Abstract
Assessing the impact of past and future urban land expansion on ecosystem services (ESs) is essential for the sustainable management of urban landscapes. Despite recent progress, it remains challenging to determine the influence mechanism of different urbanization stages and areas on ESs and their trade-offs/synergies. Here, we analyzed the responses of four ESs and their trade-offs/synergies to the spatial and temporal differentiation of urbanization in the Su-Xi-Chang region of China from 1990 to 2050. The results showed that the relationship between ESs and urbanization followed a U-shaped curve from 1990 to 2050. Between 1990 and 2020, urbanization weakened trade-offs and enhanced lose-lose situations, exhibiting an S-shaped curve. From 2020 to 2050, lose-lose situations are expected to diminish, and the curve is projected to display a downward trend. Our findings suggest the implementation of compact urban development, sustainable agricultural practices, reforestation, and the conversion of farmland to lakes.
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Introduction
Ecosystem services (ESs) refer to the various benefits that humans derive from ecosystems1, usually including supplying services, regulating services, cultural services, and supporting services. With the acceleration of urbanization, people’s living standards have gradually improved. However, at the same time, ESs have declined and the contradiction between ESs and human needs has become more pronounced2. Complex changes have also occurred between ESs, which can be reflected in trade-offs and synergies. Trade-offs refer to an increase in one ES at the expense of another3. Synergies mean tending in the same direction (a win-win or lose-lose situation)4. Understanding the impacts of urbanization on ESs and their trade-offs/synergies can help inform scientific management decisions to maximize ESs in urban areas5.
Trade-offs and synergies have been an important topic of academic interest. Internationally, the research began with a focus on biodiversity. After 2000, research topics gradually diversified to include agriculture, climate change, sustainability, and resilience. At present, the research is mainly conducted from the perspective of stakeholder preferences, and supply and demand6. The main methods used to investigate trade-offs and synergies are statistical analysis, descriptive analysis, spatial mapping, and scenario modeling7,8. Among these, most articles used statistical analysis methods such as correlation9 and cluster analysis10.
ESs and their trade-offs/synergies are not only influenced by the intensity of urbanization11, but also vary over time and space12,13,14. However, the impact of the spatiotemporal evolution of urbanization on ESs and their trade-offs/synergies has not been investigated in many studies. In terms of temporal variation, trade-offs, and synergies increase, decrease, appear, emerge, or reverse over time15. In Switzerland, for example, the relationship between food production and timber production became increasingly negative over time16. In China, carbon sequestration and water yield gradually moved from irrelevance to trade-off and then reversed from trade-off to synergy17. However, most studies focus on ESs and their trade-offs/synergies at several time points, rather than continuous years. Therefore, the evolution of ESs and their trade-offs/synergies at different urbanization stages is not clear. In terms of spatial change, scholars have conducted rich research on the landscape, river basin, regional, nationwide, protected area, transnational, and site-specific7. These results showed that ESs and their trade-offs/synergies exhibited significant spatial heterogeneity18. For example, due to land use distribution patterns, trade-offs and synergies showed significant spatial heterogeneity19. Most studies focus only on trade-offs and synergies in the whole study area, often ignoring the differences between different urbanization-level areas.
Located in the Yangtze River Delta region of eastern China, the Su-Xi-Chang region, which includes the three cities of Suzhou, Wuxi, and Changzhou, is a typical representative of rapid urbanization. From 1990 to 2020, the Su-Xi-Chang region experienced rapid urbanization, including economic development (annual GDP growth rate of 15.54%), population growth (annual population growth rate of 2.18%), and urban expansion (annual urbanization growth rate of 3.49%). While rapid urbanization has improved the lives of residents in terms of employment, education, and health care, it has also produced many negative effects. For example, declining grain yield, eutrophic water quality, weakened climate regulation, and habitat isolation20,21. These issues not only threaten the sustainability of local ecosystems but also affect economic growth. But urbanization will continue. Therefore, it is urgent to understand the ESs and their trade-offs/synergies in order to achieve sustainable urban development.
To this end, we divided the Su-Xi-Chang region into three urbanization-level areas and divided the period from 1990 to 2020 into three urbanization stages. We simulated and quantified the supply of four typical ESs (grain production (GP), water retention (WR), heat mitigation (HM), and flood mitigation (FM)) in the past (1990–2020) and in the future (2025–2050, including three scenarios of business as usual (BAU), cropland protection (CP) and ecological conservation (EC)). Then, we analyzed the spatial and temporal evolution of ESs and their trade-offs/synergies to explore the specific impacts of urbanization (Fig. 1). The objectives of the study are: (1) to reveal the temporal and spatial evolution of four ESs under different urbanization-level areas and stages. (2) to analyze the temporal and spatial evolution of trade-offs and synergies under different urbanization-level areas and stages. Our study could improve the differential understanding of ESs and their interactions in urban regions. The methodology and results could also provide the scientific basis for similar studies in other rapidly urbanizing regions around the world.
Note: The first step of the study is to classify urbanization-level areas and stages. Subsequently, the past (1990–2020) and future (2025–2050) supply of four typical ESs was simulated. In the third step, the spatial and temporal evolution of ESs and their trade-offs/synergies were analyzed to explore the specific impacts of urbanization.
Results
Classification of urbanization
Based on the nighttime light imagery data from 1990 to 2020, we categorized the urbanization development patterns in the Su-Xi-Chang region into three areas: developed urban areas, developing urban areas, and rural areas. The three areas account for 1.31%, 47.57%, and 51.12% of the total area of the Su-Xi-Chang region, respectively (Fig. 2). The developed urban areas are mainly concentrated in the urban core areas of Suzhou, Wuxi, and Changzhou. The land types in these areas were mainly built-up area (56.48%) and cultivated land (35.16%) in 1990, and built-up area (89.26%) in 2020. The main transition in land use between 1990 and 2020 was observed from cultivated land to built-up areas (72.86 km2). Developing urban areas are mainly distributed in the surrounding areas of the urban core areas, the east and north of the study area. The most significant expansion of the built-up area (2824.89 km2) took place in the developing urban areas. In 1990, this area was largely characterized by cultivated land (81.74%), while in 2020 the cultivated land decreased to 48.79% and the built-up area increased to 40.25% (Supplementary Table 1). The expansion of the built-up area mainly comes from cultivated land (2729.7 km2), water area (89 km2), and forest (38.97 km2). Rural areas are mainly distributed in the western and southern parts of the study area. The composition of cultivated land, waters, forest, and the built-up area in the rural area was 54.89%, 35.27%, 7.61%, and 2.21% respectively in 1990, which shifted to 47.91%, 37.05%, 6.71%, and 8.32% respectively in 2020 (Supplementary Table 1). The main land use changes in rural areas are conversion from cultivated land to the built-up area (507.33 km2), from cultivated land to water area (178.27 km2), and from forest to cultivated land (136.21 km2).
Note: The study categorized the urbanization development patterns in the whole study area into developed urban areas, developing urban areas, and rural areas based on the nighttime light imagery data from 1990 to 2020.
Based on the characteristics of population migration, this study divided the period from 1990 to 2020 into three urbanization stages: the initial urbanizing stage (1990–2000), the rapidly urbanizing stage (2000–2010), and the highly urbanizing stage (2010–2020). From 1990 to 2000, the total population of the whole study area showed an increasing trend (Table 1). The share of the population in the developed urban areas increased, while the share of the population in the remaining two areas decreased. During this period, the population mainly migrated to the developed urban areas. From 2000 to 2010, the population proportion in the developing urban areas increased, while the population proportion in the other two areas decreased. During this period, the population mainly migrated to the developing urban areas. From 2010 to 2020, the share of the population in the developing urban area decreased slightly, with the population mainly migrating to developed urban areas and rural areas. This is the highly urbanized stage guided by the Chinese government’s planning, namely the urban-rural integrated stage. Our study area is the demonstration area for the development of urban-rural integration in China. Therefore, it has the characteristics of balanced urban-rural migration.
Spatial distribution and relations of ESs in 2020
In general, various ESs showed significant spatial heterogeneity (Supplementary Fig. 1). The mean values of four ESs in the different urbanization areas were as follows: rural areas > developing urban areas > developed urban areas (Supplementary Fig. 2). We performed a hot spot analysis of four ESs by creating hexagonal honeycomb grids. The results showed that the three regulating services had similar spatial distribution patterns. The hotpots with high GP supply (red patches in Fig. 3) were mainly located in the western and northeastern parts of the study area. The hotpots with high supply of WR, HM, and FM were mainly located in the southwestern part of the study area, where the land use types were mainly cropland and forest. The low-value areas for the four ESs (blue patches in Fig. 3) were mainly concentrated in the urban cores and their surroundings, indicating the negative influence of high urbanization on the ESs. The remaining gray areas showed insignificant clustering, indicating a moderate supply of ESs and a relatively low degree of clustering.
a grain production; b water retention; c heat mitigation; d flood mitigation.
Temporal and spatial change of ESs from 1990 to 2020
The spatial variation trend of the four ESs in the Su-Xi-Chang region from 1990 to 2020 is shown in Fig. 4. The areas of GP growth were mainly distributed to the west and northeast of the study area (Fig. 4a). There was no significant change in the urban core area because there was no grain yield there for many years. The surrounding areas of urban core areas showed a constant or decreasing trend due to the fact that part of the cultivated land was occupied by built-up areas, and the increasing trend was mainly due to the gradual reclamation of cultivated land between 2010 and 2020. WR mainly showed a weak increasing trend (Fig. 4b). The southwest of the study area was located in a high-altitude area with dense vegetation and rich biodiversity, resulting in a significant increase in WR in the area. HM mainly showed an extremely significant decrease trend (Fig. 4c). This is linked to the continued reduction of cultivated land and the fragmentation of forests and landscapes. FM showed a significant increase trend in the west of the study area and around Taihu Lake. The FM in the eastern part of the study area, the urban core area, and its surrounding areas showed an extremely significant decrease trend (Fig. 4d)
a grain production; b water retention; c heat mitigation; d flood mitigation.
We further assessed the ESs in the three urbanization stages and three urbanization-level areas to analyze the impact of urbanization on ESs (Fig. 5). From 1990 to 2020, GP in the Su-Xi-Chang region showed a trend of first stabilizing, then significantly declining and then slowly declining (Fig. 5a). In different urbanization-level areas, the decline in GP was manifested as developing urban area > rural area > developed urban area. From the perspective of different urbanization stages, the GP in the whole study area remained stable at 0.33 kg m−2 in the initial urbanizing stage. Between 1997 and 2003, GP decreased significantly. From 2003 to 2015, GP remained stable at 0.169 kg m−2, and then decreased to 0.118 kg m−2 (Supplementary Fig. 6a).
a Grain production; b water retention; c heat mitigation; d flood mitigation. Note: initially urbanizing stage (1990–2000), rapidly urbanizing stage (2000–2010), highly urbanizing stage (2010–2020); every 1 year as a step; Savitzky-Golay algorithm used for smoothing.
The WR in the Su-Xi-Chang region generally showed a fluctuating trend of first decrease and then increase (Fig. 5b). The decrease in WR was manifested as developed urban area > developing urban area, and WR in the rural area increased. WR in the Su-Xi-Chang region was the highest in 1991 with 0.497 mm m−2, and the lowest in 2013 with 0.123 mm m−2 (Supplementary Fig. 6b). Due to the high proportion of the impervious surface in the developed urban area, its average surface runoff coefficient was higher than that of other regions, and the WR in the developed urban area was the lowest level in the study area.
HM gradually decreased as urbanization accelerated (Fig. 5c). The decrease of HM was manifested as developing urban area > developed urban area > rural area. The supply of HM is related to the level of urbanization. HM was highest in rural areas, followed by developing urban areas and developed urban areas. In the initially urbanizing stage and rapidly urbanizing stage, the HM in the developed urban area decreased significantly and approached 0.
From 1990 to 2020, FM in the study area showed a generally fluctuating downward trend (Fig. 5d). The decrease in FM was manifested as developing urban area > developed urban area > rural area. Before 2005, the supply of FM was expressed as developing urban area > rural area > developed urban area. After 2005, the supply of FM was expressed as rural area > developing urban area > developed urban area.
Temporal and spatial change of the ES trade-offs and synergies from 1990 to 2020
We further analyzed the impact of urbanization on trade-offs and synergies (Fig. 6). Overall, the relationships between ESs were mainly synergistic, except for the trade-offs between GP and HM, WR and HM, and HM and FM in rural areas. The changing trend of the correlation coefficients between ESs in rural areas and developing urban areas was similar to that of the whole study area, showing a gradually increasing or stable trend. The developed urban area showed the opposite trend of change, with a gradually weakening trend. From the evolutionary characteristics of the relationship between ESs, we found the following characteristics.
a GP vs. WR; b GP vs. HM; c GP vs. FM; d WR vs. HM; e WR vs. FM; f HM vs. FM. Note: trade-offs (background in light red), synergies (background in light blue); initially urbanizing stage (1990–2000), rapidly urbanizing stage (2000–2010), highly urbanizing stage (2010–2020); every 1 year as a step; Savitzky-Golay algorithm used for smoothing.
From 1990 to 2020, GP and WR showed a synergistic relationship in the whole study area and three urbanization-level areas. In the initial urbanizing stage, the synergies in developed urban areas were the largest; however, with the progress of urbanization, the synergies in developing urban areas gradually increased, ultimately surpassing other urban areas in the highly urbanizing stage. GP and HM showed synergies in developed urban areas and developing urban areas; over time, these synergies gradually weakened in developed urban areas and gradually increased in developing urban areas; GP and HM showed strong trade-offs in rural areas. The synergies of WR and HM gradually weakened in developed urban areas and increased in developing urban areas. In the rural areas, WR and HM showed trade-offs and gradually weakened. In the rapidly urbanizing stage, WR and HM changed from trade-offs to synergies across the whole study area. HM and FM showed stable trade-offs in rural areas, gradually increasing synergies in developing urban areas, and gradually decreasing synergies in developed urban areas. GP and FM, WR and FM showed synergies in the whole study area and three urbanization-level areas, and the synergy in the developed urban area showed a gradually weakening trend.
Temporal and spatial change of ESs from 2025 to 2050
The results of the multi-scenario simulations (BAU, CP, and EC) demonstrate that with the exception of WR, the remaining three ESs mainly exhibit a gradual decline in the BAU scenario (Fig. 7). It is noteworthy that, for the first time, rural areas experience the greatest decline in GP, HM, and FM after developing urban areas. In contrast, the CP and EC scenarios present positive alternatives. Under the CP and EC scenarios, GP exhibits an upward trend from 2025 to 2050 (Fig. 7a), reversing the downward trend of the past 31 years (Fig. 5a). While the supply of HM and FM does not reverse the overall trend of the curve, it should be noted that it is predominantly higher than that of the BAU scenario (Fig. 7c, d). WR shows an upward trend in all three scenarios (Fig. 7b). In summary, the CP scenario exerts a positive influence on GP and WR, while also moderating the decline in FM in the Su-Xi-Chang region. The EC scenario helps to mitigate the reduction in HM. The combination of CP and EC may provide benefits for the future supply of ESs.
a Grain production; b water retention; c heat mitigation; d flood mitigation. Note: every 5 years as a step; the Savitzky-Golay algorithm is used for smoothing.
Temporal and spatial change of the ES trade-offs and synergies from 2025 to 2050
Under different development objectives (BAU, CP, EC), different development areas (rural areas, developed urban areas, and developing urban areas), and different development stages (initially urbanizing stage, rapidly urbanizing stage, highly urbanizing stage), the impacts on the trade-offs/synergies between different ESs are different (Fig. 8). Overall, the correlation coefficients between the ESs (GP and WR, GP and FM, WR and HM, and WR and FM) under the BAU, CP and EC scenarios show a predominantly decreasing trend throughout the study area. This suggests that the development pattern of the Su-Xi-Chang region consistent with the highly urbanizing stage (2010–2020), or the policy orientations (CP and EC) derived from this, may help alleviate the lose-lose situations. The correlation coefficients between GP and HM, HM, and FM show an increasing trend. Where HM and FM shift from a trade-off to a lose-lose situation in the BAU, CP, and EC scenarios. In the BAU scenario, GP and HM also changed from a trade-off to a lose-lose situation. While in the CP and EC scenarios, the slope of the correlation coefficient between GP and HM gradually decreased.
a GP vs. WR; b GP vs. HM; c GP vs. FM; d WR vs. HM; e WR vs. FM; f HM vs. FM. Note: trade-offs (background in light red), synergies (background in light blue); every 5 years as a step; Savitzky-Golay algorithm used for smoothing.
Compared to the past 30 years (1990–2020), the results of future scenario simulations indicate that with further urbanization, significant changes will occur in the interactions among ESs. Under the BAU scenario, the relationship between HM and FM, as well as between GP and HM, shifts from trade-offs to lose-lose situations. This implies that if the current development pattern continues, the provision of these services will decrease simultaneously in the future. In contrast, the CP and EC scenarios can alleviate such lose-lose situations. Although the synergistic effect between GP and HM still shows a declining trend in these two scenarios, the rate of decline is significantly lower than that in the BAU scenario. This suggests that by implementing policies for the protection of cropland and ecological land, a win-win situation for the future provision of ESs is more likely to be achieved.
Discussion
Compared with previous studies, this study comprehensively analyzed the spatial and temporal changes of ESs and their trade-offs/synergies in the process of urbanization. In this section, we attempt to discuss two aspects: (1) to analyze the characteristics of the spatial and temporal evolution of ESs, trade-offs, and synergies, and their reasons; (2) to provide management recommendations to enhance ESs in the Su-Xi-Chang region.
From 1990 to 2020, there was a gradual decrease in GP, FM, and HM in the Su-Xi-Chang area, which is consistent with the findings of previous studies9,22. The areas with the greatest loss of ESs were developing urban areas (Fig. 5), in accordance with the findings of Haase et al.4 and Spyra et al.23. This is due to the fact that in rapidly urbanizing areas, the increase in the built-up area is often at the expense of the occupation of land on the periphery of the core area (such as cultivated land, water, and forest). The loss of ESs in rural areas was mainly caused by the occupation of cultivated land by rural industrial development. Compared to the developing urban areas, the impact of rural industrialization on cultivated land and green space was small, and the loss of ESs was low. In addition, in the highly urbanizing stage, the supply of the four ESs was expressed as rural area > developing urban area > developed urban area. This shows that the supply of ESs in the Su-Xi-Chang region increases with the distance from the city center, with a significant change characteristic of the urban-rural gradient24.
Based on the environmental Kuznets curve, we attempt to speculate that the relationship between urbanization and ESs in the Su-Xi-Chang region may exhibit a U-shaped curve. The left side of the U-shaped curve indicates that urbanization leads to the degradation of ESs. This indicates that the Su-Xi-Chang region was still in the phase of economic development at the expense of the environment from 1990 to 2020. The rate of decline on the left side of the U-shaped curve is as follows: rapidly urbanizing stage (2000–2010) > initially urbanizing stage (1990–2000) > highly urbanizing stage (2010–2020). The curve appears as a turning point when a city reaches a certain development stage. Recent studies have found that some core urban areas in the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, Wuhan and Sichuan-Chongqing urban agglomerations in China have reached and crossed the inflection point of the U-shaped curve25. Multi-scenario combinations can provide significant benefits for the future provision of ESs26. Combined with the results of the multi-scenario simulation in the Su-Xi-Chang region, we further argue that the CP and EC scenarios contribute to the relationship between urbanization and ESs crossing the inflection point and moving to the right side of the U-shaped curve. In summary, the left side of the U-shaped curve improves our understanding of urban impacts on ESs during the formation, development, and maturity phases of cities. The right side of the U-shaped curve suggests that we can improve the provision of ESs through the development of food and environmental protection programs.
Our study found that in developing urban areas, rural areas, and the whole study area, GP and the three regulating services (WR, HM, and FM) mainly showed a gradual weakening of trade-offs and a shift to synergies, or a gradual increase in synergies from 1990 to 2020 (Fig. 6). This is consistent with some studies suggesting that synergies may be greater than trade-offs in urban areas27,28. On the one hand, this may be related to whether or not there is a land use conflict29. Land use conflict means that once a certain land is used to provide one ES, it can no longer provide another ES; land use consistency means that a certain land use pattern is beneficial to two or more ESs simultaneously30. In this study, the water area is beneficial to mitigating the heat island effect, but cannot provide food; cultivated land provides more grains while also regulating floods. Therefore, GP in rural areas is trade-off with HM and synergistic with HM (Fig. 6). On the other hand, the conversion of forest ecosystems with high ES values into agricultural systems may result in a reduction of regulating services such as WR, HM, and FM. However, agricultural systems with low woodland cover and large areas of cultivated land (e.g., in the Su-Xi-Chang region) still have important ecosystem functions and provide multiple ESs beyond grain production31,32,33. Conversion of urban and peri-urban ecosystems with low ES values to agricultural systems may increase the supply of GP, WR, and FM in the urban landscape. Conversely, the conversion of cropland to built-up land reduces its area and fragmentation, leading to a reduction in the supply of ESs (Fig. 5) and an increase in lose-lose situations (Fig. 6).
In the initial urbanizing stage (1990–2000), many immigrants from other places and people originally living in developing urban areas and rural areas were attracted to developed urban areas to obtain better employment opportunities and quality of life (Table 1). This leads to the gradual degradation of the ecosystem functions in developed urban areas and a significant decrease in synergies. At the same time, township enterprises continued to establish and develop during this period. However, due to the limitation of objective conditions such as technical level and human capital, the scale of township enterprises was small and the location was scattered. This leads to the sporadic distribution of built-up areas in developing urban areas and rural areas. In the rapidly urbanizing stage (2000–2010), built-up areas gradually gathered and formed small town centers, i.e. lower-level centers. At the same time, many development zones were established in the Su-Xi-Chang region by introducing foreign investment. Therefore, built-up areas gradually spread outward from urban core areas and small town centers; the popularity of highways and private cars also connects cities and suburbs34. However, during this period, most cultivated land was transformed into industrial land, resulting in a significant reduction in ESs. As a result, lose-lose situations become more obvious in developing urban areas (Fig. 6). The interrelationships between GP and HM, WR, and HM also changed from trade-offs to lose-lose situations at this stage. In the highly urbanizing stage (2010 ~ 2020), the development strategy of the local government changed to a win-win situation of economic development and ecological protection, and the protection and restoration of the ecological environment were put in an important position. During this period, cultivated land and green space decreased to a smaller extent, the supply of WR increased, and the loss of other ESs decreased. The interrelationships between ESs in the whole study area did not change much during this period.
Based on this, we attempted to speculate that the relationship between urbanization and trade-offs/synergies in the Su-Xi-Chang region may exhibit an S-shaped curve. In the initial urbanizing stage (1990–2000), the S-shaped curve showed a slow upward trend. The whole study area showed a gradual weakening of trade-offs (Fig. 6, GP and HM, WR, and HM, HM and FM) or a gradual increase in lose-lose situations (Fig. 6, GP and WR, GP and FM, WR and FM). In the rapidly urbanizing stage (2000– 2010), the S-shaped curve showed a linear upward trend. The whole study area showed a significant reduction in trade-offs (Fig. 6, GP and HM, HM, and FM), a reversal of trade-offs into synergies (Fig. 6, WR and HM), or a significant increase in synergies (Fig. 6, GP and WR, GP, and FM, WR, and FM). In the highly urbanizing stage (2010–2020), the S-shaped curve gradually flattens. In a word, past urbanization weakened trade-offs and enhanced the lose-lose situations35, especially in the rapidly urbanizing stage (2000–2010). Compared to the past 30 years, the lose-lose situations in future scenarios gradually weaken, and the curve shows a downward trend (Fig. 8). This provides important insights for sustainable urban development: by formulating reasonable land use planning and ecological protection policies, it is expected to alleviate the trade-offs among ESs to some extent, promote the play of synergistic effects, and ultimately achieve coordinated development between urbanization and the ecological environment.
Based on the results, we have the following thoughts on urban land management strategies. First, the development priorities of different urbanization-level areas should be clarified and compact urban development should be promoted. In developed urban areas, vertical urban development could be encouraged. Developing urban areas should intensify and conserve existing urban land resources, which could help mitigate the expansion of built-up areas and negative impacts on ESs. Second, sustainable agriculture should be promoted. Given the resource endowment of the Su-Xi-Chang region, sustainable agriculture (e.g., farmland conservation, crop diversification, conservation tillage, integrated nutrient management, crop rotation, and residue incorporation) may be a more conducive development approach in the future to reach and cross the inflection point of the “U” curve to achieve food security while minimizing environmental degradation36. In addition, increasing the cereal production capacity of urban landscapes can reduce the need for agricultural expansion, thereby conserving land and protecting ecosystems with high ES values37. Thirdly, further afforestation and “returning farmland to lakes” could be carried out in the study area.
Although our findings can reveal the spatial and temporal patterns of ESs and their trade-offs and synergies in urban areas, there are also uncertainties. Due to the limitations of the study year and field measurement conditions, the parameters we input into the InVEST model refer to results from studies in nearby or the same regions. At the same time, the InVEST model simplifies complex ecological processes and enhances the operability of the assessment, but also introduces uncertainty into the assessment results. In addition, cultural services are important in urbanized areas. However, the inherent complexity of cultural services is difficult to reconstruct over time due to the large scale of the study area and the long-time span (1990–2020). To more comprehensively assess the provision of different ESs, we plan to conduct future surveys and interviews with residents around Taihu Lake to explore their ecological perceptions of different ESs (including cultural services).
Methods
Study area
The Su-Xi-Chang region (119°08′–121°15′E, 30°46′–32°04′N) is located in the southern part of Jiangsu Province, China, with a total area of about 17,670 square kilometers (Fig. 9). The region includes the core areas of Suzhou, Wuxi, and Changzhou, surrounded by towns and villages. The terrain is mainly flat, with an average altitude of less than 50 m. The region is located in the subtropical monsoon climate zone, with an annual average temperature of 15–17 °C and an average annual precipitation of 816–1322 mm. The temperature in the urban core areas of Suzhou, Wuxi, and Changzhou is higher than that in the surrounding suburbs and rural areas, with a significant heat island effect. The vegetation in the region mainly includes subtropical evergreen broad-leaved forests, subtropical coniferous forests, deciduous broad-leaved forests, and subtropical bamboo forests. In the western part of the study area, there are Maoshan Mountain and Tianmu Mountain with high vegetation coverage. Lakes include Taihu Lake, Yangcheng Lake and Gehu Lake.
a The location of Jiangsu province in China; b the location of the whole study area in Jiangsu province; c, d, e, f the land cover pattern of the whole study area in 1990, 2000, 2010, and 2020, respectively.
The Su-Xi-Chang region was chosen as the study area because it is typical and representative of the temporal evolution and spatial pattern of urbanization38. Since the early 1980s, many people from other parts of China have come to the Su-Xi-Chang region. During this period, much of the land in the region was developed into cultivated land and rural settlements to meet the rapidly growing demand for food. In the mid-1980s, a development model of rural urbanization through the development of township enterprises emerged in the Su-Xi-Chang, known as the “South Jiangsu Model“2. This development model enabled local rural residents to earn considerable incomes in enterprises close to their homes without having to go to the city. The existence of these enterprises promoted the economic development and infrastructure construction of townships in the Su-Xi-Chang region, giving them the characteristics of urban landscape and culture. Around 2000, with the help of the Shanghai Pudong development, development zones were established in various places in the Su-Xi-Chang region. From 2000 to 2010, the built-up area in the region increased significantly, entering an era of rapid urban expansion34. After 2010, under the background of national ecological civilization construction, the Su-Xi-Chang region transformed into a high-quality development model that paid more attention to ecological protection. The development model has been changing, but the gathering of population and the continuous progress of urbanization have not changed. Therefore, the relationship between people and land in this region has been in a state of tension. Spatially, Suzhou, Wuxi, and Changzhou have similar characteristics in ecosystem structure, social culture, and industrial layout, and present an obvious circle structure around the urban core areas, which is very typical in China’s metropolitan circle. From 1990 to 2020, the built-up area in the Su-Xi-Chang region occupied a large amount of cultivated land, resulting in landscape fragmentation and degradation of various ecosystem services39. Among them, the main land use types in 1990, 2000, 2010, and 2020 were cultivated land (accounting for 67.19%, 60.63%, 50.27%, and 47.65% of the total area of the study area, respectively), water area (23%, 25%, 26.32% and 23.65%), forest (4.96%, 5.07%, 4.68% and 4.28%) and built-up area (4.83%, 9.27%, 18.67% and 24.41%).
Quantification of ESs
In 2015, the United Nations General Assembly adopted the “Zero Hunger” Sustainable Development Goal to address global food security and environmental sustainability challenges40. This has positive implications for agricultural and natural environment linkages and policy development. Combining the availability of data, the feasibility of assessment methods, and the importance to local residents’ lives, our study selected four typical ESs for analysis, including one supplying service (grain production (GP)) and three regulating services (water retention (WR), heat mitigation (HM), flood mitigation (FM)). Firstly, the Su-Xi-Chang region is one of the important grain production bases in China, but studies have shown that local grain yield is declining with urbanization41. Assessing GP to protect food security is crucial because urbanization often leads to the loss of agricultural land, especially in peri-urban areas11. Secondly, as one of the important regulating services, WR reflects the hydrology, vegetation, and soil conditions in the study area. The large number of artificial surface constructions in the study area affects the geomorphological and hydrological processes, resulting in a decline in WR42, which has become more prominent under the effect of climate warming and drying in recent years43,44. With the acceleration of urbanization, the heat island effect has become more pronounced, not only affecting the living conditions of local residents but also causing a number of diseases45. A significant strong correlation between mean land surface temperature and the density of impervious surface (positive) and green space (negative) along the urban-rural gradients46. In addition, the Su-Xi-Chang region has a flat topography and sufficient precipitation, and the continuous expansion of impervious surface also increases the possibility of flooding47. Therefore, FM is also one of the ESs focused on in this study. GP was calculated using NDVI and total grain yield. WR, HM, and FM were all quantified using the InVEST model (See Supplementary Method 1).
Hot spot analysis of ESs
Hot spot analysis is used to determine whether there are statistically significant high and low values in the spatial distribution of a particular element11. In this study, honeycomb hexagons were created to calculate the average value of each ES for each grid in the Su-Xi-Chang region. Then we used the hot spot analysis (Getis-Ord Gi*) tool to identify the hotspots of ESs provision.
Classification of urbanization
We used digital numbers (DN) in nighttime light imagery data to identify different urbanization-level areas in the Su-Xi-Chang region from 1990 to 2020. The higher the DN, the higher the urbanization level. The nighttime light imagery data used in this study were fitted by DMSP/OLS (1992–2013) and VIIRS/NPP (2013–2020), and the data source is shown in Supplementary Table 8. Referring to the study of Fu et al.34 and Li et al.48, we used the ArcGIS 10.8 platform to overlay the areas with DN values greater than or equal to 50 in both 1992 and 2020 to determine developed urban areas. Similarly, according to the classification criteria in Table 2, we identified developing urban areas and rural areas. Based on this, we got three urbanization-level areas. It is important to note that the physical boundaries of the three urbanization-level areas changed between the years 1990 to 2020. The three urbanization-level areas divided by nighttime light imagery data are an abstract regional boundary to distinguish the different urbanization development patterns in the Su-Xi-Chang region over the past 30 years.
In addition, referring to our previous study34 and statistical analysis tools of the ArcGIS 10.8 platform, we analyzed the changes in the total population and the proportion of the population in the three urbanization-level areas over time. By revealing the characteristics of population migration, this study identifies three urbanization stages in the Su-Xi-Chang region (Table 1).
Spatial-temporal change analysis of ESs
In terms of temporal variation, we calculated the average of four ESs in developed urban areas, developing urban areas, and rural areas from 1990 to 2020. Referring to the research of Sen et al.49 and Geng et al.50, we used Theil-Sen median trend analysis to reflect the spatial change trend of ESs from 1990 to 2020. Referring to the study of Kisi et al.51, we used the Mann-Kendall test to determine the significance. We analyzed the changing trends in the MATLAB R2022b software. See Supplementary Method 2 for codes and formulas.
Trade-off and synergy analysis of ESs
Referring to two studies in the Taihu Lake Basin, China15,52, and considering the area, sample size, and visualization of the results in the Su-Xi-Chang region, we used the ArcGIS 10.8 platform to randomly select 850 points in the study area. Then, the supply value of ESs of these points was extracted. Referring to the study of Xia et al.53, the Spearman correlation coefficient in SPSS software was used to identify the trade-offs and synergies between each pair of ESs in the whole study area and different urbanization levels (rural areas, developing urban areas, and developed urban areas). The correlation coefficient value can be used to determine the strength of trade-offs and synergies. The higher the absolute value of the correlation coefficient, the stronger the strength of the trade-off or synergy. A negative correlation coefficient indicates that there is a trade-off, and vice versa. Before the analysis, we adopted the Min-Max normalization method to unify the supply of the four ESs in the range of −1 to 1, so as to reduce the influence of the unit and size among the ESs. The reasons for using the Spearman correlation coefficient are as follows: (1) The relationship between ESs may be highly non-linear. Spearman correlation coefficient may reflect a non-linear correlation. (2) The data are not normally distributed. (3) If there are obvious outliers in the data (such as developed urban areas, where the supply of most ESs is zero), it is more robust to use the Spearman correlation coefficient.
Future multi-scenario simulation of land use
The patch-generating land use simulation (PLUS) model is a land use simulation model proposed by Liang et al.54, which can better simulate the patch-level change of multiple classes of land use types. The PLUS model integrates two modules, a land expansion analysis strategy (LEAS) mining framework and a multi-class random patch seed-based CA model (CARS). The PLUS model takes into account the driving and guiding role of planned development zones in the urban development process. Therefore, the simulation of future land has a better explanation and higher simulation accuracy. Based on the Jiangsu Provincial Development Plan (the planning period is 2021–2035, with a vision up to 2050), three scenarios are proposed based on the principles of adhering to the priority of cropland protection, adhering to the priority of ecology and green development, and the principle of land conservation and intensive utilization: business as usual (BAU), cropland protection (CP), and ecological conservation (EC) (see Supplementary Method 3).
Data availability
All data analyzed during the current study are available from the corresponding author upon reasonable request. The intermediate data files generated in this study are provided as Datasets in a Figshare repository under accession code https://figshare.com/s/62540411a00134eeb401. Data supporting the results of this study are available in the Supplementary Table 8. The resolution of all raster data is 30 m and the projected coordinate system is WGS_1984_UTM_Zone_51N.
Code availability
Theil-Sen median trend analysis and Mann-Kendall test were performed using MATLAB R2022b software. The code is available in Supplementary Method 2.
References
Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).
Wang, Y., Fu, Q., Wang, T., Gao, M. & Chen, J. Multiscale characteristics and drivers of the bundles of ecosystem service budgets in the Su-Xi-Chang Region, china. Int J. Environ. Res Public Health 19, 12910 (2022).
Bennett, E. M., Peterson, G. D. & Gordon, L. J. Understanding relationships among multiple ecosystem services. Ecol. Lett. 12, 1394–1404 (2009).
Haase, D., Schwarz, N., Strohbach, M., Kroll, F. & Seppelt, R. Synergies, trade-offs, and losses of ecosystem services in urban regions: an integrated multiscale framework applied to the Leipzig-Halle Region, Germany. Ecol. Soc. 17, 22 (2012).
Costanza, R. et al. Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosyst. Serv. 28, 1–16 (2017).
Feng, Y. et al. Study on trade-offs and synergies of ecosystem services: development history and research characteristics. JARE 39, 11–25 (2022).
Aryal, K., Maraseni, T. & Apan, A. How much do we know about trade-offs in ecosystem services? A systematic review of empirical research observations. Sci. Total Environ. 806, 151229 (2022).
Babí Almenar, J. et al. Nexus between nature-based solutions, ecosystem services and urban challenges. Land Use Policy 100, 104898 (2021).
Gu, Y. et al. Assessing the impacts of urban expansion on bundles of ecosystem services by Dmsp-Ols Nighttime Light Data. Sustainability 11, 5888 (2019).
Haberman, D. & Bennett, E. M. Ecosystem service bundles in global hinterlands. Environ. Res. Lett. 14, 84005 (2019).
Sylla, M., Hagemann, N. & Szewrański, S. Mapping trade-offs and synergies among peri-urban ecosystem services to address spatial policy. Environ. Sci. Policy 112, 79–90 (2020).
Mouchet, M. A. et al. An interdisciplinary methodological guide for quantifying associations between ecosystem services. Glob. Environ. Change 28, 298–308 (2014).
Cord, A. F. et al. Towards systematic analyses of ecosystem service trade-offs and synergies: Main concepts, methods and the road ahead. Ecosyst. Serv. 28, 264–272 (2017).
Obiang Ndong, G., Therond, O. & Cousin, I. Analysis of relationships between ecosystem services: a generic classification and review of the literature. Ecosyst. Serv. 43, 101120 (2020).
Qiao, X. et al. Temporal variation and spatial scale dependency of the trade-offs and synergies among multiple ecosystem services in the Taihu Lake Basin of China. Sci. Total Environ. 651, 218–229 (2019).
Jaligot, R., Chenal, J., Bosch, M. & Hasler, S. Historical dynamics of ecosystem services and land management policies in Switzerland. Ecol. Indic. 101, 81–90 (2019).
Yang, Y. et al. Spatiotemporal variation of essential ecosystem services and their trade-off/synergy along with rapid urbanization in the Lower Pearl River Basin, China. Ecol. Indic. 133, 108439 (2021).
Cueva, J. et al. Synergies and trade-offs in ecosystem services from urban and peri-urban forests and their implication to sustainable city design and planning. Sustain. Cities Soc. 82, 103903 (2022).
Li, Y. et al. Spatially explicit quantification of the interactions among ecosystem services. Landsc. Ecol. 32, 1181–1199 (2017).
Fu, Q. et al. Spatiotemporal patterns and drivers of the carbon budget in the Yangtze River Delta Region, China. Land 11, 1230 (2022).
Ding, G. Q., Guo, J., Ou, M. H. & Prishchepov, A. V. Understanding habitat isolation in the context of construction land expansion using an ecological network approach. Landsc. Ecol. 39, 56 (2024).
Wu, Y. et al. Impact of land use change on multiple ecosystem services in the rapidly urbanizing Kunshan City of China: past trajectories and future projections. Land Use Policy 85, 419–427 (2019).
Spyra, M., La Rosa, D., Zasada, I., Sylla, M. & Shkaruba, A. Governance of ecosystem services trade-offs in peri-urban landscapes. Land Use Policy 95, 104617 (2020).
González-García, A., Palomo, I., González, J. A., López, C. A. & Montes, C. Quantifying spatial supply-demand mismatches in ecosystem services provides insights for land-use planning. Land Use Policy 94, 104493 (2020).
Pan, Y., Dong, F. & Du, C. Is China approaching the inflection point of the ecological Kuznets curve? Analysis based on ecosystem service value at the county level. J. Environ. Manag. 326, 116629 (2023).
Padilha, J., Carvalho-Santos, C., Cássio, F. & Pascoal, C. Land cover implications on ecosystem service delivery: a multi-scenario study of trade-offs and synergies in river basins. Environ. Manag. 73, 753–768 (2024).
Yang, G. et al. Using ecosystem service bundles to detect trade-offs and synergies across urban–rural complexes. Landsc. Urban Plan. 136, 110–121 (2015).
Lyu, R., Zhang, J., Xu, M. & Li, J. Impacts of urbanization on ecosystem services and their temporal relations: a case study in Northern Ningxia, China. Land Use Policy 77, 163–173 (2018).
Fienitz, M. Taking stock of land use conflict research: a systematic map with special focus on conceptual approaches. Soc. Nat. Resour. 36, 715–732 (2023).
Xu, S., Liu, Y., Wang, X. & Zhang, G. Scale effect on spatial patterns of ecosystem services and associations among them in semi-arid area: a case study in Ningxia Hui Autonomous Region, China. Sci. Total Environ. 598, 297–306 (2017).
Renting, H. et al. Exploring multifunctional agriculture. A review of conceptual approaches and prospects for an integrative transitional framework. J. Environ. Manag. 90, S112–S123 (2009).
Bommarco, R., Kleijn, D. & Potts, S. G. Ecological intensification: harnessing ecosystem services for food security. Trends Ecol. Evol. 28, 230–238 (2013).
Sanou, J., Tengberg, A., Bazié, H. R., Mingasson, D. & Ostwald, M. Assessing trade-offs between agricultural productivity and ecosystem functions: a review of science-based tools? Land 12, 1329 (2023).
Fu, Q., Xu, L., Zheng, H. & Chen, J. Spatiotemporal dynamics of carbon storage in response to urbanization: a case study in the Su-Xi-Chang Region, China. Processes 7, 836 (2019).
Liu, Z., Liu, Z., Zhou, Y. & Huang, Q. Distinguishing the impacts of rapid urbanization on ecosystem service trade-offs and synergies: a case study of Shenzhen, China. Remote Sens. 14, 4604 (2022).
Rehman, A., Farooq, M., Lee, D. & Siddique, K. H. M. Sustainable agricultural practices for food security and ecosystem services. Environ. Sci. Pollut. 29, 84076–84095 (2022).
Wilhelm, J. A. & Smith, R. G. Ecosystem services and land sparing potential of urban and peri-urban agriculture: a review. Renew. Agr. Food Syst. 33, 481–494 (2018).
Chen, S. et al. Trade-offs and synergies of ecosystem services in the Yangtze River Delta, China: response to urbanizing variation. Urban Ecosyst. 25, 313–328 (2022).
Yirsaw, E. Socioeconomic drivers of spatio-temporal land use/land cover changes in a rapidly urbanizing area of China, the Su-Xi-Chang region. Appl. Ecol. Env. Res. 15, 809–827 (2017).
Blesh, J., Hoey, L., Jones, A. D., Friedmann, H. & Perfecto, I. Development pathways toward “zero hunger”. World Dev. 118, 1–14 (2019).
Yirsaw, E., Wu, W., Temesgen, H. & Bekele, B. Effect of temporal land use/land cover changes on ecosystem services value in coastal area of China: the case of Su-Xi-Chang region. Appl. Ecol. Env. Res. 14, 409–422 (2016).
Alberti, M. Maintaining ecological integrity and sustaining ecosystem function in urban areas. COSUST 2, 178–184 (2010).
Fu, Q., Li, B., Hou, Y., Bi, X. & Zhang, X. Effects of land use and climate change on ecosystem services in Central Asia’s arid regions: a case study in Altay Prefecture, China. Sci. Total Environ. 607-608, 633–646 (2017).
Zhai, R., Tao, F. & Xu, Z. Spatial–temporal changes in runoff and terrestrial ecosystem water retention under 1.5 and 2 °C warming scenarios across China. Earth Syst. Dyn. 9, 717–738 (2018).
Marando, F., Salvatori, E., Sebastiani, A., Fusaro, L. & Manes, F. Regulating ecosystem services and green infrastructure: assessment of urban heat island effect mitigation in the municipality of Rome, Italy. Ecol. Model 392, 92–102 (2019).
Estoque, R. C., Murayama, Y. & Myint, S. W. Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Sci. Total Environ. 577, 349–359 (2017).
Marino, D. et al. Linking flood risk mitigation and food security: an analysis of land-use change in the metropolitan area of Rome. Land 12, 366 (2023).
Li, B. et al. Spatio-temporal assessment of urbanization impacts on ecosystem services: case study of Nanjing City, China. Ecol. Indic. 71, 416–427 (2016).
Sen, P. K. Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
Geng, W. et al. Analyzing spatio-temporal changes and trade-offs/synergies among ecosystem services in the Yellow River Basin, China. Ecol. Indic. 138, 108825 (2022).
Kisi, O. & Ay, M. Comparison of Mann–Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. J. Hydrol. 513, 362–375 (2014).
Bai, Y., Chen, Y., Alatalo, J. M., Yang, Z. & Jiang, B. Scale effects on the relationships between land characteristics and ecosystem services- a case study in Taihu Lake Basin, China. Sci. Total Environ. 716, 137083 (2020).
Xia, H., Yuan, S. & Prishchepov, A. V. Spatial-temporal heterogeneity of ecosystem service interactions and their social-ecological drivers: Implications for spatial planning and management. Resour. Conserv Recycl 189, 106767 (2023).
Liang, X. et al. Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: a case study in Wuhan, China. Comput. Environ. Urban Syst. 85, 101569 (2021).
Acknowledgements
The authors acknowledge the support of the National Natural Science Foundation of China (42101253, 72174089), the Jiangsu Social Science Foundation (19GLC016), the Major Projects of Philosophical and Social Sciences Research in Colleges and Universities in Jiangsu Province (2019SJZDA043), and the Jiangsu Province Social Sciences Application Research Boutique Project (23SYC-180).
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Conceptualization, Q.F. and J.C.; methodology, Q.F. and Y.W.; software, Y.W.; formal analysis, Q.F.; resources, T.W.; data curation, Y.W.; writing—original draft preparation, Y.W. and Q.F.; writing—review and editing, Q.F., J.G., and J.C.; visualization, Y.W.; supervision, J.C.; project administration, Q.F.; funding acquisition, Q.F. and J.G.; Y.W. and Q.F contributed equally to the work and are considered “co-first authors”. All authors have read and agreed to the published version of the manuscript.
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Wang, Y., Fu, Q., Guo, J. et al. Unveiling the dynamics of urbanization and ecosystem services: insights from the Su-Xi-Chang Region, China. npj Urban Sustain 4, 36 (2024). https://doi.org/10.1038/s42949-024-00177-6
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DOI: https://doi.org/10.1038/s42949-024-00177-6
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