Coupling ecosystem service value and ecological vulnerability for spatial zoning and adaptive pathways under future scenarios: a case study of Northern Shaanxi, China

Introduction: The imbalance between ecosystem service supply and demand, combined with increasing ecological vulnerability, poses a significant challenge to the sustainable development of ecologically sensitive regions. Northern Shaanxi, a typical agro-pastoral zone in China with ecological fragility, is highly susceptible to severe soil erosion, desertification, and water scarcity. These stresses are intensified by intensive human activities and climate change. Therefore, it is necessary to systematically clarify the spatial differentiation of ecosystem service value (ESV) and vulnerability in this region, and to propose regionally tailored optimization and regulation strategies.

Methods: This study introduces a “classification–simulation–strategy alignment” analytical framework to examine the interaction between ESV and ecosystem vulnerability (EV). Using Northern Shaanxi as a case study, the spatiotemporal dynamics of ESV and EV are quantitatively analyzed from 2000 to 2020. According to the spatial coupling relationship between ESV and EV, the region is divided into four functional zones: Low ESV–Low EV (general utilization areas), High ESV–Low EV (ecological stability areas), Low ESV–High EV (key restoration areas), and High ESV–High EV (core protection areas). The PLUS model is applied to simulate various future development scenarios, assess the ecological responses across these zones, and propose targeted adaptive management strategies for Northern Shaanxi.

Results: The results indicate that ESV is generally higher in the southeast and lower in the northwest, whereas EV declines from northwest to southeast. Under future scenarios, key restoration areas and core protection areas show elevated ecological sensitivity and should be prioritized in governance efforts. Scenario simulations support the development of region-specific management strategies and contribute to a systematic governance pathway based on “classification–simulation–strategy alignment” tailored to the challenges of Northern Shaanxi.

Discussion: This study advances understanding of the coordinated evolution of ESV and EV, providing both theoretical and practical foundations for precise zoning management and adaptive ecological governance in Northern Shaanxi. The findings also offer valuable insights for other ecologically sensitive regions with similar characteristics.

1 Introduction

Under ongoing global environmental change and the intensification of land use transformation, ecosystem service functions and ecological stability are continuously declining, particularly in ecologically vulnerable areas (Babí Almenar et al., 2020; Li A. L. et al., 2023; Wang and Cai, 2010). The spatial distribution of ecosystem service directly reflects regional ecological supply capacity and serves as an essential basis for constructing ecological security patterns and optimizing territorial spatial planning (Shen et al., 2023; Feng et al., 2021; Evans et al., 2022; Cao et al., 2021; Li et al., 2021). Ecosystem vulnerability (EV), as an important indicator for measuring ecosystem sensitivity and resilience to disturbances, plays a critical role in risk identification and the prioritization of governance strategies (Zhang et al., 2022; He et al., 2018). Increasing complexity in ecosystem structure has led to a pronounced spatial overlap between service value and vulnerability risk, necessitating holistic analysis of their coupling characteristics to provide a scientific basis for targeted governance. In complex social–ecological systems, mismatches frequently occur—such as the coexistence of high value and high risk or the neglect of low-value areas (Li et al., 2024; Bi et al., 2021; Ni et al., 2022; Hu et al., 2021)—highlighting the necessity of integrated spatial identification methods that combine service supply with ecological risk. Moreover, given the uncertainty of ecosystems under environmental change, traditional static assessments are insufficient, making the incorporation of dynamic simulation approaches essential for enhancing the adaptability of ecological strategies (Li C. et al., 2021; Li and Wu et al., 2022; Guo et al., 2023).

Northern Shaanxi, located in the central Loess Plateau, is a representative ecologically vulnerable agro-pastoral transitional zone and an important water and soil conservation area in China’s ecological security framework. The region has long been confronted with severe soil erosion, desertification, land degradation, and water scarcity, exacerbated by climate change and intensive human activities (Fu et al., 2017; Wang et al., 2016). In recent decades, large-scale ecological restoration projects—especially the Grain for Green Program—have significantly improved vegetation cover and watershed regulation capacity (Feng et al., 2016). However, the fragility of loess soils, complex terrain, and an economy dominated by resource-based industries render the regional ecosystems highly sensitive to external disturbances.

Research on ecosystem service value (ESV) and EV has made significant progress in functional quantification, high-resolution spatial modelling, and driving mechanism analysis (Yuan et al., 2022; Ma et al., 2021; Yan et al., 2023; Ran et al., 2022; Wei et al., 2024), leading to comprehensive frameworks that integrate supply–demand relationships, pattern evolution, and trade-off/synergy analysis (Burkhard et al., 2012; Liao et al., 2021). EV assessments have also evolved from single-element evaluations to coupled social–ecological analyses, deepening the understanding of ecosystem stress capacity and resilience (Hou et al., 2022; Laterra et al., 2016). Although growing attention has been paid to the coupling of ecosystem service and EV in spatial zoning (Liu et al., 2023; Vihervaara et al., 2010), studies on Northern Shaanxi still mainly rely on static overlay methods and lack systematic integration of coupling identification and dynamic simulation.

This study overcomes the limitations of traditional static methods for coupling ESV and EV by introducing a scenario-driven, closed-loop “classification–simulation–strategy alignment” framework. Using multi-scenario simulations, the study systematically examines changes in spatial patterns and risk characteristics of functional zones under different development and policy conditions. Simulation results are directly translated into spatially explicit policy recommendations. Northern Shaanxi is used as a case study. Advanced spatial coupling analysis and quantitative assessment methods identify and simulate ecosystem pattern changes. These approaches deepen the understanding of ESV–EV interactions and provide empirical evidence for adaptive regional governance. The study’s main innovations are: (1) the development of an integrated, dynamic analytical framework that combines spatial zoning, scenario simulation, and policy alignment, enabling comprehensive, multi-objective analysis of ecosystem services and vulnerability; and (2) the formulation of targeted ecological policies based on zoning characteristics, which strengthens the scientific basis and precision of regulatory actions. These methods and findings serve as valuable references for optimizing and managing other ecologically vulnerable regions worldwide.

2 Methods

2.1 Research framework

This study develops a comprehensive analytical framework based on the principle of “classification–simulation–strategy alignment” (Figure 1). First, after preprocessing foundational datasets and standardizing spatial scales, ESV and EV of the study area are quantitatively evaluated. The index of EV is developed through a multi-index comprehensive evaluation to reveal spatial heterogeneity in ecological functions across the region. A spatial matching model is then applied to determine the coupling status between ESV and EV. The study area is classified into four coupling types: Low ESV–Low EV, High ESV–Low EV, Low ESV–High EV, and High ESV–High EV. This classification clarifies the spatial patterns of ecological function and risk structure.

Figure 1

Figure 1. Analytical framework for ecosystem coordination zoning and scenario-based simulation

Next, three representative land-use scenarios are defined according to alternative regional development and governance strategies: the inertial evolution baseline scenario, the urbanization-dominated scenario, and the ecological protection priority scenario. A multi-scenario land use change dataset is built to illustrate potential future development pathways. The Patch-generating Land Use Simulation (PLUS) model is applied to simulate spatiotemporal dynamics of land use change under these scenarios. This approach enables evaluation of ESV and EV response trajectories across different coupling zones and identification of sensitive ecosystem transitions and emerging risk areas.

Finally, based on the typological characteristics of each zone and simulation outcomes, differentiated ecological regulation strategies are proposed for specific spatial units. This leads to the development of a forward-looking regulatory sequence of “classification–simulation–strategy alignment,” which supports regional ecological governance and optimal resource allocation.

Overall, this research framework achieves full-process integration from static spatial evaluation to dynamic scenario simulation, and from spatial coupling identification to targeted regulation. It offers strong scientific rigor, contextual adaptability, and broad applicability, thereby providing a systematic theoretical foundation and strategic guidance for the spatial governance of ecologically vulnerable areas.

2.2 Study area

Northern Shaanxi, China, was selected as the study area. Located in the northern part of Shaanxi Province, this region includes Yulin and Yan’an cities and lies at the core of the Loess Plateau (Figure 2). Characterized by a temperate semi-arid climate and receiving 300–500 mm of annual precipitation, it experiences significant spatial and temporal variability, with alternating droughts and intense rainfall events indicating pronounced ecological risks.

Figure 2

Figure 2. Comprehensive diagram of geographical location and geographical divisions in Northern Shaanxi.

The landscape is mainly composed of loess ridges, hills, and plateaus. The northern section borders the Mu Us Desert and features fragmented terrain, including loess hills and aeolian sand dunes. In contrast, the southern section is characterized by broad, flat plateaus and serves as the primary agricultural production area. Land use comprises cultivated land, forest land, and grass land, creating a mosaic of agricultural–pastoral systems and significant spatial heterogeneity in ecosystem services.

Historically, intensive land reclamation, overgrazing, and expansion of resource-intensive industries have led to ecological degradation. Ecological restoration policies, such as the Grain-for-Green Program and ecological migration, have gradually improved ecological conditions. However, balancing land-use restructuring and ecological restoration remains challenging. Northern Shaanxi faces a clear mismatch between ecosystem service supply and demand and shows high sensitivity to ecological risks, making it a key area for investigating the coupled identification and regulation of ecosystem services and EV.

2.3 Data sources and preprocessing

The data used in this study comprise both spatial and statistical data corresponding to the years 2000, 2010, and 2020. Spatial data include land use, topography, meteorological variables, vegetation indices, soil characteristics, and accessibility metrics. Detailed descriptions and data sources are provided in Table 1. To ensure the consistency and comparability across multi-source datasets, all raster data were resampled to a uniform spatial resolution of 30 m, and the coordinate system was standardized to WGS_1984_UTM_Zone_49N. Vector datasets were processed through boundary clipping, hole filling, and topological consistency checks to ensure the integrity, standardization, and analytical readiness.

Table 1

Table 1. Overview of data sources and types used in this study.

2.4 Research methods

2.4.1 Calculation of ESV

This study utilizes the equivalent factor method proposed by Xie Gaodi to quantitatively assess ESV (Xie et al., 2008). According to this methodology, the economic value of the equivalent factor is defined as 1/7 of the national average market value of grain yield. Based on the primary grain crop structure in the study area, wheat and corn are selected as the representative crops. By incorporating their respective sown areas, unit prices, and average yields per unit area, the ESV coefficient for Northern Shaanxi is locally adjusted. This approach is used to estimate ESV for the years 2000, 2010, 2020, and 2030 under different scenarios. The calculation formula is as follows:

$E=\frac{1}{7}{\displaystyle \sum _{i=1}^n}\frac{F_i{x}_i{y}_i}{F}(1)$

$ESV={\displaystyle \sum _{j=1}^m}{\displaystyle \sum _{i=1}^n}\left(A{h}_j\times E_{ji}\right)(2)$

In Equations 1, 2: E represents the unit area ESV in Northern Shaanxi (yuan/hm²); $F_i$ denotes the sown area of the ith grain crop; $X_i$ is the unit price of the ith grain crop; $y_i$ indicates the average yield per unit area of the ith grain crop; and F is the total sown area of all grain crops. ESV represents the total ESV in Northern Shaanxi; ${Ah}_j$ denotes the area of land use type j; $E_{ji}$ is the value of the ith ecosystem service function for land use type j; m is the number of land use types; and n represents the number of ecosystem service categories.

Because the contribution of construction land to ecosystem services is negligible, its unit service value is set to zero in this study (Wang and Pan, 2019). The unit values for different land use types are shown in Table 2. Specifically, the value for cultivated land is calculated as the weighted average of paddy fields and dry farmland. The value for forest land is the weighted average of coniferous and broad-leaved forests. The value for grass land is the weighted average of shrub land, grass land, and meadow types.

Table 2

Table 2. Unit ESV for major land use types in Northern Shaanxi.

2.4.2 Construction of an ecosystem vulnerability assessment system

This study develops a comprehensive EV assessment system based on three key dimensions: habitat conditions, ecosystem structure, and ecosystem function (Pan et al., 2022). EV is calculated using standardized normalization and a weighted composite evaluation method. The entropy weight method (Mon et al., 1994) is used to objectively determine the weights of each index based on the degree of dispersion in their values. The relationships between each ecosystem vulnerability assessment index, EV, and their corresponding weights are shown in Table 3. The ecosystem vulnerability index (EVI) is calculated using the following formula:

$EVI=HCI\times {\omega }_{hci}+ESI\times {\omega }_{esi}+EFI\times {\omega }_{efi}(3)$

In Equation 3: HCI, ESI and EFI denote the habitat condition index, ecosystem structure index, and ecosystem function index, respectively. Each is normalized to the range [0, 1]; ${\mathrm{\omega }}_{hci}$, ${\mathrm{\omega }}_{esi}$ and ${\mathrm{\omega }}_{efi}$ represent the corresponding weight coefficients for each dimension. Based on the equal-interval classification method, EVI is divided into five vulnerability levels: potential, slight, moderate, severe, and extreme, reflecting the spatial heterogeneity and risk distribution of EV across the study area.

Table 3

Table 3. EV assessment indicators and weights.

To assess the robustness of the EVI to uncertainties in indicator weights, the sensitivity index Si was employed (Zhang et al., 2025). For each evaluation index, its weight was perturbed by ±10%, and the remaining weights were re-normalized to ensure the total sum remained at 1. The EVI was then recalculated using the adjusted weight set, and its sensitivity coefficient was determined.

$S_i=|\frac{\left({EVI}_j-{EVI}_i\right)/{EVI}_i}{\left(W_j-W_i\right)/W_i}|(4)$

In Equation 4: $W_i$ and $W_j$ represent the weights of the ith EVI before and after adjustment, respectively; ${EVI}_i$ and ${EVI}_j$ are the values of EVI before and after the adjustment. ($W_j$-$W_i$)/$W_i$ denotes the relative change in the index weight. S_i is a dimensionless coefficient representing the percentage change in the EVI resulting from a 1% change in the index weight. A larger S_i value indicates that the EVI is highly sensitive to changes in that weight, emphasizing the importance of data and weight accuracy. Conversely, a smaller S_i value means the EVI is less sensitive to changes in the corresponding index weight.

2.4.3 Regional governance zoning from the perspective of “value-vulnerability” synergy

ESV reflects the capacity and benefits of regional ecosystems in delivering multiple ecological services (Costanza et al., 1997), while the EV quantifies the sensitivity and resilience of ecosystems to external disturbances (Cai et al., 2021). A synergistic analysis of ESV and EV enables the identification of ecological functional advantages and potential risk areas, thereby offering a scientific foundation for targeted ecological conservation and restoration strategies.

To ensure comparability, ESV and EV data are first standardized. The annual regional mean of each indicator was then used as the threshold to classify the study area into four zone types. This choice was made because the mean more accurately reflects the overall regional level, ensures consistency with standardized data, and is sensitive to spatial heterogeneity and extreme values. Specifically: areas with both ESV and EV below the mean are designated as Low ESV-Low EV, forming the general utilization areas; regions with ESV above the mean and EV below the mean are classified as High ESV-Low EV zones, representing the ecological stability areas; regions with ESV below the mean and EV above the mean are classified as Low ESV-High EV, representing the key restoration areas; areas with both ESV and EV above the mean are designated as High ESV-High EV, corresponding to the core protection areas. This four-quadrant classification framework accounts for both ecological function and ecological risk, allowing for the spatial identification of priority zones for ecological governance. It provides an operational foundation for the implementation of differentiated, adaptive regional management strategies based on ecosystem functionality and vulnerability.

2.4.4 Future scenario simulation

To explore the impact of regional land use pattern evolution on ecosystem services and vulnerability under different development paths, this study constructs a multi-scenario simulation system using the PLUS model and 2020 zoning data. Three typical development paths were set: “inertial evolution baseline scenario,” “urbanization-dominated scenario,” and “ecological protection priority scenario,” to simulate land use changes in Northern Shaanxi by 2030 (Wang and Zhang, 2022). The specific basis for scenario setting, probability conversion adjustments, and selection of driving factors is provided in Table 4.

Table 4

Table 4. Scenario simulation assumption table.

In land use simulation, the selection of driving factors is crucial for model accuracy. Based on the current situation in Northern Shaanxi, data availability, and existing research, this study selected socioeconomic and climate-environmental factors as drivers for the PLUS model (Yang et al., 2022; Li D. et al., 2023).

Finally, neighborhood weight parameters for different land use types were calculated using the Fragstats tool, with specific values for each type provided in Table 5. These parameters, which range from 0 to 1 (higher values indicate stronger expansion potential), were subsequently used to simulate the future expansion trends of the respective land use types (Wang et al., 2019).

$Xi=\frac{T{A}_i-T{A}_{\min }}{T{A}_{\max }-T{A}_{\min }}(5)$

In Equation 5: X_i represents the neighborhood weight parameter of the ith land use type; ${TA}_j$ represents the expansion area of the ith land use type; ${TA}_{\mathit{min}}$ represents the minimum expansion area among all land use types; ${TA}_{\mathit{max}}$ represents the maximum expansion area among all land use types.

Table 5

Table 5. Neighborhood weight parameters table.

The simulation results were validated using the actual 2020 land use map. A Kappa coefficient of 0.828 was obtained, indicating high simulation accuracy and model reliability.

3 Results

3.1 Temporal and spatial evolution characteristics of ESV

3.1.1 Temporal variation characteristics of ESV

Overall, the ESV in Northern Shaanxi shows a consistent upward trend in Figure 3, but the growth rate is gradually slowing. Between 2000 and 2010, the ESV in Northern Shaanxi increased by 2.891 billion yuan, representing a growth rate of 2.99%. From 2010 to 2020, the ESV increased by only 188 million yuan, with a growth rate of 0.19%. The trend in per unit area ESV change is consistent with the trend in total ESV. In 2020, the per unit area service value reached 12,445.14 yuan/hm², increasing by 23.52 yuan/hm² compared to 2010 and by 361.68 yuan/hm² compared to 2000. This also reflects the gradually slowing growth rate.

Figure 3

Figure 3. Distribution of ESV function types in different periods.

From the perspective of service type composition, during the period from 2000 to 2020, the proportion structure of various ecosystem services remained relatively stable, with regulation and supporting services contributing the most to the overall ESV. The proportion of supply services decreased each year, while the value of adjustment services, support services, and cultural services increased. Cultural services showed the most significant growth, mainly due to the expansion of forest land and grass land areas, which enhanced the region’s landscape aesthetic value. The absolute increase in regulation service value was the greatest. The expansion of ecological land improved water conservation, enhanced soil conservation, reduced the risk of landslides and debris flows, and strengthened climate and hydrological regulation.

3.1.2 Spatial distribution characteristics of ESV changes

From 2000 to 2020, the spatial distribution of ESV in Northern Shaanxi showed a gradual improvement. High-value areas became more aggregated, transitioning from a scattered distribution, while the extent of low-value areas decreased. Generally, ESV in the southern part of Northern Shaanxi is significantly higher than in the northern part. This difference is closely related to geomorphology, soil, and hydrological conditions. The southern region consists mainly of loess plateaus and earth-rock hills. Loess plateaus have deep, fertile soil and favorable hydrological conditions, which support robust vegetation. Although earth-rock hills have exposed rocks, the soil is stable and retains water well. The region has an extensive river system, which promotes vegetation growth. Relatively speaking, the northern region is located in the sand and wind hills, loess wide valley hills, and sand-covered loess hills. The terrain is fragmented, the soil quality is poor, and water retention is low. Desertification from the Mu Us Desert severely impacts this area, resulting in a fragile ecological environment and low ESV. High ESV areas are mainly concentrated in the southern river banks and the areas with high vegetation coverage in the ecological protection area.

The hotspot analysis tool (Getis-Ord Gi*) in ArcGIS was used to analyze the ESV hotspot and coldspot areas in 2000, 2010, and 2020, and the natural breaks method was used to classify the degree of ESV spatial aggregation (Figure 4). The results indicate that ESV hotspots are primarily located in the loess plateau and earth-rock hills of the southern part of Northern Shaanxi, forming areas of high ESV aggregation due to favorable hydrological conditions and high vegetation coverage. In contrast, coldspots are concentrated in the northwestern sandy hills, where low ESV is associated with an arid climate, sparse vegetation, and severe soil erosion. The spatial change trends of hot and cold spot areas show that the ecological restoration projects in Northern Shaanxi have achieved remarkable results in recent years, and the regional ecological environment has been continuously improved.

Figure 4

Figure 4. Spatiotemporal distribution of ESV cold and hot spots. (a) 2000, (b) 2010, (c) 2020.

3.1.3 Analysis of ESV change driving factors

Considering the unique natural geographic environment, ecological background, and socio-economic conditions of Northern Shaanxi, eight variables were selected as driving factors: elevation (DEM), slope, population density (POP), GDP density (GDP), normalized difference vegetation index (NDVI), mean annual temperature (TEM), mean annual precipitation (RAIN), and drought index (SPEI). The total value of ecosystem services was used as the dependent variable. The geographic detector method was employed to identify the dominant factors influencing the spatial differentiation of ESV.

The analysis results indicate that the explanatory power (q value) of different driving factors exhibited distinct phases of evolution from 2000 to 2020 in Figure 5. Specifically, the influence of POP on the spatial variation of ESV decreased markedly from 0.1771 in 2000 to 0.0587 in 2020. This suggests that, during the early stages of regional development, population distribution played a dominant role in shaping ecosystem service patterns. However, with accelerated urbanization, population concentration in towns, and the implementation of ecological projects, its influence has gradually weakened. As a key natural factor in arid areas, TEM has always maintained a high explanatory power, showing its stabilizing role in regulating the spatial pattern of ecosystem services. The explanatory power (q value) of topographic factors, such as DEM, showed a declining trend. This indicates that increased human activities and ecological engineering have reduced the restrictive effect of topography on ESV spatial heterogeneity. The explanatory power of SPEI and RAIN showed obvious fluctuations, reflecting that the regulatory ability of moisture factors on ecosystem services under the background of climate change is dynamic, especially in 2020, the impact of precipitation on ESV has significantly increased, indicating that the sensitivity of water resources changes to ecosystem service supply has increased. The explanatory power of NDVI increased in 2010, indicating that ecological engineering positively influenced vegetation coverage. In contrast, the decline in NDVI explanatory power in 2020 may be associated with balanced regional vegetation coverage or slowed growth.

Figure 5

Figure 5. Interannual variation of single-factor q values for the ESV in Northern Shaanxi.

Overall, the changes of ESV in Northern Shaanxi are influenced by the complex interplay of climate, hydrothermal conditions, ecological restoration outcomes, and socio-economic adjustments. The intensity of the driving factors has obvious phases and spatial heterogeneity.

3.2 Temporal and spatial evolution characteristics of ecological vulnerable

3.2.1 Temporal evolution characteristics analysis

In 2000 and 2010, the moderately fragile zones in Northern Shaanxi accounted for the largest proportion of the area in the region, representing 31.04% and 28.41% respectively, as shown in Figure 6. In 2020, the potentially fragile zones occupied 36.8% of the total area, becoming the largest zone by proportion. Over the 20-year period, the area of regions with lower EV in Northern Shaanxi continuously expanded. The area proportion of potentially fragile zones and slightly fragile zones increased from 23,700 km² in 2000 to 51,800 km² in 2020, rising from 29.66% of the total area in 2000 to 43.05% in 2010, and finally to 64.84% in 2020. Meanwhile, areas with higher EV (severely and extremely fragile zones) decreased from 39.3% in 2000 to 14.89% in 2020. This indicates a downward trend in overall EV and continuous improvement of the regional ecological environment.

Figure 6

Figure 6. Changes in proportions of EV levels in different periods.

Among the transitions in vulnerability levels, the most significant changes occur between adjacent levels (Figure 7). From 2000 to 2020, the total area transferred from low vulnerability (potentially and slightly vulnerable areas) to high vulnerability (severely and extremely vulnerable areas) reached 52,900 km², accounting for 10.33% of the total area. Specifically, 6,400 km² and 6,300 km² were transferred from potentially vulnerable areas to severely and extremely vulnerable areas, respectively. Additionally, 14,100 km² and 13,100 km² moved from slightly vulnerable to severely and extremely vulnerable areas, respectively. Conversely, the total area transferred from high vulnerability (severely and extremely vulnerable areas) to low vulnerability (potentially and slightly vulnerable areas) was 68,900 km², accounting for 13.47% of the total area. Among them, 21,400 km² and 23,500 km² were transferred from severely vulnerable areas to potentially and slightly vulnerable areas, respectively. For extremely vulnerable areas, 11,200 km² and 12,800 km² were transferred to potentially and slightly vulnerable areas, respectively. Overall, from 2000 to 2020, the downward transition from high to low levels vulnerability was significantly greater than the upward trend from low levels to high levels, indicating that the ecological environment in Northern Shaanxi is constantly improving. Specifically, the transfer from high levels to low levels is mainly concentrated from severely vulnerable areas to slightly vulnerable areas and potentially vulnerable areas, while the transfer from low levels to high levels is relatively small. This result shows that from 2000 to 2020, the overall horizontal structure of Northern Shaanxi has changed, the EV has decreased significantly, and the ecological environment has continued to improve.

Figure 7

Figure 7. Conversion of different EV levels during 2000–2010, 2010–2020, and 2000–2020.

3.2.2 Spatial pattern evolution characteristics

From 2000 to 2020, the overall EV level in Northern Shaanxi showed a downward trend, and the spatial pattern characteristics showed that the EVI in the central and southern parts was relatively low, and that in the northwestern part was higher (Figure 8). Low vulnerability areas are mainly located in the southern part of Northern Shaanxi. These regions are characterized by loess plateaus and earth-rock hills with deep, fertile soil, adequate water supply, high groundwater levels, and strong water retention. These conditions promote vegetation growth and ecosystem stability. From 2000 to 2010, northern areas, including Dingbian County, Jingbian County, and Hengshan District, exhibited high EV. These locations are situated in loess wide valley hills and sand-covered loess hills, characterized by rugged terrain, prominent gullies, thin soil layers, loose texture, poor water and nutrient retention, a simple vegetation structure, low biodiversity, and limited ecosystem stability, all contributing to high EVI. The central area of Northern Shaanxi is dominated by loess ridge-shaped hills and loess mound-shaped hills. Its EV level fluctuated significantly between 2000 and 2020. The regional vegetation restoration has achieved remarkable results. The ecosystem functions in some areas have been effectively enhanced, and the vulnerability level has gradually decreased, showing that the ecological restoration in the central region has a good spatial response.

Figure 8

Figure 8. Spatial distribution characteristics of EVI.

Overall, the spatial pattern of EV in Northern Shaanxi is characterized by higher values in the northwest and lower values in the southeast. Supported by ecological protection and vegetation restoration projects, ecological environment quality in the southern and central regions has continuously improved, resulting in a general decrease in vulnerability.

3.2.3 Identification of evolutionary driving mechanisms

To identify the driving mechanisms of EV changes in Northern Shaanxi, eight representative factors were selected for geographical detector analysis, considering regional natural environment characteristics and data availability. Among them, climate factors included RAIN, TEM, humidity index (MI), and drought index (AI); human activity-related factors include artificial impervious area ratio (AIAP), land use intensity (LUI), ecological protection area ratio (EPAP), and POP. The EVI was used as the dependent variable to quantitatively assess the explanatory power of these factors.

In Figure 9, the analysis results show that RAIN is the most important factor affecting the ecosystem vulnerability of Northern Shaanxi, with a q-value of 0.4037, indicating that water supply plays a decisive role in the stability of the ecosystem. The second most important factor is the MI, with a q-value of 0.2449, which also shows a strong explanatory power, reflecting the key role of regional humidity in maintaining ecosystem functions. The q values of TEM, LUI, and AI are all close to 0.1236, categorizing them as medium-impact factors. This suggests that climate heat conditions and human activity intensity also exert notable effects on EV. The reduction of precipitation and humidity will significantly increase the risk of ecological degradation in arid and semi-arid areas. LUI is a characterization indicator of the degree of human interference. Greater LUI leads to higher ecosystem disturbance and increased vulnerability. Interaction analysis shows that the combined effect of RAIN and MI has the strongest explanatory power for EV, followed by the interaction between RAIN and LUI. Both demonstrate significant two-factor enhancement effects, indicating a nonlinear interaction between climate factors and human activities.

Figure 9

Figure 9. Heatmap of Correlations among EV driving factors.

In summary, changes in EV in Northern Shaanxi are driven by a combination of natural and human factors, with a general synergistic enhancement observed among the primary driving factors.

3.2.4 Robustness analysis of EV assessment

This study performed a robustness analysis by applying a ±10% perturbation to the weights of each evaluation indicator for 2020. In Table 6, the results show that soil erosion intensity and FVC are the primary drivers of EV and its spatial distribution. Perturbing the weights of these dominant indicators substantially increases the rate of change in EV and causes disproportionately large changes in the area covered by different EV grades, compared to adjustments to other indicators. This underscores the critical role of these indicators in the spatial differentiation of ecosystem sensitivity. Topographic factors exhibited moderate sensitivity. In contrast, landscape pattern indicators and other functional indicators showed relatively low sensitivity, primarily serving supplementary roles in shaping the hierarchical structure.

Table 6

Table 6. Sensitivity analysis of EV evaluation indicators.

These findings confirm the robustness of the assessment system under uncertainty analysis. Indicators with lower weights, as identified by the entropy method, exert limited influence on the spatial distribution of EV, while those with higher weights demonstrate greater sensitivity. Notably, although perturbing FVC—the highest-weighted indicator in 2020—had a minimal effect on the mean EVI, it resulted in the reclassification of approximately 8% of the EV grade area. This suggests that spatially-oriented ecological assessments should focus not only on changes in the overall index, but also on how key individual indicators affect the stability of spatial classification patterns.

3.3 Regional governance zoning from a “value-vulnerability” synergy perspective

In Figure 10, from 2000 to 2020, the spatial distribution of “value-vulnerability” synergy types in Northern Shaanxi underwent distinct phased adjustments and demonstrated significant spatial heterogeneity. These changes reflect the dynamic succession processes of regional ecosystems under the combined influence of natural forces and human interventions.

Figure 10

Figure 10. Spatial distribution of ecological functional zones in northern Shaanxi region.

General utilization areas remained relatively stable over time, with minimal spatial variation. These areas are primarily located in long-established agricultural zones or low-disturbance flatlands. These areas have relatively low ecosystem service provision capacity and ecological risk pressure. With well-preserved original ecosystem structures and strong self-sustaining capacity, these areas provide ecological buffering functions and can serve as reserve spaces for future land remediation and ecological restoration.

Ecological stability areas exhibited a steady expansion. In 2000, these areas were primarily concentrated in Huangling County and Huanglong County of Yan’an City (both critical ecological barriers and water conservation zones) covering 11.32% of the study area. By 2020, this proportion increased to 18.03%, with the spatial pattern becoming more continuous and aggregated. This suggests ongoing enhancement of ecosystem service capacity and system stability, driven by effective ecological protection and sustainable land use. These areas are critical for supporting the transformation of ecological products into economic value and for promoting green development pathways.

Key restoration areas showed a generally declining trend, decreasing from 52.49% in 2000 to 38.29% in 2020. This contraction mainly occurred in southern to northern parts of Zhidan County, Zi chang City, Qingjian County, and Ansai District. At present, such areas are mainly concentrated in Dingbian County, Hengshan District, and Shenmu City, along the fringes of the Mu Us Desert. These areas, located in the hilly-gully region of the Loess Plateau, have long suffered from vegetation destruction, soil erosion, overexploitation of natural resources, and acute human-land conflicts. Although ecological restoration projects have alleviated degradation to some extent, these remain the most fragile ecological units in the region and are thus primary targets for ecological restoration and resource regulation.

Core protection areas exhibited a trend of fluctuation and contraction. In 2000, these zones were mainly distributed in Jingbian County and Wuqi County, accounting for approximately 5.98% of the total area. Twenty years of ecological governance have seen a reduction in its area, with ecological conservation projects effectively lowering the EVI. High ESV and high EV indicate that the area possesses both significant ecological function and high sensitivity, making it a priority for ecological regulation and risk prevention. Efforts should focus on minimizing the loss of ecosystem function caused by external disturbances.

From a regional perspective, the spatiotemporal evolution of “value–vulnerability” types in Northern Shaanxi shows a general transition from high-risk zones to stable ecological zones, accompanied by the outward expansion of core ecological functional areas. The continuous expansion of ecologically stable areas and the reduction of key restoration zones indicate improvements in overall ecosystem resilience and health. However, a significant part of the region continues to experience weakened ecological function and ongoing degradation, underscoring the urgent need for intensified interventions and targeted governance strategies.

3.4 Dynamic response characteristics of partitions under multi-scenario simulation

Simulation analysis of three typical development paths in 2020 (inertial evolution baseline scenario, urbanization-dominated scenario, and ecological protection priority scenario) reveals that regional ESV exhibits significantly different dynamic evolution patterns, as illustrated in Figure 11.

Figure 11

Figure 11. Dynamic evolution of ESV in ecological functional zones under multiple scenarios.

Under the inertial evolution baseline scenario, ESV in different regions continues the historical trend observed from 2000 to 2020. Specifically, the ESV of general utilization areas shows a slow expansion and is expected to reach approximately 31.83 billion yuan by 2020, largely maintaining the existing spatial pattern. The spatial extent of ESV in ecological stability areas has decreased, and the overall ESV is projected to decline to around 26.65 billion yuan. The ESV in key restoration areas has gradually decreased to 41.14 billion yuan. Overall, the inertial evolution baseline scenario reflects the slow recovery and limited improvement of regional ESV, with no significant breakthroughs in ecological or economic development.

Under the urbanization-dominated scenario, spatial changes in ESV across various regions are more pronounced. The ESV of general utilization areas decreases significantly and is projected to reach approximately 31.45 billion yuan in 2020. Areas with good original ecological service functions are significantly affected by urbanization expansion, and there is a risk of ESV degradation. Some regions have even been converted into key restoration areas. The ESV in ecological stability areas has declined significantly, dropping to 26.32 billion yuan. Ecologically sensitive areas are significantly affected by construction activities, and the carrying pressure of ecosystems has increased. The ESV in key restoration areas also shows a growing trend, decreasing to 40.26 billion yuan, which further exacerbates pressure on regional ecological governance. Overall, the ESV under the urbanization-dominated scenario declines significantly, indicating a clear risk of ecological degradation.

Compared with the other two scenarios, the ecological protection priority scenario has a positive effect on regional ESV. In 2020, the ESV of general utilization areas expanded significantly, reaching 32 billion yuan. The ESV in ecological stability areas increased to 26.8 billion yuan, while the ESV in key restoration areas rose to 41.2 billion yuan. Overall, the ecological protection priority scenario significantly enhanced regional ESV, although it may partially inhibit the economic development potential of some areas. Therefore, it is necessary to actively explore effective paths for the transformation of ecological product value and the coordinated development of ecological economy in the future.

Based on the above analysis, the spatial dynamics and response characteristics of regional ESV vary significantly under different development scenarios. The inertial evolution baseline scenario follows historical trends in regional ESV, but the potential for ESV recovery remains limited. Although the urbanization-dominated scenario promotes rapid economic development, it exacerbates the decline in ESV and weakens ecosystem service functions. The ecological protection priority scenario significantly enhances ESV stability and recovery capacity, aligning more closely with long-term sustainable development goals. Therefore, future adaptive regional governance strategies need to comprehensively consider the coordination and unification of ecological and economic goals to achieve a dynamic balance between protecting the value of ecosystem services and improving social and economic benefits.

4 Discussion

4.1 Adaptive zoning pathways under future scenarios

4.1.1 Strategic implications of scenario-based development choices

In contrast to other typical arid regions, the mismatch between ESV and EV in Northern Shaanxi is strongly affected by large-scale policy interventions, such as the Grain for Green Program, which has partially reduced regional EV. This is markedly different from arid areas where systematic policy support is absent.

Under the inertial evolution baseline scenario, regional ecological risks decrease, yet ESV improvement in ecological stability areas is limited and ecosystem recovery potential is not fully achieved. This suggests that relying solely on current policy inertia cannot sufficiently stimulate ecological-economic synergy (O’Farrell et al., 2010). Future governance should overcome this inertia by enhancing ecological protection and optimizing industrial layout to achieve coordinated regional development.

The urbanization-dominated scenario offers economic growth potential, but leads to severe degradation of ecosystem services and sharply increased ecological risks. Notably, key restoration areas experience significant ESV declines, indicating a critical ecological security threat (Xu et al., 2017). Therefore, adaptive governance should promote an ecologically constrained urbanization model through ecological red lines, compensation mechanisms, and green infrastructure. Such measures can balance urban expansion with ecological conservation and prevent irreversible ecosystem degradation.

The ecological protection priority scenario substantially enhances ecosystem stability and resilience, reduces high-risk areas, and reinforces ecological security, although it may limit economic growth. Future governance should prioritize effective transformation of ecological value into economic benefits (Weißhuhn et al., 2018), promote integrated development of ecological industries, tourism, and specialty agriculture, and ensure sustained ecological protection benefits. Dynamic monitoring and flexible management should be implemented in core protection areas to enhance risk control.

Overall, the future development of Northern Shaanxi should prioritize ecosystem service protection, strengthen mechanisms for ecological product value, and incorporate adaptive strategies to achieve a win-win situation for ecosystem health and socioeconomic resilience (Yan et al., 2023).

4.1.2 Region-specific optimization of adaptive zoning pathways

Future governance in ecological stability areas should enhance the synergy between ecological functions and economic interests by promoting the marketization of ecological assets and benefit-sharing mechanisms. This approach can stimulate endogenous development momentum (O’Farrell et al., 2010). In core ecological barrier regions such as Huanglong County, Huangling County, Qingjian County, and Wuqi County, priority should be given to developing eco-agriculture, understory economies, and eco-health tourism. Establishing community-led cooperatives and local industry alliances will link ecological value realization with local wellbeing and facilitate green development transitions.

General utilization areas should focus on spatial reserves and flexible management. Ecological buffer zones and green infrastructure planning should be optimized through active community participation (Metzger et al., 2008). Overall, regional governance should adopt a collaborative government-market-community model (Yan et al., 2023), strengthen capacity building and information disclosure, and establish long-term monitoring and early warning systems to ensure sustainability and adaptive management.

Core protection areas should transition from basic protection to resilience-focused governance. Mechanisms such as ecological resilience banks, risk-sharing for ecological services, and dynamic asset trading can increase regional resistance to disturbances (Weißhuhn et al., 2018). Implementing ecological asset mortgages and risk transactions enables the sharing of sudden ecological risks and minimizes regional losses. This strategy also promotes government-led, community-participated co-management and strengthens monitoring and early warning systems.

Key restoration areas should shift from single restoration projects to holistic social-ecological system restoration. Strengthening community organizations and engaging multiple stakeholders will deeply integrate ecological restoration with rural revitalization (Xu et al., 2017). Mechanisms such as eco-employment, poverty alleviation, and ecological welfare projects should be developed to increase resident participation and motivation, thereby achieving both ecological recovery and socioeconomic resilience.

4.2 Limitations and prospects

This study makes progress in ESV and EV coupled zoning and scenario simulation optimization, but still has limitations. First, ESV assessment mainly relies on the equivalent factor method, which cannot fully capture the unique ecological value of Northern Shaanxi and its subregions, and the adjustment of coefficients remains subjective. Although the EV assessment integrates multiple indicators, data limitations mean some key ecological processes and micro-level risks are not included, reducing the representation of actual vulnerability. The use of the PLUS model for scenario simulation relies on idealized trends, lacking full consideration of policy changes, market dynamics, population migration, and sensitivity to external factors like climate change and technological advances, affecting forecast reliability.

At the practical governance level, this study proposes an integrated “zonal supervision–mechanism innovation–early warning platform” framework, transforming methodological achievements into effective governance and monitoring tools. While this framework enhances zoned management capacity and crisis warning, it still faces challenges in responding to sudden ecological emergencies and balancing conservation, economic objectives, and land use conflicts, which involve uncertainty. Therefore, the proposed “classification–simulation–strategy alignment” theoretical framework offers strong coherence and adaptability, being applicable to risk assessment and zoned governance in other ecologically vulnerable regions. It also allows for localized adjustment of indicators and parameters, thereby increasing scientific accuracy and relevance in practical applications.

Future improvements should focus on innovating assessment methods, developing region-specific equivalent factor systems with multi-source data, enhancing ESV assessment with long-term observation and model calibration, constructing process-based service assessment models, and strengthening the dynamic coupling between governance strategies and spatial zoning. This will better serve multiple stakeholders and promote sustainable, effective regional ecosystem governance.

5 Conclusion

This study uses Northern Shaanxi in China as a case to systematically analyze the spatial coupling characteristics between ESV and EV, establish an ecological function zoning system driven by “value-vulnerability” synergy, and propose differentiated governance paths under multiple scenarios to support regional ecological security and sustainable development. The results show that ESV in Northern Shaanxi is generally higher in the south and lower in the north, while EV is higher in the northwest and lower in the southeast, indicating significant spatial coupling differences. From 2000 to 2020, overall ecosystem service function improved and EV declined considerably, with different zones displaying distinct responses under various scenario simulations. Ecological protection priority scenarios enhanced services in stable zones, whereas urbanization-dominated scenarios increased degradation risks in ecologically sensitive areas. In response to zoning governance needs, the study proposes tailored governance paths including protection priority, risk control, restoration integration, and green utilization, emphasizing the coordinated progress of ecological function improvement and sustainable development.

Looking ahead, it is necessary to improve the accuracy of ESV process simulation, deepen the dynamic predictive capabilities of scenario simulations, and strengthen the dynamic linkage between governance strategies and spatial zoning outcomes. This research framework provides scientific evidence for precise governance, resource optimization, and high-quality development in ecologically fragile regions, features strong transferability, and highlights the importance of local adaptation and stakeholder collaboration, offering theoretical and practical guidance for resilient ecological governance globally.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MW: Writing – original draft, Software, Data curation, Methodology. JF: Writing – original draft, Investigation, Visualization. XH: Software, Writing – original draft, Validation. YB: Funding acquisition, Writing – review and editing, Supervision.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by the National Natural Science Foundation of China (Grant No. 42401321); Innovation Capability Support Program of Shaanxi, No. 2025KG-YBXM-021; Fundamental Research Funds for the Central Universities, CHD, No. 300102354101. The authors are grateful to the reviewers for their insightful comments and suggestions that helped improve the quality of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Babí Almenar, J., Elliot, T., Rugani, B., Philippe, B., Navarrete Gutierrez, T., Sonnemann, G., et al. (2021). Nexus between nature-based solutions, ecosystem services and urban challenges. Land Use Policy 100, 104898. doi:10.1016/j.landusepol.2020.104898

CrossRef Full Text | Google Scholar

Bi, J., Hao, R., Li, J., and Qiao, J. (2021). Identifying ecosystem states with patterns of ecosystem service bundles. Ecol. Indic. 131, 108195. doi:10.1016/j.ecolind.2021.108195

CrossRef Full Text | Google Scholar

Burkhard, B., Kroll, F., Nedkov, S., and Müller, F. (2012). Mapping ecosystem service supply, demand and budgets. Ecol. Indic. 21, 17–29. doi:10.1016/j.ecolind.2011.06.019

CrossRef Full Text | Google Scholar

Cai, Z., Page, J., and Cvetkovic, V. (2021). Urban ecosystem vulnerability assessment of support climate-resilient city development. Urban Plan. 6 (3), 227–239. doi:10.17645/up.v6i3.4208

CrossRef Full Text | Google Scholar

Cao, Y., Kong, L., Zhang, L., and Ouyang, Z. (2021). The balance between economic development and ecosystem service value in the process of land urbanization: a case study of China’s land urbanization from 2000 to 2015. Land Use Policy 108, 105536. doi:10.1016/j.landusepol.2021.105536

CrossRef Full Text | Google Scholar

Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., et al. (1997). The value of the world’s ecosystem services and natural capital. Nature 387, 253–260. doi:10.1038/387253a0

CrossRef Full Text | Google Scholar

Evans, D. L., Falagán, N., Hardman, C. A., Kourmpetli, S., Liu, L., Mead, B. R., et al. (2022). Ecosystem service delivery by urban agriculture and green infrastructure – a systematic review. Ecosyst. Serv. 54, 101405. doi:10.1016/j.ecoser.2022.101405

CrossRef Full Text | Google Scholar

Feng, X., Fu, B., Piao, S., Wang, S., Ciais, P., Zeng, Z., et al. (2016). Revegetation in China’s Loess plateau is approaching sustainable water resource limits. Nat. Clim. Change 6 (11), 1019–1022. doi:10.1038/nclimate3092

CrossRef Full Text | Google Scholar

Feng, Z., Jin, X., Chen, T., and Wu, J. (2021). Understanding trade-offs and synergies of ecosystem services to support the decision-making in the Beijing–Tianjin–Hebei region. Land Use Policy 106, 105446. doi:10.1016/j.landusepol.2021.105446

CrossRef Full Text | Google Scholar

Fu, B., Wang, S., Liu, Y., Liu, J., Liang, W., and Miao, C. (2017). Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess plateau of China. Ann. Rev. 45, 223–243.

CrossRef Full Text | Google Scholar

Guo, X., Zhang, Y., Guo, D., Lu, W., and Xu, H. (2023). How does ecological protection redline policy affect regional land use and ecosystem services? Environ. Impact Assess. Rev. 100, 107062. doi:10.1016/j.eiar.2023.107062

CrossRef Full Text | Google Scholar

He, L., Shen, J., and Zhang, Y. (2018). Ecological vulnerability assessment for ecological conservation and environmental management. J. Environ. Manag. 206, 1115–1125. doi:10.1016/j.jenvman.2017.11.059

PubMed Abstract | CrossRef Full Text | Google Scholar

Hou, K., Tao, W., He, D., and Li, X. (2022). A new perspective on ecological vulnerability and its transformation mechanisms. Ecosyst. Health Sustain. 8 (1), 2115403. doi:10.1080/20964129.2022.2115403

CrossRef Full Text | Google Scholar

Hu, X., Ma, C., Huang, P., and Guo, X. (2021). Ecological vulnerability assessment based on AHP-PSR method and analysis of its single parameter sensitivity and spatial autocorrelation for ecological protection – a case of Weifang City, China. Ecol. Indic. 125, 107464. doi:10.1016/j.ecolind.2021.107464

CrossRef Full Text | Google Scholar

Laterra, P., Barral, P., Carmona, A., and Nahuelhual, L. (2016). Focusing conservation efforts on ecosystem service supply may increase vulnerability of socio-ecological systems. PloS One 11 (5), e0155019. doi:10.1371/journal.pone.0155019

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, A. L., Zhou, Y., Tang, L. Y., Niu, L. D., and Pan, M. (2023). Land use simulation and ecosystem service value assessment in Nujiang state: multi-scenario analysis based on the PLUS model. Chin. J. Agric. Resour. Regional Plan. 44 (1), 140–149. (in Chinese).

Google Scholar

Li, C., and Wu, J. (2022). Land use transformation and eco-environmental effects based on production-living-ecological spatial synergy: evidence from Shaanxi Province, China. Environ. Sci. Pollut. Res. Int. 29 (27), 41492–41504. doi:10.1007/s11356-022-18777-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, C., Yang, M., Li, Z., and Wang, B. (2021). How will rwandan land Use/Land cover change under high population pressure and changing climate? Appl. Sci. 11 (12), 5376. doi:10.3390/app11125376

CrossRef Full Text | Google Scholar

Li, D., Yang, W., and Huang, R. (2023). The multidimensional differences and driving forces of ecological environment resilience in China. Environ. Impact Assess. Rev. 98, 106954. doi:10.1016/j.eiar.2022.106954

CrossRef Full Text | Google Scholar

Li, J., Hu, D., Wang, Y., Chu, J., Yin, H., and Ma, M. (2024). Study of identification and simulation of ecological zoning through integration of landscape ecological risk and ecosystem service value. Sustain. Cities Soc. 107, 105442. doi:10.1016/j.scs.2024.105442

CrossRef Full Text | Google Scholar

Li, S., Zhao, X., Pu, J., Miao, P., Wang, Q., and Tan, K. (2021). Optimize and control territorial spatial functional areas to improve the ecological stability and total environment in karst areas of Southwest China. Land Use Policy 100, 104940. doi:10.1016/j.landusepol.2020.104940

CrossRef Full Text | Google Scholar

Liao, G., He, P., Gao, X., Lin, Z., Fang, C., Zhou, W., et al. (2021). Identifying critical area of ecosystem service supply and demand at different scales based on spatial heterogeneity assessment and SOFM neural network. Front. Environ. Sci. 9, 714874. doi:10.3389/fenvs.2021.714874

CrossRef Full Text | Google Scholar

Liu, X., Yang, C. Y., Gao, Y. N., Sun, Q. Y., Wang, S. X., Feng, C. Y., Liu, X., et al. (2023). Identification and hierarchical management of ecological security patterns and ecological control zones in Xiamen City. Acta Ecol. Sin. 43 (13), 5357–5369. doi:10.5846/stxb202203160641 (in Chinese).

CrossRef Full Text | Google Scholar

Ma, S., Qiao, Y., Wang, L., and Zhang, J. (2021). Terrain gradient variations in ecosystem services of different vegetation types in mountainous regions: vegetation resource conservation and sustainable development. For. Ecol. Manag. 482, 118856. doi:10.1016/j.foreco.2020.118856

CrossRef Full Text | Google Scholar

Metzger, M. J., Schröter, D., Leemans, R., and Cramer, W. (2008). A spatially explicit and quantitative vulnerability assessment of ecosystem service change in Europe. Reg. Environ. Change 8 (3), 91–107. doi:10.1007/s10113-008-0044-x

CrossRef Full Text | Google Scholar

Mon, D., Cheng, C., and Lin, J. (1994). Evaluating weapon system using fuzzy analytic hierarchy process-based on entropy weight. Fuzzy Sets Syst. 62 (2), 127–134. doi:10.1016/0165-0114(94)90052-3

CrossRef Full Text | Google Scholar

Ni, Z., Yang, J., and Razzaq, A. (2022). How do natural resources, digitalization, and institutional governance contribute to ecological sustainability through load capacity factors in highly resource-consuming economies? Resour. Policy 79, 103068. doi:10.1016/j.resourpol.2022.103068

CrossRef Full Text | Google Scholar

O’Farrell, P. J., Reyers, B., Le Maitre, D. C., Milton, S. J., Egoh, B., Maherry, A., et al. (2010). Multi-functional landscapes in semi arid environments: implications for biodiversity and ecosystem services. Landsc. Ecol. 25 (8), 1231–1246. doi:10.1007/s10980-010-9495-9

CrossRef Full Text | Google Scholar

Pan, Z., Gao, G., and Fu, B. (2022). Spatiotemporal changes and driving forces of ecosystem vulnerability in the Yangtze River Basin, China: quantification using habitat-structure-function framework. Sci. Total Environ. 835, 155494. doi:10.1016/j.scitotenv.2022.155494

PubMed Abstract | CrossRef Full Text | Google Scholar

Ran, P., Hu, S., Frazier, A. E., Qu, S., Yu, D., and Tong, L. (2022). Exploring changes in landscape ecological risk in the Yangtze River Economic Belt from a spatiotemporal perspective. Ecol. Indic. 137, 108744. doi:10.1016/j.ecolind.2022.108744

CrossRef Full Text | Google Scholar

Shen, J., Li, S., Wang, H., Wu, S., Liang, Z., Zhang, Y., et al. (2023). Understanding the spatial relationships and drivers of ecosystem service supply-demand mismatches towards spatially-targeted management of social-ecological system. J. Clean. Prod. 406, 136882. doi:10.1016/j.jclepro.2023.136882

CrossRef Full Text | Google Scholar

Vihervaara, P., Rönkä, M., and Walls, M. (2010). Trends in ecosystem service research: early steps and current drivers. Ambio 39 (4), 314–324. doi:10.1007/s13280-010-0048-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, R., and Cai, Y. L. (2010). Restoration patterns of degraded ecosystems in the karst region of Southwest China. Chin. J. Appl. Ecol. 21 (4), 1070–1080. doi:10.13287/j.1001-9332.2010.0118 (in Chinese).

CrossRef Full Text | Google Scholar

Wang, Y., and Pan, J. H. (2019). Optimization of ecological security pattern based on ecosystem service value reconstruction: a case study of Zhangye, Gansu. Acta Ecol. Sin. 39 (10), 3455–3467. doi:10.5846/stxb201803130495 (in Chinese).

CrossRef Full Text | Google Scholar

Wang, J. N., and Zhang, Z. (2022). Land use change and simulation analysis in the northern margin of Chai based on the Markov-PLUS model. J. Northwest For. Univ. 37 (3), 139–148+179. doi:10.3969/j.issn.1001-7461.2022.03.20 (in Chinese).

CrossRef Full Text | Google Scholar

Wang, S., Fu, B., Piao, S., Lü, Y., Ciais, P., Feng, X., et al. (2016). Reduced sediment transport in the Yellow river due to anthropogenic changes. Nat. Geosci. 9 (1), 38–41. doi:10.1038/ngeo2602

CrossRef Full Text | Google Scholar

Wang, B. S., Liao, J. F., Zhu, W., Qiu, Q. Y., Wang, L., and Tang, L. N. (2019). Neighborhood weight setting of the FLUS model based on historical scenarios: a case study of land use simulation in Fujian Triangle cities cluster in 2030. Acta Ecol. Sin. 39 (12), 4284–4298. doi:10.5846/stxb201808021649 (in Chinese).

CrossRef Full Text | Google Scholar

Wei, W., Wang, N., Yin, L., Guo, S., and Bo, L. (2024). Spatio-temporal evolution characteristics and driving mechanisms of urban–agricultural–ecological space in ecologically fragile areas: a case study of the upper reaches of the Yangtze River Economic Belt, China. Land Use Policy 145, 107282. doi:10.1016/j.landusepol.2024.107282

CrossRef Full Text | Google Scholar

Weißhuhn, P., Müller, F., and Wiggering, H. (2018). Ecosystem vulnerability review: proposal of an interdisciplinary ecosystem assessment approach. Environ. Manag. (New York) 61 (6), 904–915. doi:10.1007/s00267-018-1023-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Xie, G. D., Zhen, L., Lu, C. X., Xiao, Y., and Chen, C. (2008). A method for valuing ecosystem services based on expert knowledge. J. Nat. Resourc. (5), 911–919. (in Chinese).

Google Scholar

Xu, W., Xiao, Y., Zhang, J., Yang, W., Zhang, L., Hull, V., et al. (2017). Strengthening protected areas for biodiversity and ecosystem services in China. Proc. Natl. Acad. Sci. U. S. A. 114 (7), 1601–1606. doi:10.1073/pnas.1620503114

PubMed Abstract | CrossRef Full Text | Google Scholar

Yan, X., Liu, C., Han, Z., Li, X., and Zhong, J. (2023). Spatiotemporal assessment of ecosystem services supply–demand relationships to identify ecological management zoning in coastal city Dalian, China. Environ. Sci. Pollut. Res. Int. 30 (23), 63464–63478. doi:10.1007/s11356-023-26704-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Yang, S., Su, H., and Zhao, G. P. (2022). Multi-scenario simulation of urban ecosystem service value based on the PLUS model: a case study of Hanzhong City. J. Arid Zone Resou. Environ. 36 (10), 86–95. doi:10.13448/j.cnki.jalre.2022.255 (in Chinese).

CrossRef Full Text | Google Scholar

Yuan, J., Li, R., and Huang, K. (2022). Driving factors of the variation of ecosystem service and the trade-off and synergistic relationships in typical karst basin. Ecol. Indic. 142, 109253. doi:10.1016/j.ecolind.2022.109253

CrossRef Full Text | Google Scholar

Zhang, X., Liu, K., Wang, S., Wu, T., Li, X., Wang, J., et al. (2022). Spatiotemporal evolution of ecological vulnerability in the Yellow River Basin under ecological restoration initiatives. Ecol. Indic. 135, 108586. doi:10.1016/j.ecolind.2022.108586

CrossRef Full Text | Google Scholar

Zhang, J. F., Yang, W. R., Li, J. J., Shen, S. Y., Ma, L., and Chen, L. (2025). Spatiotemporal change and driving forces of ecosystem service value in Beijing. Acta Ecol. Sin. 45 (1), 306–318. doi:10.20103/j.stxb.202312312885 (in Chinese).

CrossRef Full Text | Google Scholar