, which reduces the carrying capacity of WEQ.

Third, among the five indicators of the SED dimension, three indicators (WG2–WG4), which represent economic structure and development level, are reflected in the SD model flowchart. In the environmental protection scenario, the carrying capacity of SED reaches its peak in 2026 and then declines year by year. As environmental protection measures are continuously strengthened, some high-pollution and high-water consumption economic activities are suppressed, leading to slower economic growth, which ultimately results in a decline in the carrying capacity for SED. This indicates that solely emphasizing environmental protection without balancing it with economic development may limit sustained socio-economic growth.

Under the economic priority scenario, the carrying capacity of SED performs the worst. Due to the proportion of industrial added value to GDP (WG4) and urbanization rate (WG2) indicators, both of which are negative indicators (see Table S2, Supplementary material), they are the main limiting factors for WECC in terms of SED. In the economic priority scenario, rapid industrialization and urbanization drive economic growth, but they simultaneously increase the pressure on water resources and the environment, which in turn suppresses the carrying capacity for SED. In contrast, under the status quo maintenance, resource conservation, and green development scenarios, the carrying capacity for SED increases year by year, benefiting from the balance between economic structure optimization and resource protection measures.

Notably, some evaluation indicators did not appear in the SD model flowchart for both the WECC and its dimensions. Therefore, while the SD model constructed in this study can relatively accurately reflect the overall trend in carrying capacity changes, it does not fully encompass all influencing factors.

In conclusion, under different scenarios, the carrying capacities of WRU, WEQ, and SED show diverse changing paths. This further reveals the limitations of a single policy or measure in promoting sustainable development. Relying solely on economic growth (e.g., the economic priority scenario) or one-sided environmental protection (e.g., the environmental protection scenario) is insufficient for achieving balanced development across all dimensions. In contrast, the green development scenario, by effectively combining economic growth, environmental protection, and resource conservation, demonstrates the most ideal performance in carrying capacity. This suggests that comprehensive, multi-dimensional policy design and implementation are critical for enhancing regional WECC.

This study introduces the concept of green development into the evaluation and simulation system of WECC for the first time, using Hubei Province, China as a case study. This provides a new theoretical perspective and methodology for WECC research. First, using a comprehensive indicator evaluation method, the study analyzed the spatiotemporal variation of Hubei's GDL and WECC. The coupling coordination model and geographic detector were then employed to explore the coordination relationship and driving factors between the two. Subsequently, an SD model, incorporating modules for population, economy, water resources, and water environment, was developed to simulate the changes in WECC under different scenarios in Hubei Province and evaluate the effectiveness of green development in enhancing WECC.

The results showed that (1) between 2012 and 2022, both GDL and WECC in Hubei Province exhibited an upward trend, increasing from intermediate to mid-high levels, with the coupling coordination relationship progressing from primary coordination to advanced coordination; (2) the improvement in GDL preceded the increase in WECC, suggesting that green development is likely a key driver in improving WECC; (3) key factors for achieving advanced coupling coordination between WECC and green development include optimizing water resource use, controlling carbon emissions, expanding green areas, and promoting technological innovation; (4) the optimal future strategy for Hubei Province is the green development scenario, with a projected 12.08% increase in WECC by 2030; in contrast, the economic priority scenario leads to a decline in WECC. Therefore, to improve WECC in Hubei Province, future efforts should prioritize coordinating energy conservation, emission reduction, environmental management, and economic growth. The evaluation and simulation method of WECC from the perspective of green development proposed in this study can be applied to other regions, providing an assessment of the potential for improving WECC and the effectiveness of environmental management strategies under a green development framework. This approach also offers scientific decision-making support for policymakers.

Meanwhile, this study also has certain limitations. First, the data coverage is limited. The study primarily relies on publicly available statistical data and fails to fully incorporate micro-level real-time monitoring data (e.g., river section water quality or enterprise emission records), which may reduce the precision of the results. Second, the assumptions of the SD model have certain constraints. The model parameters are based on historical trends and existing policies, without fully accounting for the impact of unpredictable factors such as extreme climate events or sudden policy changes, potentially underestimating the fluctuation risks of WECC. Third, this study focuses on the provincial scale and does not delve into spatial differences at the city or county level, resulting in insufficient detail in explaining regional heterogeneity. Future research could improve accuracy and applicability by incorporating high-resolution data (e.g., satellite-based water quality data), adopting dynamic uncertainty analysis, and refining the analysis to city or watershed scales.

This research was supported by funding from the National Key R&D Program of China (2023YFC3205600).

M.W. and S.G. prepared the methodology. J.L., Y.B., and H.H. prepared the materials, collected and analyzed. M.W. and S.G. prepared and wrote the original draft . M.W. acquired funds. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.