Spatio-Temporal Variations of Soil Conservation Service Supply–Demand Balance in the Qinling Mountains, China

Abstract

The ecological conservation of nature reserves has garnered considerable attention and is subject to stringent management in China. However, the majority of these areas have a history of underdeveloped economies and require urgent improvements in the well-being of local communities. Effectively coupling and harmonizing the dynamic relationship between ecosystem services and socio-economic development has emerged as a crucial concern for nature reserves. Therefore, further exploration is needed to achieve a spatio-temporal balance and alignment between the supply and demand of ESs in nature reserves in China. Utilizing multiple datasets, RULSE, and bivariate autocorrelation methods, this study investigated the spatio-temporal evolution of the ecosystem services supply–demand ratio (ESDR) and supply–demand spatial matches for soil conservation services (SCSs) in the Qinling Mountains (QMs) from 2000 to 2020. The results indicated the following: (1) Over the years, the supply of SCSs exhibited a consistently high level, with an upward trend observed in 63.10% of the QMs, while the demand for SCSs is generally low, with a decreasing trend observed in 82.68% of the QMs. (2) The supply and demand of SCSs remained favorable, with a positive ESDR reaching 82.19% of the QMs. From 2000 to 2010, there was a significant decline in ESDR; however, a substantial rebound was observed in the ESDR across the region from 2010 to 2020. (3) Over years, the majority of counties and districts exhibited positive values in ESDR. When examining cities, Weinan, Xi’an, and Ankang demonstrated relatively consistent patterns with higher ESDR values over time. In 2000, the ESDR on the northern slope exhibited lower values than that on the southern slope; however, this situation subsequently underwent a reversal. (4) The spatial distribution of SCS supply and demand was predominantly characterized by matching regions exhibiting either High Supply–High Demand or Low Supply–Low Demand for years. This study suggests that the supply and demand dynamics of SCSs in the Qinling Mountains have been favorable in recent years, with consistent spatial supply–demand matches. These findings can provide valuable insights for similar nature reserves aiming to implement ecological environmental protection and achieve sustainable development. The future research endeavors, however, should strive to expand upon these findings by exploring the supply and demand patterns associated with other ESs across diverse nature reserves, while considering their unique geographical characteristics, in order to promote more rational ecological management strategies.

1. Introduction

Ecosystem services (ESs) serve as a crucial intermediary between ecosystems and human well-being, encompassing the tangible products and intangible benefits that ecosystems offer to humans, along with the reciprocal impacts of human society’s utilization of these ecosystem goods and services [1,2]. This process encompasses the production, distribution, and consumption of ESs, as well as the supply–demand relationship associated with these services [3,4]. The supply of ESs represents the capacity of ecosystems to provide goods and services to human society, while the demand for ecosystem services reflects the amount of ecological products and services that humans desire [5,6,7] or are actually consumed [4,8,9]. Currently, within the context of global climate extremes, unprecedented anthropogenic activity intensity, escalating risks of biodiversity loss, and the disparity between supply and demand for regional ESs continue to be exacerbated [10,11,12,13]. When utilizing ecosystem services within human society, the decline in their supply capacity will exert a significant impact on the stability of regional socio-economic development, while the surge in demand for ESs may also disrupt both the supply capacity of ESs and the stability of ecosystems [14,15]. Therefore, it is crucial to evaluate the supply and demand status of regional ESs, identify spatial balance patterns, and, subsequently, propose effective ecological management measures to foster regional sustainable development [16,17].

After years of continuous development and dedicated efforts, scholars from various countries have conducted extensive and in-depth research on the supply and demand of ESs across various spatial scales, such as continents and transnational regions [4,18], countries [19,20], provinces [21,22], cities, districts and counties [7,23,24,25,26,27,28], agricultural [29] and watershed regions [30,31,32]. The primary focus of scholarly research lies in investigating the regulating, supporting, and provisioning of services, with particular emphasis placed on soil conservation services, water yield services, carbon sequestration services, habitat quality, and grain productions [4,6,28,33]. Additionally, some scholars have also examined the evolutionary aspects pertaining to the supply and demand of cultural services [34,35,36]. In terms of research methodology, during the initial stages of their study, Burkhard et al. employed expert experience and knowledge to construct a comprehensive matrix depicting the supply and demand of ecosystem services across various land use types, garnering significant attention from scholars [8]. Secondly, the unit area value methods proposed by Costanza et al. [1] and the equivalent factors for different land use types developed by Xie et al. [37] are exemplary in assessing ecosystem values. However, there is still a relative scarcity of research on service demands utilizing these methods [3]. Thirdly, models such as the Invest [32,38], ARIES [39], and RUSLE [16] models have gained widespread recognition for their robust support of processes and mechanisms. Moreover, the continuous advancements in remote sensing data and the integration of socio-economic data into raster format in recent years have significantly bolstered the credibility and popularity of model research as a commonly employed methodology [17,40,41].

In terms of research content, the consistent focal point has been on quantifying and mapping ecosystem supply and demand [3,8]. However, a comprehensive understanding of ES supply and demand ultimately requires a quantitative comparison, as well as an evaluation of the spatial matches between supply and demand [28]. The mutual relationship and dynamic changes between the ES supply and demand constitute crucial aspects in regional ecological risk assessment. The spatial and temporal dynamics of the supply–demand relationship in ESs necessitate a rational evaluation and monitoring to effectively prevent ecological risks and degradation resulting from imbalances [17]. Consequently, scholars have proposed various indices, such as the supply:demand ratios (S:D ratios) [42], the supply–demand relationship (SDR) [43], and the ecosystem services supply–demand ratio (ESDR) [44], to quantitatively evaluate the magnitude and extent of imbalances between the supply and demand in both temporal and spatial dimensions. In comparison to evaluation methods such as map overlay [45], these approaches offer enhanced operability, dimensionless analysis, and improved capability in identifying dynamic changes in supply–demand imbalances. For instance, Liang-Jie Wang et al. [33] conducted a quantitative assessment of the supply and demand of ESs in Zhejiang Province, China from 2000 to 2020 at both pixel and county scales using the supply–demand ratio index.

In conclusion, previous studies have demonstrated that the utilization of models such as Invest and RULSE for assessing supply and demand is a widely embraced approach in ESs, and they are characterized by well-defined mechanisms and accurate research findings. In terms of achieving a supply–demand balance, the ESDR model enables effective quantitative evaluation of ES supply–demand disparities at different temporal and spatial scales, making it one of the most frequently employed assessment methods due to its operational simplicity. However, it is worth noting that previous studies on the ESDR have primarily focused on examining the temporal and spatial scales of supply–demand balance, while overlooking the crucial aspects related to spatial agglomeration and matching characteristics between supply and demand. The spatial autocorrelation method is a crucial approach for examining the spatial clustering characteristics of geographical phenomena. Based on this, the bivariate spatial autocorrelation method can effectively explore the spatial clustering and matching features of ES supply and demand [6]. Thus, the investigation of spatial matches between supply and demand through bivariate spatial autocorrelation methods deserves greater attention in ES studies.

Furthermore, there remains a lack of research concerning the balances between ES supply and demand in nature reserves. Soil conservation services (SCSs) play a crucial role as ecosystem regulation services in mountainous natural reserves. Soil erosion, as influenced by external forces, can be categorized into hydraulic erosion, gravitational erosion, and wind erosion [46,47,48]. Therefore, SCSs represent the regulatory capacity of ecosystems in controlling soil erosion under the influence of water flow, gravity, wind, and other factors. The Qinling Mountains (QMs), as a nationally significant mountainous nature reserve in China, play a pivotal role as an ecological barrier for the country. The ecological environment quality of the QMs not only profoundly impacts regional sustainable development, but also holds implications for the high-quality economic and social progress of the entire nation [49]. With the growing national attention to ecological conservation, it has become an urgent issue for natural reserves such as the QMs to scientifically assess the relationship between the supply and demand of ESs, while concurrently fostering a harmonious equilibrium between ecological preservation and developmental activities in the region [49,50].

Therefore, selecting the QMs as a case study area, employing the ecological mechanism model and utilizing mathematical and statistical methods such as the ESDR and bivariate autocorrelation, this study can quantitatively analyze the temporal and spatial balances and matches between the supply and demand from 2000 to 2020. Specifically, the objectives of this study were to achieve the following: (1) analyze the spatial patterns and trends of supply and demand for the SCSs within the region through applying the RUSLE model; (2) conduct an analysis on the spatial patterns and trends of the balance between the supply and demand of SCSs at pixel, district, county, and prefecture-level city scales through employing the ESDR index; and (3) investigate the spatial matching status of the supply and demand of SCSs at the regional, district, and county scales using the bivariate spatial autocorrelation method. The findings of this study can serve as valuable scientific references for optimizing ecosystem service management, promoting socio-economic sustainable development, and facilitating other fundamental work within the QMs and similar regions.

2. Materials and Methods

2.1. Study Area

The Qinling Mountains (QMs), in conjunction with the Huaihe River to its east, serve as the north–south demarcation line in China for a multitude of geographical features [50,51], encompassing climate, hydrology, soil composition, biodiversity, etc. Situated at the heart of China, the QMs serve as a vital strategic barrier within central China, providing essential ESs such as water conservation, soil retention, biodiversity preservation, and climate regulation. Revered as China’s “Central Water Tower” and “Central National Park”, the QMs play an indispensable role in maintaining regional stability and safeguarding China’s ecological environment, thus holding immense strategic significance in the country’s construction of ecological civilization [52,53].

The QMs, typically categorized into a broader and narrower sense, primarily denote the Qinling Mountains in Shaanxi Province in the narrower sense [49,54]. This region represents the core area of the QMs, featuring elevations ranging from 156 to 3473 m (Figure 1). The QMs function as the hydrological divide between the two largest river basins in China, namely the Yangtze River and Yellow River. Consequently, the southern slope (SS) and northern slope (NS) are attributed to the Yellow River basin and Yangtze River basin, respectively, each of which are characterized by notable geographical disparities. Moreover, serving as a vital water source and conservation area for the two largest rivers, the QMs region plays a pivotal role in supplying water to significant projects like China’s South-to-North Water Diversion [49].

The northern and western regions of the QMs generally exhibit higher elevations. The watershed of the QMs is situated in the central–northern area, with its apex at Mount Taibai, which is located within Taibai County in the west. Conversely, the eastern and southern parts of this region exhibit a relatively lower topography and higher population density. The region located north of the dividing line is characterized by a temperate climate, whereas the region situated south of the dividing line exhibits a subtropical climate [49]. The distinctive geographical location, intricate terrain features, and diverse climatic conditions create an optimal environment for the proliferation of various communities of plants and animals, rendering Qinling Mountains an ecological haven. Hence, the QMs emerge as one of the most biologically diverse regions globally and serve as a crucial protected area for biodiversity conservation in China. It has gained global recognition as a renowned international repository for genetic resources, serving as a vital ecological hub within China while also standing out as a prominent geographic landmark in the country. Meanwhile, in recent years, the ecological environment quality of Qinling Mountains has witnessed significant improvement owing to the guidance of national policies and rigorous control by local management authorities. As of 2022, the vegetation coverage rate in the Qinling region has soared to an impressive 90.31%, establishing it as the preeminent area in China with unparalleled forest coverage [55].

However, the QMs primarily consist of rocky middle mountains, which are characterized by thin soil layers and steep slopes, thereby rendering the ecological environment highly susceptible. Influenced by global extreme climate patterns, the region experiences concentrated summer precipitation, resulting in frequent occurrences of heavy rain and flooding events [50,56,57]. Meanwhile, the continuous development of regional socio-economy, population growth, urbanization dynamics, and anthropogenic disturbances exert additional influence on the stability of the ecological environment [53]. The combined influence of these factors poses a significant potential risk of landslides, mudslides, and soil erosion in this region.

In terms of administrative divisions, this region encompasses a total area of 58,800 km², accounting for 28.59% of the entire Shaanxi Province. In terms of administrative divisions, this region comprises prefecture-level cities, including Xi’an, Weinan, Baoji, Hanzhong, Ankang, and Shangluo, as well as 11 districts and 27 counties [49]. Notably, Xi’an serves as the capital city of Shaanxi Province, while Xunyang and Huayin function as county-level cities. The six cities can be ranked by their involved areas as follows: Weinan < Xi’an < Baoji < Ankang < Hanzhong < Shangluo.

2.2. Research Framework

The specific research process in this study is illustrated in Figure 2.

Firstly, considering the natural environmental characteristics of the QMs region, we selected soil conservation services (SCSs) as a representative ecosystem service type in this area and conducted preliminary work on organizing and processing multi-source data.

Secondly, the Revised Universal Soil Loss Equation (RUSLE) was employed to separate the actual soil erosion from potential soil erosion, enabling the calculation of the supply and demand of the SCSs in the local area. The spatio-temporal evolution characteristics of SCSs were subsequently analyzed. As the parameters required for soil conservation capacity calculation primarily exist in raster data format, the SCSs model initially presents the results of supply and demand calculations in a rasterized form. Considering the inclusion of multiple districts, counties, and cities, as well as the north and south slope divisions within the Qinling region, we employed a partition statistics function of geographic information system software to analyze and present these results through charts, aiming to effectively showcase them at different regional scales.

Thirdly, by utilizing the ecosystem service supply–demand ratio (ESDR), we examined the surplus and deficit status of the supply and demand for SCSs across various research periods and partition scales, as illustrated above.

Finally, given that this article primarily presents recommendations for ecological management at the district and county levels, this study evaluated the spatial matching characteristics and trends in SCS supply and demand by utilizing the local Moran’s index and global Moran’s index at both the county and regional scales on the basis of bivariate spatial autocorrelation. In addition, the research findings of this article were thoroughly discussed and analyzed. Based on the research outcomes, corresponding development recommendations have been formulated to effectively address the local situation.

2.3. Data Sources

The primary data utilized in this study consist of three categories: climatic data, remote sensing data, and socio-economic data.

Specifically, the climate data were obtained from the China Meteorological Data Service Centre established by the China Meteorological Administration with a monthly time resolution for precipitation as the collected climate elements. We carefully selected meteorological stations in the Qinling area and its surrounding regions, meticulously scrutinizing their meteorological data. Ultimately, a total of 18 meteorological stations exhibiting exceptional data continuity were chosen (Figure 1b). The annual data were computed and derived using monthly data obtained from meteorological stations. Subsequently, the precipitation data were incorporated into the latter erosivity factor of rainfall calculations at both monthly and annual scales. As the meteorological data were site-specific, spatial interpolation methods were employed to transform the calculation results into spatial data in tif format with a resolution of 1 km.

The Digital Elevation Model (DEM) data in this study were derived from the widely accepted Shuttle Radar Topography Mission (SRTM) dataset, which was acquired through collaborative measurements conducted by National Aeronautics and Space Administration (NASA) and National Imagery and Mapping Agency (NIMA) using the SRTM system installed on aircraft, resulting in the generation of the currently prevalent SRTM terrain product data. Currently, this dataset is a widely used and readily available high-resolution DEM product with extensive coverage. To ensure accuracy, we specifically chose the 30 m data and extracted DEM data for the QMs region to facilitate precise calculations of slope lengths and gradients.

The NDVI data were derived from the monthly composite product MODND1M of the Moderate-Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 500 m for China. To ensure data quality, the Maximum Value Composite method was employed in this study to convert the monthly data into annual data. The resolution was standardized to 1 km through a resampling tool, and, subsequently, the NDVI time series data for the QMs were acquired using an extraction tool.

The raster data was uniformly resampled to a resolution of 1 km prior to spatial raster calculation, while the vector data remained unchanged. Please refer to

Table 1. Data source information of this study.

Category	Index	Temporal Coverage	Spatial Resolution	Data Sources
Climate data	Precipitation	2000–2020	Meteorological station	China Meteorological Data Service Centre (http://data.cma.cn accessed on 15 December 2021)
Remote sensing data	DEM	2000	30 m	Shuttle Radar Topography Mission (SRTM) dataset (https://srtm.csi.cgiar.org/srtmdata accessed on 18 January 2022)
	MODND1M	2000–2020	500 m	Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences. (http://www.gscloud.cn accessed on 18 July 2022) (https://search.earthdata.nasa.gov/search accessed on 10 January 2022)
Socioeconomic data	Administrative seat	2021	Vector data	National geographic information resources directory service system (https://www.webmap.cn/commres.do?method=dataDownload accessed on 26 December 2021)
	Administrative boundary

for the specific characteristics and acquisition methods of the data.

2.4. Methods and Processes

2.4.1. Supply and Demand of Ecosystem Services

We conducted a comprehensive quantification on the supply and demand of ecosystem services, specifically focusing on soil conservation services as a quintessential type of ecosystem service in the QMs.

The Universal Soil Loss Equation (USLE), developed by Wischmeier and Smith in 1965 [58], stands as the world’s pioneering comprehensive and scientifically rigorous quantitative assessment model for soil erosion, encompassing various influencing factors. In 1997, Renard et al. significantly enhanced the conceptualization and algorithms of these influencing factors, meticulously assessed each factor, accomplished the modification of USLE, and subsequently developed the Revised Universal Soil Loss Equation (RUSLE) model [59]. Over the years, this model has successfully integrated GIS technology with remote sensing data and regional field data, turning it into a universally recognized approach for soil conservation assessment. It has gained global recognition and been extensively applied [48,60,61], and it was rigorously evaluated in the Qinling Mountains and its surrounding regions [38,62,63]. Drawing upon previous research findings and taking into account local conditions, this study quantitatively assesses the supply and demand of soil conservation services by employing the RUSLE model.

• Quantifying the supply of Soil Conservation Services (SCSs)

The supply of SCSs denotes the actual quantity of locally provided measures for conserving soil and mitigating soil erosion. The quantification formula is as follows:

$$S_{SC}=P_e-A_e=R\times K\times L\times S-R\times K\times L\times S\times C\times P,$$

(1)

where $S_{SC}$ denotes the supply of SCSs (unit: t·hm⁻²·yr⁻¹), and its value is determined by the disparity between the potential erosion ($P_e$) and the observed amount of soil erosion (actual erosion, $A_e$). Among them, $P_e$ refers to the theoretical estimation of the soil loss in a given area, assuming the absence of vegetation cover on land and a lack of implementation of any water conservation measures; conversely, $A_e$ represents the actual measured amount of the soil loss in this area with local actual vegetation cover and implementation of soil conservation.

Additionally, the calculation methods for these parameters in the Equation (1) are as follows.

The parameter $R$ denotes the erosivity factor of rainfall for a particular year within the local region (unit: MJ·mm·hm⁻²·h⁻¹·yr⁻¹), which is determined through the application of the empirical formula developed by Wischmeier and Smith [61].

$$R={\sum }_{m=1}^{12}1.735\times {10}^{\left(1.5\times \lg {\displaystyle \frac{P_m^2}{P_y}}-0.8188\right)},$$

(2)

where $P_m$ denotes the cumulative precipitation for a specific month within a given year, whereas $P_y$ represents the total annual precipitation over the entire year in a given area.

The parameter $K$ represents the erodibility factor of local soil, which remains relatively stable over time (unit: t·ha·h·ha⁻¹·MJ⁻¹·mm⁻¹) and is utilized to assess soil erosion and transport vulnerability under local environmental conditions. The calculation process of this parameter is based on the EPIC model developed by Sharpley and Williams [38].

$$\begin{gathered} K=0.1317\left\{0.2+0.3e^{\left[-0.0256S_a\left(1-0.01S_t\right)\right]}\right\}{\left({\displaystyle \frac{S_t}{C_l+S_t}}\right)}^{0.3}\times \\ \left\{1-{\displaystyle \frac{0.25O_r}{C_l+e^{\left(3.72+2.95O_r\right)}}}\right\}\left\{1-{\displaystyle \frac{0.7\left(1-0.01S_a\right)}{\left(1-0.01S_a\right)+e^{\left(-5.51+22.91-0.01S_a\right)}}}\right\}, \end{gathered}$$

(3)

where $S_a$, $S_t$, $C_l$, and $O_r$ denote the mass percentages of sand particles, silt particles, clay particles, and organic matter in the local soil, respectively.

The slope length factor, denoted as $S_l$, and the slope gradient factor, denoted as $S_g$, are utilized to comprehensively assess the impact of terrain undulations such as slope length and slope gradient on local soil erosion. The specific calculation formulas are presented below.

$$S_l={ \left({\displaystyle \frac{\lambda }{22.13}}\right)}^n,\left\{\begin{array}{cc}n=0.5\hfill & \hfill \theta <1°\\ n=0.4\hfill & \hfill 1°\le \theta <3°\\ n=0.3\hfill & \hfill 3°\le \theta <9°\\ n=0.2\hfill & \hfill 9°\le \theta \end{array}\right.,$$

(4)

$$\lambda ={\displaystyle \frac{h}{\sin \left(\frac{\theta \times \pi }{180}\right)}},$$

(5)

$$S_g={\left({\displaystyle \frac{\sin \theta }{0.0896}}\right)}^{0.6},$$

(6)

where $\lambda$ denotes the length of the slope (unit: m), $n$ represents the slope length index, $\theta$ represents the slope steepness, and $h$ represents the height of the slope.

$C$ denotes the vegetation cover factor, which can comprehensively reflect the impact of vegetation cover on local soil erosion and $V_c$ represents the local vegetation coverage status. The presence of vegetation coverage serves as a mitigating factor against soil erosion, whereby areas with abundant vegetation experience reduced rates of soil erosion, while the opposite also holds true.

$$C=\left\{\begin{array}{cc}1\hfill & \hfill V_c=0\\ 0.6508-0.3436\lg V_c\hfill & \hfill 0<V_c\le 78.3\%\\ 0\hfill & \hfill V_c>78.3\%\end{array}\right..$$

(7)

The value of $V_c$ can be derived from the utilization of the NDVI (normalized difference vegetation index) data [64].

$$V_c={\displaystyle \frac{NDVI-{NDVI}_{min}}{{NDVI}_{max}-{NDVI}_{min}}},$$

(8)

where ${NDVI}_{min} and {NDVI}_{max}$ denote the minimum value and maximum value for a particular year within the local region, respectively.

$P$ denotes the soil and water conservation factor, which is determined by the local terrain’s slope gradient index $\alpha$ based on the Wener method [65].

$$P=0.2+0.3\alpha .$$

(9)

2.

Quantifying the demand of Soil Conservation Services (SCSs)

When assessing the demand for SCSs, many scholars have considered the quantification of local actual soil erosion in a local area as an indicator. This is because humans rely on ecosystem services and other related approaches to address existing soil erosion issues. Consequently, within the field of soil conservation, people’s desired quantity of SCSs reflects their demand for such interventions [5,6,33]. On this basis, utilizing the $A_e$ value in a RUSLE equation, we can estimate the extent of local demand for SCSs as follows:

$$A_e=R\times K\times L\times S\times C\times P.$$

(10)

The calculation methods for each parameter in this formula can be found in the preceding text.

2.4.2. Budgets of SCS Supply and Demand

The current universal indicator for assessing the budgets of the supply and demand of ecosystem services is the ecosystem service supply–demand ratio (ESDR) [6,28]. The calculation principle is outlined as follows:

$$ESDR=2\times {\displaystyle \frac{S_e-D_e}{S_{max}-D_{max}}}, \left\{\begin{array}{c}>0, Surplus\\ =0 , Balance\\ <0 , Deficit\end{array}\right.,$$

(11)

where $S_e$ represents the supply of soil conservation services at a specific location and year, while $D_e$ denotes the demand for soil conservation services for the specific location in that year. The parameter $S_{max}$ signifies the maximum supply of soil conservation services available within the entire region for that particular year, whereas $D_{max}$ represents the maximum demand for soil conservation services across the entire region during that same year. By integrating this indicator, it becomes possible to quantitatively evaluate and categorize the status of the ecosystem service budgets as demand exceeds supply ($ESDR<0,$ referred to Deficit) and neutral balance ($ESDR=0,$ referred to Balance), and supply exceeds demand ($ESDR>0,$ referred to Surplus) [8], thereby facilitating a more convenient assessment and management of ecosystem services.

2.4.3. Spatial Matching of SCS Supply and Demand

Geographic elements typically exhibit spatial dependence or interrelationships, which can be observed as the clustering or dispersion characteristics of attribute values in space. Therefore, spatial autocorrelation methods construct a spatial weight matrix to quantify the correlation between the value of a geographic feature at a specific location or partition and the values of neighboring areas [66].

The Moran’s I is a widely employed approach for analyzing spatial autocorrelation, encompassing both the global index (global Moran’s I) and local index (Local Indicators of Spatial Association—LISA) [67]. Among them, the global Moran’s index reflects the spatial autocorrelation status of geographic feature values averaged over all pixels or zones in space, while the local Moran’s index captures the spatial clustering patterns of geographic feature values at each individual location or zone. The calculation principle is outlined as follows:

$${LISA}_i={\displaystyle \frac{\left(a_i-\overline{a}\right)\sum _j{w}_{ij}\left(a_i-\overline{a}\right)}{n\sum _i{\left(a_i-\overline{a}\right)}^2}},$$

(12)

where $a_i$ denotes the attribute value of a geographic feature on either a pixel or zone $i$, $\overline{a}$ denotes the average value of the geographic feature across the entire space, $n$ denotes the number of pixels or zones within the space, and $w_{ij}$ denotes the spatial weight (spatial adjacency) between pixel or zone $i$ and $j$.

The spatial autocorrelation of geographical elements ranges from −1 to 1, with a stronger correlation indicated by the absolute value of the spatial autocorrelation being closer to 1, and the greater the randomness in the spatial distribution is represented by the absolute value being closer to 0 [68]. Specifically, the coexistence of high (low) values in a specific location and its neighboring areas indicates positive spatial autocorrelation within a range from 0 to 1, suggesting that the geographic feature exhibits either high–high clustering or low–low characteristics with surrounding areas. Conversely, if there is a discrepancy between the value at a certain location and its neighboring areas, with one being high and another being low, it signifies negative spatial autocorrelation ranging from −1 to 0, indicating either high–low or low–high clustering between the geographic feature at that location and its surrounding areas [31]. A value of zero signifies a random distribution of this geographic feature in space without any discernible clustering [69].

The LISA formula primarily investigates the spatial autocorrelation of a given geographic element and can be utilized to examine bivariate spatial autocorrelation [70]. In principle, the calculation formulas for both scenarios remain identical; however, in the case of two variables, the LISA formula can be employed to examine the spatial correspondence between Element A at a specific location and its surrounding Element B. The Moran’s index is highly regarded in the field of spatial autocorrelation due to its exceptional convenience, objectivity, and minimal limitations, making it a widely adopted tool for spatial clustering analysis [6]. The bivariate autocorrelation method, based on this, can employ LISA maps to visually depict the spatial matches between ES supply and demand, thereby emerging as an effective approach for investigating ES supply and demand [6,16,71]. Therefore, based on existing research, this study employs GeoDa software to investigate the spatial concordance between ES supply and demand using bivariate spatial autocorrelation at a significance level of 0.1. The spatial patterns of the ecosystem service’s supply and demand are classified into five categories: High Supply–High Demand (H-H), Low Supply–Low Demand (L-L), High Supply–Low Demand (H-L), Low Supply–High Demand (L-H), and Not Significant (N-S).

Within the research framework of supply–demand matching, further subdivisions can be made for the aforementioned types of supply–demand agglomeration [16]. The categories of High Supply–High Demand and Low Supply–Low Demand represent regions where there is a relatively balanced alignment between SCS supply and demand, corresponding to an optimal spatial match of H-H and L-L types, respectively. Conversely, the categories of High Supply–Low Demand and Low Supply–High Demand depict regions characterized by a mismatch between supply and demand, namely H-L and L-H type spatial mismatches, respectively.

3. Results

3.1. Spatial Patterns and Trends of SCS Supply and Demand

3.1.1. Spatial Patterns and Trends of Supply from 2000–2020

The supply of SCSs in the QMs exhibited a generally high level, with the maximum value reaching 2718.10 t·hm⁻² (Figure 3). However, notable disparities existed in the supply levels among the eastern– and central–western regions. The central and western regions were predominantly characterized as high supply areas for SCSs, particularly in Yangxian and Foping, where the SCS supply generally exceeds 1500 t·hm⁻². The counties and districts of Shangluo City, the eastern counties and districts of Ankang City, as well as areas like Ningshan, Lueyang, and Fengxian in the western region experienced inadequate SCS supply. This inadequacy was primarily characterized by maintaining levels below 500 t·hm⁻². At the district and county scale, it became increasingly evident that the central region generally exhibited a higher supply of SCSs, while the western and eastern districts and counties demonstrated comparatively lower levels of SCS supply.

Spatially, the proportion of the area exhibiting an increasing supply of SCSs reached 63.10%. The regions experiencing the most rapid growth in SCS supply were primarily situated along the southern and northern peripheries of the QMs, as well as in counties such as Zhen’an and Shanyang within the east. In these areas, the rate of increase for SCS supply generally surpassed 40 t·hm⁻²·a⁻¹. The regions experiencing a decline in SCS supply were primarily situated along the eastern and western peripheries, as well as on both sides of the QMs’ watershed. Over the years, the regions experiencing the most rapid decline in SCSs exhibited sporadic distribution across Lueyang and Fengxian within the western region, as well as near the watershed in Yangxian, Foping, Ningshan, and Zhashui. The rate of decline in SCSs generally exceeded 20 t·hm⁻²·a⁻¹. From a county-scale perspective, there had been a gradual decline in SCS supply observed in Shangnan County to the east, as well as in Lueyang and Feng County to the west. Conversely, other counties exhibited an upward trend in SCS supply, with notably higher growth rates primarily observed among counties situated along the northern and southern peripheries of the QMs.

3.1.2. Spatial Patterns and Trends of Demand from 2000–2020

There was a significant variation in the SCS demand in the QMs, with values ranging from 0 to 1933.30 t·hm⁻² (Figure 4). The spatial disparities and trends of SCS demand were categorized into six levels for further analysis. It was generally observed that there was a spatially skewed demand for SCSs. Approximately 67.61% of the total area exhibited a SCS demand of less than 300 t·hm⁻². Meanwhile, the regions with a demand below 100 t·hm⁻² were primarily concentrated in the northern slope of QMs, eastern regions, and in counties including Fengxian, Taibai, Liuba, and Mianxian in the west. These regions inherently possessed relatively lower levels of soil erosion. The areas exhibiting high demand for SCSs were sporadically distributed across Taibai, Lueyang, and Ningqiang in the west; Yangxian, Foping, and Ningshan in the center; as well as districts and counties including Hanbin, Xunyang, Zhashui, Zhen’an, and Shanyang in the east. The SCS demand within these regions generally surpassed 500 t·hm⁻². Consequently, these areas experienced substantial soil erosion and faced inherent risks associated with landslides and mudslides. Spatially, the average demand for SCSs in most districts and counties was relatively low, while districts and counties demonstrating higher demand were primarily Ningqiang and Lueyang in the southwest, as well as Zhen’an and Ziyang in the east.

The demand for SCSs in most areas exhibited a consistent downward trend, with the declining regions encompassing 82.68% of the total QMs. The regions exhibiting an upward trend in SCS demand were primarily concentrated in the western and central QMs. The pixels with the highest growth rate of SCS demand were predominantly situated in Yangxian, Foping, Taibai, and Ningshan within the central QMs. The increase rate of demand for these pixels generally exceeded 90 t·hm⁻²·a⁻¹. In contrast, the areas experiencing a decline in demand for SCSs were widely distributed across the eastern QMS, the northern slope, and the peripheral areas in the southern QMs. Among these regions, districts, and counties, such as Zhen’an, Zhashui, Shanyang, Danfeng, Shangnan, Xunyang, and Shangzhou, which were located within the eastern QMS, particularly high rates of decline exceeding 30 t·hm⁻²·a⁻¹ had been shown. The growth rate of demand varied significantly across different districts and counties, with the eastern counties exhibiting a gradual decline or increase in demand, while the central and western counties demonstrated a more pronounced upward trend. Based on the spatial distribution analysis of Figure 4, it was evident that regions experiencing a rapid decline in SCS demand coincided with higher average SCS requirements. Consequently, this rapid decline could effectively mitigate the pronounced demand for SCSs in these areas.

3.2. Spatial Patterns and Trends of ESDR from 2000–2020

3.2.1. Temporal Variations from 2000–2020

The spatial distribution maps of the ESDR for the years 2000, 2010, and 2020, respectively, were presented in Figure 5. In 2000, the ESDR exhibited consistently high values throughout the entire region, with an average of 0.19 and a range spanning from −0.79 to 1.17. Spatially, positive ESDR values encompassed a significant proportion (78.39%) of the total area, and these were predominantly characterized by moderate surplus, accounting for 42.35% areas. These surplus areas were widely distributed in the central and western regions. The slight surplus of the ESDR was primarily distributed in the northeastern and southeastern corners of the region, with slight deficits being concentrated in the eastern part, while moderate deficits were predominantly observed in Ningqiang and Lueyang. In 2010, the ESDR variation within the entire region exhibited a relatively narrow range of −0.39 to 0.59, indicating limited fluctuations. Furthermore, there was a significant decline in the overall ESDR value, reaching a minimal level of 0.04. ESDR revealed a prevalence of areas exhibiting slight surplus and slight deficit, accounting for 59.10% and 37.44%, respectively, and these were extensively distributed across the region. Moreover, there was also a notable proportion of areas displaying moderate surplus, primarily concentrated in the northeastern periphery, Hanbin and Xunyang in the southeast, as well as Foping in the central part. Remarkably, the overall level of the ESDR in the entire region exhibited a general improvement in 2020, with the average ESDR rebounding to 0.14 and an expanded range from −0.88 to 0.82. Furthermore, there had been an increased prominence of spatial disparities in the ESDR. The ESDR revealed a substantial proportion of regions exhibiting a slight deficit, slight surplus, and moderate surplus, respectively. Areas with slight deficit were primarily concentrated in the western region, while areas with slight surplus were predominantly observed in the eastern part. Areas displaying moderate surplus were widely distributed across the entire QMs.

The average values of the ESDR in the entire Qinling region were recorded as 0.19, 0.04, and 0.14 for these respective years. These findings indicated a significant decline in the ESDR from 2000 to 2010; however, there was a partial recovery observed by the year 2020. In combination with the spatial variation at the district and county scales, a larger number of districts and counties exhibited high surplus across the central and western regions in 2000, while the eastern region predominantly experienced slight-to-moderate surpluses alongside areas with a deficit status. In 2010, the central and western regions predominantly exhibited moderate surpluses, while the eastern region predominantly experienced slight surpluses; however, Ningqiang and Lueyang displayed slight deficits. Surprisingly, a significant spatial redistribution of the ESDR was observed in 2020, with the central and eastern regions predominantly exhibiting high and moderate surpluses, while mild surpluses had shifted to the western region; no counties or districts within the entire area were found to be experiencing an ESDR deficit state.

The partition statistics function in GIS was utilized to analyze and compare the average ESDR values of different cities and regions, thereby unveiling variations at various scales. There had been significant fluctuations in the ESDR levels among prefecture-level cities over the years (Figure 6). In 2000, the ESDR levels were generally high in cities, with Baoji, Hanzhong, and Ankang ranking as the top three cities with ESDR values exceeding 0.2. By 2010, there was a decrease in the ESDR levels across all cities, wherein Weinan, Ankang, and Xi’an emerged as the top three cities with relatively higher ESDR values; however, these values only slightly exceeded 0.05. In 2020, a substantial rebound in the ESDR was observed across all cities, with Weinan, Xi’an, and Ankang remaining at the forefront as the top three cities exhibiting ESDR values exceeding 0.20. Weinan, Xi’an, and Ankang had consistently exhibited a relatively high and stable ESDR over the years, whereas the remaining three cities demonstrated a comparatively lower ESDR. Regarding the scale of north–south slopes, the ESDR was lower on the north slope compared to the south slope in 2000. However, in 2010 and 2020, the ESDR exhibited higher values on the north slope than on the south slope. These findings further underscore substantial spatial and regional variations in the ESDR over the past two decades within QMs.

3.2.2. Average Spatial Patterns and Trends

From 2000–2020, the soil conservation services in the QMs maintained a spatially favorable supply–demand balance (Figure 7). The proportion of areas exhibiting positive values in the ESDR reached an impressive 82.19% of the total area. The distribution pattern of the ESDR in the entire region exhibited high values in the central part and low values on both the eastern and western sides. Specifically, negative values were predominantly observed in the counties of Shangluo City and the eastern part of Ankang City in the eastern QMs, as well as counties such as Ningshan, Lueyang, and Fengxian in the western QMs. However, these regions generally exhibited an ESDR ranging from −0.2 to 0, indicating a moderate deficit in their supply–demand conditions. The spatial distribution of pixels exhibiting ESDR values below −0.2 in the specific regions of Lueyang and Taibai appeared to be sporadic. The supply–demand conditions for soil conservation in these areas exhibited a moderate-to-severe imbalance, indicating a significant potential risk for soil erosion. Meanwhile, the pixels exhibiting a positive ESDR were widely distributed across various zones within the QMs. Notably, pixels with a relatively high ESDR primarily concentrated on the northern slope, as well as in the central and western regions. The pixels, which generally had an ESDR exceeding 0.5 and exhibited a favorable supply–demand situation, were primarily concentrated in the central QMs, encompassing Chenggu, Yangxian, and Foping along with their surrounding areas. These territories represented typical regions with a surplus supply over demand of SCSs, indicating relatively lower susceptibility to soil erosion and higher ecological carrying capacity.

Subsequently, the partitioning statistics tool in the GIS was employed to assess the spatial distribution of the mean ESDR value across districts and counties. At the district and county level, a relatively favorable supply and demand situation was observed as sporadic negative pixel values were averaged out, resulting in positive values. Consequently, from 2000 to 2020, all the districts and counties within the region exhibited a positive ESDR. The average ESDR values exhibited a range of 0.03 to 0.34 across districts and counties, indicating diverse levels of supply–demand dynamics. Based on the observed disparities in the ESDR, the supply–demand level can be classified as mild oversupply, moderate oversupply, or relatively high oversupply. The county-level analysis revealed a persistent high value of the ESDR in the central region, while lower values were observed on both sides. Notably, the central region exhibited a predominant surplus supply exceeding demand, with the ESDR generally above 0.2. Additionally, there was an expanding moderate level of supply exceeding demand toward both east and west directions, and it was characterized by an ESDR ranging from 0.1 to 0.2. The mild oversupply was primarily concentrated in Ningqiang and Lueyang, Ziyang and Xunyang, as well as most of the counties in Shangluo City, which is situated on the eastern boundary.

The spatial distribution of the ESDR exhibited an eastward increase and a westward decline, with areas showing upward and downward trends covering approximately half of the entire region, respectively. However, the rate of change in the ESDR across the entire region exhibited a relatively gradual pattern, with fluctuations ranging from −0.09 to 0.07. A substantial decrease in the ESDR was observed in the central and western regions, including Huyi, Ningshan, and Hanyin as their demarcations. In this region, a majority of pixels indicated a decline rate surpassing 0.02. Conversely, an evident upward trend in the ESDR could be observed on the eastern side of this boundary, as well as at the southern and northern peripheries of the QMs, where most of the pixels displayed an increase rate exceeding 0.02.

Compared to the pixel-scale results, the rate of change in the ESDR at the district and county level exhibited a relatively slower pace, ranging from −0.01 to 0.015 only. However, notable regional disparities emerged in the rate of ESDR change. Districts and counties located in the central and western regions generally demonstrated a declining trend in the ESDR, whereas districts and counties in the eastern regions (excluding Zhashui) showed an increasing trend in the ESDR.

Meanwhile, the standardized average values of supply, demand, and ESDR were analyzed to investigate the variation characteristics of the supply–demand relationship across diverse cities and regions from 2000 to 2020 (Figure 8). It could be observed that, over the past two decades, Shangluo and Baoji have been characterized by a low supply and high demand in SCSs, resulting in their relatively lower ESDR. Conversely, Weinan and Xi’an have experienced a high supply and low demand situation, leading to their comparatively higher ESDR. Hanzhong and Ankang fall into the category of high supply and high demand, thereby exhibiting a moderate level of ESDR.

3.3. Spatial Matches of the SCS Supply and Demand

3.3.1. Moran Scatterplots of the SCS Supply and Demand

The global Moran’s index of the supply–demand conditions in the QMs, obtained through bivariate spatial autocorrelation, effectively captured the overall characteristics of the SCS supply–demand across diverse regions (Figure 9). The global Moran’s index exhibited positive values and surpassed the significance level of 0.1 during the years 2000, 2010, and 2020, as well as from 2000 to 2020. This signifies a substantial positive spatial correlation between the supply and demand concerning their distribution across these years, as well as over multiple years on average. In essence, there exists a propensity for the clustering of High–High or Low–Low patterns in the spatial distribution of supply and demand within the QMs. Simultaneously, the global Moran’s index for each year ranged from 0.2 to 0.3, indicating a relatively high level of average spatial clustering of supply and demand during these years, with the most optimal clustering observed in 2010. However, during the period from 2000 to 2020, the global Moran’s index for the supply–demand situation was merely 0.045, indicating a decline in the spatial clustering and an increase in the spatial randomness of supply and demand under average circumstances.

Meanwhile, the Moran scatterplots illustrate the spatial distribution patterns of the supply and demand in four quadrants. Specifically, the first and third quadrants depict a positive spatial correlation between the supply and demand in SCSs, representing two clustering types: H-H (High Supply–High Demand) and L-L (Low Supply–Low Demand). Conversely, the second and fourth quadrants indicate a negative spatial correlation between supply and demand in SCSs, representing two dispersion types: H-L (High Supply–Low Demand) and L-H (Low Supply–High Demand). According to the statistics, in 2000, the Moran scatterplots in both the third and fourth quadrants accounted for more than 30% of the total count, indicating that regions characterized by L-L and L-H exhibited a predominant proportion. In 2010, the scatterplots in the third quadrant accounted for nearly half of the total count in 2010, indicating a predominant presence of L-L-type regions. Following closely were H-H-type regions, constituting 20% of the overall distribution. This observation suggests that the entire region is primarily characterized by a positive supply–demand correlation. The year 2020 witnessed a significant prevalence of Moran scatterplots in the first and second quadrants, indicating the dominant spatial presence of regions characterized by H-H and H-L. However, during the period from 2000 to 2020, the average proportion of Moran scatterplots in the second quadrant exceeded 30%, while the proportions for the other three quadrants remained approximately at 23%. This suggests a relatively equitable spatial distribution among these four types.

3.3.2. Spatial Distribution of the Matches of the SCS Supply and Demand

The bivariate spatial autocorrelation analysis function from GeoDa software was utilized to generate Bivariate LISA Cluster Maps, enabling further examination of the spatial matching status between the SCS supply and demand in different periods for each district and county within the QMs region (Figure 10). The software detected spatial matching areas that failed to meet a significance level of 0.1. Consequently, this section focused on analyzing the distribution and variability patterns exhibited by the spatial matches in H-H and L-L, along with significant spatial mismatches in the H-L and L-H observed from 2000 to 2020.

In 2000, a total of 11 districts and counties in the QMS were identified as spatially matched or mismatched areas through significant level tests. These districts and counties were located in the central and western regions, with Zhouzhi, Taibai, Yangxian, Foping, and Ningshan exhibiting the highest proportion of H-L mismatched areas. The remaining districts and counties represented spatially matched regions; Mian County, Ningqiang, and Lueyang in the west demonstrated H-H-matched areas, while Huyi, Hanyin, and Shiquan exhibited L-L-matched areas. The number of districts and counties that passed the significance test significantly increased to 16 in 2010, and they were primarily distributed in the eastern and southern regions of Qinling. Among them, the spatial L-L-matched type accounted for the highest proportion with 10 districts and counties concentrated in the eastern region, belonging to Weinan, Xi’an, and Shangluo. Following this were H-H-matched areas, which were mainly located in Ningqiang in southwestern Qinling, as well as Hanbin, Ziyang, and Langao in southwestern Qinling. Lueyang and Mian County belonged to the L-H-mismatched regions situated in southwestern Qinling. In 2020, the H-H-matched region exhibited the highest spatial proportion, encompassing nine districts and counties predominantly concentrated in the central part of the region. There were only four counties characterized by spatial mismatch: Liuba and Taibai in the west were classified as L-H-mismatched, and Huayin and Ziyang in the east were classified as H-L-mismatched.

During the period from 2000 to 2020, regions exhibiting spatially matched types accounted for a substantial proportion on average. Specifically, there were five regions characterized by L-L match: Chencang and Fengxian in the southwest, as well as Lin Tong, Lan Tian, and Lin Wei in the southeast. Regions displaying the H-H-matched type were distributed in Hanyin and Zhen’an. Spatial mismatches of the L-H type were observed in Mianxian, Hanbin, Ziyang, and Langao in the south, along with spatial mismatches in H-L being found in Taibai and Huayin in the north.

4. Discussion

4.1. The Spatio-Temporal Disparities and Patterns in the SCS Supply and Demand

As a national-level nature reserve, the QMs possess significant ecosystem services, including soil conservation services. This study revealed that between 2000 and 2020, the majority of areas exhibited a substantially high supply of SCSs with an upward trend and low demand for SCSs in a declining trend. The results indicate a positive trend of improvement in the actual soil erosion condition in the QMs over the years, which is consistent with previous research findings [62,72]. Spatially, most areas exhibit positive values for the ESDR, further confirming relatively less pressure on the SCSs in the region. These findings align with the research conducted by scholars in the Loess Plateau [73], Zhejiang Province [33], and Northeast China [6].

However, spatial characteristics of the ESDR exhibit certain variations at different scales. At the pixel scale, the proportion of positive ESDR regions was 78.39%, 62.44%, and 61.03% in 2000, 2010, and 2020, respectively. Conversely, at the county level, only a few counties displayed negative ESDR values in both 2000 and 2010, whereas all counties exhibited positive values in 2020. Moreover, across all three years, each city demonstrated positive ESDR values. Significant variations in the proportion of positive values were observed across three different scales. During partition statistics, regions exhibiting smaller negative ESDR values or fewer distributions with negative values may have been aggregated as positive within their respective areas. Meanwhile, it was observed that the ESDR on the northern slope exhibited a smaller magnitude compared to the southern slope in 2000, while displaying greater values in subsequent years. These findings imply distinct variations in both spatial distribution patterns and temporal trends of the ESDR across different scales within the QMs. Therefore, employing time and space multi-scale methodologies enables the comprehensive examination of supply and demand dynamics in the SCSs from various perspectives.

Furthermore, the spatial distribution of the ESDR in the QMs region exhibited a distinct pattern characterized by significant increases in the eastern part and decreases in the central and western parts. Approximately half of the area displayed pixels indicating increasing trends while the other half showed decreasing trends. This suggests that there still exists a certain degree of risk in the supply and demand dynamics of the SCSs in the central and western regions, thus deviating from previous research findings on comparing the SCS supply with demand. The EDSR model integrates both supply and demand parameters to enable quantitative evaluation, thereby showcasing its scientific and rational superiority over simplistic supply–demand comparisons. The identified central and western regions are anticipated to encounter substantial supply and demand pressures in the forthcoming years. Hence, local authorities should enhance supervision and implement ecological measures for safeguarding purposes.

From 2000 to 2020, a significant positive spatial correlation was observed between the supply and demand in the region. The prevalence of spatial matching clusters in H-H or L-L was relatively high throughout the entire Qinling region, indicating a consistently favorable spatial matching condition for SCSs over the years. As previously mentioned, this study also indicates that there was a consistently positive ESDR in most areas, suggesting a favorable balance between supply and demand. However, these two phenomena are not equivalent. A better supply–demand balance denotes a state of surplus between supply and demand, wherein the supply exceeds the demand. The spatial matching reflects the collaborative state of supply and demand, which is only achieved when both supply and demand exhibit high or low values. Hence, it is important to note that regions exhibiting a surplus in supply and demand may not necessarily correspond to spatial matching regions. Therefore, the integration of the ESDR and spatial matching research is imperative to attain a comprehensive comprehension of the supply and demand status of ecosystem services in a given region, thereby furnishing scientific references for the implementation of strategies in regional ecological management.

4.2. Driving Factor Analysis of the SCS Supply and Demand

The supply and demand status of SCSs reflects the capacity of a regional natural ecosystem to provide effective measures for soil erosion control, while also encompassing the societal demand for such services, which is represented by the quantifiable extent of soil erosion in the local area. The processes of soil erosion and soil conservation are intricate, and they are influenced by a myriad of factors encompassing topography and gravity factors; climate (including water, wind, freeze–thaw action, and temperature); the physical and chemical properties of the soil; vegetation cover; land use; and socio-economic development, among others.

According to the different external forces, water is identified as the primary driver of erosion in the QMs region, followed by gravitational and debris flow factors. Wind and freeze–thaw erosions are virtually negligible in this region [63]. Temperature, as another crucial determinant of climate change, exerts its influence on soil erosion processes through its impact on local hydrological circulation and precipitation patterns, vegetation growth, and agricultural activities, among others [74]. The widely employed RUSLE model is specifically designed for investigating water erosion, with due consideration being given to factors such as topography, slope, vegetation.

The QMs region experiences predominant precipitation during the summer flood season, which, in conjunction with the comprehensive influence of the local mountainous environment, designates it as the primary period for soil erosion. Within the RULSE formula, the rainfall erosivity factor (R) is regarded as a pivotal indicator for evaluating the influence of precipitation on soil erosion. In this study, we conducted a Pearson correlation analysis to investigate the relationship between historical precipitation levels and both the supply and demand of SCSs. The results revealed a significant positive correlation between precipitation and both variables, with correlation coefficients of 0.86 and 0.67, respectively. The findings underscore the significant impact of precipitation on the supply and demand dynamics of SCSs. In 2000, the limited supply and demand for SCSs were observed due to relatively low rainfall in the Qinling region. Conversely, in recent years such as 2010 and 2020, which were characterized by abundant rainfall [75], both the supply and demand for SCSs witnessed substantial growth. However, the ESDR was contingent upon the magnitude of the disparity between supply and demand of the SCSs. Consequently, in comparison to 2000 and 2020, the ESDR observed in 2010 exhibited a significantly diminished scale. Nevertheless, this does not imply that there was a reduced amount of precipitation in 2010; rather, it can be attributed to a narrower margin between the supply and demand during that particular year.

Furthermore, vegetation plays a crucial role as a natural protective barrier for soil. Its dense canopy effectively intercepts and redistributes rainwater, while its intricate root system contributes to soil stabilization and enhances its water retention capacity [73]. Consequently, there exists a positive correlation between the extent of vegetation coverage and the provision of soil conservation services. The central and western parts of the Qinling Mountains are predominantly characterized by mid-to-high mountainous terrain with minimal anthropogenic disturbance, thereby fostering the persistence of numerous undisturbed closed forests in this region over an extended period and resulting in commendable vegetation coverage [76]. Under the protective cover of vegetation, these areas also exhibit significant soil conservation functions, positioning them as high-value regions for SCSs in the Qinling region. Consequently, local soil erosion is relatively minimal, thereby indicating a limited demand for SCS interventions in this area. Accordingly, considering the balance of supply and demand, these regions can be identified as areas characterized by a higher ESDR, indicating a surplus situation due to an abundance in supply but relatively lower demand. On the contrary, the eastern and southern fringe region encompass a medium–low mountainous terrain that exhibits concentrated human population and heightened anthropogenic activities. Consequently, these areas exhibit a relatively higher prevalence of artificial forests, secondary forests, and shrubs with diminished vegetation coverage [76]. The supply of soil conservation services in these regions is relatively inadequate, resulting in a high risk of soil erosion. Consequently, they fall into the category of areas with low supply and high demand for SCSs.

The impact of human factors on SCSs is also significant, encompassing both positive and negative effects. The intricate underlying processes and mechanisms governing this influence are noteworthy. As previously mentioned, anthropogenic activities have the potential to impact SCSs through their influence on vegetation. If humans continue to deforest and exploit wood resources, it will disrupt the natural equilibrium of soil conservation and escalate the potential risk of soil erosion. Notably, recent years have witnessed significant attention toward ecological preservation in the QMs, leading to extensive implementation of diverse ecological initiatives such as afforestation, forest enclosure for regeneration, and aerial seeding. The previous studies have demonstrated a significant enhancement in the vegetation quality, coverage, and ecological environment within the QMs [49,76,77,78]. Consequently, the local ecosystem is expected to exhibit a positive developmental trajectory while gradually augmenting its capacity for SCSs. Furthermore, changes in land use play a pivotal role as crucial indicators that accurately reflect the extent of human activities. Throughout the course of social and economic production, humans change local land use patterns, thereby inducing changes in vegetation coverage, surface roughness, hydrological characteristics such as infiltration and runoff, ultimately exerting an impact on the soil conservation capacity of the land. The capacity of soil conservation service varies significantly across different land use types, with forest land having the highest capacity, followed by grassland, cultivated land, built-up land, water bodies, and bare land [79]. Zhen et al. conducted a simulation to evaluate the equilibrium between the supply and demand of SCSs in the Chinese Loess Plateau by 2030 [44]. Their findings suggest that transforming cultivated and unused land into ecological areas would enhance precipitation patterns and facilitate vegetation growth, resulting in a substantial surplus of SCS supply and demand. Since 2000, significant changes have occurred in land use within the QMs due to ecological initiatives such as the Grain for Green Project. Notably, there has been a discernible decrease in cultivated land extent, while the areas of other land types have witnessed an increase [80]. In terms of land use conversion, this primarily manifests as a shift from agricultural cultivation toward forested and grassland ecosystems. The cultivated land is primarily concentrated in the low-altitude regions of the eastern and southern QMs, thereby leading to an increased provision of SCSs within these areas. Consequently, this alteration in land use concurrently mitigates the potential risk of local soil erosion, ultimately enhancing the supply–demand ratio of SCSs and aligning with the findings presented in this study. Additionally, it is noteworthy that factors such as topography, soil characteristics, temperature, and landscape patterns also exert discernible influences on the variations of SCSs [73,79], thereby warranting further investigation.

4.3. Management Suggestions

The Qinling region is globally recognized as a biodiversity hotspot and holds paramount significance as one of China’s most crucial ecological functional areas. However, owing to the enduring constraints of the mountainous environment, the socio-economic development in this region lags behind. Firstly, for the entire region, the local management authorities should effectively tackle the challenge of balancing ecological and economic development [49], that is, the local area should strive for moderate economic development, prioritize the well-being of its residents, foster the growth of tertiary industries such as eco-tourism and cultural creativity, gradually limit the expansion of primary and secondary industries, and rigorously safeguard the ecological environment. The ecological background and the ecosystem structure and function will thus steadily improve, leading to a positive development in the supply and demand dynamics for the SCSs.

Furthermore, the regional spatial development direction is synthesized based on land use management and planning [81]. Utilizing frameworks such as “ achieving the supply–demand balance of ecosystem services through zoning [28]”, ecological management zones can be established in the Qinling Mountains region to formulate diverse development strategies [54,82]. The core protection areas serves as the primary source area for regional ecosystem services, thus resulting in a predominance of High Supply–Low Demand or Low Supply–Low Demand of ESs. The ecosystems in these areas require systematic protection, enabling indigenous residents to engage in sustainable activities that fulfill their basic needs, while prohibiting other forms of construction and industrial operations. The key protected areas primarily encompass national parks, scenic spots, forest parks, etc. The regions under consideration exhibit remarkable biodiversity; however, their ecological environment is highly susceptible to degradation. Consequently, any form of real estate development and the establishment of new hydropower facilities are strictly prohibited in order to ensure ecosystem stability and to gradually enhance local ecosystem services. The general protected areas, which are characterized by a certain population concentration and industrial development, possess delicate ecological environments with a Low Supply–Low Demand of ESs. Therefore, it is urgent for this region to prioritize ecological restoration while simultaneously promoting the growth of ecotourism and the green economy. The peripheral area of the protected zone primarily serves as a hub for population concentration and industrial development, and it is characterized by a low supply but high demand for ESs. The region should adhere to ecological restoration, optimize its industrial structure, promote intensive and environmentally friendly production practices, mitigate the negative impact of socio-economic development on the ecological environment, and progressively enhance the quality of the ecological environment.

Additionally, the local management authorities should enhance spatial planning management and facilitate a coordinated development among the different regions in the Qinling Mountains region [36]. This study revealed that the Qinling region exhibits a relatively higher proportion of areas with supply–demand matches; however, certain areas still fall short of achieving this match. Therefore, we need to continue conducting comprehensive research on the spatial matching and agglomeration of regional ESs, with a focus on identifying the key areas characterized by significant spatial agglomeration in the mismatch between supply and demand. Subsequently, targeted regulations should be implemented throughout the region to gradually achieve coordinated supply and demand at a regional scale.

4.4. Limitations and Prospects

This article utilized the QMs region as a case study to systematically examine the dynamics of spatial balance and matches between the supply and demand of SCSs in protected areas. The research findings demonstrated that, in comparison to other types of areas, protected areas exhibited a superior ecological background. Consequently, the budgets between the supply and demand of ESs were relatively favorable, and they were accompanied by a higher degree of spatial matches. Thus, this represents a commendable endeavor and exploration. However, due to data limitations, this study exclusively examined the dynamics of the ES supply and demand in the QMs from 2000. Future research endeavors should strive to integrate additional data encompassing climate change scenarios and socio-economic scenarios in order to further explore the dynamic changes in the ES supply and demand under future scenarios, as well as to investigate the underlying driving mechanisms of these changes on an extended temporal scale.

Simultaneously, it is important to note that this article solely focuses on the selection of SCSs as a representative ES type. In future studies, it would be beneficial to incorporate additional ES types in order to investigate the balance between supply and demand, as well as the spatial matching characteristics exhibited by various ES types within nature reserves. By considering these distinct spatial matching characteristics, it becomes possible to identify key protected areas and suitable development zones for implementing precise ecosystem management at a regional scale.

5. Conclusions

Taking the Qinling Mountains as a representative case study, this research employed the RUSLE model, bivariate autocorrelation methods, and multi-source data to investigate the spatio-temporal evolution characteristics of supply and demand statuses, the supply–demand index, as well as the matches between supply and demand from 2000 to 2020.

The key conclusions are as follows.

The supply of the SCSs in the QMs demonstrated a consistently high level, with a majority of areas demonstrating an increasing trend. Conversely, the demand for SCSs was predominantly low during this period, with most areas experiencing a decreasing trend. Therefore, based on the comparison of supply and demand, the pressure of SCS supply and demand in the QMs tended to be alleviated.

Over the years, the majority of regions exhibited positive values in the ESDR. However, there was a gradual decline in the SCS demand observed in the central and western QMs, while the eastern QMs predominantly demonstrated a slow upward trend. Consequently, concerning ESDR trends, significant pressure and risks were observed for soil conservation in the central and western QMs.

According to the analysis of the global Moran’s index, a significant positive spatial correlation between the supply and demand in the region was observed across various typical years, indicating that the spatial distribution of the SCS supply and demand primarily forms H-H and L-L clusters. The results of the local Moran’s index also suggest a higher prevalence of spatial matches in H-H and L-L areas, indicating a robust spatial alignment between the supply and demand in the QMs throughout the years.

In summary, the ecological environment quality of the QMs exhibited a progressive improvement in recent years, and it was characterized by a relatively low pressure on soil conservation services and an optimal spatial alignment between the supply and demand for SCSs. These favorable conditions will facilitate effective ecosystem management by local authorities and contribute significantly to the sustainable development of the region. However, significant disparities in supply and demand dynamics, and the matching status within the region at various temporal and spatial scales, necessitate the implementation of corresponding management measures. To effectively promote the stability and health of ecosystems, it is crucial to conduct supply and demand assessments of ESs across various temporal and spatial scales when implementing ecosystem management in similar nature reserves. This approach holds significant reference value for undertaking ecological management efforts within nature reserve areas.