Open AccessArticle

Coupling Coordination Analysis Between Reclaimed Water Utilization Capacity and Effect in China

Xiaohui Chen

¹,

Fengping Wu

² and

Xiaoyu Wang

^2,*

School of Humanities, Nanchang University, Nanchang 330031, China

Business School, Hohai University, Nanjing 211100, China

Author to whom correspondence should be addressed.

Water 2024, 16(22), 3283; https://doi.org/10.3390/w16223283

Submission received: 2 October 2024 / Revised: 10 November 2024 / Accepted: 13 November 2024 / Published: 15 November 2024

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Abstract

Reclaimed water utilization is one of the major strategies used to achieve sustainable development in China and for alleviating issues linked to insufficient water supply. This study leverages panel data encompassing 30 provinces in mainland China from 2011 to 2022 to establish a comprehensive evaluation index system for measuring the development level of reclaimed water utilization capacity and utilization effect. On the basis of this index, the development rule of coupling coordination between reclaimed water utilization capacity and utilization effect is explored using the coupling coordination degree model, Moran’s I index, and the grey GM (1,1) model. Experimental results show that the current utilization capacity and effect of reclaimed water in most provinces of China do not match. Moreover, China’s reclaimed water utilization capacity index was shown to gradually increase from 2011 to 2022. The reclaimed water utilization capacity index of the eastern region is significantly higher than the central region, which is lower than the national average, followed by the northeast region, and the western region obtains the lowest index. The calculation results of the coupling coordination model further demonstrate the coupling coordination degree between the reclaimed water utilization capacity; moreover, the effects of provinces and cities in China steadily increased, and there is a clear spatial clustering. The predicted results indicate that the abovementioned situation will likely continue until 2030. This study is anticipated to become a point of reference for relevant departments to optimize the coupling coordination degree of reclaimed water systems across different regions in China, implement differentiated measures, and promote a rational allocation of reclaimed water resources.

Keywords:

reclaimed water; utilization capacity; utilization effect; coupling coordination degree; development trend prediction

1. Introduction

Given the significant alterations in global climate patterns and the increasing prevalence of human activities, water scarcity has emerged as a crucial issue in numerous countries [1]. Since the dawn of the 21st century, global water resource imbalance has emerged as a pivotal challenge for the international community. The United Nations predicts that by the middle of the 21st century, about 33% of global nations will face a severe freshwater shortage. This predicament is expected to affect over 40% of the global population, thereby underscoring the water resource crisis as a pressing global challenge that cannot be overlooked [2,3]. The China Water Resources Bulletin reports the country’s water resources in 2023 at 2578.25 billion cubic meters, which is 6.6% lower than the annual average. In addition, the per capita water availability in China is far below the worldwide average [4]. By 2030, the total water demand in China is expected to reach 1 trillion cubic meters, the per capita water resources to decrease to 1700 cubic meters, and the amount of water resources available to each person to decrease to 1700 cubic meters [5]. In response to these issues, the international community has expressed significant interest in the development and utilization of reclaimed water [6,7,8]. Reclaimed water is defined as water resources that have undergone additional or supplementary treatment to adjust their quality to meet their intended use [9]. Additionally, reclaimed water refers to urban sewage or industrial and domestic wastewater that has undergone secondary treatment and further deep purification in sewage treatment plants. It is non potable water that can be reused within a certain range. Although the water quality standards of reclaimed water are not as high as those of drinking water, they far exceed the general discharge standards, making it an important water resource that can be utilized. Reclaimed water be usable in agricultural irrigation, landscape farming, industrial operations, and other fields [10,11,12,13]. The reclaimed water market is dominated by agricultural irrigation (32%), followed by landscape irrigation (20%) and the industrial sector (19%). Environment, non-drinking urban use, recreation reuse, groundwater recharge, and indirect drinking reuse are also potential uses [14].

Internationally, the United States is among the first nations to utilize reclaimed water in response to the increasing severity of the freshwater resources shortage. By the end of the 1960s, more than 300 cities in the United States were capable of recovering treated wastewater [15]. Meanwhile, faced with several challenges, including limited per capita water resources and frequent natural disasters, Japan has been at the forefront of large-scale reclaimed water utilization since the 1980s and has demonstrated a forward-looking approach to water resource management [16]. Other countries, such as Singapore and Israel, have also demonstrated strong commitments to reclaimed water utilization. These countries have not only established sophisticated systems for managing the allocation, setting prices, and balancing the supply and demand of reclaimed water, but have also advanced reclaimed water technology and management models to the international forefront through extensive practice, thus offering invaluable insights and inspiration to the global community [17,18,19,20,21]. The United States, Japan, Australia, and other countries have also carried out relevant research on the recycling and utilization of urban sewage. From the perspective of system dynamics, Suwan Park [22] discussed the impact of the development of alternative water sources on the management of urban water supply systems and proved the feasibility of alternative water supply. Ching [23] found that residents do not reject the use of reclaimed water but are suspicious of the quality of reclaimed water. This distrust has had a significant impact on the promotion and use of reclaimed water. Marchi [24] and other scholars discussed the improvement scheme in the original rainwater collection system and added the design of the management system; Ashbolt [25] and other scholars have proposed a water supply mode that can be operated in the short term, namely using multiple water sources such as groundwater, seawater, and sewage to realize the reclaimed of water resources.

Reclaimed water utilization in China has undergone remarkable advancements in recent years [26,27]. However, due to considerable regional disparities in water scarcity and eco-systemic conditions across China, the utilization of reclaimed water remains inadequate. If the capacity and effectiveness of reclaimed water utilization remain imbalanced in the long term, then such imbalance will directly result in economic losses and exacerbate ecological pressures. Reclaimed water utilization remains in its infancy in China, a nation with a sizable population and an unequal distribution of limited water resources over time and space [28,29,30]. Nevertheless, the Chinese government has issued some regulations and initiatives to promote reclaimed water utilization. For instance, the government published several documents on 11 January 2021 where they established clear objectives for the effective utilization of water resources and aimed to achieve reclaimed water utilization rates of no less than 25% and 35% in cities facing water scarcity. These policies offer strategic guidance and direction for reclaimed water utilization. China has also recently launched a three-year initiative to promote reclaimed water utilization in key cities, specifically the water-scarce, environmentally sensitive, and ecologically fragile areas. Therefore, the reclaimed water utilization capacity across different regions of China warrants a thorough investigation considering its spatial variability. To foster sustainable societal development, the degree of coupling and coordinated growth between reclaimed water utilization capacity and efficiency should also be objectively analyzed.

Significant research advancements have been reported in the field of reclaimed water utilization. However, international studies on the optimization of reclaimed water utilization systems are more advanced than those in China. In these studies, urban reclaimed water utilization systems are treated as independent components of urban water resource systems. Oron et al. established a planning model for wastewater recycling [31] while considering a range of constraints, including costs and environmental pollution. Zeeman et al. aimed to minimize freshwater usage and maximize the efficiency of freshwater resource utilization and considered sewage treatment and decentralized reclaimed water systems in their optimal design of reclaimed water utilization systems [32].

When analyzing reclaimed water utilization efficiency, several countries have considered the multifaceted aspects of economy, society, and environment and tailored their strategies and development plans based on their unique characteristics. Yerri and Piratla compared and assessed the estimated financial benefits and lifespan costs of decentralized greywater recycling systems, evaluated the sensitivity of cost–benefit trade-offs associated with sewage reuse options, and affirmed the advantages of decentralized sewage systems [33]. Lee and Jepson noted that while external environmental shocks, such as drought, can act as catalysts, the local variables, along with political, governmental, and regulatory alignments, are more likely to trigger systemic changes in water systems [34]. From a stakeholder perspective, Cagno et al. simulated the transition from urban to reclaimed water utilization and argued that suitable policy measures can enhance the economic sustainability of reclaimed water utilization. Given that various management strategies can have different impacts on each stakeholder in the water system, the value of sewage reuse can be manifested in their influence on the profit margins of public utilities [35,36].

The coupling coordination degree of composite systems has progressively emerged as a major area of study in recent years [37,38]. Using Chongqing panel data from 2017 to 2018, Li [39] investigated and forecasted the linking coordination of tourism, urbanization, and ecological circumstances in the province, developed a coupling coordination degree model by using tourism, urbanization, and ecological circumstances as its focal points, and used the GM (1,1) prediction model to forecast the coupling coordination degree of the three systems in Chongqing. By focusing on Shandong as the research area. Gu [40] used the GM (1,1) prediction model to forecast the coupling coordination schedule after investigating the coupling coordination while taking China’s agricultural industrialization and ecological conditions into account. The predicted data show that coordinated development can only be achieved via the integration of agriculture and the natural environment.

Our motivation is to propose an evaluation model of the coupling and coordination degree of re-claimed water use capacity and utilization effect to become a point of reference for relevant departments to promote a rational allocation of reclaimed water resources. While the previous literature offers a solid foundation for this study, several limitations remain:

First, at present, there are few studies that systematically evaluate the capacity of renewable water use; these studies usually focus on one or some aspects of renewable water use, for example, reclaimed water for irrigation [41], chloride ions in reclaimed water [42], and the safety of reclaimed water [43]. An evaluation approach specifically tailored for China’s reclaimed water system should be developed by drawing upon international best practices in reclaimed water allocation and considering China’s unique conditions.
Second, only a few studies have incorporated reclaimed water into coupling coordination, with most coupling analyses focusing on functional, economic, and environmental systems and ignoring the interactions within reclaimed water utilization systems [44,45].
Third, those studies that use coupling coordination models to analyze the interactions between functions and systems typically focus on the current level of coupling coordination. While some scholars integrate spatial autocorrelation analysis into these models to examine spatial clustering [46,47], only a few have explored the future trends in coupling coordination [48,49].

To address these limitations, the novel contributions of this study are summarized as follows:

Propose the reclaimed water utilization capacity and utilization effect indexes. Taking 30 provinces and cities in China as the research object, the entropy method [50] is used to comprehensively evaluate the development level of reclaimed water at the national and subsystem levels from 2011 to 2022.
Establish a coupling coordination model from 2011 to 2022 for the reclaimed water system [51,52,53] and analyze the evolution law of coupling coordination degree between reclaimed water use capacity and utilization effect. Employ the gray GM (1,1) [54,55,56] to forecast the coupling coordination between reclaimed water utilization capacity and effectiveness in the upcoming years.
Provide policy suggestions to improve the development level of reclaimed water.

2. Materials and Methods

In this study, an evaluation model of the coupling and coordination degree of reclaimed water use capacity and utilization effect is constructed. The evaluation model is divided into five processes. The specific research methods are shown in Figure 1. Process 1 involves the construction of an indicator system. The index of reclaimed water utilization capacity was established from four dimensions: basic support capacity, supply-driving capacity, demand-pulling capacity, and policy-support capacity. The index of reclaimed water utilization effect was evaluated by the amount of reclaimed water used. Process 2 uses the entropy method to calculate the reclaimed water utilization capacity and utilization effect index. The calculated index is used to measure the degree of the coupling relationship between reclaimed water use capacity and utilization effect. Process 3 established the coupling coordination model to reveal the evolution law of coupling coordination degree. It includes three levels of analysis, given as follows: time series trend, spatial feature, and comprehensive analysis. Process 4 uses the gray GM (1,1) model to predict the coupling coordination degree development level of 30 provinces in China from 2024 to 2030. Process 5 proposes policy implications to maximize the utilization of reclaimed water resources from two perspectives: the utilization capacity, and the effectiveness of reclaimed water. The five processes are interrelated and gradually deepened to form a complete evaluation model.

2.1. Calculation of Reclaimed Water Utilization Ability and Utilization Effectiveness Index

2.1.1. Evaluation Index of Reclaimed Water Utilization Capacity

In this study, 17 indicators are selected from the 4 dimensions of basic support ability, supply-driving ability, demand-pulling ability, and policy-support ability of reclaimed water to build an evaluation index system of reclaimed water utilization ability. These dimensions are selected for four reasons. First, the continuous establishment and improvement of infrastructure for reclaimed water utilization has greatly improved the recycling level of sewage and wastewater. Second, the continuous optimization of the reclaimed water supply side has improved the efficiency and quality of sewage treatment. Third, there is a pressing need for water consumption in society, and by utilizing reclaimed water for industrial, environmental, and agricultural irrigation, the issues of traditional water scarcity and pollution can be effectively mitigated. Fourth, ensuring reclaimed water utilization greatly depends on raising the standard of financial growth and putting reclaimed water utilization policies into action. Therefore, the abovementioned factors work together in the reclaimed water system.

Figure 1. Research framework for the coupling and coordination degree of reclaimed water utilization capacity and effect.

2.1.2. Evaluation Index of Reclaimed Water Utilization Effect

Reclaimed water consumption directly reflects actual water consumption in terms of actual production, living, and ecological activities, thus serving as the most direct representation of water resource demand. This measure accurately reflects the actual consumption of reclaimed water resources, thus eliminating errors resulting from predictions and estimations. This study uses the amount of reclaimed water to assess its effectiveness as illustrated in Table 1.

This study uses panel data from 30 Chinese provinces and municipalities between 2011 and 2022 because pertinent data from Tibet, Hong Kong, Macao, and Taiwan are unavailable. Interpolation methods are applied to address the missing values in individual years.

2.1.3. Computational Procedure

To thoroughly assess the capacity and effectiveness of reclaimed water utilization in China’s provinces, the entropy method is used for the scientific calculation, which involves the following key steps:

1.: To eliminate the influence due to the differences in orders of magnitude, the maximum, minimum, intermediate, and mean values are applied in order to sort the original data through dimensionless processing.

Positive indicators:

X_{i j k} = \frac{A_{i j k} - m i n (A_{i j k})}{m a x (A_{i j k}) - m i n (A_{i j k})}

(1)

Negative indicators:

X_{i j k} = \frac{m a x (A_{i j k}) - A_{i j k}}{m a x (A_{i j k}) - m i n (A_{i j k})}

(2)

where i denotes the year, j denotes the province, k denotes the indicator,

A_{i j k}

represents the original data of the k-th index value in the j-th region in the i-th year,

X_{i j k}

represents the data of the k-th indicator in the j-th region in the i-th year after dimensionless processing, and

m i n (A_{i j k})

and

m a x (A_{i j k})

represent the minimum and maximum values of the k-th index in the j-th region in the i-th year, respectively.

2.: The entropy method is applied to assign weights to each index. First, the proportion $P_{i j k}$ of the k-th index value in the j-th region in the i-th year is calculated as follows:

P_{i j k} = \frac{X_{i j k}}{\sum_{i = 1}^{N} \sum_{j = 1}^{M} X_{i j k}} (i = 1,2, \dots, N; j = 1,2, \dots, M; k = 1,2, \dots, R)

(3)

Second, the entropy value of the k-th index is calculated using the definition of information entropy as indicated by Formula (4) below:

e_{k} = - (\frac{1}{l n (N \times M)}) \times \sum_{i = 1}^{N} \sum_{j = 1}^{M} P_{i j k} l n (P_{i j k})

(4)

where

P_{i j k}

is the proportion of the value of the k-th indicator in the j-th region in the i-th year, and

e_{k}

is the entropy of the k-th index. In this study, N = 12 and M = 30.

3.: Determine the weight of each indicator:

Formula (5) expresses the difference coefficient of the k-th index as follows:

{g_{k} = 1 - e}_{k}

(5)

The entropy value of index

λ_{k}

can be expressed as follows:

λ_{k} = \frac{g_{k}}{\sum_{k = 1}^{R} g_{k}}

(6)

4.: The reclaimed water utilization capacity index of area j in year i can be calculated as follows:

U_{i, j} = \sum_{k = 1}^{R} λ_{k} \times X_{i j k}

(7)

where u is the reclaimed water utilization capacity index of area j in year i. When measured separately according to different dimensions, the development index of the reclaimed water utilization capacity in four dimensions can also be obtained. Similarly, the reclaimed water utilization effect index can also be calculated based on the formula given above.

2.2. Coupling Coordination Degree Model

The coupling and coordination degrees of the reclaimed water “utilization capacity–utilization effectiveness” system in 30 provinces and cities from 2011 to 2022 are then calculated to jointly build a coupling coordination degree model.

1.: Calculation of coupling degree. The coupling degree is typically used to reflect the degree of interaction or interconnectedness among systems or elements. The reclaimed water utilization capacity index and utilization efficiency index are then applied to construct the coupling degree C for the reclaimed water utilization capacity and utilization efficiency systems as depicted in Formula (8) below:

C = 2 \sqrt{U_{1} U_{2}} / (U_{1} + U_{2})

(8)

where U₁ is the reclaimed water utilization capacity index, and U₂ is the reclaimed water utilization efficiency index. The coupling degree C lies in the interval [0,1]. If C is close to 1, then the interaction between the reclaimed water utilization capacity and utilization efficiency systems is strong, and vice versa.

2.: Calculation of coordination degree. The coordination degree is used to evaluate the degree of system coordination. This process simulates the mutual development and progress of a system. The reclaimed water utilization capacity and utilization efficiency indexes are used to build the comprehensive coordination index T as follows:

T = α \times U_{1} + β \times U_{2}

(9)

where T is the comprehensive coordination index, and α and β are undetermined coefficients that indicate the importance and contribution of the subsystems to the overall complex system. In this study, the capacity and effectiveness of reclaimed water utilization are assumed to have the same status. Therefore, the values of α and β are set to 0.5.

3.: Construction of coupling coordination degree model. The coordination between the utilization effectiveness and utilization capacity of reclaimed water across different locations in China is thoroughly evaluated using the coupling coordination degree model, which considers the developmental stage and the coupling degree. The coupling degree C and comprehensive coordination index T obtained using Formulas (8) and (9), respectively, are utilized in the evaluation. The resulting D value for the coupling coordination degree of the reclaimed water utilization capacity and effectiveness systems is then calculated as follows:

D = \sqrt{C \times T}

(10)

where a higher D value indicates coordinated support between these two systems. The D value is computed in five stages as described in detail below (Table 2).

2.3. Moran’s I Test for Spatial Dependence

Moran’s I [57] is used to investigate the spatial characteristics associated with the coordinated advancements in the “utilization capacity–utilization effectiveness” of reclaimed water. This index defines the spatial distribution patterns with an aim to enhance and optimize the use of spatial resources. The calculation process is outlined as follows:

Calculate the global Moran’s I index as follows:

I = M \sum_{a = 1}^{M} \sum_{b = 1}^{M} W_{a b} (y_{a} - \bar{y}) (y_{b} - \bar{y}) / (\sum_{a = 1}^{M} \sum_{b = 1}^{M} W_{a b}) \times \sum_{a = 1}^{M} {(y_{a} - \bar{y})}^{2}

(11)

2.: Calculate the local Moran’s I index as follows:

I^{'} = M (y_{a} - \bar{y}) \sum_{b = 1, a \neq b}^{M} W_{a b} (y_{b} - \bar{y}) / \sum_{a = 1}^{M} {(y_{a} - \bar{y})}^{2}

(12)

where M is the number of regions,

y_{a}

and

y_{b}

are the coupled coordination between province a and province b, respectively,

\bar{y}

is the average coupling coordination degree, and

W_{a b}

is the weight matrix of the geographic economic nested space. When Moran’s I is ∈ (0,1), the closer the index is to 1, and the more significant the spatial positive correlation. However, when Moran’s I ∈ [−1,0), the closer the index is to −1, the greater the spatial difference. If Moran’s I = 0, then no spatial correlation is observed.

2.4. Grey GM (1,1) Model

The grey GM (1,1) model is used to calculate and predict the coupling coordination of reclaimed water utilization capacity and effectiveness. The basic principle of this model is to generate an accumulated generation sequence from the input raw data and establish a prediction model based on this sequence to output the prediction results. The modeling process is described as follows.

The original data is set as

x^{(0)} = x^{(0)} (1), x^{(0)} (2), \dots, x^{(0)} (n)

, and the accumulated data sequence is

x^{(1)} = x^{(1)} (1), x^{(1)} (2), \dots, x^{(1)} (n)

, where

x^{(1)} (k) = \sum_{i = 1}^{k} x^{(0)} (i), k = 1, 2, \dots, n

. The white form differential equation is as follows:

\frac{d x^{(1)} (t)}{d t} + a x^{(1)} (t) = u

(13)

where

a

is the development coefficient,

u

is the grey action quantity, and t is the time series. The matrix composed of a and u is denoted as a gray parameter

\hat{a} = {[a, u]}^{T}

, which is solved as follows in Formulas (14) to (16) using the least squares method:

\hat{a} = {(B^{T} B)}^{- 1} B^{T} Y_{n}

(14)

B = [\begin{matrix} \begin{matrix} - z^{(1)} (2) 1 \\ \begin{matrix} - z^{(1)} (3) 1 \\ ⋮ ⋮ \end{matrix} \end{matrix} \\ - z^{(1)} (4) 1 \end{matrix}] = [\begin{matrix} \begin{matrix} - \frac{1}{2} [x^{(1)} (1) + x^{(1)} (2)] 1 \\ \begin{matrix} - \frac{1}{2} [x^{(1)} (2) + x^{(1)} (3)] 1 \\ ⋮ ⋮ \end{matrix} \end{matrix} \\ - \frac{1}{2} [x^{(1)} (n - 1) + x^{(1)} (n)] 1 \end{matrix}]

(15)

Y_{n} = [\begin{matrix} \begin{matrix} x^{(0)} (2) \\ \begin{matrix} x^{(0)} (3) \\ ⋮ \end{matrix} \end{matrix} \\ x^{(0)} (n) \end{matrix}]

(16)

The discrete solution of the white form differential equation is as follows:

\hat{x} (1) (k + 1) = (x^{(0)} (1) - \frac{u}{a}) e^{- a k} + \frac{u}{a}

(17)

The predicted value is calculated as follows:

\hat{x} (1) (k + 1) = \hat{x} (1) (k + 1) - \hat{x} (1) (k)

(18)

3. Results and Discussion

3.1. Analysis of Reclaimed Water Utilization Capacity Index

3.1.1. Spatio-Temporal Trend

To intuitively observe the reclaimed water utilization capacity index of various provinces in China, the Moran I index is introduced in this study. Figure 2 reflects the temporal and spatial variation features. From the perspective of temporal differences, the reclaimed water utilization capacity index has been steadily increasing from 2011 to 2022 with some degree of regional variability. Specifically, the utilization capacity index in the eastern region exceeds the national average, whereas it falls below this level in the central, northeastern, and western regions. Meanwhile, the reclaimed water utilization capacity index in Beijing, Tianjin, Shanghai, Guangdong, Hubei, Jiangsu, and Shandong is higher than those in other provinces, indicating that most eastern provinces are in a leading position in the utilization of reclaimed water resources. By contrast, Guizhou, Heilongjiang, Guangxi, and Xinjiang have a relatively low reclaimed water utilization capacity index. The reasons for this difference are as follows: due to the earlier urbanization process and stronger economic strength in the eastern region, significant investment has been made in the planning and construction of sewage treatment plants. When designing and constructing these sewage treatment plants, the subsequent function of producing reclaimed water is often taken into account. For example, in some cities in the Yangtze River Delta and Pearl River Delta, sewage treatment plants adopt advanced secondary or even tertiary treatment processes, which can effectively remove pollutants such as organic matter, nitrogen, and phosphorus from sewage, providing high-quality raw water for the production of reclaimed water. Moreover, the tertiary industry dominates the eastern region, and having more high-tech industries can greatly promote the level of renewable water utilization. The construction of sewage treatment facilities in the central and western regions is relatively lagging behind. Due to the vast geographical area and relatively dispersed population distribution, the coverage of sewage treatment plants is limited. The upgrading of sewage treatment facilities in Northeast China is relatively slow, and some sewage treatment plants are still in the primary or simple secondary treatment stage, which is difficult to meet the requirements of reclaimed water production.

3.1.2. Element Feature

The entropy method is applied to calculate the reclaimed water utilization capacity index of the four subsystems as shown in Figure 3. From 2011 to 2022, the basic support capacity index shows the most obvious upward trend, which is in line with the national policy needs of China to vigorously develop its reclaimed water resources. Meanwhile, demand-driven capacity and supply-driven capacity have the highest and lowest development indexes among these subsystems, respectively, which suggests that China has a large demand but an insufficient supply of reclaimed water, thereby resulting in a significant discrepancy between its supply and demand. The development index of the policy-support capacity is in a stable transition state due to the fact that policy-support capacity is measured by various elements of economic advancement, and the level of economic growth does not fluctuate significantly in the short term.

3.2. Analysis of Reclaimed Water Utilization Effect Index

The reclaimed water utilization effect index is obtained by selecting the amount of reclaimed water as the evaluation index of utilization effect and using the entropy method as displayed in Table 3. The overall reclaimed water utilization effect index in China’s provinces has risen annually, but some variations remain. For example, Guangdong, Shandong, Jiangsu, and Beijing, which are located in the eastern region, have a high reclaimed water use efficiency index, but Hainan, which is also located in the eastern region, has a very low reclaimed water use efficiency index, which indicates that there are differences within the same region. The reason for these differences may be related to the terrain. For example, the island terrain in Hainan has a weak regulation and storage capacity, which affects the production and utilization of reclaimed water. In addition, the differences between different regions are more obvious. Specifically, the order of average value of the reclaimed water use efficiency index is eastern region, central region, northeast region, and western region. Taking 2022 as an example, the reclaimed water use efficiency index of Chongqing, Shanxi, Qinghai, Guizhou, and other central and western regions is far lower than the average value of other regions. The reasons for this difference may be related to the economic development level and policymaking. Specifically, the eastern region has a relatively developed economy and abundant local finances. This enables the eastern region to invest a large amount of funds in the construction of reclaimed water utilization facilities, including the upgrading and renovation of sewage treatment plants, the laying of reclaimed water transportation pipelines, and so on. However, due to the relatively low level of economic development in the central and western regions, it is difficult to allocate sufficient funds for reclaimed water utilization projects. This has led to a lag in the construction of reclaimed water production facilities in the central and western regions, and the treatment process may be relatively backward, thereby reducing the utilization efficiency of reclaimed water.

Figure 3. Development index of basic support capacity, supply-driving capacity, demand-pulling capacity, and policy-support capacity.

3.3. Analysis of Coupling Coordination Degree

3.3.1. Time Series Trend

As indicated in Table 4, a coupling coordination model is applied to determine the degree of coupling coordination between the capacity and effect of reclaimed water utilization in 30 Chinese provinces from 2011 to 2022. Experimental data show that from 2011 to 2022, the coupling coordination of the reclaimed water utilization capacity and demand across all provinces showed a steady upward trend. According to the pre-defined coupling coordination types in Table 2, in 2011, only Beijing, Shandong, Jiangsu, Hebei, and Liaoning were in the state of barely coupling coordination, accounting for about 17.86% of all provinces in China. Among the remaining provinces, Qinghai and Sichuan were in a state of imbalance, while the other provinces were on the verge of imbalance. These phenomena indicate that the two systems of reclaimed water utilization capacity and utilization effect across the 30 selected provinces are not mutually exclusive and have a relatively weak interconnection, which may hinder the sustainable growth of these systems. Meanwhile, in 2022, all 30 provinces were in a primary coupling coordination state, with the exception of Ningxia, Xinjiang, Hainan, Guizhou, Chongqing, Qinghai, Gansu, and Heilongjiang, which were on the verge of imbalance. Guangdong even reached a high coordination state as a result of increased public awareness about environmental protection and sustainable development.

3.3.2. Spatial Feature

Table 5 presents the results of applying Moran’s I index to calculate the spatial autocorrelation of the coupling coordination degree of the reclaimed water “utilization capacity–utilization effect” systems. The degree of coupling coordination observed at each province exhibits a statistically significant positive correlation and generates a significant agglomeration phenomenon in the regional space. However, the time series from 2011 to 2022 show that the Moran’s I index decreases from 0.333 to 0.193, while the value of Z decreases from 3.027 to 1.874, thereby indicating that the spatial agglomeration effect is gradually weakening. If this trend continues, then the spatial pattern boundary of “high-level regions are close to one another, and low-level regions are close to one another” will become blurred. These findings provide novel ideas for further optimizing the strategies for allocating reclaimed water resources and formulating targeted policies and measures.

However, the global Moran’s I index cannot easily reflect the spatial evolution characteristics of reclaimed water utilization capacity and utilization effect in these 30 provinces. Therefore, Table 6 reports the results of using the local Moran’s I index to further investigate the spatial correlation characteristics among neighboring provinces. The calculation results divide the spatial relationship into four intervals, and the following observations are obtained:

Except for Liaoning, most provinces within the first interval in 2011 and 2022 are located in the eastern region. Two additional central regions were also reported in 2022 compared with 2011, which is consistent with the temporal change trend of the eastern region. These observations indicate some stability in the coupling coordination degree development in the eastern region. Meanwhile, the observations for Liaoning, which dropped from the first to the second interval, indicate that this province is not yet fully developed.
Only a few provinces are distributed in the second interval, and the coupling coordination degree of Shanxi, Fujian, and Hainan is low, which indicates that these provinces are less affected by surrounding provinces with higher development levels.
Most provinces distributed in the third interval belong to remote regions, such as the west and northeast regions. The change trend for these provinces in 2011 and 2020 is not obvious, thereby indicating that their and their surrounding neighbors’ development are low. Sichuan, Yunnan, and Shaanxi have evolved from the third interval to the fourth interval, indicating that the development level of their own coupling coordination degree has improved, while that of their surrounding provinces has not changed significantly.
The number of provinces in the fourth interval in 2011 was significantly lower than that in 2022 for three reasons. First, Jiangxi and Guangxi changed from the fourth interval to the second interval, indicating that the development level of their own coupling coordination degree has declined and that their gap with surrounding provinces has gradually widened. Second, Hubei evolved from the fourth interval to the first interval, indicating that its surrounding provinces have a radiating and pulling effect on its coupling coordination degree. Third, Guizhou and Xinjiang dropped from the fourth interval to the third interval, indicating that their coupling coordination development level declined and that they no longer have advantages in the surrounding provinces.

In sum, more provinces fell into the first and third intervals than in the second and fourth intervals combined, thereby suggesting that the general spatial pattern remains dominated by the phenomenon of high-level regions being adjacent to low-level regions.

3.3.3. Comprehensive Analysis

To clearly analyze the spatio-temporal evolution rule of coupling coordination among provinces, vertical and horizontal comparative experiments are performed on the coupling coordination degree of reclaimed water utilization capacity and utilization effect in 30 provinces in China. Figure 4 presents the spatial evolution maps for 2016, 2018, 2020, and 2022 constructed using ArcGIS. These maps highlight a tendency toward the optimization of the coupling and coordination degree of reclaimed water utilization capacity and utilization effect, which is in line with the current focus of China on reclaimed water and its proactive approach to the development of infrastructure for reclaimed water utilization. Moreover, the coupling coordination degree of reclaimed water utilization capacity and utilization effect shows obvious spatial differences and aggregation.

Figure 5 shows the changes in coupling coordination degree across China’s regions over the past four years. Substantial disparities can be observed across these provinces during the same year, which may be due to the notable disparity in the overall development degree of their reclaimed water utilization effect and capacity. The coupling coordination degree between reclaimed water utilization effect and capacity is also dynamic, showing significant fluctuations in certain provinces, such as Guangdong. Some provinces, such as Beijing, Hainan, and Chongqing, only showed very little variations.

3.4. Development Trend Prediction of Coupling Coordination Degree

Prediction model verification. The class ratio test is commonly used for assessing the accuracy of a model. To pass this test, the order ratio of the original sequence data must be within the interval (

e^{- \frac{2}{n - 1}}

e^{\frac{2}{n + 1}}

). The interval (0.834, 1.154) encompasses all level ratios of the coupling coordination degree sequence data in this paper, thus confirming its appropriateness for building the grey prediction model. The GM (1,1) model accuracy is then confirmed using the posterior error ratio C and the small error probability p. Table 7 reports the test criteria.

When the GM (1,1) model is used to predict the data, an average relative error analysis (MRE) of the predicted coupling coordination degree should be conducted to confirm the prediction effectiveness of the GM (1,1) model. MRE indicates the fitting degree of the GM (1,1) model, where a smaller average relative error corresponds to a higher fit. An average relative error of less than 0.2 indicates a good fit, while an average relative error of less than 0.1 indicates a high fit. Table 8 reports the test results for the GM (1,1) model, which show that this model can be utilized to forecast the development standard of the coupling coordination degree.

Comparative analysis of prediction results. The coupling coordination degrees of 30 provinces in China from 2012 to 2022 are inputted into the GM (1,1) model as historical data to predict the coupling coordination degree of each province in the next 7 years; the results are shown in Table 9. These provinces demonstrate a consistent increase in their coupling coordination degree of reclaimed water utilization capacity and utilization efficiency, and the number of provinces in the better coupling coordination type has also steadily increased. These findings can be primarily ascribed to the expansion of reclaimed-water-related projects and the enhancement of sewage treatment capacity in these provinces. The coupling coordination degree of the central region is balanced as a whole, and the differences between provinces are gradually narrowing. This is mainly because the central region has sufficient conditions to form a stable market demand, such as the large demand for reclaimed water in industries, agriculture, municipal and other fields. There is still a gap in the degree of coupling and coordination between provinces in the western region, which may be due to the limited effect of renewable water use in some provinces due to the restrictions of resource endowment and market demand, such as Guizhou and Chongqing.

4. Conclusions and Policy Implications

4.1. Conclusions

The conclusions are summarized as follows:

In terms of time difference, China’s reclaimed water utilization capacity index gradually increased from 2011 to 2022, whereas its supply-driven capacity was at its lowest level among the four subsystems. In terms of spatial differences, some significant variations can be observed in the level of reclaimed water utilization in the eastern, northeast, central, and western regions. The reclaimed water utilization capacity index of the eastern region is significantly higher than the national average and that of the other regions. Meanwhile, the reclaimed water utilization capacity index of the central region is lower than the national average, followed by the northeast region, and the western region obtains the lowest index.
Between 2011 and 2022, the coupling coordination degree between the reclaimed water utilization capacity and effect of provinces and cities in China steadily increased. The top three cities in terms of average coordination degree were Beijing, Shandong, and Jiangsu, whose reclaimed water utilization capacity was relatively high, thus placing them in a leading position in the utilization of reclaimed water resources. Except Xinjiang, Guizhou, Ningxia, Qinghai, Hainan, and Chongqing, which were on the verge of imbalance in their coupling coordination degree, all other cities achieved a coordinated level. In terms of spatial distribution, Beijing, Shandong, and Jiangsu form the core of coordinated development in China. Their neighboring cities also exhibit a significantly higher level of coordinated development than the other cities, with the level of coordination gradually decreasing as one moves toward the periphery.
The grey GM (1,1) model is employed for forecast the degree of coupling coordination between reclaimed water utilization effect and capacity between 2024 and 2030. The results indicate that the reclaimed water utilization capacity and efficiency progressively increase along with the coupling coordination degree. Moreover, the number of provinces falling under the category of good coupling coordination has also steadily increased.

4.2. Advantages and Limitations of the Model

In this study, the reclaimed water system is introduced and a coupling coordination model of “utilization capacity utilization effectiveness” system is established. The effectiveness of the model is proved by an empirical study in China. The advantages of the model are as follows:
First, combined with China’s national conditions, the typical experience applicable to the evaluation of China’s reclaimed water system has been successfully extracted; this has also been deeply studied and widely applied. This advantage enables us to more accurately understand the utilization of reclaimed water and provide a scientific basis for policymaking and practical operation.
Second, bringing reclaimed water into the study of coupling and coordination will help to reveal the unique role and value of reclaimed water in the overall ecosystem, and provide a new perspective and ideas for water resource management and environmental protection.
Third, when applying the coupling coordination model to study the relationship between functions and systems, we not only considered the current coupling coordination degree, but also actively explored the future trend of coupling coordination degree. This advantage enables us to better predict and respond to possible problems and challenges in the future, and provide strong support for the sustainable use of water resources and environmental protection.
Due to the late start of reclaimed water use in China, the research is limited by the amount of data and the available information. This paper needs to be further explored and improved in detail and in depth. For example, this paper constructs two different types of index system, but the index system of reclaimed water use efficiency is not perfect, and the index constructed by referring to the existing research is relatively single. In the future, we will continue to conduct in-depth research and improve the deficiencies.

4.3. Policy Implications

In order to enhance the coordinated development degree of reclaimed water utilization capacity and utilization effect and to strengthen the benign interaction between these two systems, the following recommendations are made.

From the perspective of utilization capacity, the following points can be realized:

The eastern region has the highest reclaimed water utilization capacity and the highest coupling coordination degree with reclaimed water utilization effect. The advancement of sustainable economic practices in this region facilitates its utilization and recycling of reclaimed water. Given that the eastern region is economically developed and has a high-quality industrial structure for using reclaimed water and other related water conservancy infrastructure, this region can further improve its reclaimed water utilization capacity by implementing policies and through market means. However, given its increasing water pollution, the eastern region should expand its field and scale of reclaimed water utilization, improve its renewable water utilization policies, and promote a market-oriented reclaimed water allocation system.
For the backward economies located in the central and northeast regions, their coupling coordination degree of reclaimed water utilization capacity and utilization effect is in the state of bare coordination. However, these regions have an abundant water supply that can be used to further optimize their industrial chain structure related to reclaimed water, develop green and low-carbon industries, and vigorously promote the use of clean energy. This water supply can also be used to develop high-tech industries, such as advanced oxidation, membrane bioreactor, and biological treatment technologies. These regions may consider expanding their diversified application of water resources, including sewage recycling and comprehensive reclaimed water utilization.
The level of reclaimed water utilization in the western region is low, and the coupling coordination degree of reclaimed water utilization capacity and utilization effect is in a state of imbalance. The arid and semi-arid areas in this region account for about one quarter of China’s total area and have limited precipitation and water resources. Although previous studies have pointed out that the average diversion rate of surface water resources in arid areas far exceeds the world average, the water resources available for development and utilization in this area are close to the upper limit. Therefore, this region should focus on increasing the efficiency of its conventional sewage treatment processes, applying efficient and low-energy treatment technologies, and developing new energy technologies, such as photovoltaic and wind power, to promote the sustainable development of its water resources in all aspects.

From the perspective of utilization effectiveness, the following points can be realized:

Government guidance should be strengthened and the reclaimed water market vigorously cultivated. Water supply systems of different qualities should be implemented, and a series of targeted reclaimed water quality standards and usage specifications should be designed according to the differences in water demand. This measure requires a clear division of water quality requirements for various uses to ensure that the reclaimed water meets the safety and functionality requirements of different application scenarios. The government should also introduce a series of encouraging policies to promote the effective utilization of reclaimed water. These policies should pay special attention to the configuration, pricing, measurement, incentives, and assessment of reclaimed water so as to provide a solid policy basis and support for promoting reclaimed water utilization. Technological innovation plays a key role in environmental protection. The government should strengthen the source control of domestic wastewater, conduct research on wastewater reuse technology, and explore new ways of reusing water resources. The government should also increase financial support for the scientific research and development of water resource reuse technology, continue to promote the upgrading and optimization of existing processes, and promote the research and market popularization of innovative processes, efficient processes, cutting-edge technologies, and advanced equipment so as to build a comprehensive technology system.
We should aim to vigorously carry out publicity and education on the use of reclaimed water, and cultivate awareness of using reclaimed water. At present, some provinces and cities in China have not provided a good development environment for reclaimed water reuse, and the public’s understanding of reclaimed water is still insufficient. They lack full understanding and acceptance of the value of reclaimed water reuse, and there is no effective demand for reclaimed water reuse. Therefore, it is necessary to strengthen the publicity and education of reclaimed water use, enhance the public’s understanding of the current situation of water shortage, and popularize the value and importance of reclaimed water. At the same time, we should encourage and support non-governmental organizations and volunteers to participate in reclaimed water promotion activities and build a water-saving culture with the participation of the whole society.

Author Contributions

Conceptualization, X.C. and X.W.; methodology, X.C., F.W. and X.W.; software, X.C.; formal analysis, X.C. and F.W.; writing—original draft preparation, X.C. and X.W.; writing—review and editing, X.C. and F.W.; supervision, F.W. and X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 42271303).

Data Availability Statement

The data presented in this study are available on request from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. (a) Reclaimed water utilization capacity index of four regions in China from 2011 to 2022; (b) average reclaimed water utilization capacity index of 30 provinces in China from 2011 to 2022.

Figure 4. Spatial evolution of coupling coordination degree between reclaimed water utilization capacity and utilization effect in China (partial years).

Figure 5. Coupling coordination degree of reclaimed water utilization capacity and utilization effect in China (partial years).

Table 1. Measurement index of reclaimed water utilization capacity.

	Classification Indicators	Secondary Indicators	Data Source/Calculation Formula	Type
Measurement index of reclaimed water utilization capacity	Basic support ability	Number of sewage treatment plants	Statistical yearbook of urban construction in China	+
		Density of sewage pipeline in built-up area	Sewage pipeline length/built-up area	+
		Density of rainwater pipeline in built-up area	Length of rainwater pipeline/built-up area	+
		Density of rainwater sewage confluence pipeline	Length of rainwater sewage confluence pipeline/built-up area	+
		Density of reclaimed water pipeline in built-up area	Length of reclaimed water pipeline/built-up area	+
	Supply-driving ability	Reclaimed water production capacity	Statistical yearbook of urban construction in China	+
		Sewage treatment capacity	Statistical yearbook of urban construction in China	+
		Total sewage treatment	Statistical yearbook of urban construction in China	+
	Demand-pulling ability	Total agricultural water consumption	China Water Resources Bulletin	−
		Total industrial water consumption	China Water Resources Bulletin	−
		Total domestic water	China Water Resources Bulletin	−
		Total ecological water consumption	China Water Resources Bulletin	−
		Water consumption per capita	China Water Resources Bulletin	−
	Policy-support ability	Level of economic development	Total GDP/annual average population	+
		Financial support	National Bureau of Statistics	+
		Industrial structure	Output value of tertiary industry/output value of secondary industry	+
		Urbanization level	Urban population/total population	+
Measurement index of reclaimed water utilization effect	Reclaimed water consumption	Total water volume actually reused after sewage treatment	China Water Resources Bulletin	+

Note: the attribute “+” indicates that the larger the index value, the stronger the reclaimed water utilization capacity is, and “−” indicates the opposite.

Table 2. Coupling coordination degree and criteria.

Coordination Type	Disorder	Verge of Maladjustment	Reluctantly Coordinate	Moderate Coordination	Highly Coordinated
Coupling coordination value	(0,0.2]	(0.2,0.4]	(0.4,0.6]	(0.6,0.8]	(0.8,1]

Table 3. Reclaimed water utilization effect index of 30 provinces in China from 2011 to 2022.

Regions	Provinces	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Eastern Region	Beijing	0.185	0.195	0.208	0.177	0.249	0.261	0.273	0.280	0.299	0.312	0.143	0.313
	Tianjin	0.006	0.005	0.006	0.007	0.006	0.007	0.068	0.076	0.068	0.092	0.101	0.108
	Hebei	0.086	0.086	0.086	0.083	0.095	0.103	0.104	0.126	0.152	0.184	0.197	0.238
	Shanghai	0.041	0.049	0.055	0.055	0.068	0.068	0.109	0.131	0.179	0.209	0.248	0.277
	Zhejiang	0.007	0.027	0.022	0.009	0.021	0.026	0.049	0.064	0.083	0.101	0.103	0.122
	Jiangsu	0.082	0.087	0.152	0.159	0.170	0.189	0.216	0.235	0.256	0.326	0.365	0.380
	Fujian	0.001	0.000	0.000	0.000	0.000	0.000	0.018	0.047	0.062	0.075	0.091	0.112
	Shandong	0.082	0.100	0.106	0.133	0.187	0.223	0.256	0.319	0.396	0.385	0.443	0.493
	Hainan	0.001	0.001	0.001	0.003	0.003	0.004	0.006	0.007	0.006	0.006	0.008	0.008
	Guangdong	0.010	0.009	0.000	0.000	0.019	0.030	0.399	0.486	0.827	0.729	0.970	1.000
Central Region	Anhui	0.002	0.003	0.003	0.003	0.004	0.010	0.035	0.053	0.060	0.203	0.260	0.182
	Jiangxi	0.013	0.015	0.017	0.017	0.021	0.021	0.034	0.041	0.056	0.065	0.077	0.086
	Hunan	0.002	0.001	0.002	0.002	0.004	0.009	0.025	0.031	0.049	0.053	0.082	0.100
	Hubei	0.042	0.040	0.039	0.039	0.039	0.040	0.074	0.074	0.089	0.124	0.146	0.162
	Shanxi	0.011	0.021	0.027	0.019	0.025	0.031	0.024	0.029	0.032	0.058	0.079	0.012
	Henan	0.014	0.015	0.018	0.020	0.027	0.036	0.080	0.143	0.141	0.187	0.279	0.304
Western Region	Guizhou	0.012	0.047	0.046	0.057	0.060	0.002	0.002	0.006	0.006	0.013	0.012	0.013
	Guangxi	0.013	0.016	0.018	0.018	0.022	0.022	0.035	0.042	0.058	0.067	0.079	0.088
	Ningxia	0.004	0.004	0.004	0.004	0.004	0.006	0.013	0.008	0.010	0.018	0.023	0.034
	Gansu	0.005	0.002	0.009	0.012	0.018	0.021	0.023	0.015	0.023	0.028	0.035	0.037
	Inner Mongolia	0.019	0.020	0.018	0.016	0.022	0.030	0.042	0.054	0.062	0.066	0.072	0.078
	Shanxi	0.008	0.010	0.019	0.019	0.018	0.022	0.022	0.013	0.016	0.018	0.072	0.103
	Chongqing	0.001	0.001	0.002	0.002	0.002	0.002	0.002	0.003	0.003	0.004	0.004	0.005
	Yunnan	0.003	0.066	0.065	0.077	0.077	0.003	0.004	0.004	0.117	0.090	0.102	0.095
	Xinjiang	0.022	0.021	0.022	0.021	0.022	0.023	0.014	0.020	0.024	0.058	0.076	0.086
	Sichuan	0.000	0.000	0.004	0.007	0.014	0.029	0.030	0.025	0.063	0.083	0.132	0.204
	Qinghai	0.000	0.000	0.002	0.002	0.002	0.002	0.005	0.005	0.002	0.008	0.010	0.012
Northeast Region	Liaoning	0.074	0.057	0.055	0.055	0.058	0.050	0.047	0.060	0.070	0.088	0.169	0.185
	Jilin	0.002	0.002	0.002	0.002	0.002	0.000	0.004	0.005	0.048	0.048	0.059	0.076
	Heilongjiang	0.009	0.010	0.013	0.014	0.014	0.015	0.020	0.020	0.042	0.070	0.047	0.046

Table 4. Coupling coordination of reclaimed water utilization capacity and utilization effect in 30 provinces and cities in China from 2011 to 2022.

Regions	Provinces	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Eastern Region	Beijing	0.576	0.588	0.596	0.579	0.625	0.646	0.653	0.658	0.671	0.675	0.554	0.673
	Tianjin	0.245	0.234	0.245	0.252	0.240	0.252	0.448	0.460	0.448	0.483	0.490	0.500
	Hebei	0.436	0.436	0.433	0.422	0.440	0.449	0.451	0.473	0.497	0.516	0.517	0.544
	Shanghai	0.384	0.402	0.413	0.417	0.437	0.441	0.499	0.517	0.559	0.575	0.607	0.624
	Zhejiang	0.233	0.326	0.308	0.245	0.310	0.328	0.389	0.417	0.448	0.470	0.473	0.496
	Jiangsu	0.445	0.456	0.528	0.526	0.538	0.553	0.572	0.583	0.595	0.633	0.648	0.649
	Fujian	0.116	0.070	0.090	0.091	0.097	0.099	0.284	0.364	0.392	0.415	0.435	0.460
	Shandong	0.443	0.470	0.477	0.499	0.551	0.575	0.589	0.624	0.662	0.654	0.676	0.696
	Hainan	0.153	0.154	0.154	0.179	0.192	0.211	0.229	0.229	0.223	0.235	0.247	0.248
	Guangdong	0.255	0.255	0.103	0.097	0.309	0.347	0.691	0.711	0.826	0.823	0.886	0.890
Central region	Anhui	0.170	0.177	0.187	0.176	0.190	0.246	0.339	0.376	0.392	0.535	0.568	0.519
	Jiangxi	0.252	0.266	0.272	0.273	0.290	0.291	0.331	0.348	0.377	0.393	0.412	0.425
	Hunan	0.151	0.127	0.155	0.160	0.186	0.240	0.307	0.325	0.368	0.377	0.420	0.442
	Hubei	0.361	0.354	0.352	0.352	0.352	0.353	0.415	0.412	0.434	0.472	0.487	0.501
	Shanxi	0.178	0.194	0.123	0.134	0.208	0.198	0.213	0.235	0.247	0.354	0.367	0.412
	Henan	0.258	0.265	0.282	0.286	0.310	0.334	0.407	0.473	0.477	0.510	0.568	0.582
Western Region	Guizhou	0.252	0.357	0.357	0.376	0.378	0.165	0.170	0.215	0.219	0.267	0.258	0.268
	Guangxi	0.251	0.268	0.274	0.274	0.289	0.292	0.330	0.346	0.380	0.398	0.414	0.428
	Ningxia	0.200	0.190	0.198	0.194	0.190	0.217	0.265	0.236	0.249	0.288	0.309	0.337
	Gansu	0.123	0.098	0.127	0.129	0.133	0.146	0.168	0.159	0.163	0.183	0.194	0.201
	Inner Mongolia	0.284	0.290	0.282	0.274	0.300	0.323	0.347	0.377	0.398	0.403	0.410	0.422
	Shanxi	0.178	0.194	0.123	0.134	0.208	0.198	0.213	0.235	0.247	0.354	0.367	0.412
	Chongqing	0.138	0.139	0.158	0.163	0.170	0.178	0.179	0.188	0.195	0.203	0.206	0.219
	Yunnan	0.178	0.395	0.392	0.410	0.411	0.189	0.192	0.192	0.464	0.435	0.452	0.440
	Xinjiang	0.266	0.282	0.278	0.272	0.277	0.283	0.249	0.265	0.275	0.344	0.386	0.393
	Sichuan	0.048	0.037	0.191	0.222	0.263	0.319	0.323	0.311	0.396	0.431	0.486	0.544
	Qinghai	0.046	0.073	0.165	0.161	0.164	0.168	0.207	0.213	0.173	0.245	0.262	0.270
Northeast Region	Liaoning	0.422	0.400	0.399	0.400	0.400	0.383	0.379	0.405	0.424	0.449	0.530	0.544
	Jilin	0.152	0.162	0.155	0.159	0.164	0.092	0.190	0.207	0.368	0.367	0.387	0.415
	Heilongjiang	0.226	0.238	0.252	0.258	0.263	0.267	0.291	0.291	0.355	0.401	0.363	0.360

Table 5. Global Moran’s I index 2011–2022.

Years	Moran’s I Index	Z Value	p Value
2011	0.333	3.027	0.001
2012	0.228	2.165	0.015
2013	0.24	2.264	0.012
2014	0.234	2.218	0.013
2015	0.176	1.739	0.041
2016	0.212	2.036	0.021
2017	0.296	2.725	0.003
2018	0.369	3.331	0.000
2019	0.251	2.357	0.009
2020	0.227	2.158	0.015
2021	0.159	1.597	0.055
2022	0.193	1.874	0.030

Table 6. Local Moran’s I index 2011–2022.

Interval	2011	2022
HH (first interval)	Beijing, Hebei, Inner Mongolia, Liaoning, Shanghai, Jiangsu, Shandong, Henan	Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Anhui, Shandong, Henan, Hubei
LH (second interval)	Tianjin, Shanxi, Jilin, Zhejiang, Anhui, Fujian, Hainan	Shanxi, Fujian, Jiangxi, Guangxi, Hainan
LL (third interval)	Heilongjiang, Hunan, Chongqing, Sichuan, Yunan, Shaanxi, Gansu, Qinghai, Ningxia	Inner Mongolia, Jilin, Heilongjiang, Hunan, Chongqing, Guizhou, Gansu, Qinghai, Ningxia, Xinjiang
HL (fourth interval)	Jiangxi, Hubei, Guangdong, Guangxi, Guizhou, Xinjiang	Guangdong, Sichuan, Yunnan, Shaanxi

Table 7. Grey GM (1,1) model test criteria.

The GM (1,1) Model Accuracy	The Posterior Error Ratio C	The Small Error Probability P
high	<0.35	>0.95
higher	<0. 5	>0.80
qualified	<0.65	>0.70
unqualified	≥0.65	≤0.70

Table 8. Test results for the GM (1,1) model.

Provinces	The Posterior Error Ratio C	The Small Error Probability P	The Average Relative Error Analysis (MRE)
Beijing	0.6957	0.767	0.0276
Tianjin	0.1538	0.833	0.0381
Hebei	0.0839	1	0.0102
Shanghai	0.0204	1	0.0098
Zhejiang	0.1171	0.917	0.0242
Jiangsu	0.0426	1	0.0103
Fujian	0.0944	1	0.0424
Shandong	0.0323	1	0.0143
Hainan	0.0953	1	0.0091
Guangdong	0.1224	1	0.0975
Anhui	0.0719	1	0.0365
Jiangxi	0.0228	1	0.0076
Hunan	0.0202	1	0.0136
Hubei	0.0918	1	0.0146
Shanxi	0.0227	0.986	0.0135
Henan	0.0276	1	0.0165
Guizhou	0.6001	0.783	0.0556
Guangxi	0.026	1	0.0084
Ningxia	0.1103	1	0.015
Gansu	0.1037	0.894	0.054
Inner Mongolia	0.0574	1	0.0117
Shaanxi	0.3028	0.75	0.0374
Chongqing	0.0235	1	0.0029
Yunnan	0.5763	0.767	0.0928
Xinjiang	0.4616	0.742	0.0276
Sichuan	0.0529	1	0.0274
Qinghai	0.1195	0.917	0.0177
Liaoning	0.3892	0.783	0.0307
Jilin	0.2227	0.917	0.044
Heilongjiang	0.1406	0.917	0.0169

Table 9. Predicted value of coupling coordination degree development level of 30 provinces in China from 2024 to 2030.

Regions	Provinces	2024	2025	2026	2027	2028	2029	2030
Eastern Region	Beijing	0.670	0.676	0.682	0.688	0.694	0.701	0.707
	Tianjin	0.614	0.654	0.694	0.735	0.777	0.820	0.864
	Hebei	0.561	0.575	0.590	0.605	0.621	0.637	0.653
	Shanghai	0.696	0.731	0.768	0.807	0.848	0.891	0.936
	Zhejiang	0.554	0.580	0.607	0.635	0.663	0.691	0.720
	Jiangsu	0.703	0.724	0.746	0.769	0.793	0.817	0.842
	Fujian	0.603	0.657	0.713	0.770	0.829	0.888	0.949
	Shandong	0.775	0.808	0.841	0.876	0.912	0.950	0.989
	Hainan	0.285	0.299	0.313	0.327	0.343	0.359	0.375
	Guangdong	0.614	0.672	0.731	0.793	0.857	0.924	0.992
Central Region	Anhui	0.678	0.735	0.794	0.854	0.917	0.946	0.982
	Jiangxi	0.480	0.506	0.534	0.564	0.595	0.627	0.662
	Hunan	0.537	0.579	0.621	0.664	0.709	0.754	0.801
	Hubei	0.531	0.549	0.568	0.587	0.607	0.626	0.646
	Shanxi	0.674	0.716	0.760	0.805	0.851	0.898	0.946
	Henan	0.447	0.496	0.523	0.545	0.583	0.604	0.629
Western Region	Guizhou	0.184	0.172	0.159	0.146	0.134	0.122	0.109
	Guangxi	0.483	0.510	0.538	0.568	0.600	0.633	0.668
	Ningxia	0.349	0.365	0.381	0.397	0.414	0.430	0.447
	Gansu	0.479	0.502	0.526	0.552	0.579	0.607	0.636
	Inner Mongolia	0.464	0.486	0.507	0.529	0.552	0.574	0.597
	Shaanxi	0.236	0.245	0.254	0.264	0.274	0.285	0.296
	Chongqing	0.403	0.410	0.416	0.422	0.428	0.434	0.440
	Yunnan	0.379	0.390	0.402	0.414	0.426	0.438	0.450
	Xinjiang	0.638	0.690	0.744	0.800	0.858	0.917	0.978
	Sichuan	0.302	0.319	0.336	0.354	0.371	0.389	0.407
	Qinghai	0.305	0.317	0.325	0.356	0.369	0.391	0.411
Northeast Region	Liaoning	0.526	0.540	0.555	0.570	0.585	0.600	0.615
	Jilin	0.476	0.513	0.552	0.591	0.631	0.673	0.715
	Heilongjiang	0.415	0.432	0.449	0.466	0.483	0.501	0.519

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Chen, X.; Wu, F.; Wang, X. Coupling Coordination Analysis Between Reclaimed Water Utilization Capacity and Effect in China. Water 2024, 16, 3283. https://doi.org/10.3390/w16223283

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Chen X, Wu F, Wang X. Coupling Coordination Analysis Between Reclaimed Water Utilization Capacity and Effect in China. Water. 2024; 16(22):3283. https://doi.org/10.3390/w16223283

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Chen, Xiaohui, Fengping Wu, and Xiaoyu Wang. 2024. "Coupling Coordination Analysis Between Reclaimed Water Utilization Capacity and Effect in China" Water 16, no. 22: 3283. https://doi.org/10.3390/w16223283

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