Research on the coupling effect of water security and socio-economy in five economic zones of Sichuan Province, China

The United Nations has developed a specific definition of ‘water security’ that includes elements of safe water use and ecological protection, and has since closely linked it to the sustainable development goals. This fully demonstrates the critical importance of water security in achieving the long-term development in the region (UN water 2013; United Nations 2015). However, a uniform theory regarding water security has yet to be established (Butte et al. 2022). Studies on water security typically revolve around themes such as water pollution and availability, human vulnerability to drought and flood, and the sustainability of resources and water ecosystems (Cook & Bakker 2012; Chang & Zhu 2020). Previous studies indicate that water security not only emphasizes the availability of water but also incorporates environmental protection and sustainable development considerations. Therefore, water security was hereby defined as the availability of ample water resources at a reasonable cost, highlighting the critical role of efficient water use and the practice of water recycling.

Assessments of water security have given rise to diverse frameworks and indicators based on varying needs, with research scales encompassing countries worldwide (Panella 2021; Kchouk et al. 2022), islands or basins (Alexandra & Rickards 2021; Hasan & Ridwan 2021), river basins (Nkiaka et al. 2022; Pacheco et al. 2022), and provinces or cities (Lu et al. 2021; Deng et al. 2022). Since the dawn of the 21st century, quantitative studies have been conducted on water security from multiple dimensions. Data sources for evaluation indicators fall into two categories, i.e., survey data obtained through interviews based on empirical scales, and public data provided by governments based on resource metrics. Assessment methods primarily involve establishing evaluation models for water security, including the pressure-state-response (PSR) conceptual model (Van Ginkel et al. 2018; Ghosh et al. 2019), the driving-pressure-state-impact-response (DPSIR) model (Cao et al. 2022), and variations of the DPSIR model incorporating carrying capacity (Zhang et al. 2019). The concept of water security is evolving with the ongoing research. Accurately assessing urban water security is fundamentally dependent on the development of an objective and reliable set of indicators. Differences in understanding of water security make it difficult to harmonize assessment indicator systems. However, most existing indicator systems include both natural and socioeconomic attributes. Currently, integrated evaluation tools involved in water security research include analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) (Nie et al. 2018) and other multicriteria decision-making techniques. In addition, grey relational analysis (Li et al. 2020), cloud matter element model (Deng et al. 2022), fuzzy comprehensive evaluation (FCE) (Ding et al. 2017; Cai et al. 2020), and projection pursuit method (Wang et al. 2020) are also extensively used. However, these methodologies necessitate highly precise data to bolster the selection of evaluative indicators, inherently posing certain limitations. Furthermore, noncomprehensive evaluative methodologies, such as system dynamics (SD) (Sahin et al. 2017; Yin et al. 2020), water footprint (WF) (Veettil et al. 2022), and other nonintegrated evaluation methods, have been frequently employed. The SD and WF methods focus on the explanation of the interaction mechanisms of the systems and the evolutionary process. Table 1 presents a detailed comparison of several major research methods.

Table 1 presents a comparative analysis of literature on water security evaluation. The evaluation covers various aspects, and a variety of scales are involved. However, there are some shortcomings in the research for areas with uneven distribution of water resources. The evaluation methods have their own advantages and disadvantages. In comprehensive evaluation, those that account for the randomness and uncertainty inherent in the evaluation process offer considerable benefits (Liu et al. 2022b). Currently, the most widely used methods based on fuzzy mathematics for evaluating water security are the FCE method and the cloud model. To ensure objective evaluation and accurate differentiation of the results when dealing with numerous evaluation indicators, a cloud model was hereby selected for water security evaluation. This model has been widely applied in many fields such as comprehensive evaluation, intelligent control, decision analysis, and prediction (Jiao et al. 2020; Liu & Zhu 2022; Zeng et al. 2023). Among them, comprehensive assessment includes security system resilience assessment, resource and environmental carrying capacity assessment, engineering construction risk assessment, and water quality assessment (Guo et al. 2020; Li et al. 2021; Ruan et al. 2021; Yang et al. 2023). Therefore, on the basis of establishing the evaluation framework, the cloud model was adopted to conduct a comprehensive evaluation of water security in this study.

Water security is intrinsically linked to the socioeconomic development context (Grasham et al. 2019). The rational exploitation and utilization of natural resources and sustainable socioeconomic development are interdependent and mutually restrictive. Research on water security evaluation and coupled coordination degree can provide suggestions for promoting coordinated development among systems (Deng et al. 2022). Conversely, promoting high-quality economic growth and long-term sustainability is vital to reducing water risk and ensuring water security (Hoekstra et al. 2018). At present, there is a scarcity of studies that explore the interplay between water security and socioeconomic development, particularly those that assess urban water security and categorize developmental stages. The coupling coordination degree (CCD) model has been employed in various fields of research, reflecting the variation characteristics and interactive relationships across systems (Xu & Hou 2019). Many scholars have utilized the model to analyze the water resources system in conjunction with other systems (Li et al. 2016; Cui et al. 2019; Liu et al. 2022a). For instance, Liu et al. (2020) established an improved CCD model for investigating the social economy – water environmental quality coupling coordination conditions of the Nansi Lake Basin from 2001 to 2017, while analyzing the dynamic evolution mechanism.

In general, the current research on water security exhibits the following characteristics and limitations. First, theoretical frameworks for water security are increasingly emphasizing ‘sustainable development’ (Chang & Zhu 2020). Therefore, water security assessments should prioritize sustainable water supply and include the rational development and recycling of water resources. This ensures that the assessments accurately reflect the healthy operation of the water cycle within natural and social systems. Second, the existing research primarily focuses on watersheds or cities (Wang et al. 2017; Zhou et al. 2019), with specific objects such as economic zones rarely explored. Third, the coupled and coordinated development of water security and socioeconomics is less considered in water security research. Furthermore, the prevailing research on coupled coordination has inherent limitations, particularly in the absence of comparative studies examining how each system's synergistic development varies across regions with diverse socioeconomic development levels. Finally, most of the current studies solely analyze the temporal trend of water security (Cao et al. 2022) and neglect to investigate its developmental variability from a spatial standpoint. This oversight disregards the impact of the spatial structural imbalance phenomenon on sustainable development.

Considering the gaps in prior research, this study was conducted primarily to address the following three questions: First, what are the spatiotemporal dynamics of water security in Sichuan's five economic zones from a sustainable development perspective? Second, what are the key factors impacting water security in these zones? Third, what are the spatiotemporal patterns and differences in the CCD of water security system (WSS)-SES across the five zones, especially considering their varying socioeconomic statuses? To this end, the 2EPRSC water security evaluation index system was constructed based on the PSR theoretical framework, considering water utilization efficiency and water cycle, and the genetic algorithm (GA) optimized entropy weighting method (GA-IEWM)-cloud model was employed to explore the spatiotemporal heterogeneity of the water security and to delve into its influencing factors. In addition, the CCD model was utilized to analyze the synergistic development of WSS-SES in combination with the SES evaluation index system.

Compared with existing studies, the main research objectives and contributions of this study could be listed as follows. First, economic zones were selected as a specific research object, focusing on the differences in the level of integration of regions. From the perspective of sustainable development, it aimed to explore the reasons for the differences in the overall WS and CCD of the economic zones, providing new ideas for related research. Second, the concept of WS was combined with the theory of sustainable development, with a particular focus on water recycling. A new theoretical framework 2EPRSC was built, covering water resources endowment, water resources pressure, water resources response, water resources efficiency, water resources utilization structure, and water resources cycle, comprehensively reflecting the support capacity of water resources systems and water security. Third, the GA-IEWM algorithm was innovatively introduced to refine the entropy weighting method (EWM) by integrating a planning model and solving it with a GA. This approach not only objectively determines indicator weights but also enhances and diversifies them, leading to more precise outcomes. Fourth, WSS and SES were combined on the basis of evaluating the WS level using the cloud model, CCD was calculated, the coordination degree between the level of water security integration and the level of socioeconomic integration was analyzed, and the reasons affecting the sustainable and coordinated development of the two systems were explored.

This article is organized as follows: Section 1 mainly introduces the research progress of the water security evaluation. Section 2 presents the research framework and the research ideas of this paper. Section 3 establishes the comprehensive water security evaluation model based on the GA-IEWM-cloud model, TOPSIS, FCE, and the WSS-SES CCD evaluation model. Section 4 exhibits the results of the study including the spatiotemporal variation characteristics of water security and CCD in the five economic zones of Sichuan. Section 5 presents the conclusions drawn from the study, while Section 6 offers a discussion of the findings.

Step 1. From the perspective of sustainable development, indicators were extracted pertaining to water resources endowment, pressure, response, efficiency, structure, and cycle. A comprehensive water security evaluation framework, dubbed the 2EPRSC, was thus constructed, reflecting the rational development, effective utilization, and recycling of water resources.

Step 2. Based on the GA-IEWM model, the weights of indicators were calculated, and key factors impacting water security were investigated. Subsequently, the cloud model was employed to divide the water security grades of the five economic zones in Sichuan and analyze their temporal and spatial variation characteristics in water security. The TOPSIS and FCE were employed to verify the reliability of the cloud model.

Step 3. By integrating socioeconomic development evaluation indicators, the CCD model was adopted to study the spatiotemporal distribution differences of WSS-SES in the five economic zones. This could serve as a crucial reference for reference water resource allocation and investment, realizing the sustainable and coordinated development of WSS-SES and bridging the development gap between economic zones.

To combat water security challenges, including pollution, and to devise more rational water resource allocation plans for sustainable regional development, this article synthesized existing research findings and tailored them to the study area's specific conditions. A comprehensive water security evaluation framework was developed, spanning six key aspects: (1) water resources endowment (E): regional natural water conditions and the supply capacity ensuring normal human living and production; (2) water resources pressure (P): the consumption and environmental burden on regional water resources caused by human activities; (3) water resources response (R): the intensity of measures taken to improve regional water security; (4) water resources efficiency (E): the efficient utilization of water resources, reflecting technological advances; (5) water resources structure (S): the rationality of regional water resources development and utilization; and (6) water resources cycle (C): limiting effluent discharge and promoting closed-loop water use to reduce resource waste and environmental stress. A total of 16 indicators were extracted, as shown in Table 2.

Table 2

The 2EPRSC evaluation framework of water security

System layer .	Criterion layer .	Indicator layer .	No. .	Type .	References .
WSS	Endowment	Average annual rainfall (mm)		+	Deng et al. (2022)
		Water resources per unit area (104 m3/km2)		+
	Pressure	Per capita water consumption (m3/person)		−	Zhang et al. (2019)
		Per capita domestic water consumption (m3/person)		−
		Average annual temperature (°C)		−
	Response	Environmental protection investment intensity (%)		+	Cao et al. (2022)
		Green coverage (%)		+
		Proportion of ecological water (%)		+
	Efficiency	Water consumption per 10-thousand-yuan GDP (m3 /10-thousand-Yuan)		−	Du et al. (2022)
		Water consumption per 10-thousand-yuan agricultural value added (m3 /10-thousand-Yuan)		−
		Water consumption per 10-thousand-yuan industrial value added (m3 /10-thousand-Yuan)		−
	Structure	Proportion of surface water supply (%)		+	Qiao et al. (2022)
		Proportion of groundwater supply (%)		−
		Degree of water resources development (%)		−
	Cycle	Wastewater discharge per 10-thousand-yuan GDP (m3/10-thousand-Yuan)		−	Chang & Zhu (2020)
		Rate of wastewater treatment (%)		+

System layer .	Criterion layer .	Indicator layer .	No. .	Type .	References .
WSS	Endowment	Average annual rainfall (mm)		+	Deng et al. (2022)
		Water resources per unit area (104 m3/km2)		+
	Pressure	Per capita water consumption (m3/person)		−	Zhang et al. (2019)
		Per capita domestic water consumption (m3/person)		−
		Average annual temperature (°C)		−
	Response	Environmental protection investment intensity (%)		+	Cao et al. (2022)
		Green coverage (%)		+
		Proportion of ecological water (%)		+
	Efficiency	Water consumption per 10-thousand-yuan GDP (m3 /10-thousand-Yuan)		−	Du et al. (2022)
		Water consumption per 10-thousand-yuan agricultural value added (m3 /10-thousand-Yuan)		−
		Water consumption per 10-thousand-yuan industrial value added (m3 /10-thousand-Yuan)		−
	Structure	Proportion of surface water supply (%)		+	Qiao et al. (2022)
		Proportion of groundwater supply (%)		−
		Degree of water resources development (%)		−
	Cycle	Wastewater discharge per 10-thousand-yuan GDP (m3/10-thousand-Yuan)		−	Chang & Zhu (2020)
		Rate of wastewater treatment (%)		+

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In total, eight indicators were extracted to assess the level of socioeconomic development, as shown in Table 3. (1) Population density reflects the degree of regional development. An increased urbanization rate further propels societal progress. Employment stands as the foremost concern for the populace and is of paramount importance to overall societal production and development. (2) Proportion of expenditure to GDP reflects the Government's focus on regional developments. A well-structured industry significantly boosts regional economic development and production levels. Per capita GDP, total retail sales of consumer goods and the proportion of science and technology investment together provide an accurate depiction of the region's economic status.

Table 3

Evaluation indicators of SES

System layer .	Criterion layer .	Indicator layer .	No. .	Type .	References .
SES	Social development	Population density (person/km2)		−	Zhang et al. (2022)
		Urbanization rate (%)		+
		Employment rate (%)		+
	Economic level	Proportion of expenditure to GDP (%)		+	Zhang et al. (2019)
		Industrial structure upgrading coefficient		+
		Per capita GDP (Yuan/person)		+
		Total retail sales of consumer goods (100 million yuan)		+
		Ratio of research and experimental development to GDP (%)		+

System layer .	Criterion layer .	Indicator layer .	No. .	Type .	References .
SES	Social development	Population density (person/km2)		−	Zhang et al. (2022)
		Urbanization rate (%)		+
		Employment rate (%)		+
	Economic level	Proportion of expenditure to GDP (%)		+	Zhang et al. (2019)
		Industrial structure upgrading coefficient		+
		Per capita GDP (Yuan/person)		+
		Total retail sales of consumer goods (100 million yuan)		+
		Ratio of research and experimental development to GDP (%)		+

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Combining the actual development and utilization of water resources in Sichuan Province and the availability of data, this article comprehensively collected 24 indicators of water resources, meteorology, ecological environment, and socio-economy in Sichuan Province from 2012 to 2022. These indicators were utilized to evaluate the urban water security and study the coupled and coordinated development of water security and socioeconomics in various economic zones. The meteorological data were generated from day-by-day data processing of surface climate data released by the ‘China Meteorological Data Service Centre (https://data.cma.cn/)’. The initial water resources data were sourced from The Sichuan Province Water Resources Bulletin (http://slt.sc.gov.cn/scsslt/szy/szy.shtml), while the ecological environment and socioeconomic initial data were extracted from The Sichuan Statistical Yearbook (http://tjj.sc.gov.cn/scstjj/c105855/nj.shtml).

Herein, the GA-IEWM was employed to calculate the weight of indicators, and the water security grade was then assessed using the cloud model, thereby identifying the spatiotemporal variation characteristics of water security in different economic zones of Sichuan. Further, using the CCD model, the CCD development trend of WSS-SES was analyzed, providing reference information for improving water security and enhancing the coordination degree of water security and socioeconomic development.

The GA-IEWM algorithm solves the optimal indicator weights on the basis of the information entropy idea of the EWM algorithm. First, the original information matrix Y was established based on the assessment object i (⁠⁠) and evaluation index (⁠⁠). To eliminate the influence of the scale, the matrix X was standardized by ⁠:

Herein, GA was employed to solve the aforementioned planning model, and the detailed steps are as follows:

Step 1: The number of iterative steps of GA was set to be >1,000, the population size was 300, the range of indicator weights as independent variables was 0–1, and the initialized population was generated by coding;

The fitness function consisted of two parts: the former was the objective function, while the latter was the penalty function. The penalty function was utilized to restrict the independent variable, ensuring was as close to 0 as possible from 1, fulfilling the first constraint of the planning model. The fitness function aimed to increase the difference in information entropy between different years and to enhance the penalty function's effectiveness.

Step 3: Selection, crossover, and mutation operations were performed on the population to generate a new population.

Step 4: It was judged whether the termination condition was satisfied. If not, return to Step 2; if so, output the optimal weights w.

Due to the constraint altering the total weight from being equal to 1, the final indicator weights were derived through a normalization process. The normalization formula is given as follows: ⁠.

The size of information entropy reflected the degree of discrete indicator data – the greater the degree of discrete, the smaller the information entropy. The GA maximized the difference in information entropy of indicators in different years, while making the degree of dispersion between the assessment values obtained after weighting the normalized values larger, minimizing the information entropy of the overall indicators. The significance of increasing the overall degree of dispersion was to increase the gap between the assessment objects, facilitating subsequent classification.

The overall properties of the cloud model are characterized by three characteristic parameters (expectation ⁠, entropy ⁠, hyper entropy ⁠). indicates the center position of the cloud droplet distribution in the domain, is a measure of the fuzziness of the qualitative concepts, and reflects the degree of cloud dispersion (Yu et al. 2023). The evaluation criteria are the basis for calculating the characteristic parameters of the cloud model. In this article, we refer to the water use quotas issued by the Sichuan Provincial People's Government (https://www.sc.gov.cn/), the ‘14th Five-Year Plan’ for water security, and other relevant documents, as well as to the classification criteria of the existing research literature on water security (Deng et al. 2022; Qiao et al. 2022), and the evaluation criteria of the cloud model were hereby employed to calculate the characteristics of the cloud model, serving as the basis for the calculation of the parameters. In addition, the water security was categorized into five levels: safer, safe, critical safe, unsafe, and extremely unsafe, with the upper and lower boundaries of the intervals corresponding to the upper and lower thresholds of each level, as summarized in Table 4.

Table 4

Range of water security indicator classifications

Indicators .	Variables .	Unit .	Ⅰ (safer) .	Ⅱ (safe) .	Ⅲ (critical safe) .	Ⅳ (unsafe) .	Ⅴ (extremely unsafe) .
Average annual rainfall		mm	>1300	[1100,1300]	[1000,1100]	[900,1000]	<900
Water resources per unit area		104 m3/km2	>65	[55,65]	[45,55]	[35,45]	<35
Per capita water consumption		m3/person	<230	[230,250]	[250,300]	[300,400]	>400
Per capita domestic water consumption		m3/person	<43.8	[43.8,54.8]	[54.8,59.8]	[59.8,73]	>73
Average annual temperature		°C	<12	[12,14]	[14,15]	[15,16]	>16
Environmental protection investment intensity		%	>3.6	[2.8,3.6]	[2.3,2.8]	[2,2.3]	<2
Green coverage		%	>45	[40,45]	[38,40]	[35,38]	<35
Proportion of ecological water		%	>3	[2,3]	[1.5,2]	[1,1.5]	<1
Water consumption per 10-thousand-yuan GDP		m3 /10- thousand-Yuan	<55	[55,70]	[70,85]	[85,100]	>100
Water consumption per 10-thousand-yuan agricultural value added		m3 /10- thousand-Yuan	<220	[220,250]	[250,350]	[350,500]	>500
Water consumption per 10-thousand-yuan industrial value added		m3 /10- thousand-Yuan	<13	[13,25]	[25,35]	[30,45]	>45
Proportion of surface water supply		%	>95	[90,95]	[85,90]	[80,85]	<80
Proportion of groundwater supply		%	<5	[5,10]	[10,15]	[15,20]	>20
Degree of water resources development		%	<5	[5,10]	[10,15]	[15,20]	>20
Wastewater discharge per 10-thousand-yuan GDP		m3 /10- thousand-Yuan	<1.5	[1.5,3.5]	[3.5,4.5]	[4.5,5.5]	>5.5
Rate of wastewater treatment		%	>95	[85,95]	[80,85]	[75,80]	<75

Indicators .	Variables .	Unit .	Ⅰ (safer) .	Ⅱ (safe) .	Ⅲ (critical safe) .	Ⅳ (unsafe) .	Ⅴ (extremely unsafe) .
Average annual rainfall		mm	>1300	[1100,1300]	[1000,1100]	[900,1000]	<900
Water resources per unit area		104 m3/km2	>65	[55,65]	[45,55]	[35,45]	<35
Per capita water consumption		m3/person	<230	[230,250]	[250,300]	[300,400]	>400
Per capita domestic water consumption		m3/person	<43.8	[43.8,54.8]	[54.8,59.8]	[59.8,73]	>73
Average annual temperature		°C	<12	[12,14]	[14,15]	[15,16]	>16
Environmental protection investment intensity		%	>3.6	[2.8,3.6]	[2.3,2.8]	[2,2.3]	<2
Green coverage		%	>45	[40,45]	[38,40]	[35,38]	<35
Proportion of ecological water		%	>3	[2,3]	[1.5,2]	[1,1.5]	<1
Water consumption per 10-thousand-yuan GDP		m3 /10- thousand-Yuan	<55	[55,70]	[70,85]	[85,100]	>100
Water consumption per 10-thousand-yuan agricultural value added		m3 /10- thousand-Yuan	<220	[220,250]	[250,350]	[350,500]	>500
Water consumption per 10-thousand-yuan industrial value added		m3 /10- thousand-Yuan	<13	[13,25]	[25,35]	[30,45]	>45
Proportion of surface water supply		%	>95	[90,95]	[85,90]	[80,85]	<80
Proportion of groundwater supply		%	<5	[5,10]	[10,15]	[15,20]	>20
Degree of water resources development		%	<5	[5,10]	[10,15]	[15,20]	>20
Wastewater discharge per 10-thousand-yuan GDP		m3 /10- thousand-Yuan	<1.5	[1.5,3.5]	[3.5,4.5]	[4.5,5.5]	>5.5
Rate of wastewater treatment		%	>95	[85,95]	[80,85]	[75,80]	<75

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Finally, the water security grade of each economic zone is judged according to the principle of maximum membership degree.

To ascertain the cloud model's dependability, it was compared with TOPSIS and FCEs, as per the analysis presented in Table 1. Leveraging the index weights derived from the GA-IEWM, both methods were utilized to calculate the overall water security scores, which were then used to assign grades based on the scores. TOPSIS and FCEs were extensively used in the comprehensive evaluation of water security and related fields of research. TOPSIS was utilized to construct the positive ideal solution and the negative ideal solution according to the sample data, and to determine the goodness of the solution by the distance between the solution and the ideal solution. The calculation process was relatively objective. Both the FCE and the cloud model considered the inherent uncertainty in classifying indicators.

The specific operational procedures for determining the water security grade by FCE are as follows:

Step 1: Establish the assessment indicator set according to the assessment object and the water security evaluation indicator system. Adopt the indicator weight set determined by GA-IEWM. Determine the evaluation set according to the indicator grade classification standard (Table 4).

According to Zhang & Dong (2022), D ranges from 0 to 1, and grades are classified into two types: imbalance and coordination, based on the coupling coordination scores with a total of five stages, as shown in Table 7.

Table 7

Classification criteria of coupling coordination degree (Zhang & Dong 2022)

Coupling coordination degree .	Coupling coordination grade .		Developmental meaning .
	Serious imbalance	Imbalance	WSS and SES are widely different and not synchronized in their development.
	Moderate imbalance
	Primary coordination	Coordination	WSS and SES work well together and develop harmoniously.
	Intermediate coordination
	Excellent coordination

Coupling coordination degree .	Coupling coordination grade .		Developmental meaning .
	Serious imbalance	Imbalance	WSS and SES are widely different and not synchronized in their development.
	Moderate imbalance
	Primary coordination	Coordination	WSS and SES work well together and develop harmoniously.
	Intermediate coordination
	Excellent coordination

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As illustrated in Figure 6, the weight distribution of the indicators calculated by EWM is more uniform, with the weight scores of WSS and SES ranging from 0.0398 to 0.1051 and 0.0807 to 0.1938, respectively. In contrast, the gap in the weights of the indicators is more pronounced after increasing the sensitivity of each indicator to the model using the GA-IEWM method. The optimized weight scores for WSS and SES range from 0.0163 to 0.1666 and 0.1230 to 0.1733, respectively. The combination of the raw data from the indicators reveals that the weights of the indicators with minor discrepancies between the data have been diminished to varying degrees, for instance, X9-X15 in the WSS and S2, S3, S5, and S6 in the SES. Despite these indicators exhibiting some disparities between regions, the alterations are not as pronounced from 1 year to the next. GA introduces more variability into the overall assessment values across different years when solving the IEWM. This means that it amplifies the differences in overall water security over time. The method focuses more on how much the changes in indicator values in various periods contribute to the overall improvement in water security levels. It also, to some extent, diminishes the impact of regional variability. As a result, the weights of the indicators determined by GA-IEWM are more in line with practical needs.

As shown in Figure 7, in the 2EPRSC water security evaluation framework, the importance of the criterion layer is ranked as response (25.66%), pressure (24.48%), endowment (17.37%), cycle (12.63%), structure (12.33%), and efficiency (7.52%). The weight of response is mainly influenced by two indicators, environmental protection investment intensity (X6) and proportion of ecological water (X8), whose weights are 12.28 and 9.30%, respectively. The importance of pressure is obviously determined by average annual temperature (X5), which has a high weight of 16.66%. The two indicators included in endowment, average annual rainfall (X1) and per capita water consumption (X2) have weights of 10.84 and 6.53%, respectively. The rate of wastewater treatment (X16) also plays an important role with a weight of 6.84%. As evidenced by the indicator weighting values, climate change exerts a profound influence on regional water security, particularly in terms of rainfall and temperature. As the average temperature rises, there is an increased likelihood of droughts and water shortages, which will intensify the conflict between water supply and demand. Concurrently, the reduction in water quantity within the basin will also give rise to more significant pollution issues and threaten the security of the water environment. Consequently, it is imperative that efficacious measures be implemented without delay to address the mounting pressure on water resources resulting from climate change and socioeconomic growth.

In this article, combining the index weights determined by GA-IEWM, water security was assessed using cloud modeling, TOPSIS, and FCE. The outcomes were compared to validate the effectiveness of the cloud model. The cloud model determined the water security grade of each economic zone according to the principle of maximum affiliation, and TOPSIS compared the scores of the assessment object with the scores of each level to obtain the water security grade of the assessment object. The FCE was divided into grades by the fuzzy comprehensive score. The division criteria are shown in Table 5. The water security grades of five economic zones in Sichuan Province from 2012 to 2022 were calculated by the cloud model, TOPSIS, and FCE, as shown in Table 8.

In Table 8, E1–E5 corresponded to Chengdu Plain Economic Zone, Northeast Sichuan Economic Zone, South Sichuan Economic Zone, Northwest Sichuan Ecological Economic Zone, and Panxi Economic Zone, respectively. As shown in Table 8, the cloud model and the results of TOPSIS and FCE could correspond to each other in terms of the excellent performance of water security. The polarization of the grades judged by the cloud model was a little more obvious. The Northwest Sichuan Ecological and Economic Zone (E4) stood out from TOPSIS, with the cloud model predominantly categorizing it as rank Ⅰ, attributed to its abundant water resources, low population density, and significantly lower development and per capita water use compared to other economic zones. Moreover, as a key area for ecological protection in Sichuan Province, the intensity of environmental protection investment was high. Therefore, it was concluded that the evaluation results of the cloud model were reasonable. The water security grades determined by the FCE were mainly concentrated in II and III, with high fuzziness, making it difficult to distinguish the performance differences between different years and regions. The cloud model could determine the size of He through the distribution of cloud droplets in different grades of the indicator, thus adjusting the uncertainty of the grading criteria. Besides, it could be better divided into grades Ⅰ–V. TOPSIS provided a more objective approach to evaluating results based on classification criteria. However, the inherent uncertainty in grading criteria introduced a level of uncertainty to the results. Thus, the objective and absolute division characteristic of TOPSIS might not be always entirely applicable (Deng et al. 2022). Overall, the cloud model took into account the uncertainty of grading compared to TOPSIS and did not have the high ambiguity of FCE. Therefore, it was confirmed that the cloud model could be more effective in comprehensively evaluating water security.

Table 8 and Figure 8 uncovered that the overall level of water security in the five economic zones of Sichuan Province exhibited a fluctuating trend, initially deteriorating and then improving between 2012 and 2021. From 2012 to 2016, the swift economic expansion and population growth intensified the strain on water resources. However, the water resource management system was still under development, and water-saving technologies were not widely adopted. As a result, the efficiency of water resource utilization in both agriculture and industry did not see significant improvement. In 2015–2016, the regions of Chengdu and Northeast Sichuan faced significant challenges due to the overexploitation of water resources. The water resource endowment of the Northeast Sichuan Economic Zone was even slightly worse than in previous years, which was the main reason for its Grade V. In 2016–2021, the state of water Security continued to improve. However, the water security status saw a downturn in 2022. This was largely due to an unusually severe drought in Sichuan. The water stress in cities such as Neijiang and Zigong in southern Sichuan, and Nanchong and Guangyuan in northeastern Sichuan, was further exacerbated by the local topography, complicating the acquisition of external water resources.

From the perspective of the spatial distribution of the water security level, the uneven distribution of water resources led to significant differences in the water security status of each region. For 11 years, the Northwest Sichuan Ecological Economic Zone consistently achieved top-tier water security. However, the plateau terrain posed challenges to the development and utilization of these water resources. The next was Chengdu Plain Economic Zone, where the water security level was basically between Critical Safe (Ⅲ) and Safer (Ⅰ). The zone contained the center of gravity of Sichuan's socioeconomic development, with developed industries and high water consumption, yet with a high level of water conservation. The region's economic growth was dynamic and innovative, with the development of the Chengdu-Chongqing Twin Cities Economic Circle creating numerous new opportunities for advancement. At the same time, it was also necessary to note that the rapid development of the city would bring the problem of overdevelopment of water in Minjiang and Tuojiang river basins, exacerbating the contradiction between supply and demand of water resources and putting pressure on water security.

The overall water security level of the South Sichuan Economic Zone and the Panxi Economic Zone was not high, mainly Critical Safe (Ⅲ) and Safe (Ⅱ). However, the overall state of water security in both economic zones exhibited greater stability. The main change in the Panxi Economic Zone was that agricultural and industrial water use efficiency was improved to some extent. In recent years, more attention has been paid to promoting the green transformation of industries, facilitating the improvement of industrial water use efficiency. The South Sichuan Economic Zone is affected by topography and resources, and the problem of unbalanced internal development is more prominent. Yibin and Luzhou, two southern cities, enjoy abundant water resources and boast advanced water infrastructure and transportation systems. However, in Zigong and Neijiang City, water scarcity and severe early water pollution in the Tuojiang River Basin have been significantly alleviated through the government's ongoing efforts in water environment management, leading to substantial improvements in water quality. As a result, the overall level of water security in southern Sichuan has also been raised from Class III to Class II or even Class I.

Overall, after 11 years of development, the government has made important efforts to improve water use efficiency and water environment management, etc. With the implementation of water conservation policies and the completion and commissioning of numerous reservoirs and water transfer projects, the water pollution problem has been well controlled, the contradiction between the supply and demand of water resources has been regulated, and the internal cooperation and integrated development of the economic zones has been strengthened. In addition, it is worth pondering that the benefits of resolving regional water shortages through water transfer methods, particularly the construction of large-scale water transfer projects, like the Dadu-Minjiang water diversion project, will take a considerable amount of time to materialize. Therefore, the government is also highly attentive to improving the modern management level of water resources, safeguarding regional water security through implementing water-saving policies, protecting the water environment, and similar initiatives.

As shown in Figure 10, the overall CCD of the five economic zones in Sichuan Province presented a positive trend from 2012 to 2022, and the differences between the regions were gradually narrowing. From 2012 to 2022, the CCD values of the five economic zones in Sichuan Province were distributed in the range of 0.2833–0.4111, in the intervals of [0.2,0.4] and [0.4,0.6], respectively. Besides, they were on the verge of dislocation and basic coordination state, and the coordination effect was general. Among them, the Chengdu Plain Economic Zone demonstrated primary coordination, with CCD scores surpassing other zones and achieving a moderate coordination level in 2020. The regional differences in the coupled coordination of WSS-SES were closely related to the regional water security situation and the level of socioeconomic development. The economic zones with the most obvious differences in WSS and SES within these 11 years were the Chengdu Plain Economic Zone and the Northwest Sichuan Ecological Economic Zone. The Chengdu Plain, as the center of economic development in Sichuan Province, exhibited a much higher SES composite score compared to the other economic zones. Its SES composite score was also significantly higher than the WSS composite score, involving problems such as water scarcity and excessive WSS loading limiting economic development. On the contrary, despite its good resource endowment, the Northwest Sichuan Economic Zone, situated in a plateau region with a vast and sparse population, had a lower socioeconomic development level, resulting in a significantly higher WSS system composite score compared to its SES score. From the coupling of these two economic zones, it could be found that the mismatch between water security and the level of socioeconomic development in Sichuan Province was more prominent. Therefore, on the one hand, it was necessary to increase the trans-regional and trans-basin water transfer and develop the economy according to local conditions. On the other hand, in plateau areas with poorly developed infrastructure, necessary efforts should also be made to strengthen water conservancy construction, give full play to the role of water resources, and improve the intra-regional water supply capacity.

The development of the CCD of each economy from 2012 to 2022 indicated that the CCD of the five economic zones was improved to different degrees, with small fluctuations in some years, and also with their own development characteristics. The CCD of the Northwest Sichuan Ecological Economic Zone was maintained at the middle level of the province, and the region experienced fluctuations in CCD and overall improvement in its efforts to balance the relationship between environmental protection and socioeconomic development. The CCD of Chengdu Plain Economic Zone was at the highest level in the province, and the CCD was gradually improving, giving full play to the positive socioeconomic role. Especially in 2016, despite widespread poor water security across the province, the Chengdu Plain maintained a better state due to its higher SES composite score. The Northeast Sichuan Economic Zone gradually improved from the lowest level in 2012 to a medium level, and the CCD score enhanced by 12.35% in 2022 compared to 2012. In addition, the South Sichuan Economic Zone followed a similar trend, but with a less pronounced degree of change. West Panxi Economic Zone developed more slowly, with the CCD perennially at the lowest level in the province. However, it began to change for the better in 2018 and maintained at a stable level, with the transformation of old industrial bases and industrial restructuring and transformation leading to improvements in environmental quality, water security, and comprehensive socioeconomic levels. In recent years, the government has paid increasing attention to the synergistic development of the region, and cities within the economic zone have been cooperating more closely with each other to form complementary industries and continuously promote the optimization of the industrial structure. Through joint construction and sharing, the optimal allocation of resources in the region as a whole is enhanced, thus improving overall competitiveness.

This study was conducted to establish an indicator system based on water security and sustainable socioeconomic development, taking five economic zones with different characteristics in Sichuan Province as the research object. The weights of each indicator were calculated through GA-IEWM, and the key factors affecting the state of water security were analyzed. On this basis, a cloud model was adopted to comprehensively evaluate water security for the period of 2012–2022, which was also combined with the existing methodology for cloud model validation. Finally, the coupled coordination model was employed to calculate the CCD of WSS-SES in Sichuan Province. By analyzing the temporal and spatial trends of water security and CCD, corresponding suggestions were proposed to improve the water security status and promote the coordinated development of water security and social economy. The specific conclusions are as follows:

• (1) In 2012–2022, precipitation and temperature were the key factors influencing water security. The application of the GA-EWM method reduced the influence of less discernible indicators and increased variability in indicator weight values.

• (2) The water security results calculated by the cloud model could correspond to the results calculated by the existing commonly used methods. In addition, the evaluation could be refined for enhanced accuracy, offering distinct advantages for a comprehensive assessment of water security. From 2012 to 2022, the water security across Sichuan Province's five economic zones exhibited variation, with an overall trend of initial decline followed by an increase.

• (3) Most of the CCDs of WSS-SES in the economic zones of Sichuan Province in 2012–2022 were on the verge of dislocation, and the average WSS development level of the province was slightly higher than that of the SES development level. Development differences were substantial among the five zones, with a positive overall trend. Water security lagged notably behind socioeconomic growth in West Sichuan and the Chengdu Plain.

Economic growth and population expansion are escalating, heightening reliance on water resources. Overexploitation in certain regions has compounded river stress and exacerbated water pollution (Alcantara et al. 2020; Shu et al. 2024). (1) As people's awareness of water conservation increases, the sustainable use of natural resources has received increasing attention. However, the existing water security evaluation index system inadequately addresses water resource recycling capacity, limiting its applicability to contemporary water security assessments (Chang & Zhu 2020). In addition, water security and socioeconomic development interact with each other, but most of the current studies only analyze the trend of water security index, a single score, failing to reflect the coordinated development of water security and socio-economy. The present study differed by incorporating sustainable development and water cycle dynamics into the proposed framework. Herein, not only the spatial and temporal distribution characteristics of water security in Sichuan Province were obtained but also the development of the CCD of water security and socioeconomic system was also analyzed. (2) While previous scholars have also paid attention to economic zones as research objects, most of the studies have only chosen one economic zone for research and analyzed the situation of each unit in the economic zone, still essentially a study of cities (Chen & Xu 2021). In contrast, the purpose of this article was to explore the differences in the level of integration and development of economic zones, facilitating a comprehensive understanding of each zone's status. It closely matched the multiple core tenets of the national and local governments' strategies on regional integration and high-quality development, reflecting a positive response to current development needs. (3) The complexity of the research object in this article ensured that the methodologies and perspectives adopted exhibited strong generalization value. The Sichuan Basin, from the southeast to the northwest, has the topographical complexity from plains, hills to plateaus, and the climatic complexity from humid to arid. It also includes the complexity of the socioeconomic environment with Chengdu as the center and the differentiated development of each region. The five economic zones divided by the government include all the cities in Sichuan Province, and each economic zone has its own characteristics and development orientation. Consequently, the study's focus on five economic zones in Sichuan Province was indicative. The research outcomes, enriched by a comprehensive context, had significant reference value for water security development in analogous regions and in individual cities. In addition, the 2EPRSC framework established in this article based on the existing theoretical PSR took into account the sustainability of water resources and contained indicators that were widely adopted by scholars in related fields (Qiao et al. 2022). The research methodology of this article was also validated by existing models, and both the theoretical framework and assessment methodology could be generalized and applied to other regions or even a larger scope of research.

Future research can focus on the following areas:

(1) Sichuan is abundant in rivers, with varying levels of development and utilization of its major waterways. Investigating water security from the perspectives of river basins, regions, and other multidimensional angles can lead to a more optimized allocation of water resources, thereby offering more practical guidance for policy proposals. (2) Water security encompasses a broad spectrum of issues. Research in this area should not merely amalgamate water security with social, economic, and ecological elements but should also delve into the interlinked vulnerabilities and risks across different systems.

All authors mentioned in the article agreed to authorship and gave their consent to the submission of the manuscript.

All authors whose names appear on the submission approved the version to be published.

This work was supported by Sichuan Science and Technology Program (2023NSFSC0807), Opening Fund of Sichuan Mineral Resources Research Center (SCKCZY2022-YB017, SCKCZY2022-YB018) and the General Program of Sichuan Center for Disaster Economy Research (ZHJJ2022-YB002).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.