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Article

Spatiotemporal Evolution and Driving Factors of Ecosystem Supply and Demand Bundles: A Case Study in the Sichuan-Yunnan Ecological Buffer Area, China

by
Bin Yang
1,
Dongqian Xue
1,* and
Peipei Miao
2,*
1
School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
2
Faculty of Geography, Yunnan Normal University, Kunming 650500, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(12), 4977; https://doi.org/10.3390/su16124977
Submission received: 22 April 2024 / Revised: 5 June 2024 / Accepted: 7 June 2024 / Published: 11 June 2024
(This article belongs to the Topic Earth Observation-Based Ecosystem Services in Support of Planet SDGs)
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Abstract

:
Identifying the spatial characteristics of ecosystem service (ES) supply and demand is crucial for effective ecosystem management and restoration. Past related studies have primarily focused on balancing ES supply and demand and supply clustering, with less attention focused on the drivers of demand clustering and their spatial evolution. This study explored the spatiotemporal supply–demand dynamics in four crucial ecosystem services (ESs) in the Sichuan-Yunnan ecological buffer area region between 2005 and 2019, namely water yield, net primary production, soil conservation, and habitat quality. Self-organizing maps and geographical detectors were used to classify supply–demand ES bundles as their main drivers, respectively. The main results of the study included: (1) A decline in habitat quality, whereas net primary productivity, water yield, and soil conservation increased. However, there were increasing demands for habitat quality, water yield, and net primary productivity, despite the decrease in demand for soil conservation. (2) Demand for habitat quality was met by supply, whereas there were deficits in soil conservation, water yield, and net primary productivity, which contributed to the demand in the east exceeding that elsewhere. (3) The proportion of ES bundle 2 increased, whereas those of the remaining ES bundles declined. Similarly, the areas of ES demand bundles (ESDBs) 1 and 4 decreased, whereas those of 2 and 3 increased. While the spatial extent of the ESBs remained relatively stable, those of the ESDBs in the northern regions increased. Key factors influencing the spatial distribution of ES supply include human activities, population density, and precipitation, whereas land use, population density, and the human activity index primarily affect demand distribution. The results of this study can act as a reference for comprehensive regional ecosystem management.

1. Introduction

Ecosystem services (ESs) are the direct and indirect benefits provided to humans by ecosystems [1,2], and they provide a crucial link between the natural ecology and human well-being [3]. Accelerated global socioeconomic development and urbanization over recent decades have altered ecosystem patterns and processes, leading to diminished areas of natural land, degraded habitat quality, and reduced capacity for ES provision [4]. These changes have adversely impacted human well-being and have emerged as significant threats to regional ecological security [5]. At the same time, the human populations of developing countries have shown growing aspirations for an improved quality of life, which have resulted in increased demands on ESs, including climate regulation, water conservation, and soil conservation. Ecosystems are increasingly unable to meet these demands, although deficits in ESs show spatiotemporal variability. Deficits in ESs impede regional sustainable development [6]. Therefore, the present study explored the spatiotemporal evolution of the supply and demand of ecosystem services as a measure of heterogeneity and to guide sustainable development [7,8,9].
Past global studies have introduced the concepts of ES supply and demand. The dominant methods used in these studies include land use estimation, ecological process simulation, expert judgment, and the use of simulation models, such as Artificial Intelligence for Ecosystem Services (ARIES). Many of these studies have focused on understanding the regulation and provision of cultural services and their associated supply–demand relationship [10,11]. Burkhard employed a land use matrix model, informed by land use types and expert input, to develop an ES supply and demand matrix [12]; Bangash emphasized the importance of soil and water conservation efforts in managing the demand for hydrological ESs [13]. Research on ES supply and demand in China has experienced rapid development over the past two decades, evolving from an initial emphasis on supply to a comprehensive examination of the interplay between supply and demand [14]. Within these studies, tools such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model [15] and the Service Path Attribution Networks (SPANs) model [16] have facilitated spatially detailed assessments of ESs. These assessments have further enabled the effective measurement of supply–demand relationships and the management of ESs and natural resources. Liu noted significant county-level heterogeneity in the models representing ES supply–demand relationships [17]; Zhang provided a comprehensive study on the spatiotemporal patterns and evolutionary characteristics of regional ES supply and demand [18]; Rong identified ecological source areas by analyzing two levels of ES supply and demand [19]. The above studies have collectively made significant progress in assessing ES supply and demand, their spatiotemporal variability, evolution, and driving factors [20].
However, these studies have frequently ignored potential spatial correlations and complex interdependencies among multiple ESs. Therefore, the present study conducted a quantitative analysis to examine the relationship between ecosystem supply and demand and their evolution to reveal the spatiotemporal heterogeneity of ecosystem service provision in the study area [21,22,23]. The spatiotemporal reoccurrence of ESs forms bundles known as ecological service bundles, and these bundles allow the quantitative study of ESs. ESBs are typically identified using methods such as principal component analysis (PCA), mean clustering, self-organizing maps, spatial autocorrelation, and random forest (RF) [24,25,26]. The application of K-means clustering by Chen to identify ESBs illustrated the relationship between ESBs and land use patterns [27]; Song applied the Kohonen package in R to classify ESBs in Fuzhou City; by studying small-scale areas in Liangjiang New Area, Chongqing [28], Wu devised an ecological land classification framework based on ESBs [29]. The above studies highlight the dynamic nature of ES research and the need for further exploration of intricate interactions between driving factors. Recent related studies have predominantly applied the ESB concept to investigate the wide range of ESs in a region and to identify the predominant types. The ESB method facilitates a deeper understanding of the distribution and interrelationships among different ESs [30]. However, most past studies on ESBs have focused on static analyses of supply bundles or single temporal snapshots, while there has been relatively less focus on the evaluation of the spatiotemporal evolution of demand bundles [27,31]. Moreover, there remain relatively few past studies on the factors influencing ESB supply and demand. There are also few studies addressing the ES supply deficits in regions with unique geological and geomorphological characteristics, such as the Sichuan-Yunnan ecological buffer area. Therefore, there remains an important need to characterize the factors influencing the spatial distribution of ESDBs. Consequently, a quantitative analysis of the various factors influencing these bundles using geographic detectors could have significant practical implications for regional ecological and environmental planning [32].
The Sichuan-Yunnan ecological barrier area is important for the conservation and rehabilitation of important ecosystems in China and is characterized as an ecologically fragile and key ecological functional zone. The ecological barrier area serves as a vital ecological buffer area in Southwest China, playing a central role in the conservation of soil and water and the preservation of biodiversity [33]. However, accelerated urbanization and socioeconomic development in recent decades have compromised the function of the Sichuan-Yunnan ecological barrier area, contributing to severe water and soil erosion in the middle and lower reaches of the Yangtze River. Simultaneously, overgrazing and extensive animal husbandry practices have led to biodiversity loss in the region [34]. This study aimed to characterize the spatiotemporal dynamics in four key ecosystem services (water yield (WY), net primary productivity (NPP), soil conservation (SC), and habitat quality (HQ)) in the Sichuan-Yunnan ecological buffer area from 2005 to 2019. The present study examined the spatiotemporal evolution and driving factors of ecosystem service supply and demand bundles in the Sichuan-Yunnan ecological buffer area from 2005 to 2019. The objectives of the present study were to: (1) conduct a quantitative assessment of the supply and demand for four critical ESs between 2005 and 2019, namely WY, NPP, SC, and HQ; (2) evaluate the supply–demand ratio of these services during the same period; (3) employ SOM to delineate spatial and temporal patterns of ESBs; and (4) apply geographical detectors to identify the key factors driving these bundles. There is a crucial need to explore the spatiotemporal characteristics and driving forces of supply–demand ES bundles in buffer areas to obtain an improved understanding of the intrinsic connections between multiple ecosystems. Analyzing the evolutionary trajectories of ecosystem service bundles over different periods is essential for clarifying the direction of regional ecological degradation and guiding the construction of ecological civilization. The findings from this research will contribute to ecological protection and the optimization of ecosystem management strategies in the ecological buffer area.

2. Materials and Methods

2.1. Overview of the Study Area

The Sichuan-Yunnan ecological barrier area is in northwest Yunnan and central Sichuan (26°32′–34°19′ N, 98°03′–104°58′ E) and encompasses 76 and 17 counties in Sichuan and Yunnan provinces, respectively, with an area of 236,700 km2. The Sichuan-Yunnan ecological barrier area has a climate ranging from plateau mountainous temperate to subtropical monsoon, with an annual precipitation of 500 to 1400 mm. The temperature in the Sichuan-Yunnan ecological barrier area shows significant spatial variation, with differences in the average annual temperature exceeding 20 °C. The area has an extensive river system with primarily dendritic or feather-like spatial patterns and includes major rivers, such as the Jinsha, Yalong, Dadu, and Minjiang rivers [35] (Figure 1).

2.2. Data Sources and Processing

As shown in Table 1, the data used in the present study included land use, meteorological, socioeconomic, and terrain data. Land use was categorized into six primary categories. Estimates of WY and HQ were produced using the InVEST model; NPP was calculated using the Carnegie–Ames–Stanford approach (CASA) model; SC was estimated using the revised universal soil loss equation (RUSLE); WY demand was determined by uniformly distributing water usage among the industrial, domestic, agricultural, and ecological sectors; and demand for HQ and NPP were determined using the InVEST model and an inversion model using nighttime light data and carbon emissions, respectively; SC demand was calculated based on actual soil erosion rates. All the data were standardized to a 1 km × 1 km grid for comprehensive analysis.

2.3. Methodology

2.3.1. Research Framework

The present study evaluated spatiotemporal changes in ES supply and demand and their relationship to delineate the spatiotemporal evolution of ESBs. In addition, the present study applied geographical detectors to explore the factors regulating these ESBs, as shown in Figure 2.

2.3.2. Estimation of the Supply of ESs

The present study focused on four key ESs, namely WY, NPP, SC, and HQ, consistent with and building upon previous studies [36,37,38] and considering the vulnerability of ecosystems. ESs were evaluated in the present study using several methodologies, including the CASA, RUSLE, and InVEST models. Given the global emphasis on achieving “carbon peaking and carbon neutrality”, carbon storage was highlighted in the present study as a critical regulatory function due to its direct link to carbon emissions. The selection of HQ as an indicator of ES-supporting functions highlighted the importance of preserving biodiversity and enhancing habitat conditions, particularly given the risks posed by deforestation and habitat destruction. Furthermore, since the assessment of water and soil conservation is pivotal for achieving sustainable development, water-driven soil erosion in the study area was regarded as a factor that posed risks to water resources sustainability and quality as well as to agricultural productivity in downstream regions.
(1) Net primary productivity (NPP)
The present study used the CASA model to estimate the NPP of vegetation. The CASA model uses the principle of light energy utilization for estimating carbon fixation services [39,40,41].
(2) Water yield (WY)
The present study applied the InVEST model to evaluate WY [42,43,44].
(3) Habitat quality (HQ)
The maintenance of biodiversity is closely related to habitat quality. The present study used the biodiversity module of the InVEST model to assess the magnitude of HQ [34,45,46].
(4) Soil conservation (SC)
The present study applied the RUSLE model to estimate SC [47,48].

2.3.3. Estimation of Ecosystem Services (ES) Demand

(1) Carbon storage services
Grid carbon storage services demand was calculated according to the stochastic benefit model of nighttime light data and carbon emissions proposed by Pan [49]. The inversion model constructed using this approach achieved an R of 0.87.
(2) Demand for WY
Demand for water conservation considers the consumption of water for industrial, domestic, agricultural, and ecological purposes. The present study estimated gridded water conservation demand by distributing water consumption across various land use types, including construction, urban, rural, cultivated, forest, and grasslands [50].
(3) Habitat quality
By assuming that the average HQ of the study area can act as the benchmark for HQ demand, the present study determined gridded demand for HQ services according to the discrepancy between this benchmark and the actual HQ of the grid [51].
(4) Accounting of soil conservation
The preset study applied soil loss through erosion as soil conservation service demand; this was calculated using the RUSLE model [35].

2.3.4. Matching ES Supply and Demand

The present study used Wang’s approach for calculating the ecological supply and demand ratio (ESDR) to analyze the relationship between the ES supply and demand [52].

2.3.5. Identification of ES Bundles

The present study applied the self-organizing maps (SOMs) method to delineate ESBs [53]. An initial 1 km × 1 km grid was chosen as the optimal spatial resolution to evaluate the interactions between the different ESs of the study area. Consequently, the data characterizing the four ESs needed to be standardized to the above spatial resolution. The optimal number of ESBs was then identified using the clusGap function in R. Lastly, ES data were processed using the self-organizing map 2 functions in the MATLAB R environment. The repeated training sessions and increasing stability of the clustering outcomes facilitated the spatial clustering of ESBs and the delineation of distinct ES functions [54].

2.3.6. Identification of Driving Factors

The present study selected eight indicators spanning five categories according to the existing research and the natural and anthropogenic properties of the ecological buffer area for the analysis of the driving factors. The natural breakpoint method was used to classify each driving factor, and the factor detection module of the geographical detector was used to determine the degree to which each factor influenced the pattern of ecological function zoning [55].

3. Results

3.1. Temporal ES Supply–Demand Patterns in the Ecological Buffer Area

3.1.1. Temporal Trends in Ecosystem Service (ES) Supply

HQ supply in the ecological buffer zone between 2005 and 2019 exhibited a declining trend with minor fluctuations. This decline was closely linked to the intrinsic habitat suitability of the land type and the disturbances resulting from the surrounding human activities and was attributable to intensified human activities and increased landscape fragmentation. Temperature and rainfall variations were the main drivers of WY variability. The increases and decreases in rainfall and temperature over this period, respectively, resulted in reduced potential vegetation evapotranspiration and significantly enhanced WY. Concurrently, there was an overall increase in NPP with variations due to rising precipitation and the resulting increased soil moisture available to vegetation. There was also an increasing trend in soil conservation supply, which was attributable to soil erosion factors, with rainfall directly impacting the rainfall erosivity factor. The synergistic relationships between the various factors contributed to a notable enhancement in soil conservation services within the study area over the study period. Therefore, despite a decline in average annual HQ, the remaining ESs showed increasing trends between 2005 and 2019 (Table 2).

3.1.2. Temporal Variations in Ecosystem Service (ES) Demand

There was a consistent increasing trend in demand for HQ in the ecological buffer areas from 2005 to 2019; this was attributable to population growth and socioeconomic development and the associated localized demand for high-quality natural habitats. The largest population density was observed in the northeastern part of the study area, mainly the Chengdu Plain (Figure 3a). Population density showed an increasing trend from 2005 to 2019 (Figure 3b). There was a similar increasing demand for water source conservation, which was primarily attributable to growing residential and industrial water demand. In addition, there was a drastic increase in NPP demand, reflecting the combined effects of population increase, industrial and agricultural development, and urban expansion, which, in turn, have amplified the demand for carbon sequestration. There was a declining trend in the demand for SC, which was largely attributable to rapid economic development and the consequent industrial and agricultural activities that have been drivers of extensive land use changes. Significant areas of forest land and other natural cover have been converted into farmland or construction sites to accommodate agricultural production and urban expansion. These transformations in land use typically result in diminished SC since construction sites and bare farmland are more prone to soil erosion compared to forests or grasslands. Notably, while there was a decrease in demand for SC from 2005 to 2019, there were significant increasing trends in the demands for the other three ecosystem services (Table 2).

3.1.3. Temporal Trends in the ESDR

There was a decreasing trend in the HQ supply–demand ratio in the ecological buffer areas from 2005 to 2019; this was attributable to the degradation of natural ecosystems and to biodiversity loss from economic expansion, urbanization, and intensified human activities. Conversely, there was an increasing trend in the water supply–demand ratio influenced by governmental water conservation initiatives and forest restoration. There was a similar increase in the NPP supply–demand ratio, reflecting the effect of urban growth and heightened energy consumption on the increase in the demand for carbon sequestration. This increase could also be attributed to expanded vegetation coverage, which enhanced the carbon sequestration capacity of the region. There was also an overall increasing trend in the soil conservation supply–demand ratio, reflecting the significant impact of various initiatives, such as returning farmland to forests and grasslands and soil conservation by terracing. In summary, there were increases in NPP, WY, and SC over the fifteen-year period, whereas HQ experienced a decline, reflecting the persistent challenge to the ecological integrity of a region posed by human activities (Table 2).

3.2. Spatial Patterns in ES Supply and Demand

3.2.1. Spatial Trends in ES Supply

Areas of high HQ between 2005 and 2019 were concentrated in the southwest, which is characterized by extensive forests and fewer threats to biodiversity; areas of heightened WY service were concentrated in the east-central part of the study area, corresponding to increased impermeability of the land surface due to construction; high soil conservation services were concentrated in the central and southwestern areas of the study area and were attributable to extensive woodlands and grasslands, leading to enhanced rainfall interception; areas of high NPP were found in the central and southwestern regions of the study area, which is characterized by extensive forest and vegetation coverage. Conversely, areas of low soil conservation were concentrated in the east and southeast of the study area; this was attributable to the construction, unused, cultivated, and grassland areas, all of which are characterized by sparse vegetation coverage; areas of low NPP were mainly found in the central and northern regions, which are characterized by unused land and grasslands (Figure 4). The areas showing elevated values for the four ecosystem services from 2005 to 2019 were mainly concentrated in the west and south. In contrast, low values were concentrated in the north (including the northeast and northwest) and central areas (including the central east and central west), with sporadic distributions in the south (including the southeast and southwest). The spatial variation in ecosystem services within the study area was significantly influenced by diverse land use types, the quality of the ecological environment, the intensity of human activity, and the levels of infrastructure development.

3.2.2. Spatial Trends in ES Demand

The demand for HQ, WY, and NPP between 2005 and 2019 generally increased from south to east, whereas the demand for soil conservation increased from south to north. Since areas with significant demand for HQ largely coincided with densely populated regions, this demand for HQ services could be attributed to the growing need for urban parks, wetland reserves, and natural tourist spots. Conversely, regions with lower demands for HQ coincided with less dense human populations and HQ. Notably, higher demand for WY coincided with areas of higher human populations and was attributable to extensive agricultural irrigation needs. In contrast, lower demands for WY were evident in mountainous regions with sparse populations. Similarly, high demands for NPP coincided with areas of dense human populations and were attributable to heightened energy consumption and carbon emissions. In contrast, areas of low NPP demand exhibited high vegetation coverage. Higher demands for soil conservation were concentrated in the northern and north-central regions and were attributable to the fact that these areas largely comprised unused lands facing significant soil and water loss challenges due to erosion. In contrast, areas of lower soil conservation demand in the southwest and east showed urbanization and agricultural development, with associated higher impermeable surfaces. The results showed increased demands for HQ, WY, and NPP in densely populated parts of the study area (Figure 3a). In contrast, soil conservation needs were regulated by natural conditions and levels of vegetation coverage (Figure 4). In summary, the demand for the four ecosystem services in the study area from 2005 to 2019 was concentrated in the east and north. In contrast, the areas of low demand for these services were concentrated in the west and south, specifically in the southeast and southwest, with a minor presence in the central area, including the north-central and west-central zones. The distinct characteristics of the spatial distribution of ecosystem service demand in the study area could be attributed to the strong influences of population density, the intensity of human activity, and the diversity of land use.

3.2.3. Spatial Dynamics of the ES Supply–Demand Ratio

The HQ supply–demand ratio between 2005 and 2019 was lowest in the eastern part of the study area, indicating a deficit in HQ supply in this area. The eastern part of the study area is characterized by construction lands, high population density, and rapid urbanization. Consequently, the significant human-induced threats to biodiversity operating in this area contributed to the lower ratio values. Conversely, higher HQ supply–demand ratios were maintained in the south and southwest and were attributable to minimal human activity. The lowest WY supply–demand ratio occurred in the east, representing a deficit in WY in this area. The eastern part of the study area is characterized by high population density and economic activity. The consequent increased areas of impermeable surfaces associated with urbanization from buildings and roads exacerbate the deficit in WY. In contrast, higher WY supply–demand ratios were maintained in areas with higher elevations and denser vegetation. Similarly, higher soil conservation supply–demand ratios were found in the north, which typically features sparse populations and extensive vegetation coverage. Deficits in soil conservation were found in the east and were attributable to increased human development. These spatial patterns in the SC supply–demand ratio highlight the challenges of balancing soil conservation efforts with anthropogenic pressures. The increased impermeability of land surfaces associated with urbanization reduces the natural water retention capacity of soil, necessitating enhanced soil conservation efforts. Areas with higher human development showed a deficit in carbon sequestration services. Weaker carbon sequestration capabilities were found in urban and industrial zones, which are characterized by limited vegetation coverage (Figure 3a). Conversely, zones with extensive vegetation exhibited robust carbon sequestration services (Figure 4). There were spatial differences in the supply–demand ratio among the four ecosystem services in the study area from 2005 to 2019. Future conservation efforts should focus on the unique local functions and characteristics, with an emphasis on spatial configuration. In addition, ecosystem services should be conserved to balance the supply–demand ratio.

3.3. ESBs

3.3.1. Results of Ecosystem Service Cluster Classification

The self-organizing map (SOM) network introduced by Kohonen [53] differs from traditional mathematical approaches due to its integration of principal component analysis and K-means clustering. The SOM preserves the topological arrangement of the input space using a nearest-neighbor relationship function and integrates spatial data into the analysis. Within the SOM, neurons compete through learning strategies to progressively refine the network and achieve classification. This study applied SOM to identify clusters of ecosystem services. Initially, the unit size and ecological spatial attributes of the study area were thoroughly evaluated. Finally, numerous comparisons to more precisely characterize the spatial interactions between the various ecosystem services resulted in a 1 km × 1 km grid unit being chosen. The four ecosystem service datasets were then subjected to dimensionless processing. The optimal number of clusters was then determined using R, primarily relying on the change curve of the intra-group sum of squares of the ecosystem services. As shown in Figure 4, the inflection point effect of the intra-group sum of squares curve increased under an initial k of 4. Consequently, the study area was partitioned into four clustering regions, all of which exhibited statistically significant results (Figure 5).

3.3.2. Temporal Dynamics in ESBs

As shown in Figure 4, there was a stable supply of ESBs between 2005 and 2019. Specifically, while the coverage of ESB1 decreased by 10.20%, that of ESB2 increased by 19.76%. In contrast, the coverages of ESB3 and ESB4 decreased by 4.45% and 5.11%, respectively. The growth of ESB2 could be attributed to the ecological conservation efforts, including the return of farmland to forest and slope terracing. The decreased coverages of ESB1, ESB3, and ESB4 could be attributed to the development of unutilized lands and human construction activities.
The northern region showed a notable increase in ES demand bundles. While the proportions of ESDB1 and ESDB4 decreased by 7.63% and 23.26%, respectively, those of ESDB2 and ESDB3 increased by 29.09% and 1.80%, respectively. The declines in ESDB1 and ESDB4 could be primarily linked to urbanization and agricultural expansion, which have led to land consolidation and, subsequently, to lower demand. The increases in ESDB2 and ESDB3 were largely in response to water and soil erosion challenges on unutilized lands and grasslands, with the increased threat of soil erosion (Figure 6).

3.3.3. Spatial Dynamics in ES Supply Bundles

The present study divided the study area into four distinct bundles, with significant differences in ES supply identified among the bundles. Significantly higher water conservation services were found in ESB1. As shown in Figure 7, ESB1 occurred in the northeast part of the study area and expanded over time. Since this region is characterized by construction and cultivated lands, construction activities are likely a key driver of ES patterns in this region. ESB2 was characterized by uniformly low levels of various Ess, with grasslands and unused lands comprising most of this area. While ESB3 showed high soil conservation services, other Ess remained at lower levels. ESB3 predominantly comprised grasslands and shrublands, serving as a transitional zone between ESB2 and ESB4. ESB4 showed high HQ and NPP services, largely influenced by dense forest cover and substantial vegetation. ESB4 benefitted from high vegetation coverage and minimal human disturbance (Figure 7). Optimization measures should be tailored to various ES supply bundles to increase the capacity of the study area to deliver diverse ES. Specifically, for ESB2, initiatives such as converting farmland to forests, conserving natural forests, and enhancing low-quality, inefficient forests are crucial to bolstering the supply capabilities of ESB2.

3.3.4. Spatial Dynamics in ES Demand Bundles

As shown in Figure 5, the present study divided the study area into four distinct ES demand bundles, with significant differences in ES demand identified among the bundles. ESDB1, which primarily encompassed urban centers and adjacent farmlands, showed increased shortfalls in various Ess. ESDB2 encompassed grasslands and unutilized lands, with human activities leading to higher risks of soil erosion. ESDB3, which encompassed the agricultural expanses of the eastern plains, showed elevated demand for HQ, which was attributable to its unique geographic setting. Conversely, ESDB4, which encompassed the southern forested areas, showed robust surplus carbon sequestration services (Figure 7). Optimization measures should be tailored to various ES demand bundles to enhance the capacity of the study area to provide diverse ES. Specifically, ESDB1 should prioritize the enhancement of the continuous supply of agricultural services by expanding cultivation areas, scaling up breeding operations, and adapting agricultural practices to meet the needs of local farmers. In the future, ESDB2 should vigorously promote grassland projects and the conversion of farmland into forests and grasslands so as to cultivate a robust ecological environment. These efforts, spearheaded by the expansion of forests and green spaces, are designed to improve the local living conditions.

3.4. Factors Driving Ess in Functional Areas

The present study identified eight factors with significantly different (p < 0.05) influences on ESBs between 2005 and 2019 (Table 1). Human activities, population density, and precipitation had the most substantial influences, collectively accounting for >95% of spatial variation in ESBs in the ecological buffer area. Apart from a minor interaction between NDVI and night light with a lower explanatory significance, the other examined factors explained over 50% of the spatial variance in ESBs. Consequently, human activities, population density, and precipitation emerged as the central factors influencing ESBs, underscoring the complex interplay between human and natural elements in shaping the distribution of ES supply bundles (Figure 8). The examination of demand bundles between 2005 and 2019 showed that all eight factors significantly (p < 0.05) influenced the distribution of ESDBs in the ecological buffer area. Specifically, land use types, population density, and human activity accounted for >95% of the spatial variation in ESDBs in the study area. Despite the minimal interaction between temperature and rainfall, the other examined factors explained >50% of spatial variations in ESDBs. Consequently, land use types, population density, and human activity were identified as the primary factors regulating ESDBs in the study area. The spatial distribution of ESDBs was primarily influenced by human-related factors (Figure 9).
X1 to X8 is the normalized difference vegetation index (NDVI), land use type, nighttime lighting, population density, human activities, temperature, precipitation, and topographic relief, respectively.

4. Discussion

4.1. Validation of Ecosystem Service (ES) Assessment in the Ecological Buffer Area

The present study evaluated temporal changes in ES supply–demand, with the spatial distribution of the results shown and the spatial relationships between ESDBs examined. The present study examined both the temporal and spatial distribution patterns of ESs in the ecological buffer area. The present study conducted a detailed verification of the outcomes of each model to achieve enhanced quality and accuracy of ecosystem service assessment. The standard method includes the comparison and verification of these results with either measured data or the simulations of other models. Since there are limited prior ecosystem service simulation studies in the study area and a scarcity of measured data, the results of the present study were compared against those for comparable periods and regions in Sichuan and Yunnan provinces. The WY, NPP, SC, and HQ for the ecological buffer area estimated in the present study were validated against those acquired through established models. The results of the CASA model showed an average annual NPP in the area between 2000 and 2015 of 435.45 gC/m2·yr and were largely consistent with the results obtained in the present study [46]. The estimated average annual WY over four periods of 339.04 mm/yr was largely consistent with the results of the present study. In addition, the average annual soil conservation for the last period determined in the present study was largely consistent with that obtained by [34]. These findings emphasize the significant environmental quality and extensive forest coverage of the ecological buffer area.

4.2. Characteristics of ES Supply and Demand

The improved understanding of the supply–demand dynamics and spatial attributes of ESs, their spatial evolution, as well as the influencing factors, is crucial for guiding regional ecosystem management strategies. Recent studies on the balance between ES supply and demand have predominantly utilized supply–demand matrices, although a few studies have utilized bespoke indicators. However, the above approaches are often inadequate for capturing the spatial heterogeneity inherent in supply–demand dynamics. This limitation emphasizes the need for more detailed quantitative investigations into this equilibrium. The present study applied ecological process models, including remote sensing inversion, and RUSLE to characterize the spatiotemporal evolution of HQ, WY, carbon sequestration, oxygen release, and soil conservation services within the ecological buffer area. The present study also applied socioeconomic indicators to measure the demands on ESs in different ecosystems, thereby facilitating an in-depth analysis of their supply–demand equilibrium. Higher average annual SC provided by the RUSLE was concentrated in areas with a high demand for SC, mainly in western Sichuan Province and Yunnan Province, with an average soil conservation demand of 1000 t/hm2. Demand for carbon fixation generally exceeded 1100 t/hm2, which was largely consistent with the results obtained in the present study [56]. The unit grid demand was between 667 mm and 1516 mm, far exceeding those of most areas in the province, and largely consistent with the results obtained in the present study [57,58]. In addition to the decrease in average annual HQ in the study area, there were increases in the other ESs. The decrease in HQ could be attributed to heightened human activity and landscape fragmentation. Variations in WY, NPP, and SC were predominantly influenced by temperature and rainfall and the interactions between them and with other factors, resulting in noticeable increases in WY, NPP, and SC over the past fifteen years. Higher demands for WY, HQ, and carbon sequestration services were primarily found in densely populated urban regions and were attributable to urbanization and industrialization resulting in elevated water usage and carbon emissions. Investment in green infrastructure could mitigate the deficits in ES services in these areas.

4.3. Factors Affecting ESDBs

The application of SOM provides a more objective analysis of regional ESBs compared to other methods and effectively minimizes the subjectivity often encountered in ecological functional zoning. SOM highlighted the pronounced variations in natural conditions across the central, eastern, and western parts of the study area. Therefore, the accuracy of ecosystem service (ES) demand and its spatial clustering was ensured through careful assessment of model parameters and the choice of clustering method. Clustering processes in future analyses should be more transparent and should provide more stable results, thereby enabling a more precise representation of ES demand and its spatial distribution. ESDB1 showed deficits in multiple ESs and comprised urban areas and suburban farmlands; ESDB2 encompassed grasslands and unutilized lands in which interactions between human activities and natural features increased the risks of soil erosion and environmental disasters; ESDB3 comprised the eastern agricultural plains and displayed an increased demand for HQ, which was attributable to favorable geographic and ecological conditions; the demand for carbon sequestration in ESDB4 was concentrated in the southern forests, where dense vegetation resulted in local surpluses in carbon sequestration services. The results of the present study showed surpluses in all four ESs, suggesting that land use–land cover (LULC) plays a pivotal role in ES supply dynamics, which is consistent with the conclusions of previous studies [12]. Furthermore, ES bundle diversity and quantity varied across the different bundles, offering valuable insights for ecosystem management. The main limitations of the present study related to the use of a 1 km × 1 km spatial resolution in the analysis of ESs. The complex interactions between natural and anthropogenic factors require the consideration of a wide range of elements to comprehensively understand the determinants of ES dynamics.
Future studies should investigate the long-term dynamics in ES supply and demand, incorporating analyses at both grid and administrative levels (city and county). Such an approach would facilitate a more comprehensive understanding of ES supply and demand across different scales. Furthermore, there is an urgent need to develop a spatial management framework to strengthen the relationship between ES supply and demand in the study area. Consequently, further exploration of the nexus between environmental management and spatial planning is crucial for the establishment of a scientifically robust and pragmatic management framework. The present study proposed an approach for the conservation and rational utilization of ecological resources in different functional areas in response to the challenges faced by each service cluster. The conservation of ecological resources should be strengthened while also making full use of ecological resources to promote the healthy development of the local economy.

5. Conclusions

There remain relatively few existing studies on the factors influencing ecosystem service bundle (ESB) supply and demand. In addition, there is little previous research on deficits in ecosystem service supply in regions with distinct geological and geomorphological characteristics, as well as in the spatial distribution of ecological service supply and demand bundles. The present study applied SOM to identify bundles of ES supply and demand and the key drivers using the SOM technique. The key findings of the present study are listed below. Gradual improvements in the ecosystem service functions of the Sichuan-Yunnan ecological buffer zone are evident. The study area experienced an overall improvement in ES supply between 2005 and 2019, including WY, NPP, and soil conservation, despite a decline in HQ. Similarly, the demand for HQ, WY, and NPP services increased, with urban areas exhibiting higher demands compared to other regions. While the study area experienced a decreasing supply–demand ratio for HQ services, those for soil conservation, WY, and NPP services increased. Notably, there were deficits in HQ, WY, and NPP services in the study area except in the eastern plains. Despite an overall excess in the supply of comprehensive ESs, the continual decline in the supply–demand index represents worrying declining trends in HQ, WY, and NPP services due to urbanization and socioeconomic factors. The proportion of the area of ESB2 between 2005 and 2019 increased significantly, whereas those of the other ESBs declined. The area proportions of ESDB1 and ESDB4 decreased, whereas those of ESDB2 and ESDB3 increased. While ESBs experienced minimal spatial fluctuations, the coverages of ESDBs showed increasing trends in the north. The establishment of ecological corridors connected to ES supply zones is recommended to promote human well-being and sustainable development. Human activities, population density, and precipitation emerged as the primary influential factors. The distribution of ES demand bundles was predominantly regulated by human activities, underscoring the need for targeted management and policy interventions to effectively address these influences.

Author Contributions

Conceptualization, B.Y. and D.X.; data curation, P.M.; formal analysis, B.Y. and D.X.; investigation, P.M.; methodology, B.Y.; resources, B.Y.; software, B.Y. and D.X.; writing—original draft, B.Y.; writing—review and editing, B.Y., D.X. and P.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Correction Statement

This article has been republished with a minor correction to the existing affiliation information. This change does not affect the scientific content of the article.

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Figure 1. Study area of the present study (digital elevation model (DEM)).
Figure 1. Study area of the present study (digital elevation model (DEM)).
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Figure 2. Research framework used in the present study.
Figure 2. Research framework used in the present study.
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Figure 3. Spatial (a) and temporal (b) distributions of population density from 2005 to 2019. (In Figure 3b, the solid line represents the population density for each year, with points indicating the trend over time. The dotted line represents a linear regression fit to the changes in population density).
Figure 3. Spatial (a) and temporal (b) distributions of population density from 2005 to 2019. (In Figure 3b, the solid line represents the population density for each year, with points indicating the trend over time. The dotted line represents a linear regression fit to the changes in population density).
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Figure 4. Ecosystem service (ES) supply and demand and supply–demand ratio in the study area.
Figure 4. Ecosystem service (ES) supply and demand and supply–demand ratio in the study area.
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Figure 5. Results of classification of ecosystem service (a) supply and (b) demand clusters.
Figure 5. Results of classification of ecosystem service (a) supply and (b) demand clusters.
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Figure 6. Changes in the proportional area of the ecosystem service supply cluster (a) and demand cluster (b) from 2005 to 2019.
Figure 6. Changes in the proportional area of the ecosystem service supply cluster (a) and demand cluster (b) from 2005 to 2019.
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Figure 7. Spatial dynamics in ecosystem service (ES) supply and demand bundles.
Figure 7. Spatial dynamics in ecosystem service (ES) supply and demand bundles.
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Figure 8. Examination of factors influencing the spatial distribution of ecosystem services supply bundles in the study area: (a) for 2005; (b) for 2019.
Figure 8. Examination of factors influencing the spatial distribution of ecosystem services supply bundles in the study area: (a) for 2005; (b) for 2019.
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Figure 9. Examination of factors influencing the spatial distribution of ecosystem services demand bundles in the study area: (a) for 2005; (b) for 2019.
Figure 9. Examination of factors influencing the spatial distribution of ecosystem services demand bundles in the study area: (a) for 2005; (b) for 2019.
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Table 1. Description and sources of data used in the present study.
Table 1. Description and sources of data used in the present study.
Influencing FactorsData NameData FormatResolutionData Source
X1NDVItif30 mhttp://data.cma.cn/
X2Land cover datatif30 mhttps://zenodo.org/
X3Nightlighttif1 kmhttp://data.tpdc.ac.cn/
X4Population tif1 kmhttps://www.worldpop.org/
X5HAItif1 kmhttp://data.cma.cn/
X6Annual precipitationtif30 mhttp://data.cma.cn/
X7Average annual temperature datatif30 mhttp://data.cma.cn/
X8Digital Elevation Model (DEM)tif30 mhttp://www.gscloud.cn/
Table 2. The annual average values of the four types of ecological services (ESs) operating in the ecological conservation area in the Sichuan-Yunnan provinces from 2005 to 2019.
Table 2. The annual average values of the four types of ecological services (ESs) operating in the ecological conservation area in the Sichuan-Yunnan provinces from 2005 to 2019.
Type of Service2005 (Supply)2019 (Supply)2005 (Demand)2019 (Demand)2005
(ESDR)		2019
(ESDR)
HQ0.8470.8160.3980.4050.8210.808
WY (mm)314.958431.183262028500.2930.360
SC (t/(hm2 a))469.632490.1006364340.1270.201
NPP (gC/(m2 a))687.173707.841203029200.6920.693
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Yang, B.; Xue, D.; Miao, P. Spatiotemporal Evolution and Driving Factors of Ecosystem Supply and Demand Bundles: A Case Study in the Sichuan-Yunnan Ecological Buffer Area, China. Sustainability 2024, 16, 4977. https://doi.org/10.3390/su16124977

AMA Style

Yang B, Xue D, Miao P. Spatiotemporal Evolution and Driving Factors of Ecosystem Supply and Demand Bundles: A Case Study in the Sichuan-Yunnan Ecological Buffer Area, China. Sustainability. 2024; 16(12):4977. https://doi.org/10.3390/su16124977

Chicago/Turabian Style

Yang, Bin, Dongqian Xue, and Peipei Miao. 2024. "Spatiotemporal Evolution and Driving Factors of Ecosystem Supply and Demand Bundles: A Case Study in the Sichuan-Yunnan Ecological Buffer Area, China" Sustainability 16, no. 12: 4977. https://doi.org/10.3390/su16124977

APA Style

Yang, B., Xue, D., & Miao, P. (2024). Spatiotemporal Evolution and Driving Factors of Ecosystem Supply and Demand Bundles: A Case Study in the Sichuan-Yunnan Ecological Buffer Area, China. Sustainability, 16(12), 4977. https://doi.org/10.3390/su16124977

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