Assessment of Sustainable Water Utilization Based on the Pressure–State–Response Model: A Case Study of the Yellow River Basin in China

Abstract

Based on the pressure–state–response (PSR) framework, in this paper, we construct an assessment system for sustainable water utilization (SWU) in 60 prefecture-level cities along the Yellow River Basin. Then, a spatial autocorrelation model is employed to analyze the spatial distribution characteristics of SWU. Further, nine related variables are selected to explore the key factors affecting the spatial differentiation of SWU with a geographic detector model. The results are as follows: (1) The overall level of SWU in the Yellow River Basin is not high, and the level of SWU in five cities is much higher than that in other cities. (2) Overall, the SWU shows noticeable spatial autocorrelation characteristics along the Yellow River Basin. Additionally, locally, it shows high–high agglomeration, low–high agglomeration, and low–low agglomeration. (3) The most important factors affecting the spatial differentiation of SWU in the Yellow River Basin are precipitation, population density, and the proportion of tertiary industry in regional GDP.

1. Introduction

Water is both a basic natural resource and a strategic economic resource for sustainable development [1]. It is very important to improve the sustainable utilization of water resources. Sustainable water utilization (SWU) refers to the rational use of water resources, which can not only meet the normal needs of people’s life, production, and economic development but also maintain a beneficial circle of ecosystems and realize the coordinated development of regional population, resources, environment, and economy [2]. Improving SWU is an important factor in balancing social, economic, and environmental sustainability, which can achieve Sustainable Development Goals (SDGs), especially integrated with SDG6, SDG11, SDG13, SDG14, and SDG15 [3,4]. Therefore, it is of great significance to reveal the status and spatial characteristics of regional SWU and identify the key influencing factors that will help promote regional sustainable development.

Water scarcity is a global problem, especially in seasonal watersheds. The Yellow River Basin is an area of extreme water scarcity in China. The annual natural runoff in the basin is 58 billion cubic meters, accounting for only 2% of the national total. The water volume per capita in the basin is 593 cubic meters, which is 25% of the national level [5]. Therefore, SWU in the Yellow River Basin is an urgent issue. Recently, Xi Jinping, President of the People’s Republic of China, has proposed the high-quality development of the Yellow River Basin as a major national development strategy, in which the problem of water resources has always been a major obstacle. Therefore, improving the SWU will not only have a major impact on improvement in the ecological environment but also greatly promote high-quality development in the Yellow River Basin.

Recently, extensive efforts have been made on measuring water sustainability under the SDG 2030 framework (Juwana et al. (2012)) [6]. Kondratyev et al. (2002) [7] calculated the indicators (driver indicators, status indicators, and response indicators) and new indicators (external loads, critical loads, water quality parameters, critical concentrations, aquatic ecosystem status indicators, and sediment geochemical indicators) proposed by the Commission on Sustainable Development, to assess the current status of water resources in Lake Ladoga and its basins. Kundzewicz (1997) [8] discussed water resources in vulnerable areas, using arid and semi-arid lands, mountains, and small islands as examples, and elaborated on the importance of water resource assessment and its component, hydrological observation, for sustainable development. Bodini et al. (2002) [9] established a flow network by measuring the water exchange between different active sectors within the city boundary of a small city in northern Italy. Based on the DPSIR model, Han (2015) [1], Yu (2017) [10], Wang(2018) [11], and Zhou (2014) [12], among other scholars, constructed an SWU evaluation model for the Yellow River Delta, Jimo City, Shandong Province, and Beijing Municipality, and analyzed the temporal and spatial changes. Based on the perspective of water footprint, Chen (2021) [13], Yang (2021) [14], Yu (2020) [15], Zhu (2018) [16], and Li (2018) [17], to mention a few, assessed the sustainable and efficient water utilization in different regions. Liu et al. (2005) [18] evaluated the SWU in 31 provinces, municipalities, and autonomous regions in China and found that the SWU basically showed a gradually decreasing trend from the southeast coast to the northwest inland. The inefficient utilization of water has become another important constraint on sustainable development.

The ecological fragility of the Yellow River Basin and its important role in economic development have attracted the attention of many scholars [19]. Based on the pressure–state–response (PSR) framework model, Niu et al. (2017) [20] and Zhang et al. (2013) [21] evaluated the ecosystem health status in the Yellow River estuary area by using the comprehensive index method (CEI) and the analytic hierarchy method (AHP), respectively. Liu et al. (2021) [22] calculated the urban ecological carrying capacity (UECC) and found that the UECC of cities in the urban agglomeration of the Yellow River Basin has steadily increased, showing strong spatial instability. However, the spatial correlation and linkage effect of UECC in urban agglomerations were not significant. The spatial distribution of cities with high centrality and close to the center is basically the same, which were mostly located in the middle and lower reaches of the Yellow River Basin. Based on the unique perspective of virtual water trade, Zhang et al. (2021) [23] used the 2012 multi-regional input–output table to measure the water trade between the Yellow River Delta and other provinces. Their findings suggested that the virtual water trade exacerbated water scarcity in the Yellow River Delta, as virtual water exports outweigh imports.

Previous studies in the literature have made important contributions to the assessment of SWU, but there is still room for expansion. Most studies have constructed a sustainability system to analyze the SWU in different regions. However, few have focused on the spatial correlation characteristics of regional SWU and the influencing factors leading to spatial differentiation. This paper mainly studies the geographical distribution characteristics of SWU in water-scarce areas, such as the Yellow River Basin in China. According to the first law of geography, every factor is related to other factors, but similar factors are more closely related. The water of the Yellow River connects various regions of the Yellow River Basin, forming a certain spatial correlation characteristic. In this paper, we further identify the influencing factors of SWU spatial differentiation in the Yellow River Basin.

2. Study Area and Methods

2.1. Study Area

The Yellow River originates from the Qinghai Tibet Plateau and flows through nine provinces and regions of Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong, before emptying into the Bohai Sea in Kenli County, Shandong Province (Figure 1). The mainstream has a total length of 5464 km and a drop of 4480 m. The Yellow River Basin is located between 96°–119° east longitude and 32°–42° north latitude, with an east–west length of about 1900 km, a north–south width of about 1100 km, and a basin area of 795,000 square kilometers [24]. Limited by data acquisition, the area studied in this paper covers 60 prefecture-level cities in 8 provinces in the Yellow River Basin.

The data taken in this paper are mainly derived from the 2019 Urban Statistical Yearbook and Water Resources Bulletin of eight provinces of Qinghai, Gansu, Ningxia, Inner Mongolia, Shanxi, Shaanxi, Henan, and Shandong.

As shown in Figure 2, the water resources per capita in the Yellow River Basin are not high, and the regional distribution is unbalanced. In the study area, Ankang city has the highest water resources per capita, which is as high as 5117.453 m³ per capita. In the rest of the region, the water resources per capita of almost all the cities under study are below 1000 m³ (except Haidong, Ordos, and Shangluo). The amount of water resources per capita in the downstream areas is generally low, and the water quality is very poor. In general, except for a few cities in the Yellow River Basin, most cities are in a state of water shortage. Water resources have become a significant obstacle to the economic and social development of the Yellow River Basin. Therefore, improving the SWU has become an important issue in the development of this region.

2.2. PSR Model

The PSR model is currently the most widely used in the fields of ecological safety and environmental protection analysis. The PSR model can reveal the chain relationship between human activities and water resources in water utilization. It not only reflects the current state of water utilization but also evaluates the causes of change and the consequences of the response to it. It is ideally suited for the assessment of SWU [25].

In this paper, we constructed an assessment system of SWU based on the PSR model, which is composed of three factor layers: water pressure, water state, and water response. Among them, water pressure refers to the effects of human economic and social activities on water resources. The higher the value, the greater the pressure on water resources, which is counterproductive to the SWU. The water state represents the changes in the condition of water resources. The higher the value, the better the state of the water environment. Water response refers to the actions taken by societies and individuals to mitigate, prevent, restore, and prevent the negative environmental impacts of human activities. The higher its value, the greater its effect on the SWU [26].

2.2.1. Determination of Indicator Weights

We used the entropy method to determine the weight of the indicators. According to the basic principles of information theory, information is a measure of the degree of order of the system, and entropy is a measure of the degree of disorder of the system. The smaller the information entropy of the indicator, the more information the indicator provides. Suppose there are n evaluation objects and m evaluation indicators. The original data matrix of the evaluation system can be expressed as X = ${\left\{x_{ij}\right\}}_{n\times m}$(i = 1, 2, 3…, n; j = 1, 2, 3…, m), where $x_{ij}$ represents the j metric value of the i evaluation object. The main steps to establish the weight of the evaluation index using the entropy method are as follows [27]:

(1)

Standardize the indicator

In order to eliminate the impact of different indicator schemas, we standardize the original data as follows:

Positive indicator:

$$y_{ij}=\frac{x_{ij}-min\left(x_{ij}\right)}{max\left(x_{ij}\right)-min\left(x_{ij}\right)}$$

(1)

Negative indicator:

$$y_{ij}=\frac{max\left(x_{ij}\right)-x_{ij}}{max\left(x_{ij}\right)-min\left(x_{ij}\right)}$$

(2)

where $y_{ij}$ is the normalized metric value.

(2)

Calculate the weight of the i indicator value under the j indicator $p_{ij}$:

$$p_{ij}=\frac{y_{ij}}{{\sum }_{i=1}^m{y}_{ij}}$$

(3)

(3)

Calculate the information entropy of each indicator $e_j$:

$$e_j=-\frac{1}{ln n}{\displaystyle \sum }_{i=1}^n{p}_{ij} ln p_{ij}$$

(4)

In Formula (4), define $\underset{p_{ij}\to 0}{\mathit{lim}} p_{ij} ln p_{ij}=0$ when $p_{ij}$ = 0. (The processing here refers to the study of Li Y. et al. [28].)

(4)

Calculate the weights of each metric $w_j$:

$$w_j=\frac{1-e_j}{m-{{\displaystyle \sum }}_{j=1}^m{e}_j}$$

(5)

2.2.2. Calculate the Score

According to the weights of each index calculated, we further use a weighted sum to determine the scores of water stress, water state, and water response in the Yellow River Basin:

$$P_i={\displaystyle \sum }_{j=1}^3{y}_{ij}{w}_j$$

(6)

$$S_i={{\displaystyle \sum }}_{j=4}^6{y}_{ij}{w}_j$$

(7)

$$R_i={\displaystyle \sum }_{j=7}^9{y}_{ij}{w}_j$$

(8)

where $P_i, S_i \mathrm{and} R_i$ indicate the water resource pressure, state, and response scores, $y_{ij}$ is the standardized value of each indicator, and $w_j$ is the weight of each indicator.

Referring to previous research [26], in this paper, we argue that the level of SWU is a function of water pressure, state, and response, so the comprehensive score formula of SWU is given as follows:

$$F_i=\frac{S_i\times R_i}{P_i}$$

(9)

In Formulas (1)–(9), i ∊ [1,60], j ∊ [1,9]. i refers to the 60 prefecture-level cities in the Yellow River Basin in China, and j refers to the 9 indicators based on the PSR model (As shown in

Table 1. Indicator system and weights.

Target Layer	Factor Layer	Index Layer	Index Explanation	Weight
The level of SWU	Pressure	Water consumption per capita (m3) (Target 6.4)	Total water consumption divided by total population	0.184
		Degree of groundwater development and utilization (Target 6.6)	Actual amount of groundwater extracted/amount of groundwater that can be extracted	0.085
		Annual discharge of industrial sewage per unit area (ton/km2). (Target 6.3)	Annual discharge/area of industrial wastewater	0.161
	State	Water share per capita (m3) (Target 6.1)	Total water resources/population	0.200
		Water production modulus (10,000 m3/km2). (Target 6.6)	Total water resources/area	0.189
		Water production coefficient (Target 6.1)	Water production modulus/regional annual precipitation	0.059
	Response	Centralized treatment rate of sewage treatment plant (%) (Target 6.3)	Sewage treatment volume/total amount of sewage discharged	0.031
		Greening rate of built-up area (%) (Target 6.3)	Vertical projection area of urban vegetation/built-up area	0.009
		Proportion of ecological water utilization (%) (Target 6.6)	Ecological water consumption as a proportion of total water consumption	0.082

Note. Target 6.1 refers to “By 2030, achieve universal and equitable access to safe and affordable drinking water for all”. Target 6.3 refers to “By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse globally”. Target 6.4 refers to “By 2030, substantially increase water-use efficiency across all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity”. Target 6.6 refers “By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers and lakes”.

).

2.3. Spatial Autocorrelation Analysis

In this paper, global spatial autocorrelation and local spatial autocorrelation analysis methods were used to identify horizontal space agglomeration in the SWU in the Yellow River Basin. Global spatial autocorrelation generally uses Global Moran’s I. The specific calculation formula is as follows:

$$I=n\frac{{{\displaystyle \sum }}_{i=1}^n {{\displaystyle \sum }}_{j=1}^n\left(X_i-\overline{X}\right)\left(X_j-\overline{X}\right)}{{{\displaystyle \sum }}_{i=1}^n{\left(X_i-\overline{X}\right)}^2{{\displaystyle \sum }}_{i=1}^n {{\displaystyle \sum }}_{j=1}^n{W}_{ij}}$$

(10)

where n is the number of units in the study area; $X_i$ and $X_j$ represent unit i and j; i,j ∊ [1,60]. $\overline{X}$ represents the average value of X; and $W_{ij}$ is the spatial weight matrix. I ∊ [−1,1]. Given the significance level, if I > 0, it means that the regional SWU shows a cluster trend. Otherwise, it means that the regional SWU shows differences. The larger the absolute value of I, the stronger the spatial correlation. Although the global spatial autocorrelation can analyze the distribution characteristics of prefecture-level cities in the Yellow River Basin, it is not yet possible to distinguish the high- and low-value agglomeration of different cities. However, the local spatial autocorrelation can effectively identify the spatial dependence and heterogeneity of the level of SWU, and the specific calculation formula is as follows [29]:

$$G_i^{*}\left(d\right)=\frac{\sum W_{ij}{X}_j}{\sum X_i}$$

(11)

where $G_i^{*}\left(d\right)$ is the local Jereh index; $W_{ij}$ is the spatial weight matrix; $X_i$ and $X_j$ are the levels of SWU representing unit i and j; i,j ∊ [1,60]. For $G_i^{*}\left(d\right)$, Z tests are performed. If Z is significantly positive, it indicates that the horizontal space for SWU in this area shows a high-value agglomeration. If Z is significantly negative, the area shows a low-value agglomeration. Thus, the level of SWU can be divided into four categories: high–high agglomeration, high–low agglomeration, low–high agglomeration, and low–low agglomeration.

2.4. Geographic Detector

A geographic detector is used to reveal the driving forces of geography activities. The core idea is based on the assumption that if an independent variable has an important influence on a dependent variable, then the spatial distribution of the independent and dependent variables should be similar.

In this paper, we mainly used the factor detection and interaction detection function of the geographic detector. Factor detection can test the spatial differentiation of univariate variables, and interaction detection can test the effect of the interaction of different factors on the dependent variable.

In this paper, the comprehensive score of SWU was used as the dependent variable, and the nine influencing factors were selected to reveal the influences on the differentiation in the SWU. The factor detection formula is as follows:

$$q=1-\frac{{\sum }_{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}=1-\frac{SSW}{SST}$$

(12)

$$SSW={\displaystyle \sum }_{h=1}^L{N}_h{\sigma }_h^2$$

(13)

$$SST=N{\sigma }^2$$

(14)

where q is a measure of the impact factor on the SWU, h is the number of layers; L is the stratification of factor X, i.e., classification or partitioning; $N_h$ and N are the numbers of units in layer h and the whole sample, respectively; ${\sigma }_h^2$ and the ${\sigma }^2$ are the variances of the dependent Y in layer h and the whole sample, respectively. SSW and SST are the sum of the variances within the layer and the total variance of the whole region, respectively. The value range of q is [0,1]. The larger the value of q, the stronger the effect on the SWU.

Then, we identify the interactions between different risk factors of X, i.e., whether the interaction of X1 and X2 increases or weakens the explanatory power of the dependent variable Y. Firstly, we calculate the explanatory forces of the two factors, q(X1) and q(X2), respectively. Then, q(X1∩X2) is calculated when X1 and X2 interact. Furtherly, we confirm whether the impact of the two-factor interaction on SWU is enhanced or weakened by comparing q(X1), q(X2), and q(X1∩X2) [30].

3. SWU in the Yellow River Basin

The Lorentz curve in Figure 3 is farther from the absolute average, indicating that the spatial distribution of the SWU in the local cities in the Yellow River Basin is not uniform. The SWU in 60 prefecture-level cities greatly varies, among which the comprehensive scores of the SWU in Shangluo, Ankang, Xinzhou, Haidong, and Dingxi are higher than others, up to 357.61. The scores of these cities greatly differ from other regions, and subsequently, the geographical distribution of the city’s SWU gradually decreases. The comprehensive scores of Baiyin, Bayan Zhuoer, Yinchuan, Xianyang, and other places are low, and the comprehensive score of Baiyin City is only 0.8182.

Among the three factor layers, the value of water pressure is the highest, which is 74.113. This indicates that the water pressure of the cities around the Yellow River Basin is relatively large, which is mainly due to the high per capita water consumption and industrial sewage discharge per unit area in these cities. Additionally, the value of the water state is 56.406, while the value of the water response is only 44.495. This indicates that the response measures to improve the SWU are not enough in the face of high water pressure and suboptimal water resources in the Yellow River Basin.

3.1. SWU in Different Regions of the Yellow River Basin

Further, this paper divides the cities in the Yellow River Basin into three regions (

Table 2. Regional division of the Yellow River Basin.

upstream	Xining, Haidong, Lanyin, Baiyin, Tianshui, Wuwei, Pingliang, Qingyang Dingxi, Yinchuan, Shizuishan, WuZhong, Guyuan, Hohhot, Baotou, Ordos, Bayannur, Wuhai
midstream	Ulanqab, Xi’an, Tongchuan, Xianyang, Baoji, Yulin, Ankang, Yan’an, Shangluo, Datong, Taiyuan, Changzhi, Yangquan, Jinzhong, Jincheng, Shuozhou, Yuncheng, Xinzhou, Linfen, Lvliang, Luoyang, Sanmenxia
downstream	Zhengzhou, Anyang, Kaifeng, Xinxiang, Jiaozuo, Puyang, Jiyuan, Hebi, Shangqiu, Qingdao, Weifang, Jinan, Zibo, Dongying, Jining, Tai’an, Dezhou, Liaocheng, Binzhou, Heze

). As shown in Figure 4, the SWU in the midstream of the Yellow River Basin is the highest, and the lowest is downstream. The midstream of the Yellow River Basin has the lowest water pressure and the best water resources. The downstream of the Yellow River has a slow flow and sediment deposition into an “above-ground river”, which has the highest water pressure and the worst water state. Although the water response score of downstream cities is the highest, the score of SWU is still very low due to high water pressure and poor water status. The upstream is the source of the Yellow River, with relatively clear water quality, stable water flow, and large water production. Moreover, the government considers the protection of upstream water sources to be a significant issue.

3.2. Spatial Distribution of SWU

Overall, the level of SWU in the Yellow River Basin is not high, and most cities are at a medium, low, or even extremely low level.

As shown in Figure 5, Figure 6, Figure 7 and Figure 8, the SWU in Haidong, Dingxi, Ankang, Shangluo, and Xinzhou is in the first level. These areas are in the midstream and upstream of the Yellow River. Most of these cities are in the national key ecological function zones, with low water pressure. These cities have good water resources and strong resource and environmental carrying capacity.

The level of SWU in Jinzhong, Yangquan, Baoji, Jincheng, Pingliang, Xi’an, and Lvliang is in the second grade. These cities are close to first-class cities, and most of them are located in the main agricultural production areas in the midstream of the Yellow River. The carrying capacity of resources and the environment in these cities is relatively weak, and the economic and demographic conditions of large-scale aggregation are not good enough, which is related to the security of agricultural supply and the ecological security of a large range.

Tianshui, Xining, Guyuan, Jiyuan, Hohhot, Luoyang, Dongying, Ordos, Kaifeng, Jinan, and Tongchuan belong to the third class, which are clustered in the upstream, midstream, and downstream, but mainly in the midstream. The SWU in these cities is of a medium level. Xining, Hohhot, Ordos, and other cities are key development zones, and the water pressure may be further increased, and the water state may also be affected and deteriorated. Cities in restricted or prohibited development zones, such as Tianshui and Guyuan, may improve the water state and even the level of SWU due to government protection measures.

Shuozhou, Zibo, Binzhou, Yuncheng, Weifang, Changzhi, Zhengzhou, Yan’an, Wuwei, Yulin, Datong, Taiyuan, Qingyang, Anyang, Linfen, and Xinxiang belong to the fourth class. Most of these cities are located in the restricted or prohibited development zones of the midstream and downstream of the Yellow River. The ecological environment is relatively fragile, and the water pressure and the water state are relatively poor.

Hebi, Shangqiu, Lanzhou, Ulanqab, Heze, Baotou, Jiaozuo, Tai’an, Jining, Dezhou, Shizuishan, Qingdao, Liaocheng, Puyang, Wuhai, Xianyang, Yinchuan, Bayannaoer, Baiyin, and Wuzhong have the lowest levels of SWU. These areas are mainly located in Gansu, Inner Mongolia, and downstream of the Yellow River, which are more water-scarce cities with high water pressure. Among them, Qingdao, Dezhou, Liaocheng, and Jining are in the optimization development zones. The level of urbanization and land development density of these cities has been relatively high, the demand for water is increasing, and the carrying capacity of resources and the environment is gradually weakening. Although the water state is not too bad, it shows signs of gradual deterioration. Additionally, some cities are located in the key development zones, and the water pressure is smaller than that of the optimized development zones. Therefore, the carrying capacity of resources and the environment in these cities is also stronger. However, most of the cities in Gansu and Inner Mongolia are in prohibited development zones, and some are in restricted development zones. Water resources are scarce, and the water state is poor.

4. Spatial Differentiation Characteristics of SWU in the Yellow River Basin

In this paper, the global Moran’s I index and the significance level p-value were calculated based on the adjacent Queen spatial weight matrix to reveal the spatial autocorrelation of SWU. Figure 9 shows that the global Moran’s I index is 0.2783, and the p-value is 0.004, which passed the significance test of 1%. This indicates that the level of SWU in 60 prefecture-level cities in the Yellow River Basin has obvious spatial autocorrelation characteristics. Cities with a high level of SWU are close to other cities with a high level of SWU, and cities with a low level of SWU tend to be close to each other. According to their properties, the four quadrants of the scatter plot can be divided into four types: HH (high–high agglomeration) area, LH (low–high agglomeration) area, LL (low–low agglomeration) area, and HL (high–low agglomeration) area.

Figure 10 and

Table 3. Regional distribution of local autocorrelation.

High–High (HH)	Low–Low (LL)	Low–High (LH)
Tianshui, Xi’an, Ankang, Shangluo, Sanmenxia, Yangquan	Puyang, Heze, Jining	Lanzhou, Taiyuan

show the results of local spatial autocorrelation. The high–high agglomeration areas are mainly distributed in Shaanxi Province and Shanxi Province, including six prefecture-level cities of Tianshui, Xi’an, Ankang, Shangluo, Sanmenxia, and Yangquan. The level of SWU in these cities and adjacent areas is relatively high. The total amount of water in these areas is relatively abundant. These areas are not important industrial and agricultural cities, so there is less demand for water, as well as low water pressure.

The low–low agglomeration area is mainly concentrated downstream of the Yellow River, including Puyang, Heze, and Jining. The level of SWU in this region and the adjacent regions is relatively low, and the spatial correlation is manifested as a low-speed growth zone. Jining is in the optimization development zone, while Puyang and Heze are in the key development zone. An increase in the intensity of urban development will lead to an increase in urban water utilization. Although the local economy has developed rapidly, the destruction of the ecological environment caused by the exploitation of traditional water resources has a great influence on the SWU. In particular, the downstream of the Yellow River, where the three cities are located, is not endowed with high water resources, so it is urgent to change the economic growth in this area from an extensive to an intensive model of growth.

The low–high agglomeration cities are Lanzhou and Taiyuan, which are scattered in Gansu Province and Shanxi Province, respectively. The total amount of water in these cities is small. However, these cities are located in key development zones, facing significant water pressure.

5. Influencing Factors of SWU in the Yellow River Basin

5.1. Selection of the Influencing Factors

In this paper, we used a geographic detector to explore the influencing factors of regional differences in the level of SWU in the Yellow River Basin. Along with the related literature, we selected natural endowments, the level of economic development, the industrial structure, and technological progress as influencing factors, to reveal the regional differences in the SWU.

First, regarding natural endowments, the abundance of water determines the total amount of water available to the region, which provides the basis for the SWU. At the same time, the population size affects the overall demand for water. Therefore, precipitation (X1) and population density (X2) were selected to represent natural endowments.

Second, in terms of the level of economic development, the environmental Kuznets curve (EKC) pointed out that environmental quality shows a U-shaped curve relationship with economic development [31]. We argue that the level of regional economic development is one of the reasons for the regional differences in the level of SWU. On this basis, the urbanization rate (X3) [32] and GDP per capita (X4) were further selected to investigate the impact of economic development on the SWU.

Third, for the industrial structure [33], the EKC shows that environmental quality is also related to the industrial structure. We argue that the industrial structure will affect the level of SWU to a certain extent. For example, if the agriculture of a region is relatively developed, the water consumption of the region will be relatively large due to the large water consumption of farmland irrigation, which will cause certain pressure on the SWU [34]. In this paper, three indicators were chosen to characterize the structural impact described above, namely the proportion of industrial water utilization (X5), the proportion of agricultural water utilization (X6), and the proportion of tertiary industry in regional GDP (X7).

Fourth, in terms of technological progress, technology has a positive effect both on the efficiency of agricultural water utilization and industrial water utilization. In this paper, two indicators were used to express technological progress: the water consumption of the industrial added value of 10,000 yuan (X8) and the water consumption of 10,000 yuan of the agricultural added value (X9).

5.2. Dominant Factors Identification of SWU

5.2.1. Factor Detection of SWU

Firstly, the quantile method was used to discretize the independent variables and convert them from numeric to type quantities, and the q values of the determinant factors are shown in Figure 11. The results show that the q values of precipitation (X1), population density (X2), and tertiary industry as a proportion of regional GDP (X7) are the largest, respectively, 0.470, 0.437, and 0.421, and the corresponding p-values pass the 10% significance test. These factors have the strongest effect on the regional differentiation of the SWU. The q values of the proportion of agricultural water utilization (X6) and the water consumption of 10,000 yuan of the industrial added value (X8) are 0.184 and 0.169, respectively. Therefore, precipitation (X1), population density (X2), and the proportion of tertiary industry in regional GDP (X7) were selected as the leading factors for the horizontal spatial differentiation of SWU in the Yellow River Basin.

5.2.2. Interaction Detection of SWU

According to the detection factors obtained from the geographic detector, interaction detection was carried out. The interaction of the three dominant factors on the SWU was analyzed, for which we considered the comprehensive score of SWU as the dependent variable (Y), and the precipitation (X1), population density (X2), and the proportion of tertiary industry in regional GDP (X7) as the independent variables. The results show that the two-factor interactions are nonlinear enhancements (

Table 4. Dominant factor interactions.

A ∩ B	Judgment	Interaction
X1 ∩ X2 = 0.989	q (X1 ∩ X2) > q (X1) + q (X2)	Nonlinear enhancement
X1 ∩ X7 = 0.999	q (X1 ∩ X7) > q (X1) + q (X7)	Nonlinear enhancement
X2 ∩ X7 = 0.943	q (X2 ∩ X7) > q (X2) + q (X7)	Nonlinear enhancement

). It can be seen that the effect of the superposition of the two factors on the SWU is very significant.

6. Conclusions and Implications

Based on the PSR framework and SDGs 2030, in this paper, we constructed an evaluation system of the SWU. The spatial autocorrelation characteristics of the SWU were further analyzed. Then, the influencing factors of spatial differentiation of the SWU were explored by using a geographic detector. The results show that (1) the overall level of the SWU in the Yellow River Basin is relatively low. The level of SWU in Shangluo, Ankang, Xinzhou, Haidong, and Dingxi is relatively high, but there are major faults in other cities. (2) The cities located in the midstream and upstream of the Yellow River generally have a higher level of SWU than those downstream of the Yellow River, among which Qinghai Province has the highest level of SWU. Most of the cities with high levels of SWU are national key ecological function zones. (3) The level of the SWU of 60 prefecture-level cities in the Yellow River Basin has obvious spatial autocorrelation characteristics. The high–high agglomeration areas are mainly distributed in Shaanxi Province and Shanxi Province. The low–low agglomeration areas are mainly concentrated downstream of the Yellow River. The low–high agglomeration areas are scattered in Gansu Province and Shanxi Province. (4) It is found that precipitation, population density, and the proportion of tertiary industry in regional GDP are the main influencing factors of the regional differentiation of the SWU in the Yellow River.

Based on the above analysis and conclusions, this paper obtains the following policy implications:

First, it is of great significance to strengthen the ecological restoration and water resource protection of the Yellow River Basin. The ecological environment of the Yellow River Basin is fragile, and soil erosion is serious [34]. Thus, the natural endowments of the cities in the Yellow River Basin play greater roles in the SWU. As the results show, cities with abundant water resources and better ecological environments have relatively high levels of SWU. Therefore, strengthening ecological restoration and water resource protection is an important basis for improving the sustainable utilization of water resources in the Yellow River Basin. Especially for the middle reaches of the Yellow River, it is of great significance to increase vegetation cover and reduce the evaporation of surface water.

Second, the government should increase investment in science and technology, actively promote scientific and technological research and development, and provide technical support for the sustainable development of water resources. Most areas of the Yellow River Basin have limited water endowments, so the development of more efficient water-saving equipment and sewage treatment equipment to maximize water conservation and water recovery has become an urgent problem to be solved in many regions. Especially in those areas with severe water scarcity, special attention should be paid to water conservation during rainy periods. At the same time, the government should increase investment in science and technology for sewage purification, develop equipment with higher purification rates, and recycle every drop of water. It is also necessary to increase environmental protection publicity, improve residents’ awareness of water conservation, and promote the construction of a water-saving society [35].

Third, the cities along the Yellow River Basin should strengthen their level of scientific and technological support to promote the improvement of water utilization structure [34]. The industrial structure is one of the main factors affecting the spatial differentiation of the SWU in the Yellow River Basin. Traditional farmland irrigation consumes a large amount of water, and the efficiency of water utilization is not high [36]. The government should actively promote drip irrigation to reduce agricultural water utilization and improve the efficiency of agricultural water utilization. For industrial water, the reuse of industrial sewage should be improved, and recycled water should be promoted [37]. For ecological water utilization, reasonable support should be given, and publicity should be strengthened [3].

This study enriches the relevant literature on the SWU in water-scarce areas; in particular, our findings on the influencing factors enrich the relevant theories of the SWU. This paper also has some implications for achieving Sustainable Development Goals. For example, in those areas where a large proportion of water is used for agriculture, water use is inefficient. At this time, the industrial structure of the region should be optimized, and the agricultural water use efficiency should be improved to better realize sustainable development in the region.

There are still some deficiencies in this study, which need to be improved in future studies. Firstly, we built an indicator system based on the PSR framework that reflects Sustainable Development Goals 2030. However, due to the lack of data, the number of indicators selected in this paper was limited; thus, it cannot fully reflect the connection between SDGs and SWU. In the future, we will continue to expand more comprehensive indicators to cover SDGs as much as possible. Second, in this paper, we only examined the SWU in 2019, but in fact, the level of SWU changes over time. In the future, it is hoped to collect panel data to study the temporal variation in the SWU. Thirdly, there are not only important industrial cities in the Yellow River Basin but also a large number of agricultural areas, so there are many factors that affect the sustainable utilization of water resources in the Yellow River Basin. Future research can further consider spatial heterogeneity and further reveal the influencing factors of the SWU in different regions.