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Article

Land Use Intensity Alters Ecosystem Service Supply and Demand as Well as Their Interaction: A Spatial Zoning Perspective

by
Yunrui Yang
1,
Jiaying Zhang
1 and
Yi’na Hu
1,2,*
1
School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
2
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station, Shanghai 201722, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(16), 7224; https://doi.org/10.3390/su16167224
Submission received: 13 July 2024 / Revised: 12 August 2024 / Accepted: 20 August 2024 / Published: 22 August 2024
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Abstract

:
Clarifying the interaction between ecosystem service supply (ESS) and demand (ESD) and its response to land use change is essential for achieving ecological security. Previous research mainly focused on how land use change affects ESS, ignoring its impact on the interaction between ESS and ESD, making it difficult to guide regional sustainability in social–ecological coupled systems. Taking the Yangtze River Delta as the study area, after evaluating five types of ESS and ESD during 2000–2020, this study analyzed the interaction between changes in ESS and ESD using Geodetector from a spatial zoning perspective and subsequently explored the impact of land use intensity on ESS, ESD, and their interaction using Spearman correlation analysis and regression analysis. The results showed that there was obvious spatial–temporal heterogeneity in ESS and ESD. Different ecosystem services varied in their interaction between ESS and ESD, and the demand changes in water conservation and soil retention significantly influenced their supply changes, while supply changes in carbon sequestration were not influenced by its demand changes. The effect of ESD changes on ESS changes also showed spatial heterogeneity. All interactions between ESS changes and ESD changes, except carbon sequestration, were significant in the Supply Increases–Demand Increases zone. Soil retention and water conservation showed a significant supply–demand interaction in the Supply Decreases–Demand Decreases zone and the Supply Decreases–Demand Increases zone, respectively, while no ecosystem service showed significant supply–demand interaction in the Supply Increases–Demand Decreases zone. Land use intensity had linear and nonlinear effects on different ESS and ESD, and it was also found that the effect of ESD changes on corresponding ESS changes was relatively obvious in regions with dramatic land use change. Finally, this paper incorporated the changes in ESS and ESD, as well as their interaction, into land use management, which can provide implications for regional sustainability.

1. Introduction

Ecosystem services (ESs) can satisfy the demand of human society, which is essential for human well-being [1,2,3]. However, it was reported that 15 out of 24 ESs have deteriorated in the last half century, posing a grave threat to ecological sustainability [4]. Notably, human activities-induced land use change resulted in 60% degradation of provisioning ESs [4]. In this context, the relationship between ecosystem service supply (ESS) and demand (ESD) as well as its response to land use change have been a burning issue in recent years [5,6,7]. Clarifying the relationship between ESS and ESD can help to understand the interaction between the natural ecosystem and human society [8,9]. Furthermore, revealing how their relationship responds to land use change can offer vital insights for land use management [10].
Early studies on ESs predominantly centered on the quantitative evaluation of ESS and the analysis of trade-offs among them [11,12,13]. With increasing attention paid to the ESD, the relationship between ESS and ESD gradually become a hotspot, and the methods for ESS and ESD assessment have gradually been diversified [14,15,16]. Currently, research on ESS and ESD mainly focuses on two aspects: quantity match and spatial match. For instance, Ala-Hulkko et al. [17] evaluated and mapped the ESS and ESD of the food supply service in Europe; Nedkov et al. [18] explored the spatial match of the ESS and ESD of the flood regulation service at the urban scale in the Etropole municipality in Bulgaria. These studies illuminated the matching status of ESS and ESD, but ignored the interaction between them. In reality, stakeholders usually generate different socio-economic activities due to their ESD, which in turn affects ESS [19]. Moreover, due to the differences in natural background, spatial heterogeneity widely existed in different ESS and ESD [20,21], which likely extends to their interaction. A global perspective in previous research might have obscured specific regional nuances [22]. Thus, a spatially zoned exploration of the ESS–ESD interaction can help to reveal its regional responses, fostering more tailored and effective regional management strategies.
Researchers have conducted a series of investigations into the driving factors of ESS and ESD [15,23,24]. It was found that land use change can affect ESS and ESD through modifying ecosystem structure and function [25,26]. Especially in rapidly urbanizing regions, drastic land use changes have significant impacts on ESS and ESD [14,27]. For example, Chillo et al. [28] found that land use intensity (LUI) affected the provision of three out of four ecosystem services; Song et al. [29] found that urban expansion acted negatively on all ESs in Wuhan, China. However, previous studies mainly emphasized the linear relationship between land use and ESS, neglecting the nonlinear relationship between them [30]. Furthermore, limited attention has been paid to the influence of land use change on the interaction between ESS and ESD, making it difficult to guide the decision making in social–ecological coupled systems. Exploring how land use change affects the interaction between ESS and ESD can help to facilitate the implementation of sustainable land use strategies.
The Yangtze River Delta (YRD) urban agglomeration embodies a region in China that has witnessed a rapid urbanization trajectory. This accelerated transformation has, unfortunately, led to profound disruptions within the ESS domain with the corresponding ESD escalating, thereby fostering a progressively acute dichotomy between the two [31]. It is urgent to explore the interaction between ESS and ESD as well as its response to land use change, so as to support sustainable development in the YRD. This study evaluated five types of ESS and ESD, analyzed their interaction from a spatial zoning perspective, and further explored their responses to land use intensity. This study mainly aims to (1) clarify the spatial heterogeneity inherent in the interaction between ESS and ESD; (2) reveal the impact of land use change on ESS, ESD, and their interaction, and ultimately integrating these insights into sustainable ecosystem management.

2. Materials and Methods

2.1. Study Area

According to ‘The Yangtze River Delta Urban Agglomeration Development Plan’, the YRD (18°33′~123°10′ E, 28°0′~33°52′ N) refers to 132 counties in 26 cities, with a total area of 211,700 km2 (Figure 1). It is a subtropical monsoon climate, with the terrain dominated by plains. According to the official statistics, there are 174.66 million permanent residents in the YRD in 2020, occupying 12.1% of the total Chinese population. The YRD is a hotspot for China’s economic development, and it contributes 15.9% of the total GDP in China. Land use change is drastic in this region, with ecological land (i.e., forest land, cultivated land, water bodies, and grassland) continuously transferring to construction land in recent years [32]. The shrinking of ecological space has seriously influenced the ESS, which threatens the sustainability of the YRD.

2.2. Data Sources

Eight types of data were applied in our study, including land use data, digital elevation models (DEM), nighttime light data, Net Primary Productivity (NPP), the China Soil Map-Based Harmonized World Soil Database, meteorological data (including precipitation, solar radiation and evapotranspiration), county-level carbon emission, and the Chinese population spatial distribution kilometer grid dataset. All the data were visualized and resampled to 30 m × 30 m resolution, and the data sources are shown in Table 1.

2.3. Methods

In this study, we explored the interaction between ESS and ESD, and further explored the influence of land use change. There were mainly four parts: firstly, five typical ESs were selected, and their ESS and ESD in 2000 and 2020 were evaluated using various models. Secondly, the whole YRD was spatially zoned based on changes in ESS and ESD during 2000–2020. Thirdly, the interaction between ESS and ESD was examined via a geographic detector. Finally, the land use intensity was assessed, and its impact on ESs was analyzed using Spearman correlation analysis and regression analysis.

2.3.1. Measurement of ESS and ESD

According to the socio-ecological characteristics of the YRD and data availability, five ESs were selected, that is, food supply, water conservation, water purification, carbon sequestration, and soil retention. Then, their ESS and ESD were calculated in 2000 and 2020. The methods applied are listed in Table 2.
(a)
Food supply
Food supply was represented by the regional grain output, and the demand for food supply was calculated as the product of population density data and per capita food consumption data [34].
(b)
Water purification
Based on the Nutrient Delivery Ratio (NDR) in the InVEST model, we calculated the supply and demand for water purification. The NDR module can calculate the total removal of nitrogen pollutants in runoff through the total nitrogen output of vegetation and soil [35]. The total retention of nitrogen pollutants can represent the supply for water purification (Equation (1)), and the nitrogen load was used as the demand for water purification [36,37]. The parameters used in the NDR module were all set based on previous studies [38,39].
Nretention = N_load − N_export
where Nretention is the water purification supply (kg); N_load is the N input (kg); N_export is the N output (kg).
(c)
Carbon sequestration
Net Primary Productivity (NPP) can present the amount of carbon fixed by vegetation through photosynthesis, which is a generally used indicator of carbon sequestration supply [40]. Our study applied the photosynthesis equation to calculate the carbon sequestration supply (Equation (2)) [31]. Meanwhile, the demand for carbon sequestration was defined as the amount of carbon emissions that need to be reduced or regulated to maintain good ecological conditions [31,41]. We applied county-level carbon emission data from Chen et al. [33] to represent carbon sequestration demand. Due to the lack of data availability, the carbon sequestration demand in 2020 was represented by the carbon emission data in 2017.
Ctot = NPP/45% × 1.63
where Ctot is the carbon sequestration supply (kg).
(d)
Water conservation
Water yield in the InVEST model was utilized to evaluate water conservation supply, which requires data including land use, precipitation, and DEM. Root depth and the z parameter were set by referring to previous studies [38,39]. Water conservation demand was calculated by combining population density data and per capita water demand data [34].
(e)
Soil retention
The Revised Universal Soil Loss Equation (RUSLE) was applied to estimate soil retention. Our study applied the actual soil erosion amount as the indicator of soil retention demand and soil retention to represent soil retention supply. The values of C and P were set according to the existing studies [42].

2.3.2. Spatial Zoning Based on Changes in ESs

YRD was spatially zoned based on changes in ESS and ESD in this study. First, each ESS and ESD were standardized (Equation (3)) and then summed as the total ESS and ESD with equal weight superposition. On this basis, the changes in ESS and ESD in the YRD from 2000 to 2020 were analyzed, and the YRD was finally classified into four types of zones, that is, Supply Decreases–Demand Decreases zone (SD–DD zone, areas where both total ESS and ESD decrease), Supply Decreases–Demand Increases zone (SD–DI zone, areas where total ESS decreases, while total ESD increases), Supply Increases–Demand Decreases zone (SI–DD zone, areas where total ESS increases, while total ESD decreases), and Supply Increases–Demand Increases zone (SI–DI zone, areas where both total ESS and ESD increase).
E S i , j , s t d = E S i , j − E S m i n , j E S m a x , j − E S m i n , j
where E S i , j , s t d indicates the standardized amount of ES j in pixel i; E S i , j indicates the amount of ES j in pixel i; E S m a x , j and E S m i n , j indicate the maximum and minimum amount of ES j.

2.3.3. Interaction between ESS and ESD

The geographic detector was applied to examine the interaction between ESS and ESD in this study. The geographic detector is a tool for detecting spatial variation and spatial attribution, which has been widely applied in analyzing causal relationships [23]. The assumption of the method is that when a certain geographic phenomenon exists in a specific geographic space, if a certain factor has similarity to the geographic phenomenon in its spatial distribution, it means that the factor has some explanatory power for this geographic phenomenon [43,44].
This study used the factor detector module in Geodetector, which is mainly used to explore the extent to which an independent variable x can explain the spatial variability of another dependent variable y [44]. Higher q-values are associated with a higher degree of explanation, and a greater degree of influence [43,44,45]. We took counties as basic units, and the factor detector in the geographic detector was used to identify how ESD changes influence ESS changes. First, each ESD change in each county was divided into five categories using the Natural Breaks method, which were input as variable x. Then, the changes in each ESS were input as variable y. The explanation rate (q value) can be used to judge the influence degree of ESD changes on ESS changes. Generally, a higher q value indicates a higher interaction between ESS changes and ESD changes.

2.3.4. The Influence of Land Use Intensity on ESs

Land use intensity (LUI) can fully reflect the specific degree of human utilization of land resources, which can connect the ecosystem and social system [46]. Previous studies have commonly applied land use intensity to investigate the impact of land use change on ESs [28,46]. Intensity values can be assigned to different types of land use. In this study, the specific assignment of intensity values refers to previous studies [47,48] and ultimately assigns different intensity values to different land use types as follows: construction land (4), cultivated land (3), grassland (2.5), forestland (2), water bodies (2), and unused land (1). LUI can be calculated as follows:
L U I = ∑ i = 1 n D i × P i = ∑ i = 1 n D i × S i S t
LUI is the land use intensity; D i is the intensity index of land use type i; P i is the area ratio of land use type i in the region; S i is the area of land use type i; S t is the total area of the region. After LUI calculation, we applied Spearman correlation analysis and regression analysis to analyze the influence of LUI on each ESS and ESD.

3. Results

3.1. Land Use Change from 2000 to 2020

Figure 2 showed the land use types in the YRD in 2000 and 2020. In the SD–DD zone, cultivated land and forest land covered the largest areas, with an area ratio of 75% and 16% in 2000. During 2000–2020, construction land and unused land increased by 4.62 × 104 ha and 84.6 ha, respectively, while other land use types declined, with massive amounts of cultivated land converting to construction land. In the SD–DI zone, forest land and cultivated land covered the largest areas, occupying a proportion of 61% and 31% in 2000, respectively. From 2000 to 2020, cultivated land declined from 9.47 × 105 to 8.27 × 105 ha, while the construction land expanded by around 150%. In the SI–DD zone, cultivated land covered around 80% of the total area in 2000. During 2000–2020, cultivated land and grasslands both presented a shrinking trend, with grassland area decreasing about 45%. Other land use types increased; among them, forest land and water bodies rose by 1.01 × 104 ha and 1.52 × 104 ha, respectively. In the SI–DI zone, the area ratio of cultivated land and forest land reached 51% and 27% in 2000. During 2000–2020, a decreasing trend appeared in cultivated land, forest, and grassland in this zone. This zone experienced the most drastic land use change, as its cultivated land area declined by 8.97 × 105 ha, while construction land area rose by 8.92 × 105 ha.
Figure 3 showed the LUI of counties in the four ecological zones. The counties with relatively high LUI were mainly located in the SI–DI zone, while those with low LUI were concentrated in the Supply Decreases–Demand Increases (Figure 3a,b). The counties with a relatively high change in LUI were also distributed in the SI–DI zone, while those with a low change in LUI were scattered (Figure 3c). From 2000 to 2020, almost all counties, except three counties in Shanghai, showed an increase in LUI (Figure 3c).

3.2. Spatial Zoning of Changes in ESS and ESD

From 2000 to 2020, an obvious spatial heterogeneity existed in the ESS and its changes in the YRD (Figure 4). Spatially, the food supply was relatively high in the northern cities, and was relatively low in the south (Figure 4a). From 2000 to 2020, the food supply decreased from 3.96 × 1010 kg to 3.93 × 1010 kg, with a reduction rate of more than 20% in 71 counties. As for water purification, northern and western counties owned relatively high supply, while the counties in the southeastern YRD had relatively low supply (Figure 4b). The total amount of water purification decreased from 2.78 × 108 kg to 2.52 × 108 kg from 2000 to 2020. Regarding carbon sequestration, the main urban area in Nanjing and Yancheng had relatively high supply, while the counties in the center and east had relatively low supply (Figure 4c). The supply of carbon sequestration increased from 1.38 × 108 t to 1.58 × 108 t from 2000 to 2020, with a growth rate of 13.98%. The supply of water conservation increased from 9.34 × 109 m3 to 1.38 × 1010 m3 from 2000 to 2020, with an increase of 48.11%. The counties with a high water conservation supply were scattered in 2000, but were concentrated in the western and central regions in 2020 (Figure 4d). From 2000 to 2020, the supply of soil retention was high in the south and low in the north (Figure 4e), and all counties exhibited an overall growth rate of 89.98%. In summary, the total ESS increased in the YRD, and ESS changes were concentrated in the north, west, and south (Figure 6a).
From 2000 to 2020, spatial heterogeneity also existed in ESD in the YRD (Figure 5). Counties exhibiting high demand for food supply were located in the central YRD, while counties with a low demand were in the southern part (Figure 5a). The total food demand decreased from 3.40 × 1010 kg to 3.20 × 1010 kg, with about 65.9% of counties experiencing a decrease. As for the demand for water purification, it was relatively high in the west and north, and relatively low in the east and south (Figure 5b). From 2000 to 2020, the demand for water purification decreased from 4.02 × 108 kg to 3.70 × 108 kg. In terms of carbon sequestration, its demand was 1.16 × 109 t in 2020, which is about 2.8-times of that in 2000 (Figure 5c). The high demand for water conservation was in the eastern center, and low demand was in the southwest (Figure 5d). The total demand increased from 7.73 × 1010 m3 to 9.58 × 1010 m3, with an increase of 23.95%. In terms of the soil retention, there was high demand in the southwest and low demand in the northeast (Figure 5e). The total amount increased from 2.36 × 109 t to 4.2 × 109 t from 2000 to 2020, with an increase of 78.13%. In total, ESD mainly showed an increasing trend, with counties with large changes in demand located in the south-central YRD (Figure 6b).
Based on ESS and ESD changes, the 132 counties were categorized into four zones (Figure 6c). The SD–DD zone was mainly in the east-central and southern region, including only five counties. The SD–DI zone contained 21 counties in Zhejiang Province and one district in Shanghai, mostly located in the southeastern region. There were 17 counties in the SI–DD zone, among which 15 counties were in Jiangsu province. The SI–DI zone covered 88 counties, accounting for 66.67% of the total number of counties. This zone comprises southern Jiangsu, northern Zhejiang, and Anhui.

3.3. Interaction between ESS and ESD

Figure 7 showed that the impact of ESD change on ESS change exhibited obvious spatial heterogeneity in different spatial zones. In the SD–DD zone, the effect of demand changes of the soil retention on its supply changes was highly significant, while the changes of the other four ESD and ESS were insignificantly correlated. In the SD–DI zone, only the demand changes of water conservation had a significant impact on its supply change. In the SI–DD zone, demand changes were not significantly correlated with supply changes for all five ESs. In the SI–DI zone, except for carbon sequestration, the other four ESs all showed that the demand changes had a highly significant impact on their supply changes.
Overall, the effect of ESD changes on ESS changes varied across ESs (Figure 7). Food supply and water purification showed a highly significant interaction between their ESS change and ESD change in the SI–DI zone. The ESS change and ESD change of water conservation had a significant interaction in both the SD–DI zone and SI–DI zone, and that of soil retention showed significant interaction in the SD–DD zone and SI–DI zone. However, for carbon sequestration, its ESS and ESD did not exhibit a significant interaction in all four zones.

3.4. Influence of LUI on ESS and ESD

The impact of LUI varies for different ESS and ESD (Figure 8). For the supply of water conservation, soil conservation, and carbon sequestration, LUI had a negative impact (Figure 8b,d,e). For the supply of water purification and food supply, LUI had a nonlinear effect. Their correlation was positive at first and then turned negative when the value of LUI was around three (Figure 8a,c).
From the perspective of ESD, LUI exhibited a notably positive and linear influence on the demand for food supply, water conservation, and carbon sequestration (Figure 8f,g,j). Conversely, LUI’s relationship with the demand for water purification was nonlinear, initially demonstrating a positive correlation before shifting to a negative one (Figure 8h). As for the demand for soil conservation, LUI had a nonlinear negative effect, initially negative but transitioning to positive when LUI surpassed a threshold of 3.5, as illustrated in Figure 8i.

4. Discussion

4.1. Potential Influence of Land Use Change on Supply–Demand Interaction

The results above showed that an obvious spatial heterogeneity existed for ESS, ESD, and the supply–demand interaction (Figure 4, Figure 5, Figure 6 and Figure 7). Meanwhile, only in the SI–DI zone, the effect of ESD changes had a significant impact on most ESS changes (Figure 7). This zone also experienced the most drastic land use change (Figure 3c). Therefore, intensified land use change may elevate the influence of ESD changes on corresponding ESS changes, which is consistent with the findings of Xu et al. [19]. This phenomenon might be explained in two aspects. On the one side, land use changes influence ecosystem structure/process, and then affect ESS (Figure 8a–e); meanwhile, land use changes may also result in changes in socio-economic activities, which in turn affect ESD (Figure 8f–j). On the other side, the demand of stakeholders influences their socio-economic activities, which changes land use patterns and other environmental conditions, and in turn affects ESS [49,50,51]. Therefore, a close link exists between land use change and the supply–demand interaction (Figure 9).
Among the four zones, the SI–DI zone was the only one where food supply and water purification had a highly significant interaction between demand and supply. In this zone, the land use intensity and its change were both relatively high compared to the other zones. Cultivated land and forest land transferring to construction land was the main land use change, which caused an increasing demand for food supply and a decrease in its supply [52]. Forests have the function of water nourishment and water purification [53], and construction land expansion would increase nitrogen output [54,55]. Therefore, forest land transferring to construction land increased the water purification demand and weakened its supply in this zone. Water conservation exhibited a significant supply–demand interaction in the SD–DI zone and SI–DI zone. Construction land has a greater supply capacity for water conservation than water, forest, and cultivated land [56,57]. Thus, as the construction land greatly increased by occupying the cultivated land in these two zones from 2000 to 2020, water conservation demand and supply increased. In the SD–DD zone and SI–DI zone, the interaction between changes in demand and supply for soil retention was highly significant. With construction land increasing and forest land decreasing, the risk of soil erosion intensified; the demand thereby tended to increase in these two zones. Comparatively, carbon sequestration showed an insignificant supply–demand interaction in all zones. A possible explanation might be that the demand for carbon emissions is more affected by regional industrial development, while its supply is affected by land use changes, which are two independent variables.

4.2. Policy Implications

Integrating ESs into land use management has become a hotspot in recent years [27,58]. Previous studies focused on ESS, neglecting the ESD of human society and the interaction between them, which makes it difficult to provide strategies in a comprehensive way. After evaluating ESS and ESD, this study clarified the supply–demand interaction for ESs from a spatial zoning perspective and discussed the potential impact of land use change, so as to guide sustainable ecosystem management in the YRD.
In the SD–DD zone, cultivated land and forest occupied a large area, and the interaction between changes in supply and demand for ESs was insignificant. ESs, such as food supply, were continuously weakening during 2000–2020. In this region, ESS should be improved through the protection of cultivated land and forest.
In the SD–DI zone, cultivated land and forest were continuously transferred to construction land, and the contradiction between ESS and ESD tends to intensify. To improve ESS, an ecological redline should be delineated and ecological protection should be strengthened. To mitigate ESD, it is necessary to optimize the structure of social demand with government policies and ultimately to promote the harmonious coexistence of human beings and nature.
In the SI–DD zone, the changes in ESS and ESD showed a sustainable development status. In this zone, Jiangsu Province has also established an ecological redline for water conservation service protection, with the objective of promoting sustainable development that balances food supply and water conservation. On the basis of consolidating existing ecological achievements, this region should take further actions, such as formulating policies to reduce the use of chemical fertilizers on farmland.
In the SI–DI zone, land use change was drastic, and ESD changes had an obvious impact on ESS changes. Therefore, rational land use planning should be carried out. As the eastern region is highly urbanized with an increasing population and a growing ESD, strictly protecting the natural land and reasonably controlling urban expansion is urgent for sustainable development. Meanwhile, ESD should be optimized to improve both ESS and ESD. For instance, reasonable measures, such as improving public water facilities and upgrading sewerage system, should be taken to reduce water pollution and water resource wastage.

4.3. Limitations and Prospects

This study took the YRD as a case, carried out spatial zoning based on the changes in ESS and ESD from 2000 to 2020, and further analyzed their interaction and its responses to land use intensity. From a spatial zoning perspective, this study revealed the spatial heterogeneity of the interaction between ESS and ESD as well the influence of land use intensity. The results can effectively support differentiated management of the ecosystem, which also provides a new insight for further studies.
However, there are also some limitations. Firstly, this study considered provisioning, regulating, and supporting services, but cultural services were ignored due to data availability. In further studies, cultural services should be emphasized so as to reveal the benefits of the ecosystem to human society in a more comprehensive way. Secondly, due to the lack of observation data, this study did not carry out validation of our results. In further studies, it is necessary to validate the result of ESS using field observation data. Thirdly, this study focused on the interaction between ESS and ESD at a local scale, ignoring its spatial flow characteristics. Actually, most ESs, such as water purification and soil retention, exhibited the characteristics of spatial flow, and their supply can not only satisfy the local demand but also downstream demand. ESs flow should be further considered in the study of the interaction between ESS and ESD.

5. Conclusions

Revealing the interaction between ESS and ESD and its response to LUI is important for sustainable land use management. Taking the YRD as a study area, we explored the spatial heterogeneity of the interaction between ESS and ESD, and further discussed their responses to land use intensity. From 2000 to 2020, ESS and ESD both increased and there was an obvious spatio-temporal heterogeneity in their changes. The impact of ESD changes on ESS changes varied among ESs and among ecological zones. Only in the SI–DI zone, the interactions between changes in ESS and ESD, except for carbon sequestration, were highly significant. In the rest of the zones, only individual or no ESs showed significant interactions. Land use intensity had a linear and nonlinear influence on different ESS and ESD. The most drastic land use change and the significant interaction between ESS and ESD both occurred in the same zone, indicating that the intensity of land use change may intensify the influence of ESD changes on their ESS changes. This study finally integrated the interaction between ESS and ESD and its response to land use change into ecosystem management, providing a new insight for ecosystem sustainability.

Author Contributions

Y.Y.: Conceptualization, Methodology, Software, Writing original draft, Visualization, Funding acquisition. J.Z.: Methodology, Review and editing. Y.H.: Conceptualization, Formal analysis, Resources, Writing, Review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the National Natural Science Foundation of China (42201102).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographical location of the study area.
Figure 1. Geographical location of the study area.
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Figure 2. Land use changes in the ecological zones from 2000 to 2020.
Figure 2. Land use changes in the ecological zones from 2000 to 2020.
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Figure 3. Land use intensity of counties from 2000 to 2020.
Figure 3. Land use intensity of counties from 2000 to 2020.
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Figure 4. Ecosystem service supply in 2000 and 2020. (a): food supply; (b): water purification; (c): carbon sequestration; (d): water conservation; (e): soil retention.
Figure 4. Ecosystem service supply in 2000 and 2020. (a): food supply; (b): water purification; (c): carbon sequestration; (d): water conservation; (e): soil retention.
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Figure 5. Ecosystem service demand in 2000 and 2020. (a): Food supply; (b): Water purification; (c): carbon sequestration; (d): water conservation; (e): soil retention.
Figure 5. Ecosystem service demand in 2000 and 2020. (a): Food supply; (b): Water purification; (c): carbon sequestration; (d): water conservation; (e): soil retention.
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Figure 6. Ecosystem services supply and demand changes and ecological zones from 2000 to 2020. (a): ecosystem services supply changes; (b): ecosystem services demand changes; (c): ecological zones.
Figure 6. Ecosystem services supply and demand changes and ecological zones from 2000 to 2020. (a): ecosystem services supply changes; (b): ecosystem services demand changes; (c): ecological zones.
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Figure 7. Interaction between changes in ecosystem service supply and demand.
Figure 7. Interaction between changes in ecosystem service supply and demand.
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Figure 8. Influence of land use intensity on ecosystem service supply (a–e) and demand (f–j).
Figure 8. Influence of land use intensity on ecosystem service supply (a–e) and demand (f–j).
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Figure 9. The framework for the interactions between ecosystem services supply and demand.
Figure 9. The framework for the interactions between ecosystem services supply and demand.
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Table 1. Data sources.
Table 1. Data sources.
DataSpatial ResolutionPeriodSourcesCollected Date
Land use data30 m2000, 2020https://www.resdc.cn/12 May 2022
Digital elevation model 90 m-http://www.dsac.cn/12 May 2022
China Soil Map-Based Harmonized World Soil Database 1:1 million-http://www.dsac.cn/12 May 2022
Meteorological data (temperature, precipitation, and solar radiation)30 m2000, 2020http://data.cma.cn/23 August 2023
Net Primary Productivity500 m2000, 2020https://lpdaac.usgs.gov/23 August 2023
County-level carbon emission-2000, 2017Chen et al. [33] 25 August 2023
Chinese population spatial distribution kilometer grid dataset 1 km2000, 2020https://www.resdc.cn/25 August 2023
Total grain output and per capita grain consumption-2000, 2020Statistical Yearbooks of Anhui, Zhejiang, Jiangsu and Shanghai23 August 2023
Data of per capita water consumption-2000, 2020China Water Resources Bulletin.23 August 2023
Table 2. The assessment methods for each ecosystem service.
Table 2. The assessment methods for each ecosystem service.
Ecosystem ServicesIndicatorsMethods
Food supplySupply: County grain outputFrom the Statistical Yearbook
Demand: County grain consumptionProduct of population density and per capita food consumption
Water purificationSupply: County removal of nitrogen pollutantsInVEST Model Nutrient Delivery Ratio
Demand: County nitrogen loadInVEST Model Nutrient Delivery Ratio
Carbon sequestrationSupply: County quantity of carbon sequestration during vegetation photosynthesisPhotosynthesis equation
Demand: County carbon emissionsFrom Chen et al. [33]
Water conservationSupply: County water yieldInVEST Model Water Yield
Demand: County water demandProduct of population density and per capita water demand
Soil retentionSupply: County soil retentionDifference between potential soil erosion and actual soil erosion
Demand: County soil erosionActual soil erosion
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Yang, Y.; Zhang, J.; Hu, Y. Land Use Intensity Alters Ecosystem Service Supply and Demand as Well as Their Interaction: A Spatial Zoning Perspective. Sustainability 2024, 16, 7224. https://doi.org/10.3390/su16167224

AMA Style

Yang Y, Zhang J, Hu Y. Land Use Intensity Alters Ecosystem Service Supply and Demand as Well as Their Interaction: A Spatial Zoning Perspective. Sustainability. 2024; 16(16):7224. https://doi.org/10.3390/su16167224

Chicago/Turabian Style

Yang, Yunrui, Jiaying Zhang, and Yi’na Hu. 2024. "Land Use Intensity Alters Ecosystem Service Supply and Demand as Well as Their Interaction: A Spatial Zoning Perspective" Sustainability 16, no. 16: 7224. https://doi.org/10.3390/su16167224

APA Style

Yang, Y., Zhang, J., & Hu, Y. (2024). Land Use Intensity Alters Ecosystem Service Supply and Demand as Well as Their Interaction: A Spatial Zoning Perspective. Sustainability, 16(16), 7224. https://doi.org/10.3390/su16167224

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