1. Introduction
Ecosystems provide basic and necessary services for human survival and social functioning, namely ecosystem services (ESs). ESs are the continuous provision of ecosystem goods and services by ecosystems and their ecological processes [
1]. However, factors such as population growth, industrialization and urbanization have led to a rapid increase in the demand for ESs, and ecosystems are facing unprecedented pressure [
2]. A comprehensive and reasonable quantitative assessment of ecosystem services value (ESV) is necessary to alleviate the contradiction between supply and demand of ESs, manage effectively and formulate relevant policies. Water resources are an essential part of ecosystems. Whereas, due to the existence of water pollution, waste of water resources and climate change, the water ecosystem is facing greater pressure than other types of ecosystems. Therefore, it is crucial to study the water ecosystem services value (WESV) and the various factors that affect WESV.
Monetization of ecosystem service value is the most recognized and practical form of ESV, and the calculation methods can be divided into two categories. One is to adopt the relevant methods of traditional ecological economics or environmental economics. Most of these methods obtain the output of physical quantity based on statistical data or ecological model, and then they calculate the value of ecosystem services by combining the market value method, willingness survey method (CV) and revealed preference method. This kind of method has high requirements on data, parameters, model accuracy and method applicability, etc. Specific to the value of water resources ecosystem services, this kind of method is suitable for calculating the value of water resources with commodity attributes. The other is the equivalence factor method, which constructs the economic value equivalent per unit area of different ecosystems based on the division of ecosystem service functions and quantifies ESV in combination with the distribution of ecosystems. Compared with traditional methods, the equivalence factor method requires relatively fewer data, and the evaluation of ESVs is more comprehensive. Costanza et al. divided the global ESs into 17 species and calculated that the ESV of 16 biomes in the world was US
$33 trillion per year [
3], and rivers or lakes were one of the 16 biomes. Meanwhile, another contribution of this study is to provide the equivalent table of services value per unit area of 17 ecosystems in each biome at the global scale, which provides a reference for future research. In 2003, the Millennium Ecosystem Assessment (MA) conducted by the United Nations classified global ESs into four primary categories, namely Provisioning Services, Regulating Services, Supporting Services, and Cultural Services [
4]. At the regional scale, Xie et al. improved Costanza’s research and obtained the equivalent factor of ecosystem service value suitable for China. Then, the ESV can be calculated combined with the area of each ecosystem. This study was accepted by a large number of Chinese scholars [
5,
6]. Whether it is a global scale or a regional scale, in the study of using the equivalent factor method to evaluate ecosystems, most of the rivers/lakes or waters are divided into one type of ecosystem for discussion. However, this method does not clarify the service value of groundwater, which is an important component of water resources. The most direct service value of groundwater is that it provides a part of water for production and domestic use, accounting for only 70% of the total global water use for agriculture, and 40% of agricultural water comes from groundwater [
7]. Therefore, it is more comprehensive and accurate to combine the two methods to assess the WESV including surface water and groundwater.
It is not the ultimate goal of scholars to study the value of WESV; the more important goal is to study the relationship between WESV and the interaction of various factors. In order to deal with more severe water and environmental problems, exploring the influence of various factors on WESV has gradually become one of the research hotspots. Due to the social nature of water resources, social and economic factors have become one of the components that affect WESV. The characteristics of cities, populations, communities, and cultures [
8] all have an impact on water resources and water ecology [
9,
10]. The urbanization level is a concentrated expression of the social and economic development degree, which profoundly affects the spatial distribution and potential functions of ecosystem services [
11]. Many researchers have conducted related research in North China [
12,
13], Yangtze River Delta [
14], Pearl River Delta [
15], Southwest Mountainous [
16,
17] and Northwest arid regions of China [
18,
19]. WESV also has a significant response to changes in natural factors. Climate conditions [
20,
21,
22], ecosystem types [
23], and environmental quality [
24] are the main influencing factors. Meanwhile, the coupling of many factors, such as nature, social economy and human activities, has a more realistic impact on WESV [
25,
26]. Many of the above studies have fully considered various factors and provided important references for explaining the changes caused by EVS or WESV. Unfortunately, the collinearity among some influencing factors and the nonstationarity in space have not attracted enough attention.
However, EVS exhibit spatial heterogeneity and spatial dependence with changes in geographic space due to differences in the socioeconomic development degree, natural resources, and geographic environment. Thus, incorporating geospatial aspects into the research scope is the key to addressing spatial heterogeneity. Geographically Weighted Regression (GWR) model is an effective tool for dealing with spatial heterogeneity, which is improved on the basis of ordinary least square [
27]. The model incorporates the spatial location information as a coefficient into the regression equation and explores to eliminate the nonstationarity caused by spatial changes based on the fitted values of geographic element parameters [
28]. The GWR model has been widely used in the fields of natural resources and ecological environment [
29]. The water footprint has been extensively researched, including concepts, methods and applications, for better management of water resources and water ecology [
30,
31]. With the deterioration of ecological environment problems, it is necessary to analyze the evolution of ecological footprint and the spatial differences of influencing factors from the perspective of spatial heterogeneity [
29]. In the related research on land use variation and ESV, the GWR model is used to solve the problem of spatial heterogeneity and compare with the OLS model [
32]. In coastal counties of Mississippi and Alabama (U.S.), GWR was used in the estimation of the monetary value of distance to different waterfront types, in the extension to a traditional hedonic pricing method, and in analyzing the value of ecosystem services associated with waterfronts differed geospatially [
33]. However, few researches utilize the GWR model to study WESV, which becomes the main content of this study.
In arid and semi-arid regions, water resources are very precious, which means that the ecological services provided by water resources play a vital role. Therefore, on the basis of accurately measuring the value of water resources ecosystem services, analyzing the impact of various factors on WESV is of great significance to effectively manage water resources and alleviate the contradiction between supply and demand of water resources ecosystem services.
Many studies have been conducted on the value of ecological services and their influencing factors. However, this study is more relevant. Specifically, the ecological service value of water resources is the object of this study. In addition, the scope of water resources is broader to include groundwater and surface water. This study serves the ecological compensation policy in China.
In this study, the typical area in the central Loess Plateau of China was taken as the research object, and the evaluation of water resources ecosystem service value and the analysis of the temporal and spatial changes of the influencing factors were carried out. The main works are as follows:
- (1)
Combining the environmental economics method and the equivalent factor method, the WESVs of 25 counties (districts) in the study area including groundwater and surface water in 2010, 2015 and 2020 were calculated, and the distribution characteristics were analyzed;
- (2)
Selecting the representative factors of nature, economy and society, and the applicability of OLS model and GWR model in studying the impact of each indicator on WESV was analyzed;
- (3)
Using the more applicable GWR model, the spatial heterogeneity and spatial and temporal distribution of the effects of various factors on WESV were shown.
4. Discussion
4.1. Necessity to Assessing WESV
The significance of evaluating the value of ecosystem services is to better manage the ecological environment and natural resources to achieve sustainable development. The water issue is prominent today, so it is more practical to discuss the value of water resources ecosystem services. In China, the value of ecosystem services is always in the form of the upper limit of the ecological protection compensation standard. The water ecosystem services value is often calculated as part of a comprehensive ecosystem service value and is rarely discussed in isolation. Even in the basin ecological compensation, only the fluctuation of the direct use value caused by the change of water quantity is calculated, and how the ecosystem service value of water resources including groundwater changes is not fully explored. In 2019, the ecological protection and high-quality development of the Yellow River Basin was established as one of China’s major national strategies. Ecological compensation is one of the key tasks of the strategy. The Loess Plateau, located in the middle reaches of the Yellow River, is the main source of sediment in the Yellow River. The region has scarce water resources, a large population, and an urgent need for development. Hence, from the perspective of the integrity of water resources, this study selects typical regions to assess WESV and analyzes the temporal and spatial variation characteristics of WESV, which can provide a basis for the ecological compensation development.
4.2. The Spatiotemporal Distribution of WESV Response to Different Influencing Factors
Previous studies have shown that the value of water resources is affected by multiple factors, such as water quantity, water quality, use of water resources, economic development, and educational level of residents [
59,
60].
With the deepening of relevant research, the water resources value has been extended to the value of ecosystem services that water resources can provide for human well-being. Correspondingly, WSVE is also affected by economic, social, natural and cultural factors. Many researches have explored the mechanism by which ESV is affected by various driving factors, including land use change, socioeconomic development indicators, and human acceptance willingness. Furthermore, the distribution characteristics of the sensitivity of ESV to various influencing factors at different temporal and spatial scales were obtained. WESV is an important part of ESV and is also affected by various factors. As mentioned above, the extent to which WESV is affected by external factors should be studied separately.
With the rapid economic development and the sharp expansion of cities, the differences between different regions in natural resources, economy, society and culture are gradually increasing. In this study, the central part of the Loess Plateau was selected as the research area, and four main influencing factors were selected. The GWR model was used to analyze the influence of per capita GDP, population density, the proportion of water areas, and water consumption on WESV in different regions.
Per Capita GDP is an important parameter to measure the development degree of a region. The rise in prices and the massive consumption of water resources brought about by economic development will lead to an increase in WESV. Meanwhile, WESV will greatly decreased because of the ecological degradation caused by development, that is, the reduction in water area. Therefore, the relationship between water conservation and economic development should be balanced. The effect of per capita GDP on ESV was confirmed by Song F. [
61] in a study on the value of wetland ecosystem services. Dai X. concluded that per capita GDP was negatively correlated with ESV in Chengdu [
62]. As can be seen from
Figure 4, per capita GDP showed time instability. The study area has undergone a process of environmental damage and recovery during a 10-year development period, which is consistent with the process in China. During 2015–2020, the construction of ecological civilization has been raised to a new level. At last, a pattern in which the degree of economic development roughly matches WESV is formed. Many studies have found that per capita GDP has an unstable impact on ESV, by influencing the level of awareness, willingness to protect the environment, and investment in environmental protection [
63,
64,
65].
It is generally believed that the increase in population density is accompanied by the expansion of human activity areas, which will encroach on other land-use types. The water area is also experiencing depletion of rivers and lakes due to the massive depletion of surface water while the water area was occupied. Under the dual effect of the two, WESV is bound to decrease, which also explains the negative correlation between population density and WESV. It was confirmed in Chen Y.’s study of the relationship between population density and agro-ecosystem services [
66]. Jiang Z. also obtained a consistent conclusion in his study of the South Four Lakes that population density was negatively correlated with Esv [
67]. In recent years, the population growth rate in the study area has slowed down as in the whole country. WESV is less sensitive to population density.
In the same region, WESV will increase with the increase in water area, which can also be explained by the calculation method adopted in this study. The equivalent factor method is to calculate the corresponding ESV according to the type of land use.
Figure 6 showed that the proportion of water area has the greatest influence on WESV. During the initial stage of the study, the drier regions in the north were more sensitive. With the comprehensive management of the Mu Us Desert, the environment in the northern region has been gradually improved, and the scarcity of water resources and the vulnerability of the water environment have been alleviated. The center of influence then transferred to the west, where the pace of governance was relatively slow.
In 2010 and 2015, the impact of water consumption on WESV had a certain regularity in space, but the spatial instability that occurs cannot be ignored.
Figure 7a,b can well demonstrate this phenomenon. From 2015 to 2020, the state strictly controlled the regional water consumption, and the strictest water resources management system was successfully implemented locally [
68], which well explained the phenomenon that the spatial distribution pattern in 2020 was more regular.
There is spatial heterogeneity in the sensitivity of WESV to explanatory variables. Using the GWR model can more clearly show the spatial pattern of the influence degree and its evolution trend over time.
4.3. Limitations
Based on the water area obtained from remote sensing image statistics and the water consumption obtained from statistical data, this study proposed a method to quickly calculate WESV and analyze the temporal and spatial distribution of driving factors. However, the impact of water quality on WESV does not depend on water consumption or water area. Deterioration of water quality not only reduces the quality of services provided by the water resource but also incurs additional remediation costs [
69]. This study did not include water quality as the basic data in the WESV assessment for three reasons. First, different types of WESVs have different sensitivity to water quality, and its mechanism of action and degree of influence are not clear, which will cause uncertainty in the calculation of WESV. Second, the equivalent factor method used in this paper is calculated from the ecological service value and the economic value of grain, and the output of grain is affected by water quality. That is to say, this study considered the impact of water quality on WESV to a certain extent. Third, the spatial and temporal scales of this study determined that water quality will not have an essential impact on WESV. In space, the county-level administrative region is the smallest research unit; in terms of time, a year is the minimum span of time. From this point of view, the water quality in the region is relatively stable, and this is also verified by the water quality data released by the environmental department. Therefore, the WESV calculated in this study is still representative and reliable without considering water quality.