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Article

Simulation and Zoning Research on the Ecosystem Service in the Beijing–Tianjin–Hebei Region Based on SSP–RCP Scenarios

by
Jinxiao Li
1,
Guijun Zhang
1,
Pengtao Zhang
1,*,
Siyu Jing
2 and
Jie Dong
1
1
College of Land and Resources, Hebei Agricultural University, Baoding 071001, China
2
College of Resources and Environmental Sciences, Hebei Agricultural University, Baoding 071001, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(8), 1536; https://doi.org/10.3390/land12081536
Submission received: 15 June 2023 / Revised: 20 July 2023 / Accepted: 27 July 2023 / Published: 2 August 2023
(This article belongs to the Special Issue Integrating Ecosystem Service Assessments into Land Use Decisions)
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Abstract

:
Understanding future trends and identifying characteristic differences in regional ecosystem services (ESs), in addition to ecological zoning, is vital for promoting the adjustment of ecological policy and the construction of sustainable ecosystems. Based on shared socioeconomic pathways and representative concentration pathways (SSP–RCP), the spatial distribution pattern of land use in the Beijing–Tianjin–Hebei region (BTH) in 2030 was simulated by using a patch-generating land use simulation model (PLUS) in this study. Water yield, carbon storage, habitat quality, and food product were simulated through the comprehensive evaluation model combining ecosystem services and trade-offs (InVEST). The comprehensive supply capacity of ESs was assessed. The ecological risk level was calculated by comparing the changes in the integrated supply capacity of ESs in 2020 and under each SSP–RCP scenario in 2030, and ecological zoning was established using a two-dimensional discriminant matrix. The results are as follows: (1) The degradation of grassland and cropland accompanied by an increase in construction land and forest to varying degrees will be the common characteristics of the three SSP–RCP scenarios in BTH. (2) Water yield and carbon storage services will exhibit an upward tendency only under SSP1-2.6, while habitat quality and food product services will exhibit a downward trend under three SSP–RCP scenarios. Obvious geographical heterogeneity exists in the comprehensive supply capacity of ESs. (3) Zones with low ecological risks will mainly be distributed in some counties of Zhangjiakou city, while zones with high ecological risks will account for a large proportion of the whole. There will be great ecological risks in the BTH overall. (4) The BTH was split into four types of ecological zones based on supply and risk. These zones comprise the ecological enhancement zone, ecological restoration zone, ecological sensitive zone, and ecological conservation zone. Corresponding control measures were also proposed. The findings of this study can be used to inform the formulation and improvement of environmental conservation policies.

1. Introduction

ESs are, both directly and indirectly, beneficial to humans, forming a connection between nature and mankind [1]. Chronic and high-intensity human activities have led to the deterioration of the global environment and intensified conflict between ecological and economic development; natural ecosystems are facing unprecedented pressure. ESs will experience unprecedented changes in the future, and human survival and development will be threatened. The United Nations Environment Programme has proposed the UN Decade on Ecosystem Restoration from 2021 to 2030 to curb the degradation of ecosystems and improve human well-being [2]. This initiative aims to promote the preservation and revitalization of ecosystems across the globe. Ecological zoning is an important component of ecological management. It is an important means of ecological protection and restoration because it can assist decision makers in formulating and adjusting regional ecological management policies [3]. Therefore, improvements to ecological zoning are crucial for achieving the United Nations Decade on Ecosystem Restoration and ensuring the sustainable growth of the regional ecology [4].
Research on ecological zoning can be traced back to natural zoning. Humboldt’s global isotherm map set a precedent for natural zoning. Further, Merriam [5] and Herbertson [6] have made outstanding contributions to improving the concept and theory of natural zoning. Bailey [7] compiled a map of the ecological regions of the US with the continuous development of natural zoning research. This marked the emergence of the first real ecological zoning plan. Research on ecological zoning has emerged globally since. Methods such as ecological networks [8], matrix analyses [9], multi-criteria decision analyses [10], self-organizing feature mapping networks [11], K-means clustering [12], and other zoning methods have been widely used in studying ecological zoning. Zoning units mainly include administrative zones [13], watersheds [14], and grids [15]. Researchers have carried out extensive studies from the perspectives of ecological risk [16], ecological restoration [17], ES clusters [18], supply and demand of ESs [19], and ecological compensation [20]. However, there is a certain lag in existing ecological zoning research, and there is a lack of consideration of the future changes of ESs because most of the aforementioned have relied upon the current scenario of the structure and processes of the ecosystem to carry out corresponding ecological zoning research.
Coping with global climate change and the degradation of ecosystems has become an urgent issue in recent years. Research on ES simulation has emerged to determine the future trends of ESs and achieve sustainable ecological development. The studies conducted by Costanza and Daily have perfected the principle, classification, and mechanism of ESs and laid the theoretical groundwork for the simulation of ESs [1,21]. The material quantity method and the value quantity method provide methodological feasibility for future ES simulation. The assessment results of the material quantity method are more scientific and reliable because its creation is based on the mechanism of ecosystem processes and ESs. Therefore, more researchers favor this method [22,23]. For instance, Fang combined InVEST and PLUS to simulate changes in ESs [24]. Arunyawat and Shrestha coupled the CLUMondo dynamic spatial model and the InVEST to simulate changes in ESs under three future scenarios in northern Thailand [25]. An increasing number of researchers are currently focusing on the simulation of future ESs. However, most only used changes in land use as the principal influencing factor for simulating future ESs because of the uncertainty of climate change, which leads to unreliable forecast results. The latest Coupled Model Intercomparison Project 6 (CMIP6) describes future socioeconomic development and global climate changes by combining SSP and RCP [26,27]. This provides a reasonable scenario for predicting future ESs and the possibility for a more accurate simulation of future ESs [28].
The economic and political center of northern China is located in the BTH [29]. The rapid economic development of the BTH and the increasing urbanization rate has aggravated ecological and environmental problems [30]. Higher standards for the simultaneous development of high-quality socioeconomic and ecological civilization have been implemented in response to the BTH’s coordinated development.
As an extension of previous studies, we constructed an ecological zoning method based on ES simulation, which fully considers the future changes of ESs and can effectively identify future regional ecological risks. From this perspective, in this study, our primary purpose is to offer a reference to guide ecological zoning management and the promotion of high-quality ecological and economic development of the BTH based on the change trends of ESs in this region. Therefore, the geographical distribution pattern of land use in the BTH was simulated using models such as PLUS and InVEST based on SSP–RCP. The comprehensive supply ability of ESs was assessed, and the regional ecological risk in the future was identified to construct ecological zoning. The following are the specific research objectives. (1) The spatial distribution pattern of land use in 2030 was revealed under SSP–RCP scenarios. (2) Water yield, carbon storage, habitat quality, and food product in 2020 and 2030 were calculated to assess the comprehensive supply capacity of ESs. Changes and differences in ESs were analyzed, and the regional ecological risks in the future were identified. (3) The BTH was ecologically zoned according to the comprehensive supply capacity of ESs and ecological risks. Different zoning control measures were proposed. The results promote the protection and sustainable management of ecosystems in the BTH, and offer a foundation for the formulation of ecological strategies.

2. Materials and Methods

2.1. Study Area

The BTH is situated within 113°04′–119°53′ E and 36°01′–42°37′ N, which includes Beijing, Tianjin City, and Hebei Province (Figure 1). With a high terrain in the northwest and a low terrain in the southeast, the landform of this region is complex. The Bashang Plateau, Yanshan and Taihang Mountains, and the Hebei Plain are three major geomorphic units that extend from northwest to southeast. The GDP of the BTH reached 9.6 trillion yuan, with a permanent resident population of 110 million in 2021.

2.2. Data Sources

Table 1 shows the data utilized in this work. This study classifies land use data from the years 2000, 2010, and 2020 as either cropland, forest, grassland, water, construction land, or unused land. The vector data of railways, highways, first-class highways, second-class highways, tertiary highways, and government residential buildings were processed by the Euclidean distance tool in ArcGIS. The precipitation, temperatures, and potential evapotranspiration were spatially downscaled in the SSP–RCP scenario utilizing the Delta method. The coordinate system of all geographical data was unified by ArcGIS10.2 software and converted into raster data with a 1 × 1 km spatial resolution.

2.3. Methods

The research framework mainly includes the following four steps (Figure 2). First, the spatial distribution pattern of land use under SSP–RCP scenarios in 2030 was simulated. Second, the water yield, carbon storage, habitat quality, and food product in 2020 and under the SSP–RCP scenario in 2030 were calculated to assess the comprehensive supply capacity of regional ESs. Third, changes and differences in ESs in 2020 and under each SSP–RCP scenario in 2030 were compared to identify regional ecological risks. Finally, the BTH was ecologically zoned according to the comprehensive supply capacity of ESs and ecological risks.

2.3.1. Selection of SSP–RCP Scenarios

The SSP scenarios described the possibility of different socioeconomic development in the future [33], while the RCP scenarios described the future greenhouse gas concentration and climate changes [34]. A combination of the two provides future scenarios that combine socioeconomic and climate change. Three typical scenarios were selected according to the wide applicability of each scenario, the availability of data, and the current condition in the BTH. The following are these three scenarios. (1) SSP1-2.6, the sustainable development pathway, prioritizes ecological protection and restoration while minimizing energy use and material consumption, as well as emissions of greenhouse gases. This scenario promotes inclusive and equal development. (2) SSP2-4.5, the intermediate pathway, maintains the inertial development trend of society, economy, and technology over the historical period with a decreasing usage intensity of resources and energy. However, the challenges of unbalanced social development and environmental fragility still exist in this scenario. (3) SSP5-8.5, the rapid development pathway relying on fossil fuels, improves scientific and technological innovation. Fossil fuel resources are extensively mined and used with a rapidly growing global economy and significant greenhouse gas emissions.

2.3.2. PLUS Model

The PLUS model consists of the land expansion analysis strategy (LEAS) and the CA model based on multi-type random patch seeds (CARS) [35]. In this study, fourteen natural and social factors (Table 2) were selected as driving factors of land use change according to previous studies and the availability of data [36,37,38]. Combined with the dynamic driving factors (GDP, population, precipitation, and temperature) under the SSP–RCP scenario, the land use under the SSP–RCP scenario was simulated using the PLUS model. First, we extract the two stages’ expansion and transformation components of land use. Then, LEAS was used to determine the probability that each land use type would grow on each unit and the degree to which each driving factor would contribute to the change in land use. Finally, CARS and Markov chains were used to obtain the patch evolution of various types. The specific parameter settings are described in Supplementary Material S1.
Based on the land use data of the BTH in 2000 and 2010, combined with 14 driving factors, the land use spatial pattern of the BTH in 2020 was obtained, and the simulation results were compared with the actual situation in 2020. The simulation accuracy verification results indicate that the overall accuracy is 0.91, and the kappa coefficient is 0.87. The PLUS model establishes a valid simulation result and can be used to predict the future spatial distribution of BTH land use.

2.3.3. Assessment of ESs

Single ESs Assessment

Based on the typicality of ESs in the BTH and the availability of SSP-RCP scenario data, four ESs were selected in this paper. The ESs are calculated, and the parameters are set based on the following (Table 3; Supplementary Materials S2).

Multiple Ecosystem Services Landscape Index (MESLI)

MESLI can characterize the comprehensive supply capacity of regional ESs [42]. The calculation formula is as follows:
M E S L I = ∑ i = 1 n x i − m i n x i m a x x i − m i n x i
where  i  is the type of ES;  n  is the number of ES types.  x i    is the observed value of the  i  ES;  m a x x i    is the maximum value of the  i  ES; and  m i n x i  is the minimum value of the  i  ES.

2.3.4. Ecological Risk Assessment and Ecological Zoning

Ecological risk refers to the possibility of a loss in the ES capacity. The changing trend of the capacity of comprehensive ESs was determined by comparing the MESLI of different regions in 2020 and under each SSP–RCP scenario in 2030. Then, the change results of MESLI in the three scenarios were superimposed to identify ecological risk zones depending on whether the MESLI of the research unit declined. We believe that the probability of occurrence of the three scenarios is equal and construct the following judgment criteria. The area is a low ecological risk zone if the MESLI of the research unit does not decline or only one scenario declines. The area is a high ecological risk zone if the MESLI of the research unit declines in two or more scenarios.
The zoning of the ES capacity and ecological risk zones represented the comprehensive capacity and the stability of capabilities of regional ESs, respectively. The current situation of the ecological supply capacity and future ecological risks were superimposed and analyzed. A two-dimensional correlation judgment matrix was established to obtain different supply risk spatial types. Ecological zoning was carried out according to the characteristics of specific areas.

3. Results

3.1. Land Use Simulation Analysis

According to the results (Figure 3 and Figure 4), which were simulated by the PLUS model, in 2030, cropland, forest, grassland, and construction land will become the significant types of land use in the BTH (the four account for more than 96%). In general, the decline in grassland and cropland, along with the increment of construction land and forest to varying degrees, will be the common characteristics of the three SSP–RCP scenarios. This is in accordance with Fan’s research results [43]. Specifically, the quantity of diverse lands varies considerably under the three scenarios. SSP1-2.6 has the fastest growth rate of forest area and decreasing rate of cropland; grassland surfaces exhibit a slightly decreasing trend relative to 2020, while the construction land area of the SSP1-2.6 scenario has a minor proportion relative to the other two scenarios. The change trends of various land use in SSP2-4.5 are similar to those of SSP1-2.6 but differ in terms of their change ranges. The expansion rate of construction land in SSP5-8.5 is the fastest, which is 1.2 times that of SSP1-2.6 in comparison to other scenarios. However, its cropland area accounts for the greatest percentage of each of the three SSP–RCP scenarios.

3.2. Temporal and Spatial Variation of ESs

3.2.1. Changes in Individual ESs

Water yield (Figure 5a) follows a regional distribution pattern that is strongly correlated with the pattern of precipitation. Significant advantages in water production service capacity are found in the eastern coastal areas of the BTH, as well as the eastern foothills of the Taihang Mountains in the southern BTH. The construction land and its immediate surroundings have a relatively high water yield service. This is a result of the high proportion of impervious surfaces [37], low precipitation infiltration, and low potential evapotranspiration of the construction land. In 2020, the average annual water yield was 68.85 mm/km2. Only the water yield service of SSP1-2.6 will improve, reaching 83.19 mm/km2. The water yield capacity under SSP2-4.5 and SSP5-8.5 scenarios will become lower than that in 2020 at 47.58 mm/km2 and 64.65 mm/km2, respectively.
High values of carbon storage (Figure 5b) are primarily found in the Yanshan–Taihang Mountains region, where the proportion of forest is relatively high. Low values of carbon storage are mostly distributed in the low hilly regions at the southern foot of the Yanshan Mountains, Bashang Plateau, and Hebei Plain. As the most significant carbon pool in terrestrial ecosystems, the forest has more organic matter accumulation and stronger carbon storage capacity [44]. Therefore, the carbon storage in the forest is relatively high. The carbon storage in the BTH was 16.88 × 108 t in 2020. Only in the SSP1-2.6 scenario will carbon storage increase, reaching 17.05 × 108 t. The carbon storages under SSP2-4.5 and SSP5-8.5 scenarios will be 16.85 × 108 t and 16.70 × 108 t, respectively, which will decrease by 0.35 × 107 t and 1.79 × 107 t, respectively, in comparison to those in 2020. The loss in carbon storage is relatively severe as a result of the rapid spread of construction land with low carbon density and encroachment on cropland and grassland with high carbon densities.
The habitat quality (Figure 5c) follows a similar regional distribution pattern to that of carbon storage; the habitat quality in 2020 was 0.509. Further, the habitat quality under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios will be 0.508, 0.502, and 0.498, respectively, which exhibit a relatively small decline. Forests, as the main habitat, will have different degrees of growth in the three scenarios, but their growth rate will be lower than that of construction land. The threat to adjacent habitats is heightened by the spread of construction land, which undermines the structure of the habitat. Therefore, biodiversity in the research area will be affected.
The Hebei Plain and the Bashang Plateau, both critical agricultural regions, are where the high capacity of food products is concentrated (Figure 5d). The total amount of food production was 10.16 × 107 t in 2020. The food production capacity of BTH will decline in all three scenarios due to the decreased amount of cropland. The total amounts of food production will be 9.47 × 107 t, 9.60 × 107 t, and 9.66 × 107 t under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively.

3.2.2. Changes in Integrated ESs

MESLI was utilized to assess the comprehensive supply capacity of ESs in different counties with the county-level administrative districts of the BTH as the unit. To more clearly identify how ESs vary in various areas, the Jenks Natural Breaks in ArcGIS was employed to categorize the MESLI of the BTH in 2020 and under the SSP–RCP scenarios in 2030 into high supply zones (1.47–1.88), middle supply zones (0.96–1.47), and low supply zones (0.19–0.96) (Table 4; Figure 6). There is apparent spatial heterogeneity in the MESLI of the BTH in terms of distribution. Zones with high supply are mainly located in the Yanshan–Taihang Mountain area, the hilly area, and the Piedmont Plain in the eastern section of the Yanshan Mountains. The Yanshan–Taihang Mountain area is dominated by forest and grassland, with high vegetation coverage and biodiversity and a strong capacity for carbon storage. The hilly area and the Piedmont plain in the eastern section of the Yanshan Mountains are mainly cropland with relatively high precipitation. Therefore, the strong food product capacity and service capacity of water yield led to a high supply capacity of regional ESs. The middle-supply zones are mainly distributed in the Bashang Plateau and the Hebei Plain, which mainly includes cropland ecosystems with a strong capacity for food production. However, this area is highly impacted by human activities and has moderate carbon storage capacity. The low-supply zones are primarily located in regions with a higher proportion of urban construction land. The service capacity of water yield is strong, but the supply capacity of other ESs is low in this area. In terms of the area proportion, the area ratio of middle- and high-supply zones is relatively high, while that of low-supply zones is relatively low. The proportion of high-supply zones to the total area is the highest (56.35%) among the three scenarios under the SSP1-2.6 scenario. The areas occupied by the low and the middle supply zones are the largest among the three scenarios under the SSP2-4.5 scenario, which are 6023.87 km2 and 107,813.96 km2, respectively. However, the middle supply zones accounted for a larger proportion under the SSP5-8.5 scenario (50.19%).
It can be seen that the comprehensive supply capacity of ESs has changed significantly after comparing the MESLI in 2020 and under the three scenarios in 2030 (Figure 7). The MESLI of 54 counties is enhanced under the SSP1-2.6 scenario, with the majority of the improvement occurring in the Taihang Mountains and the Bashang Plateau. The service function of water yield and carbon storage in the research area will continuously increase, and the ability to maintain biodiversity will stabilize. This is mainly due to the increased average annual precipitation in comparison to 2020 and the continuous increase in the forest area. However, the food product capacity will decrease significantly due to the continuous reduction of cropland area. The area with declines in the MESLI will mainly be located in the Hebei Plain, which has a high proportion of cropland, and the hilly regions near the Yanshan Mountains under the SSP1-2.6 scenario. Only one county’s MESLI will be improved under the SSP2-4.5 scenario. Water yield will decrease significantly to only 70% of the average depth of water yield of 2020 due to the reduction of precipitation and the increase of evaporation under the SSP2-4.5 scenario. Although there will be a slight expansion in forest area, grassland and cropland will gradually recede, and construction land will expand, resulting in food production capacity, carbon storage capacity, and capacity to maintain biodiversity, lowering in comparison to 2020. The MESLI of eight counties will exhibit an upward trend under the SSP5-8.5 scenario. Located primarily in Northwest Hebei, these counties are dispersed across the Bashang Plateau and the northwest and southern portions of the Intermountain Basin. SSP5-8.5 will have the highest temperature of the three scenarios, along with increased precipitation. However, the potential evaporation will be higher than that in 2020, leading to a slight decline in the average capacity of water yield in the SSP5-8.5 scenario. The carbon storage capacity and the ability to maintain biodiversity will be the weakest of all three scenarios because of the widespread encroachment of grassland and other ecosystems by construction land under the SSP5-8.5 scenario.

3.3. Identification of Ecological Risk Zones

The ecological risk zones (Figure 8a) of the BTH were identified by superimposing the change results of MESLI in different counties under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Low-risk zones include Guyuan County, Kangbao County, Shangyi County, Zhangbei County, Chongli District, Chicheng County, Yangyuan County, and Yu County. These regions coincide with the MESLI enhancement regions under the SSP5-8.5 scenario. Zones with high risk will mainly be distributed in areas other than Zhangjiakou. The zones with low risk in the BTH will mainly be distributed in some areas of Zhangjiakou, with obvious spatial agglomeration characteristics, under the SSP–RCP scenarios. It has been shown that precipitation and land use are the primary variables influencing ESs [45,46]. Land use changes and precipitation were compared in the work. This study compares the land use changes of the BTH and Zhangjiakou in 2020 under SSP–RCP scenarios. This comparison is accomplished by conducting an analysis of the dynamic degree of integrated land use. The dynamic degrees of land use in the BTH and Zhangjiakou are 0.34%, 0.32%, and 0.31%, and 0.28%, 0.09%, and 0.12%, respectively, in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Zhangjiakou has a lower degree of dynamic integrated land use than the BTH. Further, terrestrial ecosystems will be relatively stable in Zhangjiakou, which benefits considerably for food production, carbon storage, and the maintenance of biodiversity significantly. The average annual precipitation in the BTH exhibits a declining trend only in the SSP2-4.5 scenario in comparison to 2020, but the precipitation in the Zhangjiakou region under all three scenarios is higher than that in 2020. This significantly affects maintaining the stability of water yield capacity. Zhangjiakou served as an important ecological protective screen in the BTH. The ecological structure of the Zhangjiakou area has been adjusted, and the ecosystem’s stability has been enhanced through the continuous promotion of ecological projects, including the Ecological Restoration of the Yongding River.

3.4. Analysis of Ecological Zoning

Given these results, we built a two-dimensional correlation judgment matrix that incorporates the ES supply capacity and ecological risk (Figure 8b). The superposition results of ES supply ecological risk were obtained. Regions with high supply–high-risk characteristics are dispersed throughout the Yanshan–Taihang Mountains. They cover an area of 92,789.52 km2, which is approximately 43.42% of the research area. Regions with high supply–low-risk characteristics are clustered principally in the northwest and south regions of the intermountain basin in northwest Hebei. The area of these regions is 19,335.07 km2, which accounts for 9.05% of the area. Zones with middle supply–high characteristics risk are mostly located in the agricultural production regions of the Hebei Plain and the center urban area of Zhangjiakou. The area is 90,542.48 km2, which accounts for 42.36% of the research area. Regions with middle supply–low-risk characteristics are concentrated in the Bashang plateau area. They occupy an area of 6775.72 km2, of which percentage reached is 3.17% of the BTH. The zones of low supply–high-risk characteristics are distributed in the central urban area of the Hebei Plain with a spotty pattern. They cover an area of 4274.90 km2, which is about 2.00% of the research area.
According to the spatial type of the supply capacity–ecological risk characteristics of ESs, and the characteristics of different regions, we divided the BTH into four ecological zones. These zones (Table 5; Figure 9) consist of the ecological enhancement zone, ecological restoration zone, ecological sensitive zone, and ecological conservation zone.

4. Discussion

4.1. Significant Differences in ESs under SSP-RCP Scenarios

ESs and ecological risks are significantly distinct under different SSP–RCP scenarios. In terms of supply capacity and ecological stability, the SSP1-2.6 scenario, which represents sustainable development, outperforms both SSP2-4.5 and SSP5-8.5. This has been confirmed by the findings of other studies [47]. Upon closer examination, we observe a declining trend in both habitat quality and food product services compared to the year 2020, corroborating earlier findings [41,48]. Similarly, the SSP1-2.6 demonstrates the highest carbon storage capacity, aligning with Wang’s results [49]. However, variations in water yield may differ due to the impact of future precipitation, evapotranspiration, and regional disparities, showing some deviations from previous research findings [50]. We consider meteorological conditions and land use to be the direct causes of significant differences in ESs under the SSP–RCP scenario. Climate changes affect the provision of ESs by altering the biological and physical cycles of ecosystems [51,52]. Land use changes directly impact the structure and functions of ecosystems, affecting the level of ecosystem service provision [53]. Therefore, land use functions and climatic conditions affect the ecosystem supply capacity. The geographical variability of ESs is mainly attributable to variations in land use and meteorological conditions between regions. For example, many woodlands and high vegetation coverage area in the Taihang Mountains of the study area leads to the maintenance of biodiversity and significant carbon sequestration abilities in this area. The hilly area on the south side of the Yanshan Mountains has a relatively high water yield in the research area due to its abundant precipitation. In addition to land use and climate change, there are substantial differences in the development paths and policies in the SSP–RCP scenarios. The transformation of the ecosystem by humans and the degree of natural resource utilization are adjusted to affect land use and climate changes, indirectly affecting ESs. Moreover, because ESs are strongly linked to human welfare, and development strategies and policies will constantly change in response to human preferences and perceptions of ecological advantages [54]. Therefore, it is necessary to take appropriate measures according to the differences in ESs and zoning results to accelerate the realization of ecologically sustainable development.

4.2. Suggestions for Future Development

Ecological enhancement zones are mainly dominated by agricultural and urban spaces, with a small proportion of ecological space. The delineation of “three zones and three lines” should be rigorously promoted to improve the ecological supply capacity and enhance the ecological sustainability of this region. The relationship between urban space, agricultural space, and ecological space should be balanced. This region, the main agricultural production area in the BTH, is the most important for ensuring food production services. The protection of cropland quantity should be promoted, and the red lines for cropland protection are strictly observed. The policy of arable-land balance must be implemented to guarantee China’s food security. Further, the quality of cropland must be continuously promoted, and the level of agricultural modernization and production efficiency should be improved. The recreational functions of agricultural production areas should be leveraged to improve ecological agriculture, optimizing the ecological pattern of the agricultural landscape [55]. Appropriate adjustments can be made to the ecological space and urban spatial structure based on food security. The adjustment of urban space should first determine urban development boundaries to ensure the rational development of construction land. The ecological space can be adjusted by appropriately supplementing a certain amount of ecological land along the Yanshan–Taihang Mountains region.
In the ecological restoration zone, there are relatively high capacities for ES supply, while the ecological risk is relatively high. Therefore, relevant work should be carried out to ensure ecosystem restoration. The encroachment of forest and grassland by construction land should be strictly controlled, and the species composition and spatial organization of plant communities should be optimized in the Yanshan–Taihang Mountains region. The construction of ecological projects should continue, including the greening of Taihang Mountain, along with the governance of small watersheds in the Yanshan Mountain Area. The coastal areas of Qinhuangdao and Tangshan, with abundant precipitation, have strong water yield capacity in the BTH. Therefore, focus should be placed on water yield and water conservation functions in this area. Not only should the massive growth of construction land be controlled, but the extent of the impervious surface should be appropriately controlled to reduce the risk of urban waterlogging in this area [37]. The proportion of ecological land should be increased to improve the water conservation capacity of this area [56]. Shifting cultivation should continue to be promoted in the region. The rotation system has capacities to enhance the water conservation of soil, water utilization efficiency, and food production services in this region [57].
The ecologically sensitive zone is distributed in the city center with a high population density. City greening should be promoted in this area to build a sponge city. Public support facilities should be improved, and greenhouse gas emissions should be reduced. It is difficult to carry out large-scale ecological projects in this area due to its particular economic, social, and political functions. The construction of an ecological safety pattern should be accelerated to enhance the landscape connectivity and the spatial linkage of the system structure. The implementation of the eco-compensation responsibility should be strengthened to expand eco-compensation modes, including economic compensation, policy compensation, educational compensation, and technical compensation [58], forming a healthy development trend.
Ecological containment zones in some counties of Zhangjiakou, with low ecological risk and relatively strong capacity of ecological supply, should be guaranteed owing to their unique ecological advantages. The development principle that “green hills are golden hills” should be strictly implemented. Ecological projects should continue to be developed, with adherence to the principle of planting trees and grass in suitable places. The ecological and post-winter Olympic tourism industry should be actively developed. The rest-rotation grazing system must be implemented, and construction activities that increase ecological sensitivity should be prohibited to improve the ecological supply capacity and stability.

4.3. Limitations and Future Prospects

To facilitate the protection of ecosystems and accelerate ecologically sustainable development in the BTH, this study is based on the SSP–RCP scenarios to explore future changes in ESs and construct ecological zones. This has implications for improving regional ecological policies and promoting green and high-quality development in the future. However, this research has certain limitations.
First, limitations exist when determining ecological zoning units. County-level administrative regions are selected as zoning units to ensure the implementation of zoning policies, but ecosystem structure and processes are not affected by administrative boundaries. The different characteristics of ecosystem service flows can have certain impacts on the results of ecological zoning [59]. Multi-scale research can facilitate the systematic identification of ecological functional mechanisms across various regions. Future studies should employ this multi-scale research to ascertain the optimal zoning units. Second, due to the difficulty of simulating certain natural environmental conditions, only four typical ESs in the BTH were selected. These services include food production of the supply service, carbon storage, water yield of the regulating service, and habitat quality of the support service. To a certain extent, these services represent all of the ESs of the BTH. However, there is no analysis of cultural services. A complete ES simulating framework should be built to capture the future evolution of all ESs. In future studies, a more comprehensive assessment framework of ESs can be constructed, building upon the ES classification framework proposed by the Millennium Ecosystem Assessment.
Third, a brief analysis was conducted on these reasons for the changes in ESs, but no in-depth research was conducted on their processes and mechanisms. Some issues will become hot topics in future research. These issues include how to strengthen the synergistic effect of ESs and the promotion of human welfare [4], along with the exploration of the interaction mechanism between various ecosystems.
Fourth, new models and methods should be applied more widely. In land use simulation, the PLUS model can only simulate the patch-level evolution of land use [35], while the MCCA model can effectively simulate sub-cell scale mixed land use structural changes [60]. The development and use of this model are of great significance for land use simulation. The ARIES model should also receive more attention when evaluating ESs [61]. In comparison to the InVEST model, ARIES focuses on the flow of ecosystem services in the landscape, which helps understand the spatial connections of ESs in different regions and can serve to construct more scientific and reasonable ecological zoning. In addition, because of the advantages of a wide detection range and a short revisit time of remote sensing technology, the ecosystem evaluation method based on remote sensing technology should also be implemented in future research [62,63]. Finally, this study primarily addresses zoning from an ecological standpoint to identify ecological risks and maintain ecological stability, and it lacks zoning perspectives from other disciplines, such as economics and sociology. As a result, future studies on zoning with multidisciplinary integration should be carried out.

5. Conclusions

By coupling the PLUS–InVEST model, this paper effectively simulated the ESs supply capacity of three SSP–RCP scenarios in 2030. Ecological risks in various regions were cataloged, and zoning was established accordingly. The results are as follows.
(1)
In 2030, there will be an increase in the amount of construction land and forests, along with a degradation in the amount of grassland and cropland in all three SSP–RCP scenarios;
(2)
The distribution of the four ESs is apparently spatially heterogeneous in the BTH.ESs under the three scenarios in 2030 exhibit markedly distinct trends of variance from those in 2020. Water yield and carbon storage services will increase only in SSP1-2.6, while the habitat quality and food production services exhibit a downward trend in the three scenarios;
(3)
Significant differences exist in MESLI changes under different SSP–RCP scenarios in 2030 compared to those in 2020. The MESLI of eight counties will be improved under the three SSP–RCP scenarios. Zones with low ecological risks will mainly be distributed in some counties and districts of Zhangjiakou. Zones with high ecological risks will account for a large proportion of the whole, and the BTH will have high ecological risks;
(4)
The conflicts among urban, agricultural, and ecological spaces in the ecological enhancement zone need to be balanced. Ecological projects in the ecological restoration zone should be constructed. City greening should be promoted in the ecologically sensitive zones to build a sponge city. Ecological protection should be the focus of the ecological conservation zone, with the appropriate development of ecotourism projects.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land12081536/s1, Table S1: The neighborhood weights for individual land use; Table S2: Cost Matrix of land use pair of SSP1-2.6 scenario; Table S3: Cost Matrix of land use pair of SSP2-4.5 scenario; Table S4: Cost Matrix of land use pair of SSP5-8.5 scenario.

Author Contributions

J.L.: Data curation, Methodology, Software, Formal analysis, Visualization, Writing—original draft. G.Z.: Formal analysis, Investigation, Supervision, Writing—review and editing. P.Z.: Resources, Project administration, Funding acquisition, Writing—review and editing. S.J.: Software, Data curation. J.D.: Validation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Social Science Development Research Project of Hebei Province, China (Grant No. 20220202232) and the Social Science Foundation of Hebei Province, China (Grant No. HB19YJ020).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 12 01536 g001
Figure 1. Topography and location of BTH.
Figure 1. Topography and location of BTH.
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Figure 2. Technology roadmap for this study.
Figure 2. Technology roadmap for this study.
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Figure 3. Quantitative changes of each land use type.
Figure 3. Quantitative changes of each land use type.
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Figure 4. Spatial simulation of land use in BTH in 2030 under SSP–RCP scenarios.
Figure 4. Spatial simulation of land use in BTH in 2030 under SSP–RCP scenarios.
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Figure 5. ESs distribution in 2020 and ESs distribution under SSP–RCP scenarios in 2030.
Figure 5. ESs distribution in 2020 and ESs distribution under SSP–RCP scenarios in 2030.
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Figure 6. Spatial distribution of MESLI in 2020 and MESLI under the SSP–RCP scenario in 2030.
Figure 6. Spatial distribution of MESLI in 2020 and MESLI under the SSP–RCP scenario in 2030.
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Figure 7. Spatial variations of MESLI under three SSP–RCP scenarios.
Figure 7. Spatial variations of MESLI under three SSP–RCP scenarios.
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Figure 8. Spatial types of BTH (a) ecological risk types and (b) ES supply ecological risk types.
Figure 8. Spatial types of BTH (a) ecological risk types and (b) ES supply ecological risk types.
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Figure 9. Ecological zoning in BTH.
Figure 9. Ecological zoning in BTH.
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Table 1. Data acquisition and description.
Table 1. Data acquisition and description.
CategoryDataYearResolutionData Sources
Historical
data		Land use data2000, 2010, 20201 kmResource and Environment Science and Data Center (http://www.resdc.cn), accessed on 6 December 2022
Potential evapotranspiration20191 kmNational Earth System Science Data Center (http://www.geodata.cn/),
accessed on 6 December 2022
Population density20191 km
Precipitation20201 km
GDP20201 km
Temperature20201 km
Soil depth20091 kmHWSD (http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/),
accessed on 5 December 2022
Soil type20091 km
Railway2020vectorOpenStreetMap (https://www.openstreetmap.org/), accessed on 6 December 2022
Highway2020vector
Primary road2020vector
Secondary road2020vector
Tertiary road2020vector
Government premises2020vectorNational Catalogue Service For Geographic Information (https://www.webmap.cn/main.do),
accessed on 6 December 2022
DEM200930 mGeospatial Data Cloud (https://www.gscloud.cn/),
accessed on 6 December 2022
Slope200930 mCalculated from DEM by Slope tool
provided by ArcGIS software
Future
data		GDP20301 km[31]
Population density20301 km[32]
Precipitation20301.1° × 1.1°MRI-ESM2-0 Model (https://esgf-node.llnl.gov/search/cmip6/),
accessed on 5 December 2022
Potential evapotranspiration20301.1° × 1.1°
Temperature20301.1° × 1.1°
Table 2. Spatial driving factors of the land use change in this study.
Table 2. Spatial driving factors of the land use change in this study.
CategoryDriving Factors
Social factorsPopulation
GDP
Distance from railways
Distance from highways
Distance from primary roads
Distance from secondary roads
Distance from tertiary roads
Distance from governments
Natural factorsSoil type
Average annual precipitation
Average annual temperature
DEM
Slope
Distance from water
Table 3. Methods of Assessment ESs.
Table 3. Methods of Assessment ESs.
Ecosystem ServicesModels and MethodsReferences
Water yieldInVEST model[13]
Carbon storageInVEST model[39]
Habitat qualityInVEST model[40]
Food productFood production capacity per unit area of land use[41]
Table 4. Area and proportion of ESs supply capacity level zones.
Table 4. Area and proportion of ESs supply capacity level zones.
ESs Supply
Capacity Level Zone		2020SSP1-2.6SSP2-4.5SSP5-8.5
Area
(km²)		Proportion
(%)		Area
(km²)		Proportion
(%)		Area
(km²)		Proportion
(%)		Area
(km²)		Proportion
(%)
High supply zone112,124.5952.47120,423.7556.3599,874.3146.73100,668.1547.10
Middle supply zone97,311.5945.5389,237.3241.75107,813.9650.45107,260.2550.19
Low supply zone4275.962.004051.071.906023.872.825783.742.71
Table 5. Statistics of ecological zoning in BTH.
Table 5. Statistics of ecological zoning in BTH.
Ecological ZoningSupply Risk Space TypeArea (km²)Proportion (%)
Ecological enhancement zoneMiddle supply–High risk90,542.4842.36%
Ecological restoration zoneHigh supply–High risk92,789.5243.42%
Ecological sensitive zoneLow supply–High risk4274.902.00%
Ecological conservation zoneHigh supply–Low risk26,119.6712.22%
Middle supply–Low risk
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Li, J.; Zhang, G.; Zhang, P.; Jing, S.; Dong, J. Simulation and Zoning Research on the Ecosystem Service in the Beijing–Tianjin–Hebei Region Based on SSP–RCP Scenarios. Land 2023, 12, 1536. https://doi.org/10.3390/land12081536

AMA Style

Li J, Zhang G, Zhang P, Jing S, Dong J. Simulation and Zoning Research on the Ecosystem Service in the Beijing–Tianjin–Hebei Region Based on SSP–RCP Scenarios. Land. 2023; 12(8):1536. https://doi.org/10.3390/land12081536

Chicago/Turabian Style

Li, Jinxiao, Guijun Zhang, Pengtao Zhang, Siyu Jing, and Jie Dong. 2023. "Simulation and Zoning Research on the Ecosystem Service in the Beijing–Tianjin–Hebei Region Based on SSP–RCP Scenarios" Land 12, no. 8: 1536. https://doi.org/10.3390/land12081536

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