Coupling PLUS–InVEST Model for Ecosystem Service Research in Yunnan Province, China

Abstract

In efforts to improve regional ecosystem service functions, coordinate land development and ecological conservation, and establish a reference for optimizing land resource allocation and policy formulation to cope with climate change, it is critical to investigate the spatial distribution of land use/cover change (LUCC) and ecosystem services (ESs) under future climate change. This study proposes a framework based on the Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP), integrating the patch-generating land use simulation (PLUS) model and the integrated valuation of ecosystem services and tradeoffs (InVEST) model to analyze the spatial agglomeration of ESs, to analyze the importance of each driving factors. The results of the study show as follows: (1) the combination of CMIP6 and PLUS models can effectively simulate land use with an overall accuracy of 0.9379. (2) In spatial correlation, ESs show good clustering in all three future scenarios, with similar distribution of cold hotspots in the SSP126 and SSP245 scenarios. Hotspots are more dispersed and cold spots are shifted to the west in the SSP585 scenario. (3) GDP is an important factor in carbon storage and habitat quality, and precipitation has a greater impact on soil retention and water production. Overall, ESs can be increased by appropriately controlling population and economic development, balancing economic development and ecological protection, promoting energy transition, maintaining ecological hotspot areas, and improving cold spot areas.

1. Introduction

Ecosystem services (ESs) are all the benefits that humans derive directly or indirectly from ecosystems [1] and are essential for human survival and sustainable development [2]. With increasing industrialization and urbanization, greenhouse gas emissions, such as carbon dioxide (CO₂), are causing global warming [3,4], leading to climate warming and land use/cover change [5], and ESs are declining in most parts of the world [6]. Changes in ESs are directly or indirectly influenced by climate, soil, vegetation and land type [7], and human-induced climate change and land use type conversion are changing ecological patterns [8]. Climate change affects the process of vegetation growth and thus ecosystem services, making it more difficult to predict future ESs.

Numerous current land use simulation models, such as CA-Markov [9,10], ANN-CA [11], CLUS-S [12], Dyna-CLUE [13] and FLUS models [14], are widely used by scholars. However, these models have some limitations; they cannot identify effectively the drivers affecting land use change, especially the inability to simulate multiple land use panels dynamically and spatially, which limits the simulation of land under future climate change. Therefore, the PLUS model is chosen to accurately simulate the spatial distribution by predicting land changes with future factor constraints. With the current in-depth research on ESs, numerous models can quantify them, such as InVEST [15], ARIES [16], ESM [17], and EcoMetrix [18], among which the InVEST model is more mature and can be used for large-scale quantitative studies, the model has lower data requirements, lower computational workload, and higher accuracy of conclusions, and is widely used [19,20,21], while studies in the Chinese region have also shown strong applicability to evaluate and analyze a variety of indicators in time space and detect dynamic distribution changes [22,23,24,25].

Based on historical research, national and international experts have conducted studies around the relationship between land change and ecosystem services [26,27]. In terms of research methods, most scholars combine land simulation models and InVEST models for prediction and simulate multiple development scenarios; in terms of research scales, most of them are based on a large number of studies conducted at global, national or watershed levels. For example, Jie Yang studied the spatial and temporal evolution of water production services in the Yellow River Basin of China and concluded that the impact of climate change is extremely significant [28]; Feng Tang conducted dynamic scenario simulations of habitat quality in the Huaihe Economic Zone [29]; Guofeng Zhu used CA-Markov and InVEST models to simulate carbon storage in the arid zone of northwest China as an example [30]. In summary, many scholars have focused on the impacts of single indicators when modeling future ESs, without considering the changes of multiple ecosystem indicators, and have been more subjective in setting up land simulation scenarios and have not studied the impacts caused by climate change in the future. Most of the current studies are based on historical evolutionary characteristics, but there is a lack of research to predict ecosystem services under the influence of future climate change and to reveal the role of drivers in the degradation of ecosystem services. With the sixth International Coupled Model Comparison Program (CMIP6) providing researchers with multiple future global climate change scenarios, it provides a standard for projection studies [31,32]. Therefore, this study changed the previous scenario setting to reveal the impact of climate change multi-induced ESs in the context of climate change.

Yunnan is located at the headwaters or sources of many international and domestic rivers, rich in natural resources, such as forest and grass, ecology, etc. It is responsible for the three ecological security barriers of “Western Plateau”, “Yangtze River Basin” and “Pearl River Basin”, and undertakes the strategic task of maintaining regional, national and even international ecological security. However, with the rapid development of Western development, human activities are more and more frequent, and the ecology should be taken into account while developing the economy. Water production, soil conservation, carbon storage, and habitat quality are often identified as key regional ecosystem services, and these four main types of ecosystem services exhibit complex interactions and interrelationships. As human activities cause climate change leading to natural ecological breakdowns, biodiversity is threatened, forests are reduced, leading to a decrease in the carbon sequestration capacity of ecosystems, water production services affect regional soil erosion, landslides and other geological disasters are prominent, and the soil environment is damaged. Therefore, four services were selected for study to contribute to adaptation and mitigation of climate change and socio-economic development. Integrating the PLUS model and InVEST model, its purpose is to address the impact of climate change on regional ecology, analyze future land use change trends under three different future scenarios, assess future ecosystem services and their spatial relevance, and reveal the main influencing factors of ecosystem services. Ultimately, based on the results of the study, recommendations are made for the government for sustainable ecosystem development.

2. Materials and Methods

2.1. Study Area

Yunnan Province is situated in southwest China, adjacent to Guizhou and Guangxi in the east, connected to Sichuan in the north, and bordering Tibet in the northwest, at an altitude ranging from 77 to 6635 m, between 21°8′ and 29°15′ north and 97°31′ and 106°11′ east (Figure 1). It is a mountainous plateau area bounded by the Yuanjiang Valley and the broad southern valley of the Yunling Mountain Range, which is subdivided into two major topographic areas to the east and west. The eastern portion consists of the east and central Yunnan Plateau, which is an integral part of the Yunnan–Guizhou Plateau and has an average altitude of approximately 2000 m. The western portion is interspersed with high mountains and canyons, with treacherous terrain and a relative height difference of more than 1000 m between the mountains and canyons. The northwest of the province is mountainous, and the southeast is flat, with a gradual descent from north to south. It covers a total area of 394,100 km², which is equal to 4.1% of the total land area of the country. As of December 2021, Yunnan Province consists of 16 administrative regions at the prefecture level and 129 district divisions at the county level. As of 2021, Yunnan Province has a population of 46.9 million and a gross regional product (GDP) of RMB 2,714,670 billion.

2.2. Data Collection and Processing

Land use, meteorological, natural, and social data from the following sources comprise the majority of the datasets included in this study. (1) Land use type data: from the Resource Environment Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 7 July 2022) with a 30 m spatial resolution, combining the original land types into six major categories (arable land, forest land, grassland, water, construction land, and unused land). (2) Natural data: DEM data from the Geospatial Data Cloud (http://www.gscloud.cn, accessed on 15 July 2022) ASTER GDEM, 30 m spatial resolution, slope from DEM analysis; soil erosion and soil type from the Resource and Environment Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 19 July 2022); soil erodibility, root restriction depth, and effective plant water content obtained from the International Soil Reference and Information Center (https://data.isric.org, accessed on 21 July 2022), 1 km spatial resolution; NDVI is obtained through Landsat image acquisition [33]. (3) Social data: population and GDP obtained from the Resource and Environment Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 23 July 2022); distances to railroads, water, and roads obtained indirectly through ArcGIS spatial analysis. (4) Meteorological data: precipitation, temperature, and evapotranspiration data were obtained from the National Earth System Science Data Center (http://www.geodata.cn, accessed on 23 July 2022), and from precipitation data, rainfall erosion force factors were calculated. In addition, future climate data for different scenarios are obtained by selecting the desired scenario for download through the IPCC (https://esgf-node.llnl.gov, accessed on 9 August 2022).

The aforementioned data were unified in ArcGIS with a single coordinate system in accordance with the model input specifications, and all data spatial resolutions were unified to 250 m in line with the appropriate situation of the study area and computer processing capacity, utilizing the techniques for determining the ideal sample size and clustering.

2.3. Research Ideas and Methods

The variables of temperature, precipitation, soil erosion, soil type, DEM, slope, population density, GDP, NDVI, and distance from water, railroad, and road were selected as the 12 drivers to investigate in this study (Figure 2). First, the land use in 2020 is simulated, beginning in 2000, and compared to the actual land use data in 2020. The overall accuracy and kappa coefficient are then applied to evaluate the accuracy of the model simulation results. Second, Markov was applied to predict the number of each category in 2040 under various climate change scenarios using land use data from Yunnan Province in 2000 and 2020 as the parameter input of the model. Then, beginning with data from 2020, various scenarios corresponding to SSP-RCP are established and simulated for 2040 based on the combination of driving factors and neighborhood weights. After which, the historical land use data and the results of the PLUS simulated climate change scenarios were utilized once more to estimate ecosystem services using the InVEST model, and four indicators were chosen: carbon storage, water yield, soil conservation, and habitat quality. Through these, the influence of drivers on ESs indicators will be indicate. At last, the findings were normalized in ArcGIS, taking into account the average value of each index over the course of the preceding years. The final results of ecosystem services in Yunnan Province were calculated using the entropy weight method and divided into 5 levels. Spatial correlation analysis was then carried out on the superimposed results (Figure 3).

2.3.1. Land Use Simulation Based on Climate Change

CMIP6 incorporates shared economy pathways (SSPs) as opposed to the four representative concentration pathways (RCPs) in CMIP5 [34], emphasizing the role of different social development models in driving climate change [35,36]. CMIP6 includes shared economy pathways (SSPs) rather than the four representative concentration pathways (RCPs) of CMIP5, emphasizing the role of various social development models in driving climate change. In this paper, three scenarios are selected as the division of future development into three scenarios for this study. (1) Ecological protection scenario: ecological protection is prioritized, forest land and grassland are protected by policy guidance, fossil energy use is slowed, green energy use is promoted, and sustainable green development is achieved, in accordance with SSP126 scenarios (SSP1 and RCP2.6). (2) Natural development scenario: evolves according to historical development, social development, and ecology, following a socio-economic and technological intermediate path with moderate levels of GHG emissions, corresponding to the SSP245 scenario (SSP2 and RCP4.5). (3) Economic development scenario: according to the SSP585 scenario, a substantial amount of fossil energy will be consumed in the pursuit of maximum economic efficiency, leading to a rapid increase in high-temperature gas emissions (SSP5 and RCP8.5).

The PLUS model, developed by Xun Liang et al. [37], incorporates the rule mining framework of the Land Expansion Analysis Strategy (LEAS) with the CA model based on multi-class random patches with multiple types of random seeds (CARS) to achieve greater simulation precision and more similar landscapes [38]. First, based on the land use data of the two periods, the LEAS model is superimposed and the areas where changes occur are identified. From these areas, a sample is taken, and the random forest algorithm is used to explore the relationship between each site type and the driving factors in order to calculate the probability of conversion of each site using the following formula:

$$P_{i,k\left(X\right)}^d=\frac{{\sum }_{n=1}^{M}I\left(h_n\left(X\right)=d\right)}{M}$$

(1)

where $P_{i,k\left(X\right)}^d$ is the expansion probability of land type k at image element i, d denotes the presence of other land types shifting to land type k, taking 0 or 1, X is a vector composed of driving factors, h_n(X) is the land type calculated when the decision tree is n, M is the total number of decision trees, and I is the indicator function for the decision tree.

To simulate the spatial evolution of each category, the traditional CA model is combined with the patch generation and threshold decreasing mechanism to generate the CARS model, which simulates the land use with respect to each category’s development potential. When the neighborhood effect of a single category is equal to zero, the PLUS model can generate the “seeds” of each category automatically and form new patch groups employing the following formula:

$${\Omega }_{i,k}^t=\frac{con\left(c_i^{t-1}=k\right)}{n\times n-1}\times w_k$$

(2)

$$O{P}_{i,k}^{1,t}=\{\begin{array}{l}{P}_{i,k}^1\times \left(r\times {\mu }_k\right)\times D_k^t\begin{array}{cc}& {\Omega }_{i,k}^t=0andr<\end{array}{P}_{i,k}^1\\ P_{i,k}^1\times {\Omega }_{i,k}^t\times D_k^t\begin{array}{cccc}& & Others& \begin{array}{cc}& \begin{array}{cc}& \end{array}\end{array}\end{array}\end{array}$$

(3)

where ${\Omega }_{i,k}^t$ is the domain weight of ground class k in image element i at moment t, and w_k is the domain weight parameter. $O{P}_{i,k}^{1,t}$ is the integrated probability of transition from image element i to ground class k at moment t, $P_{i,k}^1$ is the suitability probability of expansion of image element i to ground class k, $D_k^t$ is the adaptive drive coefficient, r is a random value between 0 and 1, and μ_k is the threshold value of newly generated patches.

2.3.2. Ecosystem Services under Climate Change

Carbon storage services are the carbon stored in vegetation and soil by ecosystems; the InVEST carbon module divides carbon storage into four categories: above-ground carbon, below-ground carbon, soil carbon, and dead organic carbon [39,40]. The carbon pool in the study area was identified using references to previous literature, and carbon storage was calculated using land use information using the equation below:

$$C_{total}=\sum _{k=1}^n{A}_k\times \left(C_{above}+C_{below}+C_{soil}+C_{dead}\right)$$

(4)

where $C_{total}$ is the total carbon storage in the study area (t), $A_k$ is the area of the kth land type in the study area, k from 1 to n, n is the number of land types, and above-ground vegetation carbon density ($C_{above}$), below-ground vegetation carbon density ($C_{below}$), soil carbon density ($C_{soil}$), and dead organic matter carbon density ($C_{dead}$) make up the carbon pool in the study area, in units.

Water yield is a crucial component of ecosystem services, which refers to the amount of water available for human use each year to promote social welfare [41,42,43]. The InVEST model requires a combination of climatic, topographic, and soil data to calculate water yield, as shown in the following equation:

$$Y_{xj}=(1-\frac{AE{T}_{xj}}{P_x})P_x$$

(5)

where, $Y_{xj}$ is the annual water yield of the jth land class in the xth image element (mm), $P_x$ is the average annual precipitation in the xth image element (mm), and $AE{T}_{xj}$ is the actual evapotranspiration of the jth land class in the xth image element (mm).

Soil retention (SDR) is the capacity of each locality to prevent soil erosion, and the SDR module in InVEST is based on the revised universal soil loss equation (RUSLE) [44,45], which factors in the sediment interception force of the plot and is calculated as follows:

$$SR=R\times K\times LS-R\times K\times LS\times C\times P$$

(6)

where SR denotes total annual soil conservation ($t·h{m}^{-2}·a^{-1}$), LS is the topographic factor calculated from the slope length factor (L) and slope factor (S), K is the soil cocoa erodibility factor, C is the vegetation cover factor, P is the soil conservation measure factor, and R is the rainfall erosion force index calculated from monthly precipitation.

Habitat quality is the condition of the various resources and conditions that the environment provides for biological development; when habitat quality is excellent, the development of biodiversity is assured [46,47,48]. Using the habitat quality module of the InVEST model, the relationship between each species and threat factors was determined, and the relative sensitivity of each species to threat factors and the distance of influence of threat factors were considered when calculating the habitat quality index, expressed as biodiversity:

$$Q_{xj}=H_j(1-(\frac{D_{xj}^z}{D_{xj}^z+K^z}))$$

(7)

where $Q_{xj}$ is the habitat quality of the jth land class in the image element x, taking values from 0 to 1. $H_j$ is the suitability of the jth land class, K and z are the parameter of the model as a constant, and $D_{xj}$ is the degradation degree of the jth land class in the image element x.

3. Results

3.1. Land Use Simulation Based on Climate Change

3.1.1. Land Use Simulation Accuracy

In order to test the accuracy of the PLUS model, the land use distribution in Yunnan Province in 2020 was simulated, starting in 2000, based on the chosen land drivers. This simulation was then contrasted with the actual land use data in 2020 (Figure 4) to verify the accuracy of the model. The kappa coefficient is verified to be 0.8947, and the overall accuracy is 0.9379, indicating that the PLUS model has a high level of simulation accuracy for this study area and can be used for simulation prediction. Different combinations of factors and various expansion probabilities were selected for various scenarios in accordance with the CMIP6 scenarios. Additionally, in the experiments, the future population, GDP, temperature, and precipitation data under the SSP126, SSP245, and SSP585 scenarios were added [49,50] in order to fully account for the development of each category under climate change and obtain the spatial distribution of land use types in 2040. (Figure 5).

3.1.2. Land Use Change under Climate Change

During the study period, land types were frequently transferred. The main directions of land use type transfers were the grassland between forest land and arable land, and the forest land between grassland. With the strengthening of water protection, water expands to the surrounding areas, occupying forest land and grassland, and water resources gradually have been restored. Meanwhile, as the demand for economic development continues to expand, the expansion of urban area has accelerated rapidly, and a large amount of cultivated land has been consumed, leading to the continuous decline of cultivated land in this region (Figure 6).

In each of the three future SSP scenarios, the climate warming caused by greenhouse gas emissions results in an increase in precipitation climate wetness, which promotes plant growth. Due to varying degrees of climate change, there are substantial differences in land area changes between each scenario (

Table 1. Area changes under climate change scenarios (unit: km²).

Land Type	2020	2040 SSP126	2040 SSP245	2040 SSP585
Arable land	67,532.25	65,671.375	66,114.1875	67,921.375
Woodland	219,452.9375	218,358.1875	217,898.9375	217,113.125
Grassland	85,837.5625	84,949.5	84,526.6875	83,773.1875
Water area	3823.1875	5525.6875	5392.5	4691.5
Construction land	4750.3125	6893.375	7477.625	7906.4375
Unused land	1554.5	1552.625	1540.8125	1545.125

). The SSP126 scenario has the slowest expansion of land for construction among the three scenarios, with an increase of 2143.0625 km² from 2020. Due to the regulation of climate change policies and the effectiveness of environmental management, as well as the maintenance of watersheds, there has been an increase of 1702.5 km² in forest and grassland areas, and a decrease of 1860.875 km² in arable land as a result of the slow rise in population and the abandonment of a significant amount of arable land. In SSP585, which has the highest temperature, precipitation, and population of the three SSP scenarios, the expansion of land for construction is evident, increasing by 3156.125 km² relative to 2020. Due to the rapid increase in population, in order to meet the demand for food, arable land increased by 389.125 km²; however, in the absence of policies to combat climate change, forest land and grassland are in a serious state of decline. As the climate warms and humidifies and rainfall increases, the area of the watershed decreases slightly in two of the three scenarios, decreasing by 2339.8125 km² and 2064.375 km², respectively. The SSP245 scenario is more evenly developed than the other two, with an increase in the area of built-up land and watershed and a decrease in the area of cropland, forest land, and grassland. By analyzing the area of each category, the area ranking of Yunnan Province is determined to be forest land > grassland > arable land > construction land > water area > unused land, with forest land accounting for approximately 57% of the province’s area.

3.2. Climate Change Based Ecosystem Services

3.2.1. Spatial Distribution of Ecosystem Services

The spatial distribution of ecosystem services in Yunnan (Figure 7) exhibits a “low in the east and high in the west” layout with a strong correlation between spatial distribution and natural features, with the northwestern and southern regions being more prominent. The northwestern part of Yunnan Province has the highest altitude, resulting in limited urban development, low intensity of human activities, the influence of several nature reserves, and the preservation of a more pristine ecological environment; the southern area has the lowest altitude, mild and suitable climate, high rainfall, and abundant vegetation, resulting in a superior ecological environment. The low value of ecosystem services is concentrated in the commercial, educational, and political center of Yunnan Province, with a dense population and high intensity of human activities, and less rainfall than the eastern part; additionally, the land cover type is predominantly construction land and arable land, with low surface vegetation cover, and although the majority of the land is mountainous but with low elevation and flat terrain, this has led to a low value of ecosystem services. Carbon storage, habitat quality, and soil conservation also exhibit a spatial distribution pattern of “low in the east and high in the west” in the three climate change scenarios for 2040; water yield services are influenced by rainfall and evapotranspiration and exhibit different spatial distributions in the three future scenarios, with SSP126 and SSP245 decreasing from the middle to the two ends, and SSP585 decreasing from the northwest to the southeast.

3.2.2. Spatial and Temporal Variation in Ecosystem Services

It was analytically determined that the ESs in the study area changed significantly from 2000 to 2020 (

Table 2. Temporal and spatial variation of the mean value of ESs.

	2000	2020	2040 SSP126	2040 SSP245	2040 SSP585
Carbon storage (t)	1584.1777	1573.379	1558.9866	1556.8187	1556.5118
Water yield (mm)	984.844	729.568	1573.2462	1499.6477	1731.8023
Soil conservation (t)	936.4495	608.5748	1672.6056	1584.5343	2075.6745
Habitat quality	0.7512	0.7484	0.742	0.7414	0.7348

) and that all four indicators exhibited a decreasing trend, with water yield and soil retention decreasing significantly. The average value of water yield decreased from 984.844 mm in 2000 to 729.568 mm in 2020, a decrease of 26.92%, and the average value of soil retention decreased from 936.4495 t in 2000 to 608.5748 t in 2020, a decrease of up to 35.01%. Compared to 2020, the ESs in 2040 under the three SSP scenarios show varying degrees of change, with significant increases in soil retention and water yield, with the fastest growth rate in the SSP585 scenario, followed by the SSP126 scenario, and the lowest growth rate in the SSP245 scenario, which is primarily caused by precipitation, which will continue to increase in the future as temperatures continue to rise, resulting in a significant increase in precipitation (Figure 8). The expansion of construction land with varying intensities results in a slight decline in carbon storage and habitat quality in the future scenario due to changing climatic conditions, altering habitat conditions, and corresponding changes in future land use types.

The ecosystem services in Yunnan Province in 2040 are compared with those in 2020 and spatially visualized and expressed using ArcGIS to determine the spatial change distribution of ecosystem services under three SSP scenarios (Figure 9). The outward expansion of the principal urban area of Kunming has led to a decline in carbon storage and habitat quality, which is primarily concentrated in the central region. The gradual conversion of land types with high carbon sequestration capacity to those with low capacity, the increase of cities and the change in cultivated land affecting habitat quality, and the weakest reduction in the central portion of SSP126 out of the three scenarios, followed by SSP245 and SSP585, are the primary factors affecting habitat quality. Significant increases in soil retention and water yield are concentrated in the northwest and central regions, with a progressively diminishing area at the two ends. This is due to the significant increase caused by the future occurrence of large amounts of precipitation in the region, with SSP126 experiencing the greatest increase across all three scenarios, followed by SSP245 and SSP585, and the area of increase gradually contracting toward the northwest.

3.3. Spatial Correlation of Ecosystem Services under Climate Change Based

The weights of the four indicators (0.24 for carbon storage, 0.27 for habitat quality, 0.24 for soil conservation, and 0.25 for water yield) were calculated using the entropy weighting method, and the normalized indicators were superimposed to determine the results of ecosystem services in Yunnan Province. The distribution of 2000–2020 ecosystem services is “low in the northeast and high in the southwest” (Figure 10). The distribution of ecosystem services from 2000 to 2020 (Figure 10) demonstrates a “low northeast and high southwest” pattern, with the high-value area in the southwest region decreasing in all directions and the overall average value decreasing from 0.5441 to 0.5197. In terms of spatial correlation, the Moran I indices of ESs were greater than 0, 0.7456, and 0.6653, indicating that the spatial distribution of ESs was clustered. According to the Getis–Ord Gi* hotspot analysis, the spatial distribution of the cold hotspots in 2000–2020 varies significantly, and the cold spots expanded to the northwest. As a result of the intense human activity in the region, the cold spots are primarily in Kunming, Yuxi, Chuxiong, and Dali City. The flat terrain facilitates urban development, and a large amount of construction land and agricultural land radiates ecologically outward. By 2020, the cities of Nujiang, Diqing, Baoshan, and Lincang became the new cold spots. The locations of the hotspots are primarily in the southern and eastern remote areas of Yunnan, including the cities of Pu’er and Xishuangbanna, which are ecologically advantageous regions with a predominant woodland land use type, abundant rainfall, mild climate, and suitable vegetation. The number of 2020 hotspot areas in the east, such as Zhaotong, Wenshan, and Qujing, increases, and the ecological environment in the east improves. Land fragmentation is the cause of the insignificant areas.

Ecosystem services in 2040 under the three SSP scenarios (Figure 11) improve in comparison to the 2020 mean of 0.5197, and the overall mean values under the three scenarios are 0.5922, 0.5794, and 0.5599, respectively, with increased precipitation allowing for ecological recovery. The western portion of the spatial distribution contains the highest values, while those in the eastern portion decrease. Regarding spatial correlation, the Moran I indices for the three SSP scenarios were 0.7286, 0.7773, and 0.6922 and were all greater than 0. The spatial clustering effect was evident, and the Getis–Ord Gi* analysis of hotspots for different scenarios revealed significant spatial differences. The hotspot areas under the SSP126 and SSP245 scenarios are primarily in the Yunling Mountains, Lincang, Pu’er, and Xishuangbanna cities, which have dense vegetation cover, high coverage, and a large amount of woodland, as well as a significant ecological improvement over Dali and Lijiang cities in 2020, which eventually aggregate into a hotspot cluster. The cold spot region consists primarily of the urban agglomeration in central Yunnan and Wenshan City, which has low precipitation, high evapotranspiration, intense human activity, and rapid development of living and production space. As a result of these factors, the ecological environment is over-exploited, which leads to a decline in environmental quality compared to 2020 in Qujing and Wenshan City, forming a cluster of cold spots. Under the SSP585 scenario, the hotspot distribution is more dispersed, exhibiting a fragmented spatial distribution without forming a large continuous area, primarily in Nujiang, Lincang, Zhaotong, and Qujing, and primarily due to the absence of rainfall and evapotranspiration concentration. The cold spot areas are concentrated in the cities of Kunming, Chuxiong, Honghe, and Yuxi, which have a low slope, high solar irradiance, and high evapotranspiration, as well as a strong urban sprawl evident in this scenario and an increase in arable land area, which has a severe impact on the ecological environment and contributes to the concentration of cold spot areas.

4. Discussion

4.1. Importance of ESs Drivers

In this study, natural and social factors for 2020 were selected to analyze the correlation with each indicator of ESs and to explore the importance of drivers on ecological impacts (Figure 12). The study shows that GDP and NDVI are the main influencing factors in carbon storage, and that high speed development expands construction land and occupies ecological land, thus leading to a decrease in carbon sequestration capacity, which is consistent with the findings of Yin et al. [51]. Among habitat quality, GDP, population and NDVI are important influencing factors, and with population growth, urban and agricultural development, human activities are changing land use types, leading to habitat degradation, and the findings are supported by previous studies [52]. In soil conservation, precipitation and slope are closely related. Soil erosion tends to occur in areas with steep topography and high rainfall, and changes in precipitation patterns can lead to regional soil loss and thus affect changes in soil conservation, which is similar to the findings of Maurya [53]. Among the water yield, the most significant effect is caused by precipitation, which influences the distribution of water yield by changing the hydrological processes and water content distribution, similar to the results of previous studies [54]. As shown in Figure 8, precipitation increases rapidly in the future, resulting in increased soil retention and water production services. Therefore, increasing the vegetation cover can weaken the infiltration of rainwater and soil erosion, and contribute to the regulation of ecological services.

4.2. Management and Recommendations under Climate Change

The experimental results show that the rapid development of Yunnan Province in future climate change disrupts the surrounding ecosystem, which leads to a slight decrease in carbon storage services and habitat quality in the three scenarios. With future precipitation increases, precipitation erosion changes, improving water production services and soil conservation in the study area, increased water production services provide water resources that promote life and production in the region, but in extreme precipitation climates, increased water production in Yunnan Province, the source of the Pearl River and the headwaters of many rivers, such as the Yangtze River, may lead to flood risks in downstream areas. Therefore, the main challenge for the government is to respond to the impact of climate change and formulate policies to optimize the land use structure, improve the ecological environment, transition from “high speed” to “high quality” economic development, and improve regional ecosystem services.

The proposed measures are: the areas with high concentration of ESs values are the ecological core areas in Yunnan Province, in the future planning and development, the focus should be on securing ecological resources in this area, planting a certain proportion of woodlands, as water-saving shrubs and grasslands on steep slopes will promote the improvement of water, carbon, soil and habitat quality and maintain the important position of ecological security barriers in western Yunnan. In the low value concentration area of ESs, the protection of forests as well as grassland ecosystems should be strengthened, the conversion to clean energy should be carried out, the use of fossil fuels should be reduced, which not only slows down the warming but also prevents the degradation of vegetation, and at the same time slows down the development intensity of construction land in central Yunnan, and increases urban green areas to reduce the negative impact of urban expansion on the ecosystem. Under different climate change scenarios, formulating reasonable response policies and choosing reasonable development directions can improve the regional ecosystem service functions and form a balanced and coordinated land use pattern.

4.3. Experimental Advantages and Limitations

This paper makes projections for 2040, combines climate, socio-economic development and land use demand, selects four ecological service indicators for quantitative analysis, discusses the impact of the drivers on each service indicator in 2020, and superimposes the quantitative results by weighting to consider the impact of climate change in a comprehensive manner. The previous prediction scenario settings were changed in the experiment to use the development model provided by CMIP6 to make the prediction more reasonable. However, the experiment has not yet discussed the relationship between the indicators. In future research, the trade-off synergistic relationship between the indicators will be analyzed in depth, and multiple future years will be predicted to form the future trend of ESs, which will provide the government with a scientific reference basis for territorial spatial planning and land resource management to achieve the double carbon target, energy saving and emission reduction and modern governance.

5. Conclusions

In this study, the coupled PLUS and InVEST models are used to simulate the land use types and ESs under three future SSP scenarios with the help of climate data from CMIP6. The results show that:

Under future climate change, the parameters provided by CMIP6 are input into the PLUS model, which can effectively simulate land use change, and the model can be used for future land use change simulation work. Land change under the SSP245 scenario is similar to SSP126, where arable land decreases more severely under the SSP126 scenario, and urban expansion accelerates and arable land decreases slowly under the SSP245 scenario. In the SSP585 scenario, an increase in arable land occurs and a serious expansion of construction land encroaches on forest and grassland areas.

In the spatial correlation, all three scenarios showed good clustering effects. There is no significant difference in the distribution of cold hotspots in the SSP126 and SSP245 scenarios, and the hotspot area is mainly near the Yunling Mountains, with cold spots concentrated in the eastern part of the study area. The SSP585 scenario is slightly different from the other two scenarios in that the cold spots are shifted westward as a whole, and the hotspots are scattered and do not form a large area of clustering.

Among the importance of drivers, changes in ESs are influenced by a combination of factors, with GDP being the main influence on carbon storage; GDP and population having a greater impact on habitat quality; and precipitation being particularly important for soil retention and water production. Therefore, the impact of these factors can be focused on in future ESs projections.