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Article

Assessing and Predicting Ecosystem Services and Their Trade-Offs/Synergies Based on Land Use Change in Beijing–Tianjin–Hebei Region

by
Shengxuan Gong
,
Yuhu Zhang
*,
Xiao Pu
*,
Xiaohan Wang
,
Qiuyu Zhuang
and
Wenhui Bai
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(13), 5609; https://doi.org/10.3390/su16135609
Submission received: 16 May 2024 / Revised: 27 June 2024 / Accepted: 27 June 2024 / Published: 30 June 2024
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Abstract

:
Exploring the spatial and temporal dynamic changes in ecosystem service functions and trade-off/synergistic relationships over a long time series in the Beijing–Tianjin–Hebei region is of great practical significance for regional high-quality development. Taking the Beijing–Tianjin–Hebei region as the research object, PLUS was used to predict the land use distribution in 2030 under three scenarios: business as usual (BAU), cropland protection scenario (CPS), and ecological protection scenario (EPS); the InVEST model was introduced to assess ecosystem services including water yield (WY), carbon stock (CS), habitat quality (HQ), and soil conservation (SC); trade-offs/synergies among ecosystem services were calculated by using R and GeoDa modeling. The results show the following: (1) Between 1980 and 2020, the study area witnessed varying trends in WY and SC, which initially decreased and subsequently increased, showing an overall upward trend. In contrast, CS and HQ consistently declined throughout the period; the HQ, CS, and SC values were high in the northwest and low in the southeast, while the WY value was high in the southeast and low in the northwest. (2) From 1980 to 2020, the relationships SC-HQ and CS-HQ, as well as CS-SC, were characterized by synergy. In contrast, the interactions CS-WY, SC-WY, and WY-HQ demonstrated trade-offs. On the whole, the trade-off/synergy degree showed a trend of fluctuating increase. From the perspective of the spatial scale, CS-HQ, CS-SC, SC-HQ, and WY-HQ were mainly cooperative relationships. CS-WY and SC-WY were trade-off relationships. (3) Compared with 2020, the total ecosystem service of the four types decreased, increased, and increased under business as usual (BAU), the ecological protection scenario (EPS), and the cropland protection scenario (CPS), respectively, and the increase was the highest under the ecological protection scenario. In terms of time scale, there were also differences in the trade-off intensity among ecosystem services under the three scenarios. In the EPS, the trade-off/synergy intensity among various ecosystem services was the highest, followed by the CPS, and the lowest was under BAU. The findings of this research offer theoretical insights and practical guidance for enhancing ecosystem services and zoning functions in Beijing–Tianjin–Hebei, while also providing fundamental support for refining the territorial spatial configuration.

1. Introduction

The ecosystem serves as the cradle for life’s survival and reproduction, providing essential ecosystem services for humans [1,2]. According to the IPBES Secretariat, ecosystem services are categorized as Material Contributions, Non-Material Contributions, and Regulating Contributions. These services play a vital role in maintaining human social well-being, economic development, and ecological balance. However, the diversity and uneven spatial distribution of ecosystem services often exhibit trade-off relationships, i.e., antagonistic situations where a kind of service might be gained in return for another [3], or synergistic relationships, i.e., positive relationships whereby one ecosystem service directly increase the benefits supplied by another service [4]. Studies have shown that topography [5], human activities [6], and land use changes [7] lead to spatiotemporal variations in ecosystem services (ESs), with land use change being the primary driving factor. In this context, studying changes in ecosystem services and their trade-offs/synergies forms the foundation of integrated ecosystem management, offering a basis for decision making in managing and utilizing regional natural resources. Achieving coordinated development between humans and the natural environment, thereby enhancing human well-being, holds substantial theoretical and practical significance [8,9,10].
Land use change mainly involves changes in land use types and spatial patterns, which are closely related to the cycling of surface materials and life processes [11]. Analyzing land use changes is a fundamental method for assessing the impact of human activities on ecosystem services [12,13]. Chen et al. proposed that simulating land use scenarios is a key method for quantifying ecosystem services [14]. The evaluation of ecosystem services based on land use, along with the assessment of trade-offs and synergies among these services, has become a major focus in the study of regional ecosystem services [15,16]. Accurately simulating land use changes and the spatiotemporal dynamics of ecosystems under different scenarios by using high-precision models is crucial [17,18]. The patch-generating land use simulation (PLUS) model integrates Markov chains, cellular automata (CA) modules, and multi-type random patch seed mechanisms [19]. It retains the adaptive inertia competition and roulette competition mechanisms from the FLUS model. The model trains on land use conversion samples between two periods to derive the development potential of various land use types, enabling more accurate simulations of dynamic changes in land use spatial distribution. Utilizing the PLUS model for multi-scenario simulations of land cover changes allows for more precise research on the evolution of various land parcels [20]. This provides a solid foundation for predicting the trade-offs and synergies of ecosystem services. There are numerous studies on the application of the PLUS model in multi-scenario simulations of land cover. Zhao et al. [21] used the InVEST-Markov-PLUS model to assess and predict ecosystem services under multiple scenarios for Liaoning Province. Lin et al. [22] predicted land use changes under three scenarios for the Guangdong–Hong Kong–Macao Greater Bay Area in 2030. They analyzed the relationships among ecosystem services, providing references for ecosystem service management and socioeconomic planning in the Greater Bay Area. The quantitative assessment of the spatiotemporal changes in ESs based on land use change, along with the analysis of trade-offs and synergies, is crucial to achieving regional sustainable development.
The Beijing–Tianjin–Hebei (BTH) region is one of the three world-class city clusters, serving as a core growth pole of the country. Extensive urban development has resulted in an increase of about 93.1% in construction land within this region from 1980 to 2020. According to the 2021 China Soil and Water Conservation Bulletin, the area of soil erosion in the BTH region is approximately 4.25 × 104 km2. As reported in China Environmental Statistics Yearbook 2021, the per capita water resources in Beijing and Tianjin in 2020 accounted for about 1/20 of the national average, while in Hebei Province, the corresponded to only 1/11, indicating an extreme scarcity of water resources. Approximately 70% of the rivers experience interrupted flow throughout the year, underscoring the extreme scarcity of water resources [23]. Located in a semi-arid area with a sensitive and fragile ecological base, the region has experienced significant ecological risks and environmental pressures due to large-scale urban construction. This has exacerbated tensions in human–land relations, has intensified resource and environmental problems, and has contributed to uneven regional development, making it one of the most challenged areas in eastern China. The InVEST and PLUS models were employed to evaluate and project the functions of ecosystem services—water yield, soil conservation, carbon stock, and habitat quality—in the BTH region from 1980 to 2020 and under three scenarios projected for 2030. The goal of this study is to provide a scientific foundation for enhancing ecosystem service configurations and developing precise, comprehensive ecological protection strategies, thereby promoting sustainable development in BTH.

2. Material and Methods

2.1. Study Area

The Beijing–Tianjin–Hebei (BTH) region (36.03° N–42.62° N, 113.52° E–119.85° E) in northern China spans 216,500 km2, making up 2.2% of the country’s total land area (Figure 1) [24]. Elevations range from −52 m to 2836 m, decreasing from northwest to southeast. The region has a temperate continental monsoon climate, with annual temperatures ranging from 3 °C to 15 °C and rainfall between 304 mm and 750 mm [25], experiencing four distinct seasons. The BTH region features a complex ecological system with diverse geological features, landforms, and biomes, all of which significantly contribute to the study of ecosystem services. Key geological features include sedimentary rock formations in the Yan Mountains and granite and metamorphic rocks in the Taihang Mountains [26,27]. These formations affect soil composition and water retention throughout the region. The BTH region features varied landforms, including the North China Plain, one of China’s key agricultural areas due to its fertile soils and extensive irrigation systems. The Taihang Mountains along the western edge form a natural barrier, significantly influencing local climate patterns and biodiversity. The Yanshan Mountains to the north also contribute to the diverse topography and serve as important watersheds. Biomes in the BTH region include temperate deciduous forests in the Taihang and Yan Mountains, which are home to various plant and animal species. The northern region features temperate grasslands, especially in transition zones between mountains and plains, which support grazing and biodiversity. Along the Bohai Sea coast, extensive wetlands provide critical habitats for migratory birds and other wildlife, playing a key role in water purification and flood control [28,29,30]. Over the past four decades, the BTH region has seen significant growth. By 2020, it had become one of China’s primary economic hubs, contributing 8.5% to the national GDP, totaling CNY 8.64 trillion. This was achieved with only 2.3% of the country’s land area and 7.8% of its population [31]. Despite economic gains, rapid development has caused considerable environmental strain, particularly impacting ecosystem services crucial for regional sustainability.

2.2. Data Sources

The data and their sources used in this study for ecosystem service assessment and driving factors are listed in Table 1. The study area was divided into 6 categories, i.e., cropland, forestland, grassland, water, construction land, and unused land, and 10 driving factors were selected to calculate the suitability probability of each land use type in PLUS, including DEM, slope, temperature, precipitation, PET, population density, gross domestic product, distance from rivers, distance from railways, and distance from highways (Figure 2). All raster data were reprojected to the WGS1984_UTM_ZONE_50N coordinate system to maintain consistent raster extents.

2.3. Ecosystem Service Assessment Methods

2.3.1. Ecosystem Service Assessment

This study employed the InVEST model for a comprehensive quantitative evaluation of ecosystem services, including water yield (WY), soil conservation (SC), habitat quality (HQ), and carbon stock (CS).

2.3.2. Carbon Stock

Ecosystem carbon stock refers to the total amount of carbon stored within an ecosystem. This carbon is stored in plants, soil, and other organic matter and plays a crucial role in mitigating climate change. The total regional CS was determined by multiplying the average carbon density (Table 2) of Cabove, Cbelow, Cdead (also known as dead organic carbon), and Csoil in each ecosystem or vegetation type by their respective areas [32]. Based on the actual situation of the study area and the difficulty of obtaining data, the study selected above-ground biomass carbon, below-ground biomass carbon, and soil carbon pool, which have a significant impact on carbon storage assessment, for estimation. The litter carbon pool is temporarily not considered [33]. The calculation formula of the carbon storage module in the InVEST model is as follows:
Ctotal = Cabove + Cbelow + Csoil + Cdead
where Ctotal is the amount of CS overall (t/ha), Cabove is the amount of CS of vegetation above the ground (t/ha), Csoil is the CS of vegetation litter (t/ha), and Cbelow takes the CS (t/ha) of the underground portion of vegetation as a unit. The storage of soil carbon (t/ha) is known as Cdead.

2.3.3. Water Yield

Water yield refers to the amount of water provided by an ecosystem through processes such as precipitation interception, soil water storage, and groundwater recharge. The water yield module in the InVEST model was adopted to evaluate the water yield. The calculation formula is as follows [34,35]:
Q W = ∑ i = 1 j P i − R i − E T i ⋅ A i
The QW is determined by various factors, including land use area (Ai), precipitation (Pi), surface runoff (Ri), and evapotranspiration (ETi), where j denotes the total ecosystem types in the study area.

2.3.4. Habitat Quality

Habitat quality indicates the potential of regional ecosystems to provide the necessary conditions for the survival and breeding of species and is the key to the protection of biodiversity [36]. The habitat quality module in the InVEST model was used for the assessment. The calculation formula is in the following form:
Q x j = H j 1 − D x j z D x j z + k z
In the formula, Qxj is the habitat quality index of grid x in land use j; Hj is the habitat suitability of habitat type j (Table 3), within the range of [0, 1]; Dxj is the habitat degradation index; R is the number of stress factors; and K is a semi-saturation constant, which is generally 1/2 of the maximum habitat degradation [37,38].

2.3.5. Soil Conservation

SC primarily includes erosion control and overland sediment retention. The InVEST model sedimentation module was used to calculate soil conservation [39]. The calculation formula is as follows:
A E R x = R K L S x − U S L E x × S D R x + T x
R K L S x = R x × K x × L S x  
U S L E x = R x × K x × L S x × C x × P x
where AERx represents the soil conservation amount (t/hm2) of grid x; RKLSx represents the potential soil erosion of grid x (t/hm2); USLEx represents the actual soil erosion of grid x (t/hm2); SDRx represents the sediment transport ratio; Tx represents the amount of sediment discharged uphill by grid x interception (t/hm2); Rx represents the rainfall erosivity factor [MJ·mm (hm2·h·y)−1]; Kx represents the soil erodibility factor [t·hm2·h (MJ·hm2·mm)−1]; LSx represents the slope length factor; Cx represents the vegetation cover factor; Px represents the soil and water conservation factor.

2.3.6. PLUS Model

The PLUS model, an enhancement of the FLUS model, is a patch-level, refined land use prediction tool that incorporates policy-driven guidance [40,41]. This study utilized land use data from 2010 and 2020 to determine the suitability probability of various categories, employing these factors as predictor variables. By using the 2010 data as a baseline for simulation, the PLUS model projected the 2020 land use patterns; these were then compared with actual 2020 data. The comparison yielded a Kappa coefficient of 0.83, indicating high reliability of the simulation results. To address diverse developmental needs and in agreement with the ecological and agricultural priorities stipulated in the Beijing–Tianjin–Hebei Cooperative Development Land Use Master Plan, we adjusted the settings of the transfer matrix. These adjustments facilitated predictions of land use distribution types for 2030 under the scenarios of business as usual (BAU), cropland protection scenario (CPS), and ecological protection scenario (EPS) within the region.
(1) Neighborhood weights: Neighborhood weights represent the expansion capacity of different land use types [35] and were calculated by using the dimensionless values of the area change in land use type from 2010 to 2020 (Table 4).
(2) Driving factors: Land use change is related to the natural environment, transportation location, and socioeconomics, and the spatial variables driving land use change in this paper are mainly natural factors (DEM and slope), transportation location factors (distance from rivers, railroads, and highways), socioeconomics (population and GDP), and meteorology (temperature, precipitation, and potential evapotranspiration) [42,43], as shown in Figure 2.
(3) Scenario design: The land use transfer matrix delineates the potential conversions between different land use types. Within this matrix, a value of 1 indicates that conversion between two types is possible, while a value of 0 signifies it is not. The study defines three scenarios (Table 5): the BAU scenario allows for free conversion among all land types; the ecological protection scenario prioritizes conversions based on ecological benefits, ranking land types from most to least beneficial as forestland, grassland, cropland, and watersheds; the cropland protection scenario permits conversions of all non-construction land types into cropland [33].

2.3.7. Correlation Analysis

The correlation analysis method can effectively reflect the change trend between two variable directions and degrees of potential. Firstly, random points were generated by using ArcGIS 10.8 software, followed by the utilization of the value extraction to the point tool to acquire the four ecosystem service values for each point. Secondly, z-score standardization was applied to normalize the obtained sample data and mitigate any impact caused by varying magnitudes. Subsequently, the Kolmogorov–Smirnov test was employed to identify non-normal distributions. Finally, the trade-offs and synergies among four ecosystem services—water yield, carbon storage, soil conservation, and habitat quality—were investigated by using correlation analysis packages in R.

2.3.8. Bivariate Spatial Autocorrelation

Bivariate spatial autocorrelation is a widely employed method utilized to uncover the correlations among different attributes within evaluation units [44]. In this study, we employed the bivariate spatial autocorrelation analysis tool provided by GeoDa 1.18.0 software to investigate the trade-offs and synergies among four ecosystem services across the study area over the period from 1980 to 2020. Additionally, we analyzed the spatial and temporal evolution characteristics. The calculation formula is as follows:
I = ∑ i = 1 n ∑ j = 1 n w i j x i − x ‾ x j − x ‾ S 2 ∑ i = 1 n ∑ j = 1 n W i j
I i = x i − x ‾ ∑ j = 1 n w i j x i − x ‾ S 2
S 2 = ∑ i = 1 n ∑ j ≠ i n w i j
where I is the Moran’s index of spatial autocorrelation, ranging from −1 to 1. A positive I value indicates that ecosystem services exhibit a synergistic relationship in their spatial distribution, while a negative I value indicates a trade-off relationship. An I value of 0 signifies a lack of spatial correlation. Here, n represents the total number of evaluation units; Xi and Xj refer to the average values of ecosystem services for units i and j, respectively, while x is the average value of ecosystem services within unit i. Wij represents the weight matrix constructed from spatial grid adjacency relationships, and S2 denotes the variance.

3. Results

3.1. Land Use Changes during 1980–2020

According to the land use data from Table 4, cropland was the predominant land use in the BTH region from 1980 to 2020, with proportions of 52.2%, 52.04%, 50.73%, 48.2%, and 46.29%. This type of land use consistently constituted nearly half of the region’s total area, but its share steadily declined over the years. The percentage of unused land was consistently the smallest, hovering around 1%, and also exhibited a gradual decrease. In contrast, the percentage of construction land rose markedly, increasing from 6.56% in 1980 to 12.72% in 2020, an increase of about 93.1%. Moreover, forest, grassland, and water areas displayed fluctuating trends. Over the past 40 years, forest and water body areas have grown by 0.41 km2 and 0.22 km2, respectively.
From the perspective of the spatial distribution of urban land use types in the BTH region (Figure 3 and Table 6), cropland emerges as the predominant type in the southeast half of the region, occupying the largest area, primarily distributed in Zhangjiakou City, Baoding City, and Cangzhou City. Cropland represents the primary category affected by urban expansion. The distribution of forest and grassland areas is relatively similar, extending from the northeast to the upper southwest of the study area, encompassing most of Chengde, the northwest of Beijing, the northeast of Baoding, and the east of Zhangjiakou. The reduction in forested regions was mainly due to their transformation into cropland and grassland. Construction land was concentrated in Beijing, Tianjin, Shijiazhuang, and neighboring urban regions in the southeast. From 1980 to 2020, the construction land area expanded significantly, especially in Beijing, where it extended into nearby areas in a continuous manner. Water and unused land regions, which were comparatively small, were primarily situated around Bohai Bay and experienced little variation over time.

3.2. Changes in Ecosystem Services during 1980–2020

The InVEST model was employed to assess the supply of carbon stock, water yield, soil conservation, and habitat quality from 1980 to 2020. From the perspective of spatial distribution, the patterns of the CS, HQ, and SC services in the BTH region consistently showed an overall pattern of “low values in the southeast and high values in the northwest” (Figure 4). In contrast, WY services exhibited the opposite trend, with their values being “high in the southeast and low in the northwest”. High-value areas for CS, HQ, and SC concentrated in the western and northern Yanshan–Taihang Mountains, where the land use is predominantly forests and grasslands, featuring high vegetation cover [45]. Concurrently, the advancement of ecological projects like “returning farmland to forest” and the “Beijing–Tianjin–Hebei wind and sand source control project” has enhanced the SC capacity, as well as CS and HQ, throughout the region.
Over the period from 1980 to 2020, CS, HQ, and WY in the BTH region declined by 102.57 Tg, 0.0282, and 13.16 × 108 m3, respectively. In contrast, SC increased by 4.712 × 106 t. Among these changes, carbon storage experienced its largest decrease during 2000–2010, with a reduction of 40.06 Tg, which represented 39.06% of the total decrease in carbon storage over the past 40 years. The average annual WY in the Beijing–Tianjin–Hebei region was 180.758 × 108 m³, peaking at 194.85 × 108 m³ in 1980. The overall water yield demonstrated a trend of initial decrease followed by an increase. The lowest water yield, 170.23 × 108 m3, occurred in 2000, and the average regional habitat quality index was 0.4499, indicating a continuous decline in habitat quality over the past 40 years. This represented a decrease from 0.464 in 1980 to 0.4358 in 2020, or 6.1%. The overall ecosystem soil conservation in the BTH region displayed a downward trend from 1980 to 2010, decreasing from 841.383 × 106 t to 710.946 × 106 t, or a decrease of 15.5%. Soil conservation began to increase from 2010 to 2020, reaching its maximum in 2020, with 846.095 × 106 t. The minimum value was recorded in 2010, 710.946 × 106 t, with the difference between the highest and lowest values being 135.149 × 106 t.

3.3. Future Changes in Land Use and Services under Three Different Simulation Scenario

3.3.1. Land Use Changes under Three Scenarios in 2030

Based on the findings from Figure 3 and Table 6, obtained through simulations conducted with the PLUS model, it is projected that by 2030, cropland, forestland, grassland, and construction land will constitute the predominant land use types in the BTH region, collectively accounting for over 98% of the land cover. Notably, the overall trajectory of change in each land category from 2020 to 2030, under the BAU scenario, closely aligns with patterns observed during the 2000–2020 period. Specifically, cropland is expected to shrink by 395.63 km2, while construction land is expected to expand by 5.34%. This expansion in construction land primarily stems from the conversion of cropland. However, under the cropland protection scenario projected for 2030, a significant reversal is expected, with an anticipated increase of 946.01 km2 in cultivated land area and a simultaneous reduction of 1318.09 km2 in construction land area. The additional cropland in the southeastern region is expected to primarily derive from repurposed construction land. Moreover, under the ecological protection scenario for 2030, a substantial increase of 912.16 km2 in grassland area is anticipated, representing approximately 18.66 times the increase observed under the cropland protection scenario.

3.3.2. Changes in Ecosystem Services under Three Different Scenarios in 2030

In contrast to the conditions observed in 2020, it is projected under the BAU scenario that Beijing, Tianjin, and Hebei will continue along their previous developmental trajectories until 2030 (Figure 5 and Figure 6). Specifically, CS is expected to decrease by 7.45 Tg, while HQ is predicted to decrease marginally by 0.0009. However, HQ is forecasted to remain relatively stable. Conversely, WY is projected to increase by 1.91 and SC by 3.983. The degradation of HQ and depletion of CS would be particularly pronounced in the central urban areas and along the Taihang and Yanshan Mountains. This phenomenon originates from the expansion of urban land and rural settlements, which encroach upon cultivated land, forested areas, and grassland, thus causing a significant decline in HQ and CS. Conversely, SC and WY demonstrate an upward trend in the northwest and eastern regions, respectively. The surge in WY would primarily occur in expanding urban areas, which are characterized by a high proportion of impervious surfaces and reduced evapotranspiration. In the projected cultivated land protection scenario for 2030, the degradation of ecosystem services surrounding cities is significantly mitigated compared with the natural development scenario, resulting in increases of 4.02, 0.05, 0.0036, and 4.063 in CS, WY, HQ, and SC, respectively. Under the ecological protection scenario for the same year, CS, HQ, and SC receive significant boosts of 24.39, 0.0148, and 3.841, respectively, while WY sees a decline of 16.19. These shifts in ecosystem services would occur predominantly in the western Taihang Mountain range, the northern Yanshan Mountain range, the Bashang Plateau, central Qinhuangdao, and the northern Tangshan mountains, following extensive conversion cropland and grassland into woodland.

3.4. Ecosystem Service Trade-off and Synergy

3.4.1. Temporal Changes in Ecosystem Trade-off/Synergy Relationships

The correlation coefficients for ecosystem services (Figure 7) indicated that from 1980 to 2020 in the BTH region, there was a high positive correlation (|r| ≥ 0.6) between habitat quality and soil retention, a moderate positive correlation (0.3 ≤ |r| < 0.6) between carbon storage and water yield, and a weak correlation (0.1 ≤ |r| < 0.3) for other ecosystem services, with exceptions of moderate correlations in certain years. In the time series, both the role and the degree of correlation among ecosystem services exhibited changes. Three groups of ecosystem services, SC-HQ, CS-SC, and CS-HQ, demonstrated synergistic relationships that fluctuated over time. Specifically, the synergy of SC-CS decreased, then increased, and decreased again, reaching its lowest point in 2000. The synergy of CS-HQ followed the same pattern, decreasing, increasing, and then decreasing again, all within 2000. Synergism was the lowest in 2010 after a period of decrease and increase, whereas the synergism of SC-HQ peaked in 2000 after increasing and then decreasing. Water yield had a trade-off relationship with both habitat quality and carbon stock, exhibiting fluctuating trends of decrease–increase–decrease, with pronounced trade-offs in 2000. The relationship between SC and WY transitioned from a trade-off to a brief period of weak synergy and back to a trade-off around 1990. Compared with 2020, the trade-offs and synergies of SC-WY, CS-SC, WY-HQ, and CS-WY strengthened across all three scenarios by 2030. Synergies among habitat quality, carbon storage, and soil conservation were enhanced only in the BAU and cropland preservation scenarios. Trade-offs/synergies among ecosystem services were the most pronounced in the ecological preservation scenario, followed by the cropland protection and BAU scenarios.

3.4.2. Spatial Variation in Ecosystem Trade-Offs/Synergies

Bivariate global spatial autocorrelation analysis of ESs in the BTH region under three different scenarios from 1980 to 2020 and in 2030 was performed, resulting in Moran’s I values for 48 groups, all of which were positive and passed the 95% confidence test (Figure 8). This indicates a synergistic relationship among the four types of ESs. Specifically, the spatial synergistic effects of SC-WY and SC-HQ were notable from 1980 to 2020, with Moran’s I values fluctuating within 0.22–0.348 and 0.247–0.409, respectively, and peaking in 2000 and 2010. Similarly, Moran’s I for WY-HQ fluctuated between 0.156 and 0.409, peaking in the same years. For the period from 1980 to 2000, Moran’s I for WY-HQ fluctuated between 0.156 and 0.331, indicating a significant spatial synergistic effect. Conversely, the spatial synergistic effect of CS-WY was weak, with Moran’s I values ranging from 0.133 to 0.182. The spatial synergistic effects of CS-SC and CS-HQ, with Moran’s I values ranging from 0.219 to 0.27 and from 0.21 to 0.255, respectively, were particularly pronounced from 1980 to 2010 for CS-SC and from 2010 to 2020 for CS-HQ. In 2030, under various scenarios, all ecosystem services exhibited synergistic effects. Notably, Moran’s I indices for SC-HQ, CS-SC, SC-WY, and CS-HQ remained stable under the cropland and ecological protection scenarios, showing significant spatial synergies. Conversely, CS-WY and WY-HQ demonstrated significant spatial synergies under the natural and ecological protection scenarios, although these effects were less pronounced under the three scenarios in 2030.
By using Geoda 1.18.0 software, local spatial autocorrelation analysis was conducted for various scenarios in the BTH region from 1980 to 2030 (Figure 9). In terms of spatial distribution, the evolution of local LISA spatial agglomerations exhibited four main patterns without significant changes: high–high (HH) agglomerations, low–low (LL) agglomerations, low–high (LH) agglomerations, and high–low (HL) agglomerations. From 1980 to 2020, the study area primarily experienced synergistic relationships among services, concentrated in the northwestern part. Spatial heterogeneity was also observed. During this period, CS-SC and SC-HQ displayed stable synergistic relationships, with minimal variations in their extents, primarily located in the cities of Zhangjiakou, Chengde, Cangzhou, and Xingtai. CS-WY transitioned from synergistic to trade-off relationships, initially strengthening then diminishing, with synergies concentrated in the cities of Zhangjiakou, Chengde, Cangzhou, and Xingtai, while trade-offs were predominant in the cities of Tangshan, Qinhuangdao, and Baoding. CS-HQ was characterized by a synergistic relationship, primarily in the cities of Zhangjiakou, Chengde, Cangzhou, and Xingtai, with a trade-off emerging in Qinhuangdao in 2020. The SC-WY relationship fluctuated over time, showing phases of strengthening and weakening synergies, primarily in the cities of Zhangjiakou, Chengde, Qinhuangdao, Cangzhou, and Xingtai, with trade-offs in the cities of Baoding and Tangshan. WY-HQ synergies, initially located in Zhangjiakou, Chengde, and Qinhuangdao, weakened over time, transitioning to trade-offs in Qinhuangdao and Zhangjiakou in 2010 and 2020, respectively.
For 2030, the spatial effects of CS-HQ and SC-HQ were consistent across the three different scenarios; CS-HQ demonstrated strong synergy and weak trade-offs, with synergistic areas located in the cities of Chengde, Zhangjiakou, and Xingtai, while Qinhuangdao served as the trade-off area. Similarly, SC-HQ exhibited a synergistic relationship with a distribution identical to that of CS-HQ. The spatial effects of CS-SC, which were consistently synergistic across all three scenarios, were located in Zhangjiakou, Chengde, Xingtai, and Cangzhou. Conversely, SC-WY, WY-HQ, and CS-WY exhibited strong trade-offs and weak synergies across all three scenarios. The CS-WY trade-off area spanned Baoding and Qinhuangdao, the SC-WY trade-off area was confined to Baoding, and the WY-HQ trade-off region, which had expanded, was primarily located in Zhangjiakou, Qinhuangdao, and Xingtai.

4. Discussion

Human activities, climate change, and ecosystem conservation and restoration initiatives have substantially changed the spatial and temporal distributions of urban ecosystem services. Grasping these alterations is essential for regional development and urban planning. Analyzing the evolution of urban ecosystem services over time and space, alongside assessing their trade-offs/synergies, provides valuable insights for decision making in sustainable urban development and environmental management [46].

4.1. Comparison of Ecosystem Services

This study revealed that the spatial distribution of ecosystem services (ESs) over the past 40 years and under three 2030 scenarios showed similarities linked to land use distribution, underscoring the close relationship between ecosystem structure and land use [43]. The differences in land use patterns across the three scenarios were subtle, due in part to the extensive conservation areas involved, which span thousands of square kilometers. In the Beijing–Tianjin–Hebei region, HQ, CS, WY, and SC displayed spatial heterogeneity closely associated with land use types [13]. High-value areas for SC, CS, and HQ were predominantly found in the northeast and southwest, characterized by high vegetation coverage, whereas densely populated urban areas mostly exhibited low values. High-value WY areas were primarily located in the northeastern coastal regions of BTH and near major cities like Beijing, aligning with prior studies [47], while low-value regions were found mostly in the eastern southern plains and northwestern mountains. Recent studies have demonstrated that changes in soil structure, surface albedo, roughness, and vegetation cover across various land types are increasingly driven by land use, affecting ecosystem services [48,49].
Under the BAU, ecological protection, and cropland protection 2030 scenarios, cropland remained the predominant land use type in the study area. The natural development scenario maintained the 2010–2020 trend in land use.
Construction land expansion replaced ecological areas like cropland, woodland, and grassland. The ecological protection scenario limits unregulated construction to preserve woodland and grassland, while the cropland protection scenario prioritizes maintaining and increasing cropland areas. The CS and HQ values were the highest in the ecological protection scenario and the lowest in the BAU scenario. This is because the ecological scenario suppresses urban expansion, reducing ecosystem disturbances, and increases forestland and grassland, reducing habitat fragmentation. WY behaved inversely, with the lowest values in the ecological protection scenario and the highest values in the BAU scenario. This pattern is attributed to increased evapotranspiration from additional forests and grasslands in the ecological scenario and greater impermeable surfaces from urban expansion in the BAU scenario, reducing precipitation infiltration and increasing surface runoff [50]. Additionally, moderate urbanization has been demonstrated to alleviate water shortages in semi-arid regions [51], consistent with previous findings on ecosystem services in the BTH region [37,52,53]. Nevertheless, although increasing water yield can alleviate scarcity, it may also exacerbate flooding and soil erosion risks. Considering groundwater depletion in certain BTH areas [54], afforestation should be cautiously expanded with ecological protections adapted to local conditions.

4.2. Trade-Offs and Synergies of Ecosystem Services

Land use changes affect ecosystem processes by altering ecosystem components and structures and then act on service supply and their interactions. The study area exhibits both trade-offs and synergies in ESs based on correlative analysis. There are trade-offs in CS-WY, SC-WY, and WY-HQ. Conversely, there are synergies in CS-SC, CS-HQ, and SC-HQ. This is consistent with the previous research results on the trade-off/synergy relationships among SC, CS, WY, and HQ in BTH in 2015 [37].
This study revealed a strong positive correlation between CS and HQ; areas with dense vegetation typically exhibit richer biodiversity and greater HQ. Additionally, increased vegetation cover enhances soil resistance to rainfall erosion, which reduces erosion, retains upslope sediment [55,56], and indirectly benefits SC, demonstrating a synergistic relationship between SC and CS. The trade-offs between WY and HQ, CS, and SC are consistent with the findings of Feng et al. [37] on ecosystem services in the Beijing–Tianjin–Hebei region and Liu et al.’s [46] study on the trade-offs of ecosystem services in Beijing. This may be attributed to the reorganization of natural and socioeconomic factors, such as land use types, topography, and slope within the study area, affecting the dynamics of ecosystem services [57]. Strong interactions exist among environmental variables such as land use types, vegetation cover, and evapotranspiration. Forests with high vegetation cover consume significant water due to transpiration. Water interception and evaporation through the forest canopy also lead to higher actual evapotranspiration, thereby reducing local WY. Additionally, built-up areas exhibit low surface vegetation cover. Although water loss through vegetation transpiration is minimal, significant water is directly evaporated into the atmosphere from the surface, resulting in poor WY capacity. Temporally, the trade-offs and synergies among ecosystem services shifted significantly after 2000, driven by intensified ecological protection and major restoration projects in the BTH region, including the greening of the Taihang Mountains and plains, the management of wind and sand sources, and extensive reforestation efforts [58].
The spatial analysis revealed that CS and SC predominantly exhibited a synergistic relationship, with stable patterns of trade-offs and synergies across all periods studied. Low–low synergy areas were primarily found in Xingtai and Cangzhou, characterized by arable and construction land, where human activities minimally interfered with CS and SC. High–high synergies were predominantly located in Zhangjiakou and Chengde. Low–high trade-offs between CS and WY occurred mainly in Chengde, with its arable land, abundant rainfall, and soil erosion contributing to high water yield but low carbon storage. Conversely, high–low trade-offs were most notable in Baoding, where the forested land’s high interception, evapotranspiration, and carbon density resulted in high carbon storage but low water yield. CS and HQ consistently showed strong synergy, especially in Zhangjiakou and Chengde, where high elevation, favorable thermal and hydrological conditions, and extensive vegetation on forested and grassland areas contribute to their high values. SC and WY typically exhibited a synergistic relationship, though this synergy varied, reaching its lowest point in 2010. For future land management policies, proactive interventions are necessary to guide land use trends towards sustainable development. Key protective measures should include enforcing forestry closures, banning grazing, and preventing deforestation activities such as reclamation and quarrying. Additionally, general ecological zones should undergo active restoration, including reforestation and afforestation, to enhance ecological preservation.

4.3. Limitations and Perspectives

This study examined the spatiotemporal dynamics of ESs in the BTH region, emphasizing the trade-off/synergy among ecosystem services by integrating the InVEST and PLUS models. The findings offer significant insights for shaping regional land ecological management policies. However, the analysis faced several limitations:
(1) The limited number of ecosystem services assessed restricted a comprehensive evaluation in the BTH region, thus limiting the results;
(2) The reliance on physical parameters from prior studies for assessing ecosystem services in the BTH region may have led to discrepancies between calculated and actual results;
(3) While temperature and precipitation are commonly used to predict future land use, their integration with land use, which significantly impacts ecosystem services, may not fully account for the development probabilities under various future climate scenarios. Therefore, future studies should explore these aspects more comprehensively from the perspective of various climate scenarios.

5. Conclusions

By coupling the PLUS–InVEST models, this paper effectively simulated and predicted the ES supply capacity in the BTH region from 1980 to 2020, as well as under three 2030 scenarios, including HQ, CS, WY, and SC. The Spearman correlation coefficient and the bivariate space method were applied to identify trade-offs/synergies among ESs in the BTH region.
(1) From 1980 to 2020, construction land in the BTH region expanded by 13,234.5 km2, while cropland areas correspondingly declined. Under the 2030 natural development scenario, regional trends are expected to follow the patterns observed from 1980 to 2020. In the ecological protection scenario, areas of grassland and forestland expanded, and the expansion of construction land slowed down.
(2) A relatively strong spatial heterogeneity of the ES supply exists in the BTH region. The distribution pattern of HQ, CS, and SC was generally high in the northwest and low in the southeast, while that of WY was high in the southeast and low in the northwest. From 1980 to 2020, the carbon storage and habitat quality in the BTH region showed a continuous downward trend, and the water yield and soil conservation showed a decreasing trend first and then an increase, reaching the lowest values in 2000 and 2010, respectively. In terms of time scale, CS-SC, CS-HQ, and SC-HQ were synergistic relationships, while CS-WY, SC-WY, and WY-HQ were trade-off relationships. On the whole, the trade-off/synergy degree showed a trend of fluctuating enhancement. Among them, the trade-off relationship was the strongest in 2000, and the synergy relationships CS-SC and SC-HQ were the strongest in 2010. The CS-HQ synergy was the strongest in 2020. In spatial terms, CS-HQ, CS-SC, SC-HQ, and WY-HQ were mainly synergistic relationships, and CS-WY and SC-WY were trade-off relations.
(3) The ecosystem services under different scenarios showed different changing trends. Compared with 2020, water yield and soil conservation in the 2030 natural development scenario increased, while habitat quality and carbon storage showed a downward trend, as did total ecosystem services. In the cropland protection scenario, all four kinds of ecosystem services were improved, and the total ecosystem services also showed an increasing trend. In the ecological protection scenario, water production decreased significantly, the other three ecosystem services increased significantly, and the total ecosystem services increased the most.
(4) Temporally, CS-SC, CS-HQ, and SC-HQ exhibited synergistic relationships, whereas CS-WY, SC-WY, and WY-HQ displayed trade-offs. The ecosystem services exhibited varying trends under different scenarios. In the cropland protection scenario, all four ecosystem services improve, showing an overall upward trend.

Author Contributions

Conceptualization, S.G., Y.Z. and X.P.; Methodology, S.G., Y.Z. and Q.Z.; Software, S.G.; Validation, S.G., Y.Z., X.P., Q.Z., X.W. and W.B.; Formal analysis, S.G. and Y.Z.; Investigation, S.G., Y.Z. and X.P.; Resources, S.G. and Q.Z.; Data curation, S.G.; Writing—original draft, S.G.; Writing—review and editing, S.G.; Visualization, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Key International (Regional) Joint Research Program of NSFC (No. 42220104004), National Natural Science Foundation of China (No. 42377337), and the National Key R&D Program of China (No. 2023YFC3306400).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographical location of study region: (a) location of Beijing−Tianjin−Hebei in China; (b) Beijing−Tianjin−Hebei elevation map; (c) Beijing−Tianjin−Hebei land use for 2020; (d) administrative map of cities in the Beijing–Tianjin–Hebei region.
Figure 1. Geographical location of study region: (a) location of Beijing−Tianjin−Hebei in China; (b) Beijing−Tianjin−Hebei elevation map; (c) Beijing−Tianjin−Hebei land use for 2020; (d) administrative map of cities in the Beijing–Tianjin–Hebei region.
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Figure 2. Spatial driving factors of land use change in BTH.
Figure 2. Spatial driving factors of land use change in BTH.
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Figure 3. The actual land use pattern from 1980 to 2020 and the simulated landscape pattern in 2030.
Figure 3. The actual land use pattern from 1980 to 2020 and the simulated landscape pattern in 2030.
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Figure 4. ES distribution in BTH from 1980 to 2020.
Figure 4. ES distribution in BTH from 1980 to 2020.
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Figure 5. Trends in ecosystem service changes over time in BTH from 1980 to 2030.
Figure 5. Trends in ecosystem service changes over time in BTH from 1980 to 2030.
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Figure 6. Spatial distribution of ESs in BTH under three scenarios in 2030 (CS: carbon stock; SC: soil conservation; WY: water yield; HQ: habitat quality).
Figure 6. Spatial distribution of ESs in BTH under three scenarios in 2030 (CS: carbon stock; SC: soil conservation; WY: water yield; HQ: habitat quality).
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Figure 7. The correlation coefficients for ecosystem services from 1980 to 2020 (CS: carbon stock; SC: soil conservation; WY: water yield; HQ: habitat quality; * p < 0.05; ** p < 0.01).
Figure 7. The correlation coefficients for ecosystem services from 1980 to 2020 (CS: carbon stock; SC: soil conservation; WY: water yield; HQ: habitat quality; * p < 0.05; ** p < 0.01).
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Figure 8. Bivariate spatial correlation analysis of ecosystem services from 1980 to 2030 (CS: carbon stock; SC: soil conservation; WY: water yield; HQ: habitat quality; * p < 0.05).
Figure 8. Bivariate spatial correlation analysis of ecosystem services from 1980 to 2030 (CS: carbon stock; SC: soil conservation; WY: water yield; HQ: habitat quality; * p < 0.05).
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Figure 9. LISA agglomeration diagram of ecosystem services in BTH from 1980 to 2030.
Figure 9. LISA agglomeration diagram of ecosystem services in BTH from 1980 to 2030.
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Table 1. Data format and sources.
Table 1. Data format and sources.
DatasetData SourceResolutionTime
Land UseGlobeLand30 data center (http://www.globallandcover.com/); accessed on 1 March 202330 m1980, 1990, 2000,
2010, 2020
DEMGlobeLand30 data center (http://www.globallandcover.com/); accessed on 1 March 202330 m1980, 1990, 2000, 2010, 2020
SlopeGeospatial Data Cloud (http://www.gscloud.cn/); accessed on 1 March 202330 m1980, 1990, 2000, 2010, 2020
TemperatureChina Meteorological Data Service Centre (http://data.cma.cn/); accessed on 1 March 20231 km1980, 1990, 2000, 2010, 2020
PrecipitationChina Meteorological Data Service Centre (http://data.cma.cn/); accessed on 1 March 20231 km1980, 1990, 2000, 2010, 2020
EvapotranspirationChina Science Data Center for Resources and Environment (http://www.resdc.cn/); accessed on 1 March 20231 km1980, 1990, 2000, 2010, 2020
Soil textureChina Science Data Center for Resources and Environment (http://www. resdc.cn/); accessed on 1 March 20231 km1980, 1990, 2000, 2010, 2020
Soil root depthChina Soil Dataset from World Soil Database (HWSD) (https://westdc.westgis.ac.cn); accessed on 1 March 20231 km1980, 1990, 2000, 2010, 2020
Plant available
water content		Cold and Arid Regions Sciences Data Center (http://westdc. westgis.ac.cn/); accessed on 1 March 20231 km1980, 1990, 2000, 2010, 2020
Population densityNational Earth System Science Data Center (http://www.geodata.cn/); accessed on 1 March 20231 km2020
GDPChina Science Data Center for Resources and Environment (https://www.resdc.cn/); accessed on 1 March 20231 km2020
River dataNational Geographical Information Resource Catalog Service System
(https://www.webmap.cn/); accessed on 1 March 2023		1 km1980, 1990, 2000, 2010, 2020
Distance from railwaysOpenStreetMap
(https://www.openstreetmap.org/); accessed on 1 March 2023		Vector2020
Distance from highwaysOpenStreetMap
(https://www.openstreetmap.org/); accessed on 1 March 2023		Vector2020
Distance from riversNational Earth System Science Data Center (http://www.geodata.cn/); accessed on 1 March 2023Vector2020
Table 2. Carbon density of different soil types (t/hm2).
Table 2. Carbon density of different soil types (t/hm2).
Land Use TypeAbove-Ground
Biomass		Below-Ground
Biomass		Soil
Cropland3.200.30112.90
Forestland69.6021.00133.80
Grassland1.1813.6060.50
Water 3.4012.108.64
Construction land0.406.9028.80
Unused land9.131.8234.08
Table 3. Habitat suitability and sensitivity of land use types.
Table 3. Habitat suitability and sensitivity of land use types.
Land Use TypeHabitat
Suitability		Threat Factors
CroplandConstruction LandUnused Land
Cropland0.300.000.700.20
Forestland1.000.600.800.30
Grassland0.900.500.600.40
Water 0.750.600.650.30
Construction land0.000.000.000.00
Unused land0.100.100.200.00
Table 4. Neighborhood weights of land use types in BTH.
Table 4. Neighborhood weights of land use types in BTH.
Land UseCroplandForestlandGrasslandWaterConstruction
Land		Unused Land
Weight0.490.150.030.160.180.05
Table 5. Land use transition matrix under different scenarios for Beijing–Tianjin–Hebei in 2030.
Table 5. Land use transition matrix under different scenarios for Beijing–Tianjin–Hebei in 2030.
BAUCPSEPS
ABCDEFABCDEFABCDEF
A111111100000111000
B111111111000010000
C111111111100011000
D111111100100011100
E111111000011111110
F111111100001011101
Note: A, B, C, D, E, and F symbolize land use: cropland, forestland, grassland, water, construction land, and unused land, respectively. The value 0 denotes prohibition of transition, while the value 1 signifies permission for transition.
Table 6. Areas of different land use types in BTH (area: km2).
Table 6. Areas of different land use types in BTH (area: km2).
YearCropland ForestlandGrasslandWaterConstruction LandUnused Land
1980113,036.5845,208.8935,465.046356.9714,215.592274.48
1990112,696.7944,788.7735,831.436262.4714,714.462261.70
2000109,343.5444,678.9235,304.816335.2817,814.552074.06
2010104,024.4545,006.1834,063.525455.4525,984.911293.16
202099,904.5945,938.6634,053.746801.0027,450.091665.59
2030 BAU99,508.9646,159.3234,078.506337.7428,915.861551.80
2030 CPS100,850.6046,767.2434,102.616625.4027,131.991535.08
2030 EPS104,754.8447,834.8934,965.896871.1520,154.551251.60
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Gong, S.; Zhang, Y.; Pu, X.; Wang, X.; Zhuang, Q.; Bai, W. Assessing and Predicting Ecosystem Services and Their Trade-Offs/Synergies Based on Land Use Change in Beijing–Tianjin–Hebei Region. Sustainability 2024, 16, 5609. https://doi.org/10.3390/su16135609

AMA Style

Gong S, Zhang Y, Pu X, Wang X, Zhuang Q, Bai W. Assessing and Predicting Ecosystem Services and Their Trade-Offs/Synergies Based on Land Use Change in Beijing–Tianjin–Hebei Region. Sustainability. 2024; 16(13):5609. https://doi.org/10.3390/su16135609

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Gong, Shengxuan, Yuhu Zhang, Xiao Pu, Xiaohan Wang, Qiuyu Zhuang, and Wenhui Bai. 2024. "Assessing and Predicting Ecosystem Services and Their Trade-Offs/Synergies Based on Land Use Change in Beijing–Tianjin–Hebei Region" Sustainability 16, no. 13: 5609. https://doi.org/10.3390/su16135609

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