Dynamic Relationship of Urban and Rural Water Shortage Risks Based on the Economy–Society–Environment Perspective

Abstract

Based on the economy–society–environment perspective, this study details the causes and characteristics of urban and rural water shortage risks, and then explores the dynamic relationship between urban and rural water shortage risks. It quantitatively analyzes the urban and rural water shortage risks of 52 areas in Northwest China during 2001–2019. Furthermore, the dynamic relationships are tested by using the exploratory spatial data analysis model. The main conclusions are as follows: (1) The water shortage risk level is gradually declining over time, while the urban water shortage risk is improving faster than the rural water shortage risk. (2) The relationships show significant synergy. There are four primary types: strong synergy areas, medium synergy areas, weak synergy areas, and very weak synergy areas. (3) The levels of synergy within the northwestern regions show a positive spatial correlation and spatial agglomeration; that is, regions with high levels of synergy are adjacent, while regions with low levels of synergy are adjacent. From the perspective of local spatial differentiation, positive spatial autocorrelation patterns (H-H and L-L) account for a large proportion and gradually increase over the research period, reflecting the patterns of H-H and L-L. The agglomeration becomes increasingly obvious.

1. Introduction

Water resources are basic, natural, strategic, and economic resources. They are the lifeblood of economic development and the ecological environment, and important resources for sustainable development [1]. Since the 21st century, with population growth, living environment changes, climate change, industrial structure changes, and other factors, water use has increased sharply, and an increasing number of countries have experienced water crises [2]. Twenty-eight countries are classified as water–deficient or severely water-deficient by the United Nations, and that number is increasing [3]. More than two billion people are at risk of having less available freshwater, and at least one-quarter of them could be living in countries affected by periodic or repeated shortages by 2050. Over 80% of the wastewater generated by human activities is discharged untreated into rivers or oceans. Approximately 2.3 billion people have no access to safe drinking water and 4.6 billion have no access to safe sanitation. Each day, nearly 1000 children die from preventable diarrheal diseases caused by water and sanitation problems [3]. In this context, water shortages should not only be considered resource shortages but also an issue concerning the economy, society, and ecology, as well as many other aspects [4]. To achieve the effective utilization of water resources, among the population, environment, and economy, comprehensive and sustainable development is needed for the pursuit of long-term goals, which requires the comprehensive consideration of water resource utilization, economic development, and the internal relationship with the ecological carrying capacity. Therefore, it is particularly necessary to use scientific methods to evaluate water shortages and to develop rational policies.

Due to increasing contradictions caused by the impacts of global climate change on China’s water resources, uneven air distribution, frequent droughts, and other disasters, water shortages have aroused widespread concern throughout the country and society, among all walks of life [5]. Due to the complex geographical conditions, changeable climate, and dense population, China is one of the few countries in the world where natural disasters occur in many forms and with high frequency, being most strongly affected by meteorological disasters. Among them, drought and high-temperature events are two natural disaster types that have great impacts on China’s economy and the lives of its citizens [6]. For example, Northwest China is an arid and semi-arid region that is deep inland, where low rainfall is a major concern. It is also characterized by a complex terrain and diverse geomorphological types, which lead to further drought events. High-temperature events are often accompanied by drought events, and high-temperature events are also often the cause of drought events [6]. Rising temperatures, increased evaporation, and reduced rainfall often lead to simultaneous meteorological and soil droughts, making water shortages more severe. With the development of the west of the country and the intensification of human activities, land desertification and water shortages are becoming increasingly obvious. The middle and upper reaches of the Yellow River suffered three consecutive droughts between 1960 and 1962. In 1999, Qinghai experienced the worst drought in 50 years, since 1949. From September of the previous year to May of that year, high temperatures and low rainfall lasted for more than 200 days in autumn, winter, and spring. In contrast, floods are a major concern in Southeast China [7].

The existing research on water shortage risk mainly focuses on three aspects. The first is the exploration of conceptual and interpretive frameworks. Early studies on water shortage risk were mostly preliminary or conceptual explorations. The Water Poverty Index is applied to evaluate the water resource shortages by combining resource, access, capacity, and use components with an environment component [8]. This index initially considered economic factors and social factors and indicated the possibility of water shortage risk; however, the Water Poverty Index ignored the impacts of natural disasters and risk on water shortages [9]. Later, the Water, Economy, Investment, Learning, and Assessment Index [10]; Water Wealth Index [11]; Climate Vulnerability Index [12]; Canadian Water Sustainability Index [13]; and Drivers, Pressure, State, Impact, and Response Model [14] were presented based on water shortages. This highlights the growing maturity of water shortage theory. The second aspect is the study of the scale of water shortages. The water shortage situation is evaluated at multiple scales, including urban [15,16], rural [17,18], basin [19,20,21], national [22], province [9], county [4], town [16], and community levels [23,24], mainly in terms of water availability, accessibility, quality, environmental impact, and social and economic factors [25]. The third aspect is the research on water shortages within different fields of study. Water shortage is considered from various perspectives across different fields, for example hydrological engineering and social economy perspectives. The water resource system is closely related to many systems of human life and production [26]. In this process, the evaluation of water shortages also involves economic development [27], food [11], urbanization and industrialization [27], and agricultural modernization [28].

The water shortage risk approach is a new method for measuring the impacts of human activities and natural disasters on water resource systems; it measures the real utilization of water resource systems from the perspectives of the society, economy, environment, and nature, and it extends the evaluation of water shortage problems from the field of hydrology to the field of social economy [29]. However, there are some weaknesses that must be addressed. On the one hand, most studies consider water shortages in urban and rural areas separately, rather than in a combined manner [14]. In China, in terms of the allocation of water resources, the state has adopted an urban-centered development strategy but has ignored the construction and development of rural water resources, which has led to a serious lag in rural water resource construction and failed to meet the needs of rural areas in terms of infrastructure, public health, basic education, and other aspects [30]. Considering the economic and social impacts comprehensively, compared with urban areas, rural areas are in a weak position in terms of water intake, usage, drainage, control, and management [31]. On the other hand, the existing research [20,29] has focused on the main cause of water resource shortages but has not fully considered the dynamic relationship between urban and rural areas in different regions. The lack of spatial econometric analysis in the existing research neglects the potential effects of the spatial correlations among economic and social activities. Change in an area can affect not only the local area but also neighboring areas through the flow of populations, policies, resource allocation, and investment channels. Therefore, ignoring the spatial relationships is likely to lead to ineffective policymaking.

To provide a foundation for policymakers through the comprehensive assessment of water shortage risk at the regional level, this study takes the economy–society–environment perspective as a starting point and analyzes the relationship between urban and rural water shortage risks in different regions. Further insight into the problem of water shortages can be gained by taking a comprehensive approach that analyzes the spatial variability in water resource conditions. This multifaceted approach overcomes limitations that arise in simplified analyses that use a single method. We believe that this article will serve as a valuable reference for urban and rural water shortage studies in developing countries.

2. Water Resources in the Study Area: Economic, Social, and Environmental Issues

Against the background of global climate change, compared with humid areas, in arid and semi-arid areas, climate change is more intense, and the warming trend is stronger in China. Drought is widespread in China, where the arid areas as a whole account for 22% of the national land area [8]. In China, with thousands of arid areas, the northwest region is the main drought distribution area, and it also has one of the highest drought rates globally for this latitude. It is a typical climate-change-sensitive area and has a fragile ecological environment. Northwest China has an arid and semi-arid climate. Northwest China represents approximately 30% of China’s land area, 7.3% of its population, and 5.4% of its GDP [32]. It is one of the poorest areas in the country. It has scarce precipitation, with less than 500 mm of annual rainfall [24], and an arid, continental arid, semi-arid, and alpine climate. The annual rainfall in the Loess Plateau is between 300 and 500 mm, that in the Qingda basin is less than 200 mm, that in the Hexi corridor (Jiayuguan, Zhangye, Jinchang) is less than 100 mm, that in Jiuquan is only 29.5 mm, and that in Turpan is less than 20 mm, amounting to almost no rain year-round. Due to the scarcity of precipitation, the prevalence of drought, and the spread of deserts, the surface water in Northwest China covers approximately 220 billion cubic meters per year, accounting for only approximately 8% of the country’s total runoff [33]. The actual per capita water resources in the northwestern regions amount to approximately 990 m³, less than half of China’s average (2239.8 m³) and less than one-tenth of the world average [33,34].

Northwest China is faced with severe problems associated with resources and the environment, especially water shortages and water pollution. On the one hand, the amount of water resources per capita is low, and the patterns of water resource utilization are different in different regions [35]. The long-term extensive water use pattern leads to the low utilization rate of water resources and serious waste in Northwest China [34]. With continuous economic growth and large amounts of untreated agricultural sewage, industrial wastewater and domestic sewage are directly discharged into local rivers, far exceeding the self-purification capacity of the water bodies, while the carrying capacity of the water environment is approaching its limit [36]. At present, water shortages, water pollution, water ecological degradation, water mismanagement, and other water problems are intertwined, which restricts the sustainable development of Northwest China to varying degrees and places unprecedented pressure on economic and social development [37]. Therefore, systematic analysis and quantitative research on the matching of urban and rural water resource utilization in Northwest China is of great significance in ensuring the ecological security of water resources, optimizing the industrial and agricultural structures, rationally utilizing and protecting resources, and providing a scientific basis for solving the problem of water shortages.

3. Materials and Methods

3.1. The Indictors of Water Resources Shortage Risk

Disaster risk refers to the degree of damage caused by disaster activities and the possibility of disaster occurrence. Domestic and foreign scholars generally believe that disaster risk is typically the result of the combined effect of the risk of the disaster-causing factor and the exposure and vulnerability of the disaster-bearing body [38]. Therefore, the disaster risk function can be expressed as: disaster risk = f (risk, exposure, and vulnerability). Human measures to cope with water shortage also have a great impact on the improvement of water resources. When evaluating the water shortage risks in urban and rural areas, the actual situation of the study area must be considered more comprehensively. Considering the research perspective of this paper, the selection of urban and rural water shortage risk evaluation indicators mainly follows the principles of scientificness, particularity, availability, and comparability. The following equation is used:

$Water shortage risk index=hazardous \left(H\right)\times exposure \left(E\right)\times vulnerability \left(V\right)\times adaptive \left(A\right)$

where the hazardous (H)—and subsequently environmental—risk is one of the factors of climate change that results from the disaster degree, frequency, and intensity, including natural, economic, and social potential hazards, which are formative factors [39]. Exposure (E) refers to the potential loss of water resources in rural areas under the influence of external and internal disaster factors, mainly including population, property, and cultivated land [16]. The vulnerability (V) of hazard-affected bodies refers to the water resource system’s response to the risk of interference factors, such as damage or disaster loss, which are closely related to the stability of each key element within the system, considering the planting structure, the structure of the rural population, and the ecological environment, because of the lack of aging data for the rural population and because the rural population aging phenomenon is generally serious in cities, where the population aging rate instead of the rural population aging rate is typically considered [16]. Adaptive (A) refers to the ability to alleviate regional water shortage. It refers to the system’s ability to deal with and respond to disasters and risks under the influence of disturbance factors, mainly depending on economic development, education, scientific and technological development, infrastructure, and other factors [23,39]. According to the mathematical model of disaster risk, and combined with the characteristics of water resource shortages, a model water resource shortage evaluation index can be established:

$W={\lambda }_H\times H+{\lambda }_E\times E+{\lambda }_V\times V+{\lambda }_A\times A$

where ${\lambda }_i$ is the weight of the index $i$.

3.2. Assigning Weights to the Indicators

To avoid the influence of subjective human factors, the entropy weighting method, which uses the objective environment to calculate the index weights, was selected [40]. Principal component analysis (PCA) is based on the analysis of measurable data but may result in variations in the attributed weights [41].

The objective of PCA is to protect data under the principle of minimum information loss and to reduce the time needed to process high-dimensional variables; that is, under the premise of ensuring the minimum loss of information, some information is discarded to reduce the variables to a small, comprehensive number that represents the multidimensional variables originally adopted [42]. In this paper, PCA was used to obtain the comprehensive weight to measure and score the urban water shortage risk and rural water shortage risks in Northwest China (Table 1 and Table 2).

3.3. The Calculation of Water Shortage Risk

Here, we set $w_{ui}^t$ and $w_{ri}^t$, respectively, to indicate urban water shortage risk and rural water shortage risk, where i is the number of indicators; t represents years; $V_{ui}^t$, $V_{ri}^t$, $v_{ui}^t$, and $v_{ri}^t$ are the maximum and minimum values of $i$ indicators for urban water shortage risk and rural water shortage risk in t years, respectively. This calculation generally takes the form of the following formula:

$w_{ui}^t=\{\begin{array}{l}\left(w_{ui}^t-v_{ui}^t\right)/\left(V_{ui}^t-v_{ui}^t\right)\hfill \\ \left(V_{ui}^t-w_{ui}^t\right)/\left(V_{ui}^t-v_{ui}^t\right)\hfill \end{array}{w}_{ri}^t=\{\begin{array}{l}\left(w_{ri}^t-v_{ri}^t\right)/\left(V_{ri}^t-v_{ri}^t\right)\hfill \\ \left(V_{ri}^t-w_{ri}^t\right)/\left(V_{ri}^t-v_{ri}^t\right)\hfill \end{array}$

where $w_{ui}^t$ and $w_{ri}^t$, respectively, indicate the first years of urban and rural water shortage risks after the standardization of the $i$ indicators of value.

If the increase in the variable value results in the best situation then:

$w_i^t=\left(w_i^t-v_i^t\right)/\left(V_i^t-v_i^t\right)$

If the increase in the variable value results in the worst situation then:

$w_i^t=\left(V_i^t-w_i^t\right)/\left(V_i^t-v_i^t\right)$

In addition, $w_u^t, w_r^t$ indicate the urban and rural water shortage risks in the first t years of overall comprehensive development; this article selects the linear weighted sum method, as shown in Formula:

$w_u^t={\displaystyle \sum _{i=1}^n{\lambda }_{ui}{w}_{ui}^t}, {\displaystyle \sum _{i=1}^n{\lambda }_{ui}=1}{w}_r^t={\displaystyle \sum _{i=1}^n{\lambda }_{ri}{w}_{ri}^t}, {\displaystyle \sum _{i=1}^n{\lambda }_{ri}=1}$

3.4. Harmonious Development Model

This paper uses the harmonious development model to measure the harmony and development levels between urban water shortage risk and rural water shortage risk [7]. The H-D model of the interaction between the two systems is as follows:

${\mathrm{H}}_{ur}^t=\{\begin{array}{c}a a<1\\ \frac{1}{a} a>1\end{array} a=\ln \left(w_u^t\right)/\ln \left(w_r^t\right)$

For ${\mathrm{H}}_{ur}^t$, the coordination degree between urban and rural water shortage risks in the first year, the t values range between 0 and 1. However, for $w_u^t$ and $w_r^t$, with similar values, the development level is low, and the degree of coordinated development shows higher values. Therefore, to accurately reflect the level of interactive development between urban water shortage risk and rural water shortage risk, we also take the development levels of the two systems into consideration and further construct a model for the degree of harmonious development between the two systems, as follows:

$D_{ur}^t=w_u^t+{\left(w_r^t\right)}^3$

$T_{ur}^t=\alpha w_u^t+\beta w_r^t$

where $D_{ur}^t$ indicates the development of urban water shortage risk and rural water shortage risk for the first t degrees and $T_{ur}^t$ reflects the harmonious development between two industries according to the first comprehensive evaluation index t, $\alpha +\beta =1$, where $\alpha$ and $\beta$ are undetermined coefficients indicating the degrees of contribution of urban water shortage risk and rural water shortage risk to the synergy level of the whole system. To avoid the bias of subjective human factors, the coefficients were each assigned a value of 0.5 [43].

3.5. Exploratory Spatial Data Analysis

Exploratory spatial data analysis (ESDA) is a methodology based on spatial correlation measures. It describes spatial agglomeration and spatial anomalies to reveal the spatial interactions affecting the object of study [45,46].

3.5.1. Global Spatial Autocorrelation Analysis

Global spatial autocorrelation reflects the spatial correlation and spatial variation of the observed variables in a given region. If Moran’s I is significantly positive, then the region with a higher (or lower) level of water shortage risk is significantly concentrated in space. The closer the value is to 1, the smaller the overall spatial difference. In contrast, a greater overall spatial difference is indicated when the global Moran’s I is 0; thus, space is irrelevant [47].

The following equation was used:

${\mathrm{Global} \mathrm{Moran}}^{’}\mathrm{s} \mathrm{I}=\frac{{{\displaystyle \sum }}_i^n{{\displaystyle \sum }}_{j\ne 1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{s^2{{\displaystyle \sum }}_i^n{{\displaystyle \sum }}_{j\ne 1}^n{w}_{ij}}$

where $x_i$ and $x_j$ are the evaluation values of region i and region $j$, respectively; $n$ is the spatial observation unit; and $S^2$ is the variance of the evaluation value. For the average of $x$, $w_{ij}$ is the spatial weight matrix of $i$ and $j$. If $i$ and $j$ are adjacent relations, $w_{ij}=1$; otherwise, $w_{ij}=0$.

3.5.2. Local Spatial Autocorrelation Analysis

For regional differences in overall spatial heterogeneity, local spatial differences are likely to expand. To comprehensively reflect the changing trend of regional economic spatial differences, the ESDA local analysis (LISA) method is needed to reveal the self-correlation of local regional units in an adjacent space [48]. Here, the following equation was used: ${\mathrm{Local} \mathrm{Moran}}^{’}\mathrm{s} {\mathrm{I}}_i=z_i{\displaystyle \sum }_{j=1}^n{w}_{ij}{z}_j$.

The value of Moran’s I is generally between −1 and 1; values less than 0 indicate a negative correlation, values equal to 0 indicate no correlation, and values greater than 0 indicate a positive correlation; $z_i$ and $z_j$ are the standardized values of the evaluation values of i and j, respectively.

4. Results

4.1. The Water Shortage Risk Results and Their Significance

Measured with reasonable weights, detailed data, and scientific methods, the water shortage risk in Northwest China presents a regular and clear trend. Firstly, on the whole, the water shortage risk values in Northwest China are increasing year by year, indicating that the water shortage risk is improving. In 2019, approximately 80% of urban areas and 69% of rural areas showed significant improvements in water shortage risk. This shows that under the influence of social adaptability, by increasing the water conservancy infrastructure in agriculture, strengthening management, and improving economic and social capacity, most urban areas in Northwest China are becoming increasingly capable of using water resources in production activities, and the overall level of water shortage risk is being reduced. In terms of the mitigation rates, Yanan, Pingliang, Weinan, Jiuquan, Haixi, Shihezi, and Kizilsu showed faster urban mitigation rates than Hanzhong, Jiayuguan, Zhangye, Yinchuan, Urumqi, Changji, and Aksu. However, the water shortage risk levels in urban and rural areas, which still account for approximately 20% and 31% of the total, respectively, showed a trend of deterioration or slow growth. The affected urban areas are Lanzhou, Jiayuguan, Baiyin, Haidong, and Yushu. The affected rural areas are Tongchuan, Yulin, Ankang, Shangluo, Jinchang, Baiyin, Tianshui, Pingliang, Jiuquan, Qingyang, Dingxi, Linxia, Gannan, Zhongwei, Xining, Haidong, Haibei, Hainan, Shihezi, Turpan, Bayango, and Kizilsu. Furthermore, the difference in water shortage risk values in Northwest China is gradually expanding, and the improvement rate in urban areas is faster than rural areas, which shows that the contradiction of water shortage risk is increasingly prominent, which is in line with China’s national conditions. As shown in Table 3, from 2001 to 2019, the imbalance between urban water shortage risk and rural water shortage risk in Northwest China showed a trend of expansion (decline), while the urban areas showed rapid growth, with growth rates of over 52% in most areas, among which the highest growth rate was 203% for Jiuquan and the highest rate of decline was 20% for Jiayuguan. Rural areas showed slow growth, with the highest growth rate of 30.49% represented by Hanzhong. The rates of decrease were as high as 20.36%. There is still uneven development of the urban and rural water shortage risks, mainly due to the large differences in the levels of economic development and water resources among different regions. Since the implementation of the “western development” strategy, economic development and water resource utilization in the western region have been highly valued by the state. Policies to improve water scarcity are often skewed towards cities, limiting further improvements in rural water scarcity [49]. Urban water shortage risk is developed based on the regional economy and culture and builds on the regional water resource endowment (but is not limited to the regional natural endowment); this process fully absorbs the social and economic development ability of rural areas and leads to the improvement of the urban and rural water shortage risks.

4.2. Types and Stages of Synergy

To accurately grasp the changes in the inequity of the urban and rural water shortage risk development in Northwest China, we selected the years 2001, 2010, and 2019 for analysis (Table 4). We found that the improvement in the balanced development of the urban and rural water shortage risks is gradual and slow. To facilitate policymaking by decisionmakers, the system clustering method in SPSS was used to divide 52 regions in Northwest China into four types: very weak areas, weak areas, medium areas, and strong areas (Figure 1). The synergy level of urban and rural water shortage risks in 2001 was used as a reference point. According to the method of systematic clustering, 21.2% of the regions had a strong synergy level, 75% of the regions had a medium synergy level, 1.9% of the regions had a weak synergy level, and 1.9% of the regions had a very weak synergy level. In 2010, 19.2% of the regions shifted from a strong level to a weak level, while 5.7% of the regions from a medium level to a weak level. In 2019, there were no fundamental changes in the distribution patterns of strong areas and medium areas.

During the period studied, 48% of the regions with a medium level shifted to a weak level, while 19% of the regions with a weak level shifted to a very weak level. This indicates that the urban and rural water risks are decreasing.

4.3. The Spatial Evolution Analysis

For the northwestern area in 2001, 2010, and 2019, the synergy values were obtained using ArcGIS software; the Moran’s I values for the synergy levels of urban and rural areas were 0.1673, 0.1082, and −0.0891, while the normal Z statistics for the Moran’s I values were significant at the 0.01 level (Figure 2). This shows that there was spatial autocorrelation in inequality between 2001 and 2019, in that the regions and autonomous regions with high inequality and those with low inequality tended to cluster together. In other words, the spatial distribution pattern demonstrated that “regions with low synergy level in urban and rural areas are surrounded by regions with high synergy levels of water shortage risk in rural areas” or “regions with high synergy levels in urban and rural areas are surrounded by regions with low synergy levels”. The global Moran’s I value shows a trend of first increasing and then decreasing and reaches its peak in 2010, which indicates that the spatial convergence of the water shortage risk in urban areas in Northwest China first increases and then gradually decreases, while the degree of spatial heterogeneity first decreases and then continuously increases. Further analysis showed that in 2001, the spatial relationship of the synergy level between the urban water shortage risk and rural water shortage risk inequality was L-L type and was mainly concentrated in Western Shaanxi and Western Xinjiang, accounting for 33% of the total number of regions in Northwest China. The regions with H-H were mainly concentrated in Southern Shaanxi, Northern Xinjiang, and Central Gansu, accounting for approximately 29% of the total number of regions in Northwest China. In 2009, the spatial relationship the between urban water shortage risk and rural water shortage risk changed greatly, and the proportion of urban water shortage risk in Northwest China decreased from 29% to 21%. The areas with L-L with a large expansion accounted for 33% to 40%, covering most of Xinjiang and Gansu. The H-L and L-H areas contracted slightly. In 2019, the spatial relationship between the urban water shortage risk and rural water shortage risk changed only slightly. The H-H areas continued to shrink slightly, and the proportion increased from 21% to 19%; the L-L areas moved to the east, and the proportion decreased from 40% to 27%; the H-L areas and the L-H areas expanded slightly. During this period, most areas of Shaanxi continuously represented H-H, while Eastern Gansu and Ningxia developed L-L areas. In general, from 2001 to 2019, the L-L areas between urban water shortage risk and rural water shortage risk in Northwest China showed a shrinking trend, and stable L-L areas were formed in the west. Stable H-H areas were also formed in the west in most parts of Shaanxi, making the distribution pattern at the urban–rural synergy level obvious. The map can be further divided into four regions corresponding to four different types of regional spatial differences (Figure 3).

• (1). The H-H area includes Karamay, Shihezi, Ankang, Baoji, Yanan, Yulin, Hanzhong, Weinan, Xian, and Haixi. The synergy level of the region itself and the surrounding area is relatively high, while the spatial difference between the two is relatively small. The synergy level of urban and rural water shortage risks in H-H areas is much higher than that in the surrounding areas. These areas should be the growth poles of regional water use. However, the intensity and direction of diffusion are different in each region. Southern Shaanxi and other regions that are economically developed play a role in promoting the utilization and development of water resources in surrounding regions due to spillover effects through regional cooperation in various aspects, factor flow, positive transfer, and technology diffusion. Natural disasters are relatively rare, social adaptability is high, and the construction of urban water supply facilities and wastewater treatment facilities is perfect. However, the diffusion effect of Southern Qinghai on the surrounding regional water resource system is relatively weak.

• (2). The H-L area includes Ili, Kashgar, Bayangol, Changji, Longnan Aksu, Lanzhou, Zhangye, Jiayuguan, Qingyang, Guoluo, Xining, Xianyang, Shangluo, Tongchuan, and Yushu. The synergy level in this area is higher, while that of the surrounding area is lower, and the spatial difference between the two is greater. There is a certain gap between H-L regions and the growth pole areas, with a high synergy level between urban and rural water shortage risks. These areas have much room for improvement in water use and are still developing their own energy sources. Therefore, the rapid improvement of their water use does not lead to the improvement of the surrounding areas but shows a certain polarization effect. Diffusion is mainly restricted by the weak attraction ability of the surrounding areas, which to some extent inhibits the rapid development of the water use efficiency in this area. Most of the natural water resources in these areas are in good condition, and their economic conditions are generally good. These districts have a large number of universities and scientific research institutes, with high levels of scientific and technological development and strong social adaptability. The popularization and utilization rates of agricultural water-saving irrigation technology are high, which makes the agricultural water efficiency in this region generally higher than that in other regions. At the same time, the standard rate of industrial sewage treatment and the popularization rate of urban water use in this area are high, indicating that the water supply facilities and wastewater treatment facilities are in good condition. Although natural water resources are under great pressure due to population and economic growth, the local water scarcity situation is alleviated through the adjustment of the economic and social capacity.

• (3). The L-H area includes Jinchang, Pingliang, Wuzhong, Bortala, Urumqi, Kizilsu, Turpan, Hotan, Hami, Tacheng, Altay, and Jiuquan. The region has a low synergy level, but the surrounding areas have relatively high levels, and the spatial difference between them is great. The L-H region itself shows slow improvements in urban and rural water shortage risks and is less affected by regions with better synergy levels. This type of distribution represents a transition from regions with better coordination in water use to regions with lower coordination, and these areas are distributed around regions with better coordination in the development of water use. The natural environment in these areas is dry, with a wide range of areas, serious land desertification, and less man-made damage to the ecological environment. In addition, there is a demand for the domestic and industrial sewage treatment capacity to be improved and facilities to be further improved in these areas. Due to the limited natural water resources and the limited economic and social capacity, affected by the rapidly developing surrounding areas, the economic and social water resources in the neighboring areas have not been maintained. In addition, the economic and social adaptability in these areas is low, and their existing water resources are not fully utilized; thus, these areas are only slightly affected by neighboring areas.

• (4). The L-L area includes Zhongwei, Guyuan, Shizuishan, Yinchuan, Linxia, Tianshui, Dingxi, Wuwei, Gannan, Baiyin, Haidong, Haibei, Hainan, and Huangnan. The water shortage risk levels of the region and the surrounding areas are low, and the spatial difference between the two is relatively small. The L-L type represents a gathering area with a low synergy level between urban and rural water resource utilization and slow improvement. These regions have poor health conditions, poor government, low per capita GDP, and a low level of science and education, which shows that these regions have poor social adaptability. At the same time, these areas are not only farmland and water conservancy facilities, but also lack capital sources for construction, maintenance, and transformation. In terms of the spatial distribution, these areas are mainly concentrated in Central Gansu and Ningxia. The water shortage risk is relatively serious. Due to market regulation, government control, and the lack of natural water resource conditions, these areas have not experienced a fundamental shift, and due to the backward economic and social development levels of the local areas, any abundant water resources are not used efficiently; thus, these are “water-poor areas”. Although the room for improvement is great, the improvement is slow and cannot be achieved in a short time.

5. Conclusions and Discussion

The consideration of water shortage risk, as a tool for understanding the complexity of water issues, demonstrates from a new perspective that improving resilience to water scarcity is an effective strategy for promoting the sustainable use of resources, thereby providing policymakers with an open and transparent systematic approach and a powerful tool for prioritizing the needs of the water sector. This study shows that the situation of water shortage risk in Northwest China is improving year by year, and the improvement rate in urban areas is much faster than that in rural areas. Effective water use requires meaningful self-management, based on clear regulations and supported by local institutions and authorities. The complexity of the overall urban and rural water systems and their linkages to human systems have been highlighted. The findings suggest that targeted policy interventions and management plans are needed to improve the water shortage risk in Northwest China. The water shortage risk can be improved through targeted and operational policies that consider factors such as water rights, water capacity, and economic development. In this way, combining the results from urban and rural areas is more likely to help determine which areas to focus on. These results will further assist policymakers and planners in studying the potential impacts of alternative programs for interventions in these research areas.

It is difficult to change the water shortage situation in the rural areas in Northwest China in a short time, and the regional differences are obvious. Therefore, more attention should be directed towards the improvement of the water shortage risk in rural areas. The government should continue to strengthen its support of irrigation and water conservancy; improve the rural water shortage risk in the areas of lower status; actively adopt advanced water-saving irrigation technology; promote drought-resistant crop varieties; guide the advancement of agricultural water management; and facilitate the flow of talent, technology, and capital to agricultural areas experiencing water shortage risk to promote coordinated development. When formulating policies on regional water shortage risk, it must be considered that the development of urban and rural water shortage risks in each region is affected by the spatiotemporal dependence between neighboring regions to varying degrees. H-H regions are strongly affected by spatial dependence, and we should give full play to the comparative advantages of each city in terms of their endowment of water resources, should encourage the flow of resources between regions, should promote the free flow of agricultural production factors, and should implement differentiated regional poverty reduction cooperation strategies. In L-L regions, which are less affected by the spatial dependence effect, we should strengthen the management of agricultural water resources, improve the water use efficiency, and strengthen the construction of farmland water conservancy to reduce the impacts of drought and other disaster events on the agricultural, industrial, and domestic water use.

There are several limitations to this paper. Firstly, different index selection criteria and weighting methods may produce different results. In future research, there is still a need to use a variety of other indicators, evaluation criteria, and methods and to analyze the robustness and reliability of the results. Secondly, this paper discusses the index selection for urban and rural water shortage risks in Northwest China. However, due to the lack of data for the agricultural labor force resource endowment and the paper’s use of the number of people having attended school instead of otherwise measuring the rural elementary level of education, it is difficult to find a unified and comparative index between urban and rural areas, so this study does not consider water prices or water rights. The process of index selection should involve macro- and micro-level data on the development of other factors of urban and rural water shortage risks for further discussion and the provision of a meaningful research perspective. Finally, based on the framework of the H-D model, this study constructs a dynamic framework for determining the synergy level, which provides a new perspective and content for the research on the relationships and interactions between urban and rural systems. Nevertheless, while the proposed model is novel, other approaches, such as composite system interactions and their effects in space, need to be studied.

Funding

This research was funded by the Social Science Major Project of Shaanxi Province (2021ND0378), China Postdoctoral Science Foundation (2021M692655), Research Funds from Northwest A&F University (Z1090220194), and the Social Science Foundation of the Ministry of Education (21YJC630086).

Institutional Review Board Statement

This study mainly focused on models and data analysis and did not involve human factors considered dangerous. Therefore, ethical review and approval were waived for this study.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the strict management of various data and technical resources within the research teams.

Acknowledgments

We would like to thank MDPI for editing this paper. We would like to thank Liu Wenxin for his valuable suggestions for revising this academic paper. We also appreciate the constructive suggestions and comments regarding the manuscript from the reviewer(s) and editor(s).

Conflicts of Interest

The author declares no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. The types synergy of urban and rural water resources risks.

Figure 2. The GISA of urban and rural water shortage risks in Northwest China.

Figure 3. The LISA of urban and rural water shortage risks in Northwest China.

Figure 3. The LISA of urban and rural water shortage risks in Northwest China.

Table 1

Details of the urban water risk components, indicators, and weights.

Component	Indicators	Data Sources	Weight
Hazardous	Lag rate of urban economy	[17]	0.065
	Population with access to clean water	[18]	0.051
	Coefficient of precipitation variation	[42]	0.072
Exposure	Growth rate with access to clean water supply pipeline	[19]	0.076
	Urban GDP as a proportion of GDP	[43]	0.047
	Per capita water resources	[18]	0.080
	Population density	[22]	0.067
	Sewage treatment	[22]	0.058
Vulnerability	Proportion of urban workers	[16]	0.036
	Sanitary toilet penetration	[19]	0.062
	Per capita water consumption	[7]	0.043
	Water consumption per unit of added industrial value	[44]	0.033
Adaptive	Volume of wastewater per 10,000 yuan	[44]	0.070
	Fiscal self-sufficiency of the government	[19]	0.064
	Per capita vegetation coverage	[18]	0.070
	Cumulative benefit rate of water reform	[43]	0.048
	Relief money for urban disasters	[25]	0.057

Table 2

Details of the rural water risk components, indicators, and weights.

Component	Indicators	Data Sources	Weight
Hazardous	Lag rate of agricultural economy	[17]	0.066
	Crop disaster area	[17]	0.054
	Intensity of pesticide application	[19]	0.048
	Coefficient of precipitation variation	[42]	0.073
Exposure	The actual irrigation capacity	[19]	0.077
	Agricultural GDP as a proportion of GDP	[43]	0.048
	Per capita water resources	[18]	0.081
	Agricultural population density	[22]	0.068
Vulnerability	Proportion of rural workers	[16]	0.037
	Sanitary toilet penetration	[19]	0.063
	Per capita water consumption	[7]	0.044
	Water consumption per unit of added agricultural value	[44]	0.034
Adaptive	Soil erosion control area	[44]	0.079
	Drainage area of farmland	[45]	0.056
	Fiscal self-sufficiency of the government	[19]	0.065
	Expenditure on agriculture and water resources	[43]	0.049
	Reduction of water consumption per ten thousand yuan	[25]	0.058

Table 3

Urban and rural water shortage risk values in northwest China from 2001 to 2019.

U/R	2001	2004	2007	2010	2013	2016	2019
Xian	0.398/0.286	0.438/0.301	0.482/0.338	0.516/0.378	0.571/0.377	0.608/0.425	0.655/0.458
Tongchuan	0.337/0.267	0.373/0.278	0.428/0.316	0.464/0.357	0.508/0.360	0.539/0.394	0.563/0.426
Baoji	0.341/0.288	0.385/0.299	0.435/0.347	0.465/0.372	0.486/0.372	0.526/0.415	0.581/0.455
Xianyang	0.366/0.284	0.406/0.300	0.454/0.335	0.474/0.371	0.515/0.370	0.558/0.413	0.600/0.448
Weinan	0.338/0.291	0.376/0.302	0.415/0.353	0.431/0.377	0.479/0.379	0.517/0.418	0.570/0.453
Yanan	0.306/0.332	0.335/0.344	0.401/0.389	0.434/0.433	0.468/0.433	0.504/0.468	0.563/0.511
Hanzhong	0.298/0.286	0.337/0.294	0.387/0.340	0.434/0.389	0.491/0.396	0.511/0.429	0.566/0.475
Yulin	0.344/0.300	0.366/0.305	0.403/0.346	0.440/0.385	0.484/0.390	0.555/0.429	0.568/0.449
Ankang	0.358/0.320	0.373/0.320	0.446/0.371	0.451/0.386	0.488/0.391	0.542/0.428	0.586/0.460
Shangluo	0.317/0.326	0.348/0.337	0.404/0.388	0.423/0.400	0.444/0.393	0.494/0.439	0.542/0.470
Lanzhou	0.403/0.273	0.341/0.279	0.412/0.325	0.411/0.363	0.465/0.371	0.514/0.407	0.557/0.443
Jiayuguan	0.492/0.296	0.518/0.298	0.573/0.565	0.471/0.353	0.540/0.418	0.566/0.453	0.592/0.487
Jinchang	0.359/0.258	0.366/0.262	0.415/0.323	0.438/0.355	0.421/0.356	0.480/0.393	0.516/0.411
Baiyin	0.348/0.262	0.316/0.266	0.367/0.314	0.385/0.347	0.453/0.354	0.463/0.390	0.491/0.407
Tianshui	0.349/0.256	0.323/0.267	0.370/0.316	0.389/0.342	0.440/0.348	0.475/0.379	0.511/0.412
Wuwei	0.288/0.266	0.296/0.272	0.357/0.321	0.353/0.358	0.426/0.364	0.446/0.399	0.479/0.435
Zhangye	0.294/0.265	0.304/0.272	0.351/0.321	0.360/0.366	0.412/0.366	0.463/0.406	0.489/0.450
Pingliang	0.260/0.270	0.336/0.280	0.364/0.329	0.381/0.357	0.445/0.361	0.470/0.397	0.507/0.428
Jiuquan	0.202/0.294	0.311/0.298	0.361/0.355	0.357/0.368	0.440/0.375	0.463/0.414	0.494/0.444
Qingyang	0.286/0.300	0.306/0.307	0.354/0.360	0.372/0.367	0.443/0.372	0.457/0.405	0.497/0.442
Dingxi	0.280/0.248	0.295/0.257	0.335/0.298	0.368/0.330	0.425/0.338	0.443/0.368	0.477/0.406
Longnan	0.293/0.258	0.295/0.264	0.338/0.308	0.357/0.345	0.404/0.345	0.445/0.381	0.481/0.427
Linxia	0.284/0.254	0.300/0.260	0.365/0.305	0.356/0.337	0.404/0.343	0.423/0.370	0.473/0.406
Gannan	0.287/0.269	0.291/0.266	0.390/0.334	0.362/0.347	0.408/0.358	0.433/0.390	0.469/0.421
Yinchuan	0.342/0.253	0.337/0.260	0.401/0.319	0.425/0.349	0.465/0.359	0.507/0.399	0.547/0.432
Shizuishan	0.317/0.266	0.324/0.271	0.405/0.323	0.433/0.359	0.482/0.368	0.512/0.406	0.538/0.433
Wuzhong	0.269/0.265	0.291/0.266	0.344/0.314	0.358/0.361	0.423/0.362	0.456/0.399	0.493/0.432
Guyuan	0.285/0.267	0.319/0.276	0.381/0.328	0.395/0.355	0.450/0.360	0.475/0.396	0.507/0.429
Zhongwei	0.268/0.290	0.275/0.298	0.342/0.351	0.356/0.361	0.411/0.368	0.459/0.404	0.477/0.436
Xining	0.323/0.302	0.353/0.310	0.400/0.290	0.388/0.298	0.475/0.380	0.482/0.415	0.522/0.451
Haidong	0.356/0.249	0.396/0.258	0.424/0.295	0.438/0.345	0.414/0.344	0.435/0.377	0.466/0.394
Haibei	0.352/0.269	0.361/0.270	0.422/0.339	0.421/0.352	0.459/0.362	0.495/0.401	0.537/0.419
Huangnan	0.378/0.253	0.384/0.255	0.424/0.311	0.442/0.343	0.469/0.345	0.483/0.381	0.545/0.413
Hainan	0.324/0.275	0.333/0.276	0.367/0.321	0.384/0.363	0.420/0.362	0.465/0.400	0.505/0.437
Guoluo	0.412/0.282	0.413/0.281	0.454/0.325	0.504/0.379	0.541/0.375	0.553/0.407	0.600/0.452
Yushu	0.441/0.270	0.434/0.271	0.463/0.328	0.505/0.370	0.478/0.365	0.506/0.381	0.591/0.435
Haixi	0.283/0.297	0.328/0.301	0.427/0.356	0.447/0.387	0.484/0.401	0.503/0.436	0.548/0.473
Urumqi	0.364/0.249	0.368/0.253	0.419/0.304	0.461/0.349	0.522/0.353	0.597/0.401	0.636/0.429
Karamay	0.410/0.304	0.399//0.310	0.419/0.325	0.468/0.365	0.525/0.377	0.593/0.422	0.627/0.455
Shihezi	0.262/0.331	0.278/0.300	0.364/0.343	0.371/0.373	0.416/0.370	0.501/0.413	0.551/0.446
Turpan	0.313/0.484	0.326/0.266	0.413/0.328	0.427/0.334	0.491/0.334	0.557/0.372	0.568/0.401
Hami	0.284/0.261	0.291/0.260	0.354/0.301	0.381/0.335	0.436/0.342	0.489/0.386	0.511/0.413
Changji	0.294/0.280	0.307/0.286	0.379/0.331	0.361/0.383	0.408/0.378	0.457/0.420	0.518/0.466
Ili	0.304/0.269	0.309/0.272	0.354/0.323	0.378/0.365	0.421/0.373	0.455/0.407	0.506/0.446
Tacheng	0.297/0.293	0.297/0.296	0.366/0.347	0.347/0.377	0.390/0.371	0.449/0.427	0.476/0.459
Altay	0.274/0.284	0.274/0.267	0.369/0.317	0.350/0.355	0.383/0.357	0.451/0.412	0.493/0.441
Bortala	0.287/0.263	0.306/0.269	0.353/0.305	0.369/0.341	0.422/0.346	0.474/0.386	0.554/0.427
Bayangol	0.311/0.325	0.322/0.291	0.375/0.310	0.384/0.361	0.443/0.384	0.498/0.411	0.540/0.448
Aksu	0.287/0.256	0.300/0.261	0.374/0.314	0.352/0.350	0.406/0.355	0.452/0.408	0.484/0.435
Kizilsu	0.276/0.380	0.291/0.398	0.374/0.360	0.395/0.389	0.464/0.419	0.520/0.371	0.620/0.402
Kashgar	0.258/0.284	0.276/0.317	0.373/0.341	0.353/0.366	0.399/0.368	0.443/0.417	0.485/0.449
Hotan	0.226/0.264	0.239/0.265	0.338/0.307	0.332/0.339	0.388/0.349	0.423/0.390	0.463/0.430

Table 4

Synergy levels of urban and rural water shortage risks from 2001 to 2019.

Region	2001	2003	2005	2007	2009	2011	2013	2015	2017	2019
Xian	0.557	0.552	0.553	0.592	0.589	0.603	0.599	0.618	0.629	0.638
Tongchuan	0.542	0.536	0.535	0.582	0.578	0.605	0.606	0.630	0.632	0.657
Baoji	0.561	0.563	0.555	0.612	0.598	0.621	0.627	0.662	0.660	0.683
Xianyang	0.557	0.554	0.556	0.596	0.591	0.614	0.614	0.646	0.643	0.662
Weinan	0.564	0.561	0.559	0.623	0.608	0.641	0.636	0.672	0.668	0.687
Yanan	0.565	0.594	0.595	0.667	0.605	0.702	0.706	0.745	0.743	0.771
Hanzhong	0.558	0.577	0.567	0.612	0.676	0.643	0.652	0.676	0.688	0.717
Yulin	0.574	0.558	0.565	0.617	0.634	0.657	0.647	0.667	0.672	0.683
Ankang	0.593	0.578	0.567	0.636	0.634	0.645	0.647	0.672	0.666	0.685
Shangluo	0.586	0.591	0.583	0.666	0.610	0.660	0.662	0.709	0.706	0.724
Lanzhou	0.544	0.551	0.547	0.594	0.594	0.632	0.632	0.654	0.660	0.680
Jiayuguan	0.550	0.545	0.569	0.857	0.610	0.602	0.658	0.681	0.708	0.719
Jinchang	0.534	0.533	0.535	0.591	0.583	0.611	0.625	0.661	0.652	0.659
Baiyin	0.537	0.539	0.533	0.587	0.587	0.618	0.616	0.652	0.659	0.664
Tianshui	0.532	0.541	0.527	0.589	0.583	0.609	0.612	0.639	0.637	0.663
Wuwei	0.537	0.545	0.543	0.595	0.605	0.633	0.633	0.670	0.671	0.704
Zhangye	0.537	0.546	0.543	0.595	0.609	0.637	0.638	0.670	0.676	0.720
Pingliang	0.521	0.554	0.539	0.603	0.601	0.632	0.626	0.653	0.657	0.684
Jiuquan	0.417	0.571	0.554	0.632	0.604	0.645	0.642	0.673	0.685	0.711
Qingyang	0.549	0.576	0.555	0.628	0.596	0.642	0.638	0.672	0.674	0.707
Dingxi	0.519	0.530	0.520	0.571	0.576	0.601	0.605	0.634	0.634	0.668
Longnan	0.530	0.536	0.525	0.582	0.583	0.615	0.616	0.648	0.647	0.693
Linxia	0.525	0.532	0.527	0.577	0.582	0.610	0.614	0.644	0.644	0.670
Gannan	0.540	0.537	0.541	0.605	0.593	0.624	0.629	0.664	0.666	0.690
Yinchuan	0.529	0.537	0.532	0.589	0.582	0.616	0.619	0.667	0.650	0.671
Shizuishan	0.540	0.545	0.544	0.592	0.599	0.615	0.624	0.653	0.656	0.678
Wuzhong	0.533	0.537	0.537	0.588	0.604	0.630	0.631	0.666	0.670	0.695
Guyuan	0.538	0.540	0.538	0.600	0.596	0.623	0.624	0.662	0.661	0.685
Zhongwei	0.525	0.549	0.541	0.613	0.597	0.635	0.640	0.678	0.680	0.706
Xining	0.575	0.576	0.567	0.560	0.529	0.636	0.638	0.682	0.683	0.707
Haidong	0.526	0.525	0.529	0.563	0.573	0.598	0.613	0.649	0.652	0.658
Haibei	0.543	0.542	0.542	0.607	0.589	0.621	0.623	0.660	0.659	0.661
Huangnan	0.528	0.526	0.528	0.578	0.574	0.599	0.603	0.637	0.641	0.650
Hainan	0.549	0.545	0.550	0.594	0.604	0.630	0.632	0.667	0.670	0.697
Guoluo	0.552	0.546	0.548	0.586	0.598	0.605	0.610	0.638	0.641	0.667
Yushu	0.537	0.540	0.537	0.587	0.586	0.604	0.621	0.640	0.638	0.653
Haixi	0.545	0.576	0.558	0.624	0.604	0.612	0.660	0.777	0.698	0.724
Urumqi	0.525	0.528	0.531	0.571	0.571	0.598	0.594	0.617	0.612	0.619
Karamay	0.573	0.580	0.564	0.592	0.589	0.618	0.619	0.643	0.641	0.654
Shihezi	0.497	0.492	0.561	0.618	0.610	0.649	0.642	0.670	0.675	0.687
Turpan	0.516	0.535	0.539	0.596	0.570	0.590	0.586	0.599	0.598	0.626
Hami	0.532	0.528	0.529	0.575	0.579	0.605	0.607	0.638	0.643	0.665
Changji	0.551	0.554	0.554	0.603	0.605	0.672	0.652	0.695	0.709	0.730
Ili	0.542	0.547	0.553	0.597	0.597	0.645	0.644	0.684	0.695	0.709
Tacheng	0.565	0.569	0.544	0.623	0.605	0.669	0.647	0.698	0.715	0.739
Altay	0.537	0.534	0.524	0.590	0.589	0.635	0.632	0.678	0.685	0.707
Bortala	0.534	0.542	0.532	0.578	0.577	0.613	0.613	0.648	0.639	0.662
Bayangol	0.577	0.557	0.531	0.582	0.590	0.634	0.652	0.660	0.657	0.695
Aksu	0.528	0.530	0.531	0.586	0.588	0.633	0.626	0.675	0.671	0.702
Kizilsu	0.499	0.507	0.465	0.637	0.630	0.672	0.688	0.614	0.619	0.600
Kashgar	0.511	0.512	0.521	0.615	0.608	0.648	0.642	0.689	0.712	0.721
Hotan	0.467	0.477	0.483	0.581	0.579	0.616	0.623	0.660	0.673	0.703

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