Responses of ecosystem services and their trade-offs and synergies to spatiotemporal differentiation of urbanization: Evidence from the Su-Xi-Chang region, China

doi:10.21203/rs.3.rs-3595878/v1

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Responses of ecosystem services and their trade-offs and synergies to spatiotemporal differentiation of urbanization: Evidence from the Su-Xi-Chang region, China

https://doi.org/10.21203/rs.3.rs-3595878/v1

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Assessing the impact of urbanization on ecosystem services is an important part of improving understanding of the harmonious coexistence between people and nature. Despite recent progress, it remains challenging for influence mechanism of different urbanization stages and areas on ecosystem services and their trade-offs and synergies. Here, we analyzed the responses of four ecosystem services (crop production, water retention, heat mitigation, flood mitigation) and their trade-offs and synergies to the spatial and temporal differentiation of urbanization in the Su-Xi-Chang region of China from 1990 to 2020. The results showed that the relationship between ecosystem services and urbanization was "U"-shaped curve. In terms of space and time, the greatest loss of ecosystem services occurred in developing urban areas and during the rapidly urbanizing stage. Also, urbanization weakened trade-offs and enhanced lose-lose situations, showing an "S"-shaped curve. Based on the results, some suggestions are proposed aimed at promoting sustainable urban development.

Earth and environmental sciences/Ecology/Ecosystem services

Scientific community and society/Developing world

Earth and environmental sciences/Environmental social sciences/Sustainability

Earth and environmental sciences/Ecology/Urban ecology

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 gradually decreased², and the contradiction between ESs and human needs has become increasingly prominent³. Complex changes have also occurred between ESs, which can be reflected in trade-offs and synergies^4,5. Trade-offs refer to the increase of one ES while the decrease of another ES (i.e., a win-lose or lose-win situation)⁶. Synergies include the simultaneous increase (a win-win situation) or decrease (a lose-lose situation) of two ESs⁷. Understanding the impact of urbanization on ESs and their trade-offs and synergies can help make scientific management decisions to maximize ESs in urban areas⁸.

Trade-offs and synergies have been an important topic of academic concern. Internationally, the research began with the focus on biodiversity. After 2000, research themes gradually diversified, covering agriculture, climate change, sustainability, and resilience, etc. At present, the research is mainly conducted from the perspective of stakeholder preference and supply and demand⁹. The research in China was relatively later, focusing on spatio-temporal dynamic change¹⁰, driving factor analysis^4,11, and scenario simulation prediction^12,13, etc. Most studies discussed the trade-off and synergy through statistical analysis such as Pearson¹⁴ and Spearman¹⁵, ecosystem service bundles^16-18, and spatial mapping, and used scenario simulation for prediction.

ESs and their tradeoffs and synergies are not only influenced by the intensity of urbanization^14,19, but also vary over time and space²⁰. However, there are not many studies have explored the impact of spatiotemporal evolution of urbanization on ESs and their trade-offs and synergies. In terms of temporal variation, trade-offs and synergies increase, decrease, appear, disappear or reverse with the passage of time¹⁰. For example, a study on the urban agglomerations in the lower reaches of the Pearl River Basin in China found that the synergy between carbon sequestration and habitat quality increased steadily over the past 25 years, and carbon sequestration and water yield had changed gradually from irrelevant to trade-off, and then reversed from trade-off to synergy¹⁹. However, most studies focus on ESs and their trade-offs and synergies at several time points, rather than continuous years²¹. Therefore, the evolution of ESs and their trade-offs and synergies at different urbanization stages is not clear. In terms of spatial change, scholars have carried out rich research from global, intercontinental, national, regional and basin scales^22,23. These results showed ESs and their trade-offs and synergies exhibited significant spatial heterogeneity²⁴. For example, Li et al.²⁵ found that trade-offs and synergies showed significant spatial heterogeneity due to the distribution pattern of land use, with synergies mainly occurring in forest and grassland, while trade-offs mainly occurring in impervious surfaces and cultivated land. Most studies only focus on the trade-offs and synergies in the whole study area, while the differences between different urbanization level areas are often ignored¹⁹.

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 in China. From 1990 to 2020, the Su-Xi-Chang region experienced rapid urbanization, including economic development (annual growth rate of GDP at 15.54 %), population growth (annual growth rate of population at 2.18 %), and urban expansion (annual growth rate of urbanization at 3.49 %). While rapid urbanization has brought a better life to local residents, such as employment, education, and medical care, it has also produced many negative effects. For example, declining crop yield²⁶, eutrophic water quality²⁷, and weakened climate regulation ability²⁸. These issues not only pose a threat to the sustainability of local ecosystems, but also in turn affect economic growth. However, the urbanization process will continue²⁹. Therefore, it is urgent to understand the ESs and their trade-offs and synergies in the Su-Xi-Chang region 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 (crop production (CP), water retention (WR), heat mitigation (HM), and flood mitigation(FM)). Then, we analyzed the spatial and temporal evolution of ESs and their trade-offs and synergies to explore the specific impacts of urbanization. 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 scientific basis for similar studies in other rapidly urbanizing regions around the world.

2.1. Classification of Urbanization

2.1.1. Division of urbanization level areas

Based on nighttime light imagery data, the Su-Xi-Chang region was divided into developed urban area, developing urban area and rural area. The three areas account for 1.31%, 47.57% and 51.12% of the total area of the Su-Xi-Chang region, respectively (Fig. 1). Developed urban areas are concentrated in the urban core areas of Suzhou, Wuxi and Changzhou. In 1990, the land types in this area were mainly built-up area (56.48%) and cultivated land (35.16%), and in 2020, built-up area (89.26%) was the main land type. From 1990 to 2020, the main land use type transition is from cultivated land to built-up area (72.86 km²)(Fig. 2b). Developing urban areas are mainly distributed in the surrounding areas of the urban core areas, the east and north of the study area. The largest expansion of built-up area (2824.89 km²) occurred in developing urban areas. In 1990, the land type of this area was mainly cultivated land (81.74%), and in 2020, it was mainly cultivated land (48.79%) and built-up area (40.25%). The expansion of built-up area mainly comes from cultivated land (2729.7 km²), water area (89 km²) and forest (38.97 km²)(Fig. 2c). Rural areas are mainly distributed in the western and southern parts of tthe study area. The proportion of cultivated land, water area, forest, and built-up area in the rural area was 54.89%, 35.27%, 7.61%, and 2.21% in 1990, and 47.91%, 37.05%, 6.71%, and 8.32% in 2020, respectively. Three main land-use changes occurred in rural area: from cultivated land to built-up area (507.33 km²),, from cultivated land to water area (178.27 km²), and from forest to cultivated land (136.21 km²)(Fig. 2d).

2.1.2. Division of urbanization stages

From 1990 to 2020, the total population of the whole study area showed an increasing trend (Table 1). The population proportion in developed urban areas increased, while the population proportion in the remaining two areas decreased. During this period, the population mainly migrated to the developed urban area. 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 developing urban areas. From 2010 to 2020, the population proportion in the developing urban area decreased slightly, with the population mainly migrated to developed urban areas and rural areas. This stage is the highly urbanized stage under the guidance of the Chinese government planning, namely urban-rural integration stage. Our study area is the demonstration area for the development of urban-rural integration in China, so it has the characteristics of balanced urban-rural migration. Based on the above population migration characteristics, this study divided the period from 1990 to 2020 into three urbanization stages: initially urbanizing stage (1990 ~ 2000), rapidly urbanizing stage (2000 ~ 2010), and highly urbanizing stage (2010 ~ 2020).

Table 1

Division of urbanization stages (Unit: ten thousand).
	1990	Proportion	2000	Proportion	2010	Proportion	2019	Proportion
Developed urban area	74.75	5.86%	162.40	10.34%	184.00	8.52%	214.05	9.54%
Developing urban area	712.44	55.85%	868.28	55.29%	1340.7	62.11%	1356.5	60.45%
Rural area	477.05	37.40%	527.93	33.62%	621.74	28.81%	657.49	29.30%
Note: In order to ensure the consistency of long-time series data, we replaced the temporarily missing 2020 population data with 2019 population data.

2.2. Temporal and spatial change of ESs

2.2.1. Spatial heterogeneity of ESs in 2020

In 2020, various ESs in the Su-Xi-Chang region showed significant spatial heterogeneity. The high-value areas of CP were concentrated in the west and northeast of the study area, with a large and relatively flat cultivated land. The highest crop yield was 391.23 kg / 900 m² (Fig. 3a). The average crop yield in the developed urban area was the lowest, at 8.56 kg / 900 m². The crop yield in the developing urban area and rural area were similar, at 105.4 kg / 900 m² and 110.78 kg / 900 m², respectively (Fig. 4a). The high-value areas of WR (Fig. 3b) and FM (Fig. 3d) were concentrated in the relatively higher elevation forest in the southwest of the study area and around Taihu Lake. The WR in the rural area was the highest, at 403.84 mm / 900 m². The second was the developing urban area, with an average of 248.54 mm / 900 m². Developed urban areas were the lowest, at 43.32 mm / 900 m² (Fig. 4b). The average value of FM in the developed urban area, developing urban area and rural area were 0.07, 0.15 and 0.17, respectively (Fig. 4d). The high-value areas of HM were concentrated in the western part of the study area and Taihu Lake (Fig. 3c). The average values of HM in the developed urban area, developing urban area and rural area were 0.14, 0.6 and 0.92, respectively (Fig. 4c). The low-value areas of the four ESs were mainly concentrated in the urban core areas of Suzhou, Wuxi and Changzhou and their surrounding areas.

2.2.2. 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. 5. The growth areas of CP were mainly distributed in the west and northeast of the study area (Fig. 5a). There was no significant change in the urban core area because grain was not produced there for many years. The surrounding areas of urban core areas showed a constant or decreasing trend due to part of the cultivated land was occupied by built-up area, and the increasing trend was mainly due to the gradual reclamation of cultivated land from 2010 to 2020 (Fig. 2d). WR mainly showed a weak increasing trend (Fig. 5b). 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. 5c). This is associated with the continuous reduction of cultivated land and forest and landscape fragmentation. FM showed a significant increase trend in the west of the study area and the surrounding areas of 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. 5d).

We further assessed the ESs in the three urbanization stages and three urbanization level areas to analyze the impact of urbanization on ESs (Fig. 6). From 1990 to 2020, CP in the Su-Xi-Chang region showed a trend of first stabilizing, then significantly declining and then slowly declining (Fig. 6a). In the spatial pattern of different urbanization level areas, the decrease of CP was manifested as developing urban area > rural area > developed urban area. From the perspective of different urbanization stages, the CP in the whole study area remained stable at 297.31 kg / 900 m² in the initial urbanizing stage. From 1997 to 2003, CP decreased significantly. From 2003 to 2015, CP remained stable at 152.12 kg / 900 m², and then decreased to 106.62 kg / 900 m².

The WR in the Su-Xi-Chang region generally showed a fluctuating upward trend of first decreasing and then increasing (Fig. 6b). The decrease of 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, at 446.89 mm / 900 m², and the lowest in 2013, at 110.32 mm / 900 m². 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, the WR in the developed urban area was the lowest level in the study area.

HM showed a gradual decline with the acceleration of urbanization (Fig. 6c). The decrease of HM was manifested as developing urban area > developed urban area > rural area. The supply of HM is related to urbanization level. The HM in rural areas were the highest, 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. 6d). The decrease of 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.

2.3. Temporal and spatial change of the trade-offs and synergies of the ESs

We further analyzed the impact of urbanization on trade-offs and synergies (Fig. 7). Overall, the relationships between ESs were mainly synergistic, except for the trade-offs between CP and HM, WR and HM, and HM and FM in the rural area. 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, showing a gradually weakening trend. From the evolutionary characteristics of the relationship between ESs, we found the following characteristics.

From 1990 to 2020, CP and WR showed a synergistic relationship in the whole study area and three urbanization level areas. In the initially urbanizing stage, the lose-lose situation in developed urban areas was the largest; However, with the progress of urbanization, the lose-lose situation in developing urban areas gradually increased, and ultimately surpassing other urban areas in the highly urbanizing stage. CP and HM showed lose-lose situations in developed urban areas and developing urban areas; with the passage of time, these lose-lose situations gradually weaken in developed urban areas and gradually increased in developing urban areas; CP and HM showed strong trade-offs in rural areas. The lose-lose situation of WR and HM gradually weakened in developed urban areas and increased in developing urban areas. WR and HM showed trade-offs and gradually weakened in the rural area. WR and HM in the whole study area changed from trade-offs to synergies in the rapidly urbanizing stage. HM and FM showed stable trade-offs in rural areas, a gradually increasing lose-lose situation in developing urban areas, and a gradually decreasing lose-lose situation in developed urban areas. CP 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.

Compared with previous studies, this study comprehensively analyzed the spatial and temporal changes of ESs and their tradeoffs and synergies in the process of urbanization. In this section, we attempt to discuss two aspects: (1) to analyse the characteristics of the spatial and temporal evolution of ESs, trade-offs and synergies, and their reasons; (2) to provide management enlightenment for sustainable development of urban ecosystem.

3.1. Spatial-temporal evolution of ecosystem services

The results showed that the four ESs in the Su-Xi-Chang region have obvious spatial heterogeneity in spatial distribution in 2020. The high-value areas of the three regulating services were mainly concentrated in the forest in the southwest of the study area, while the low-value areas were concentrated in the urban core areas of Suzhou, Wuxi and Changzhou. From 1990 to 2020, CP, FM and HM in the Su-Xi-Chang region showed an overall downward trend, similar to the results of Gu et al.¹⁴ and Wu et al.⁶¹ The losses mainly come from land use changes, especially built-up area occupying more cultivated land, reducing the supply of ESs⁶².

We found that the area with the largest loss of ESs was developing urban areas (Fig. 6). This result is consistent with the findings of Haase et al.⁷ and Spyra et al.⁶³. This is because in rapidly urbanizing areas, the increase of the built-up area often comes at the expense of occupying the peripheral land of the core area (such as cultivated land, water and forest)³¹. In addition, the loss of ESs in developed urban areas was also very high, and no longer provided ESs in the highly urbanizing stage. The loss of ESs in rural areas was mainly caused by the occupation of cultivated land by rural industrial development. However, compared with the other two areas, the impact of rural industrialization on cultivated land and green space was small, and the loss of ESs was small. In different urbanization stages, the loss of ESs was the largest in the rapidly urbanizing stage (2000 ~ 2010), followed by the initially urbanizing stage (1990 ~ 2000), and finally the highly urbanizing stage (2000 ~ 2010) (Fig. 6). 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 Su-Xi-Chang region increases with the distance to the city center, and has a significant change characteristic on the urban-rural gradient⁶⁴.

Based on the environmental Kuznets curve, combined with the results of this study and the finding of Wu et al.⁶⁵ and Chen et al.⁶⁶, we attempt to speculate that the relationship between urbanization and ESs in the Su-Xi-Chang region may exhibit a "U" curve (Fig. 8). The left side of the "U" curve indicates that urbanization leads to the degradation of ESs. This indicates that from 1990 to 2020, the Su-Xi-Chang region was still in the stage of economic development at the expense of sacrificing the environment. When a city develops to a certain development stage, the curve appears an inflection point. 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" curve⁶⁷, that is, urbanization will promote the improvement of ESs. As the center of economic development in Jiangsu Province, the Su-Xi-Chang region should focus on moving to the right side of the "U" curve in the future, and accelerate the realization of a win-win situation between economic development and ecological environment improvement.

3.2. Spatial-temporal evolution of trade-offs and synergies

Many studies have found that there is trade-offs between supplying services and regulating services⁴, but some studies believed that this cannot be regarded as a general result^68,69. For example, some studies suggest that synergies may be greater than trade-offs in urban areas^70,71. Our study found that in developing urban areas, rural areas and the whole study area, supplying services and regulating services mainly showed a gradual weakening of trade-offs and shifted to synergies, or a gradual increase in synergies from 1990 to 2020.This may be related to whether land use patterns are conflicting or consistent⁷². Land use conflict means that once a certain land is used to provide one ES, it could no longer provide another ES; land use consistency means that a certain land use pattern is beneficial to two or more ESs simultaneously⁷². For example, water area is beneficial to mitigating the heat island effect, but cannot provide food; cultivated land provide more crops while also regulating floods. Therefore, CP in rural areas is trade-off with HM and synergistic with HM (Fig. 7). In addition, cultivated land in the study area accounts for a large proportion, and the forest coverage rate is low. Large areas of cultivated land play an important ecosystem function in the Su-Xi-Chang region, providing multiple ESs at the same time, reflecting the consistency of land use patterns. At the same time, the land use change driven by urban expansion is mainly reflected in the transformation of cultivated land into built-up area, resulting in a reduction and fragmentation of cultivated land. This approach also leads to a gradual reduction in the supply of the four ESs, enhancing lose-lose situations.

In the initially 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 area in developing urban areas and rural areas. In the rapidly urbanizing stage (2000 ~ 2010), built-up area gradually gather and form 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 area gradually spread outward from urban core areas and small town centers; the popularity of highways and private cars also connects cities and suburbs³¹. 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. 7). The interrelationships between CP 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 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 at 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.

Urbanization significantly changes the evolutionary characteristics of trade-offs and synergies. Based on the results, we attempted to speculate that the relationship between urbanization and trade-offs/synergies may be an "S" curve (Fig. 9). From 1990 to 2000, the whole study area included trade-offs (curve I: CP and HM, WR and HM, HM and FM) and lose-lose situations (curve II: CP and WR, CP and FM, WR and FM); from 2000 to 2010, the whole study area showed lose-lose situations; from 2010 to 2020, the interrelationship between ESs remained stable. That is, urbanization weakens trade-offs and enhances lose-lose situations, especially in the rapidly urbanizing stage (2000 ~ 2010). A study in Shenzhen, China, also found that the trade-offs between regulating services, and between regulating services and supplying services weakened significantly from 1978 to 2018⁷³. In order to achieve a win-win situation between economic development and ecological protection, we imagine a future vision between urbanization and trade-offs/synergies, which may be a high-order curve (Fig. 9). In the future, through the improvement of science and technology and the implementation of policies, synergies between various ESs will be significantly enhanced, which is represented by win-win situations. When limited by objective conditions, the interrelationship between ESs remains stable.

3.3. Implications for urban ecological management

The results show that rapid urbanization reduces the supply of ESs, weakens the trade-offs and enhances lose-lose situations. In the spatial pattern of different urbanization level areas, ESs gradually decreased and tended to zero in developed urban areas; the supply of ESs in developing urban areas and rural area is decreasing year by year, it still has the supplying capacity. Based on this, we have the following thoughts on urban ecological management:

(1) Clarify the development priorities of different urbanization level areas. Developed urban areas could focus on strengthening scientific and technological innovation capabilities to improve social and economic levels. This area can promote the vertical development of the city. Developing urban areas could not only serve as the main crop production area, but also innovate agricultural planting technology combined with regional development advantages. This area could implement the urban-rural integrated development mode. Rural areas have excellent ecological environment and should focus on protecting and restoring the ecological environment.

(2) Promote high-level synergies of ecosystem services. In the near term, the Su-Xi-Chang region should continue to protect and restore the ecosystem, and reach and cross the inflection point of the "U" curve as soon as possible. Further control the total amount of built-up area, give priority to protecting and increasing the area of forest and water area, and maintain keep the area of cultivated land at a certain scale. In the long future, we will promote the transformation of urban development mode in the Su-Xi-Chang region from a high degree of urbanization to coordinated with social economy and the ecological environment. The quantitative assessment results of ESs and the interactions among various ESs are introduced into urban management.

3.4. Limitations and Future Perspectives

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. First, due to the limitations of the study year and field measurement conditions, the parameters we input into the InVEST model refer to the results of studies in nearby or the same regions. At the same time, the InVEST model simplifies complex ecological processes, enhances the operability of assessment, but also leads to the uncertainty of the assessment results. Secondly, this paper only analyzed the trade-offs and synergies between two ESs, but the relationships between ESs are often "many-to-many". Future research should combine model simulations with field survey methods to accurately assess the supply of ESs. And innovative research methods to assess the relationships between multiple ESs.

4.1. 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. 10). The region includes the core areas of Suzhou, Wuxi and Changzhou, surrounded by towns and villages. The terrain is mainly plain, 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°C ~ 17°C and an an annual average precipitation of 816 mm ~ 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 forest, subtropical coniferous forest, deciduous broad-leaved forest and subtropical bamboo forest. 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.

The reason for selecting the Su-Xi-Chang region as the study area is that it is typical and representative in the temporal evolution and spatial pattern of urbanization³⁰. Since the early 1980s, a large number of 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 food needs. 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"³. This development model enabled local rural residents to earn considerable incomes in enterprises close to their homes without going to the city. The existence of these enterprises promoted the economic development and infrastructure construction of townships in the Su-Xi-Chang region, making them have 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. During 2000 ~ 2010, the built-up area in the region increased significantly, entering an era of rapid urban expansion³¹. After 2010, under the background of national ecological civilization construction, the Su-Xi-Chang region transformed to 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. In space, 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 area, which is very typical in China's metropolitan circle. During 1990 ~ 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 services³². 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%).

4.2. Quantification of ESs

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 (crop production (CP)) and three regulating services (water retention (WR), heat mitigation (HM), flood mitigation (FM)). The Su-Xi-Chang region is one of the important crop production bases in China, but studies have shown that local crop production is declining with urbanization³⁴. The large number of artificial surface construction in the study area affects the geomorphological and hydrological processes, resulting in a decline in WR³⁵, which has become more prominent under the effect of climate warming and drying in recent years^36,37. The heat island effect in the Su-Xi-Chang region is significant, which not only affects the living environment of local residents, but also causes a series of diseases^38,39. The heat island effect has urban-rural differences, and the mitigation capacity of green spaces and water area in different urbanization level areas is different^40,41, so we took HM into account. 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 flooding⁴². Therefore, FM is also one of the ESs focused on in this study.

4.2.1. Crop Production

Crops are irreplaceable material resources for local residents' lives, and the assessment of CP is crucial to protecting food security. Referring to the method of Liu et al.⁴³, we used NDVI data to calculate the vegetation condition index (VCI_i) of the ith cultivated land pixel, and allocated the total crop yield to each cultivated land pixel. The calculation formula are as follows.

$${\text{C}\text{P}}_{\text{i}}={\text{C}\text{P}}_{\text{y}}\times \frac{{\text{V}\text{C}\text{I}}_{\text{i}}}{\sum _{\text{i}=1}^{\text{a}}{\text{V}\text{C}\text{I}}_{\text{i}}}$$

$${\text{V}\text{C}\text{I}}_{\text{i}}=\frac{\left({\text{N}\text{D}\text{V}\text{I}}_{\text{i}}-{\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{i}\text{n}}\right)}{{\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{a}\text{x}}-{\text{N}\text{D}\text{V}\text{I}}_{\text{m}\text{i}\text{n}}}\times 100\text{%}$$

Among them, CP_i is the crop yield of the ith cultivated land pixel, kg; CP_y is the total crop yield over the years, kg; a is the total number of pixels of the cultivated land; NDVI_i is the annual NDVI value of the ith cultivated land pixel; NDVI_max and NDVI_min represent the maximum and minimum annual NDVI values of the cultivated land, respectively.

4.2.2. Water Retention

As one of the important regulating services, WR reflects the hydrology, vegetation and soil conditions in the study area. We adopted the method of Bai et al.⁴⁴ to calculate WR according to the difference between water yield and surface runoff. Water yield was calculated using the Water Yield module of the InVEST model. The calculation formula are as follows.

${\text{W}\text{R}}_{\text{i}\text{j}}={\text{W}\text{Y}}_{\text{i}\text{j}}-{\text{R}\text{F}}_{\text{i}\text{j}}$	(3)
${\text{R}\text{F}}_{\text{i}\text{j}}={\text{P}}_{\text{i}}\times {\text{C}}_{\text{i}\text{j}}$	(4)
${\text{W}\text{Y}}_{\text{i}\text{j}}={\text{P}}_{\text{i}}-{\text{A}\text{E}\text{T}}_{\text{i}}$	(5)
${\text{A}\text{E}\text{T}}_{\text{i}}={\text{P}}_{\text{i}}\times \left(\frac{1+{\text{w}}_{\text{i}}{\text{R}}_{\text{i}\text{j}}}{1+{\text{w}}_{\text{i}}{\text{R}}_{\text{i}\text{j}}+\frac{1}{{\text{R}}_{\text{i}\text{j}}}}\right)$	(6)

where WR_ij is the annual WR of land use type j on pixel i, mm; WY_ij is the annual water yield, mm; RF_ij is the annual surface runoff, mm; P_i is the annual precipitation, mm; C_ij is the average surface runoff coefficient of land use type j on pixel i; AET_i is the annual actual evapotranspiration, mm; w_i is a non-physical parameter characterizing natural climate and soil properties, dimensionless; R_ij is the Budyko Dryness Index, dimensionless.

Table 2

WR coefficient.
lucode	LULC_desc	K_c²⁶	root_depth²⁶	LULC_veg	C_ij⁴⁵
1	Cultivated land	0.6	1000	1	0.347
2	Forest	1	7000	1	0.0267
3	Grassland	0.65	1500	1	0.0937
4	Water area	1	1000	0	0
5	Built-up area	0.3	500	0	1
6	Unused land	0.2	10	0	1
Note: K_c is the evapotranspiration coefficient; root_depth is the maximum root depth; LULC_veg refers to the presence or absence of vegetation. 1 indicates the presence of vegetation and 0 indicates the absence of vegetation.

4.2.3. Heat Mitigation

We applied the InVEST Urban Cooling module to calculate HM. The module calculates the cooling capacity (CC_i) of the pixel i based on shade_i, evapotranspiration (ETI_i), albedo_i, and distance to cold islands such as parks. If pixel i is unaffected by any large green spaces (more than 2 hectares), HM is equal to CC_i, otherwise equal to a distance-weighted average of the CC_i from the large green spaces and the pixel i. The calculation formula are as follows.

${\text{C}\text{C}}_{\text{i}}=0.6\times {\text{s}\text{h}\text{a}\text{d}\text{e}}_{\text{i}}+0.2\times {\text{E}\text{T}\text{I}}_{\text{i}}+0.2\times {\text{a}\text{l}\text{b}\text{e}\text{d}\text{o}}_{\text{i}}$	(7)
${\text{E}\text{T}\text{I}}_{\text{i}}=\frac{{\text{K}}_{\text{c}}\times {\text{E}\text{T}}_{0}}{{\text{E}\text{T}}_{\text{m}\text{a}\text{x}}}$	(8)
${\text{G}\text{A}}_{\text{i}}={\text{p}\text{i}\text{x}\text{e}\text{l}}_{\text{a}\text{r}\text{e}\text{a}}\times \sum _{\text{j}\in \text{d} \text{r}\text{a}\text{d}\text{i}\text{u}\text{s} \text{f}\text{r}\text{o}\text{m} \text{i}}{\text{g}}_{\text{j}}$	(9)
${\text{C}\text{C}}_{\text{p}\text{a}\text{r}\text{k}\text{i}}=\sum _{\text{j}\in \text{d} \text{r}\text{a}\text{d}\text{i}\text{u}\text{s} \text{f}\text{r}\text{o}\text{m} \text{i}}{\text{g}}_{\text{j}}\times {\text{C}\text{C}}_{\text{j}}\times {\text{e}}^{\left(\frac{-\text{d}(\text{i},\text{j})}{{\text{d}}_{\text{c}\text{o}\text{o}\text{l}}}\right)}$	(10)
${\text{H}\text{M}}_{\text{i}}=\left\{\begin{array}{c}{\text{C}\text{C}}_{\text{i}}, if {\text{C}\text{C}}_{\text{i}}\ge {\text{C}\text{C}}_{\text{p}\text{a}\text{r}\text{k}\text{i}} or {\text{C}\text{A}}_{\text{i}}<2ha\\ {\text{C}\text{C}}_{\text{p}\text{a}\text{r}\text{k}\text{i}}, otherwise\end{array}\right.$	(11)

where HM_i is the HM on pixel i; CC_i is the cooling capacity on pixel i. Its value is comprised between 0 and 1. 1 indicates maximum cooling capacity, 0 indicates no cooling capacity. ET₀ is the potential evapotranspiration, mm; ET_max is the maximum value of ET₀; pixel_area is the area of a pixel in ha; CC_parki is the cooling capacity of each park; d(i, j) is the distance between pixel i and pixel j; d_cool is the distance for which a green space has a cooling effect.

Table 3

HM coefficient.
lucode	LULC_desc	shade⁴⁶	K_c	albedo⁴⁷	g_j⁴⁶
1	Cultivated land	0	0.6	0.2	1
2	Forest	1	1	0.2	1
3	Grassland	0	0.65	0.2	1
4	Water area	0	1	0.05	1
5	Built-up area	0	0.3	0.15	0
6	Unused land	0	0.2	0.25	0
Note: shade_i refers to the ability of trees to provide shade. 1 indicates that it can provide shade, and 0 indicates that it cannot provide shade. albedo_i is the proportion of reflected solar radiation, with values between 0 and 1, 0 indicates the maximum absorption, and 1 indicates the maximum reflectivity. g_j indicates whether it can be regarded as a green space, 0 indicates that it cannot be regarded as a green space, and 1 indicates that it can be regarded as a green space. albedo_i refers to the study of Stewart et al.⁴⁷, and shade_i and g_j refers to the study of Zawadzka et al⁴⁶.

4.2.4. Flood Mitigation

In the study, the Urban Flood Risk Mitigation module of the InVEST model was used to calculate FM. The module estimates runoff using SCS-Curve Number.The calculation formula are as follows.

${\text{F}\text{M}}_{\text{i}}=1-\frac{{\text{R}}_{\text{i}}}{{\text{P}}_{\text{m}\text{a}\text{x}}}$	(12)
${\text{R}}_{\text{i}}=\left\{\begin{array}{c}\frac{{\left(\text{P}-0.2{\text{I}}_{\text{i}}\right)}^{2}}{\text{P}+0.8{\text{I}}_{\text{i}}}, P>0.2{\text{I}}_{\text{i}}\\ 0 , P\le 0.2{\text{I}}_{\text{i}}\end{array}\right.$	(13)
${\text{I}}_{\text{i}}=\frac{25400}{{\text{C}\text{N}}_{\text{i}}}-254$	(14)

Among them, FM_i is the annual FM of pixel i, dimensionless, with values between 0 and 1; P_max is the design storm depth, mm; R_i is runoff, mm; I_i is the potential retention, mm.

Table 4

FM coefficient.
lucode	LULC_desc	CN_A	CN_B	CN_C	CN_D
1	Cultivated land	54	70	80	84
2	Forest	36	60	73	79
3	Grassland	49	69	79	84
4	Water area	0	0	0	0
5	Built-up area	85	90	92	94
6	Unused land	77	86	91	94
Note: CN_i is the runoff curve value of pixel i, dimensionless. The value range of CN_i is between 0 ~ 100. CN_i equal to 100 indicates that rainfall is completely converted to runoff. We refer to the data from the US Department of Agriculture⁴⁸.

We found that the Urban Flood Risk Mitigation module mainly affects the value of each pixel based on changes in land type and soil characteristics. In the process of urbanization, the original natural open soil in the core area has been transformed into artificial surface, building and other closed soil, and its soil physical and chemical properties will change significantly. However, due to the huge workload of soil survey, the Harmonized World Soil Database is widely recognized and used in relevant research in China⁴⁹. Therefore, the FM simulated by the InVEST model in this study is mainly determined by the LULC from 1990 to 2020. Although the SCS-Curve method used in the module could well reflect the ranking of FM on different land uses, we found that for the areas where the land type has not changed for many years, the FM obtained by the module is basically consistent. Vegetation loss caused by human activities such as agricultural production, deforestation and settlement is also prone to flooding⁵⁰. Many studies use NDVI to measure vegetation coverage in study areas⁵¹ and to detect flood events⁵². These findings indicate that the higher the NDVI value, the lower the possibility of flooding^53,54. Therefore, we tried to use NDVI to correct the Urban Flood Risk Mitigation module, and use the corrected calculation results to participate in the analysis. The correction formula is as follows.

$${\text{F}\text{M}\text{C}}_{\text{i}}={\text{N}\text{D}\text{V}\text{I}}_{\text{i}}\times {\text{F}\text{M}}_{\text{i}}$$

Among them: FMC_i is the corrected FM_i. NDVI_i is the NDVI value of pixel i, NDVI∈[-1, 1]. NDVI greater than 0 indicates vegetation coverage, and the value of NDVI increases with the increase of vegetation coverage Areas with NDVI close to 0 are mainly impervious surfaces. Areas with NDVI less than 0 are mainly water area. This study sets water area to not provide FM. Therefore, replace the negative value with 0, which is 0 ≤ NDVI_i ≤ 1.

4.3. 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 value is, the higher the urbanization level is. The nighttime light imagery data used in this study have been fitted by DMSP/OLS (1992 ~ 2013) and VIIRS/NPP (2013 ~ 2020), and the data source is shown in Table 7. Referring to the study of Fu et al.³¹ and Li et al.⁵⁵, 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 5, we identified developing urban areas and rural areas. Based on this, we got three urbanization level areas.

In addition, referring to our previous study³¹ and statistical analysis tools of ArcGIS 10.8 platform, we analyzed changes in the total and proportion of the population in the three urbanization level areas over time. By revealing the characteristics of population migration, this study determines three urbanization stages in the Su-Xi-Chang region.

Table 5

Classification of urbanization levels in the whole study area.
Classification criteria	Urbanization Levels
DN₁₉₉₂value ≥ 50 and DN₂₀₂₀value ≥ 50	Developed urban area
DN₁₉₉₂value < 50 and DN₂₀₂₀value ≥ 50	Developing urban area
DN₁₉₉₂value < 50 and DN₂₀₂₀value < 50	Rural area

4.4. Spatial-temporal change analysis of ecosystem services

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.⁵⁶ and Geng et al.⁵⁷, 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.⁵⁸, we used the Mann-Kendall test to determine the significance of changing trends. We analyzed in the MATLAB R2022b software. The formulas for Theil-Sen median trend analysis and the Mann-Kendall test are as follows.

$${\beta }=\text{m}\text{e}\text{d}\text{i}\text{a}\text{n}\left(\frac{{\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}}{\text{y}-\text{x}}\right), \forall \text{y}>\text{x}$$

Among them: β is the change trend of ES. Β > 0 indicates that ES is on an increasing trend, and β < 0 indicates that ES is on a downward trend. ES_y and ES_x are time series data of ES; y and x are time series.

$\text{S}=\sum _{\text{x}}^{\text{N}-1}\sum _{\text{y}=\text{x}+1}^{\text{N}}\text{s}\text{g}\text{n}({\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}})$	(17)
$\text{s}\text{g}\text{n}({\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}})=\left\{\begin{array}{c} 1 , {\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}>0\\ 0 , {\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}=0\\ -1, {\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}<0\end{array}\right.$	(18)
$\text{V}\text{a}\text{r}\left(\text{S}\right)=\frac{\text{N}(\text{N}-1)(2\text{N}+5)}{18}$	(19)
$\text{Z}=\left\{\begin{array}{c}\frac{\text{S}}{\sqrt{\text{V}\text{a}\text{r}\left(\text{S}\right)}}, S>0\\ 0 , S=0\\ \frac{\text{S}+1}{\sqrt{\text{V}\text{a}\text{r}\left(\text{S}\right)}}, S<0\end{array}\right.$	(20)

Where, S is the test statistic; N is the length of the time series, i.e. N = 31; Z is the normalized test statistic. Referring to the division method of Shah et al.⁵⁹, when |Z| is greater than 1.65, 1.96 and 2.58, it means that the trend analysis has passed the significance tests of 90%, 95% and 99% reliability, respectively (Table 6).

Table 6

Trend characteristics of ESs⁵⁹.
$\varvec{\beta }$	$\mathbf{Z}$	Trend features of ESs	Trend types of ESs
${\beta }>0$	$\left\|\text{Z}\right\|>2.58$	Extremely significant increase	Increase***
	$1.96<\left\|\text{Z}\right\|\le 2.58$	Significant increase	Increase**
	$1.65<\left\|\text{Z}\right\|\le 1.96$	Sub-significant increase	Increase*
	$\left\|\text{Z}\right\|\le 1.65$	Weak increase	Increase
${\beta }=0$	$\left\|\text{Z}\right\|$	No relationship	No relationship
${\beta }<0$	$\left\|\text{Z}\right\|\le 1.65$	Weak decrease	Decrease
	$1.65<\left\|\text{Z}\right\|\le 1.96$	Sub-significant decrease	Decrease*
	$1.96<\left\|\text{Z}\right\|\le 2.58$	Significant decrease	Decrease**
	$\left\|\text{Z}\right\|>2.58$	Extremely significant decrease	Decrease***

4.5. Trade-offs and synergy analysis of ecosystem services

Referring to two studies in the Taihu Lake Basin of China^10,26, taking into account 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.⁶⁰, the trade-offs (i.e., a win-lose or lose-win situation) and synergies (i.e., a win-win or lose-lose situation) between each pair of ESs in the whole study area and different urbanization level areas (rural area, developing urban area and developed urban area) from 1990 to 2020 were identified by the spearman correlation coefficient in SPSS software. The correlation coefficient value can be used to determine the strength of trade-offs and synergies. The larger the absolute value of the correlation coefficient is, the greater the strength of the trade-off or synergy is. When the correlation coefficient value is negative, it is considered 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 ~ 1, so as to reduce the influence of the unit and size among ESs. The reasons for using spearman correlation coefficient are as follows: (1) The relationship between ESs may be highly nonlinear. Spearman correlation coefficient can reflect a nonlinear correlation. (2) The data do not conform to normal distribution. (3) When there are obvious outliers in the data (such as developed urban area, where the supply of most ESs is zero ), it is more robust to use spearman correlation coefficient.

Table 7. Data used in this study.

Data Name	Data Type	Year	Source
LULC	30 m × 30 m Raster data	1990 ~ 2020	30 m annual land cover and its dynamics in China from 1990 to 2019 (Version 1.0.0)³³. (https://doi.org/10.5281/zenodo.4417810 accessed on 6 October 2022)
Total population	1 km × 1 km Raster data	1990, 2000, 2010, 2019	Resource and Environment Science and Data Center (https://www.resdc.cn/DataList.aspx accessed on 5 October 2022)
Nighttime light imagery	1 km × 1 km Raster data	1992 ~ 2020	An improved time-series DMSP-OLS-like data (1992 ~ 2021) in China by integrating DMSP-OLS and SNPP-VIIRS - Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GIYGJU/ accessed on 10 October 2022)
Normalized difference vegetation index	30 m × 30 m Raster data	1990 ~ 2020	Resource and Environment Science and Data Center (https://www.resdc.cn/DataList.aspx accessed on 8 October 2022)
Total crop production	Whole study area Statistical data	1990 ~ 2020	Statistical yearbook of CNKI (https://data.cnki.net/Yearbook accessed on 7 October 2022)
Average precipitation	1 km × 1 km Raster data	1990 ~ 2020	National Earth System Science Data Center (http://www.geodata.cn accessed on 6 October 2022)
Potential evapotranspiration	1 km × 1 km Raster data	1990 ~ 2020	National Earth System Science Data Center (http://www.geodata.cn accessed on 6 October 2022)
Soil depth, sand, silt, clay, soil organic matter content	1 km × 1 km Raster data	1995	Agriculture Organization of United Nations (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12 accessed on 1 October 2022)
Hydrologic soil groups	250 m × 250 m Raster data	2015	Global Hydrologic Soil Groups for Curve Number-Based Runoff Modeling (https://daac.ornl.gov/ accessed on 18 October 2022)

Data availability

The data generated and analyzed during this study are described in the Table 7. Unify all raster data with a resolution of 30 m × 30 m and a projected coordinate system of WGS_1984_UTM_Zone_51N.

CODE AVAILABILITY

Theil-Sen median trend analysis and Mann-Kendall test were performed using MATLAB R2022b software. The code that supports the findings of this study is available from the corresponding author upon reasonable request.

ACKNOWLEDGEMENTS

The authors acknowledge the support of the National Natural Science Foundation of China (42101253), 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 Open fund of State Key Laboratory of urban and regional ecology (SKLURE2021-2-2).

AUTHOR CONTRIBUTIONS

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. and J.C.; visualization, Y.W.; supervision, J.C.; project administration, Q.F.; funding acquisition, Q.F. All authors have read and agreed to the published version of the manuscript.

COMPETING INTERESTS

The authors declare no competing interests.

ADDITIONAL INFORMATION

Correspondence and requests for materials should be addressed to Qi Fu or Jinhua Chen.

Reprints and permission information is available at http://www.nature.com/reprints

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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You are reading this latest preprint version

Responses of ecosystem services and their trade-offs and synergies to spatiotemporal differentiation of urbanization: Evidence from the Su-Xi-Chang region, China

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Version 1

Abstract

Figures

1. Introduction

2. Result

2.1. Classification of Urbanization

2.1.1. Division of urbanization level areas

2.1.2. Division of urbanization stages

2.2. Temporal and spatial change of ESs

2.2.1. Spatial heterogeneity of ESs in 2020

2.2.2. Temporal and spatial change of ESs from 1990 to 2020

2.3. Temporal and spatial change of the trade-offs and synergies of the ESs

3. Discussion

3.1. Spatial-temporal evolution of ecosystem services

3.2. Spatial-temporal evolution of trade-offs and synergies

3.3. Implications for urban ecological management

3.4. Limitations and Future Perspectives

4. Materials and Methods

4.1. Study Area

4.2. Quantification of ESs

4.2.1. Crop Production

4.2.2. Water Retention

4.2.3. Heat Mitigation

4.2.4. Flood Mitigation

4.3. Classification of Urbanization

4.4. Spatial-temporal change analysis of ecosystem services

4.5. Trade-offs and synergy analysis of ecosystem services

Declarations

References

Additional Declarations

Supplementary Files

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Version 1

\({\text{W}\text{R}}_{\text{i}\text{j}}={\text{W}\text{Y}}_{\text{i}\text{j}}-{\text{R}\text{F}}_{\text{i}\text{j}}\)	(3)
\({\text{R}\text{F}}_{\text{i}\text{j}}={\text{P}}_{\text{i}}\times {\text{C}}_{\text{i}\text{j}}\)	(4)
\({\text{W}\text{Y}}_{\text{i}\text{j}}={\text{P}}_{\text{i}}-{\text{A}\text{E}\text{T}}_{\text{i}}\)	(5)
\({\text{A}\text{E}\text{T}}_{\text{i}}={\text{P}}_{\text{i}}\times \left(\frac{1+{\text{w}}_{\text{i}}{\text{R}}_{\text{i}\text{j}}}{1+{\text{w}}_{\text{i}}{\text{R}}_{\text{i}\text{j}}+\frac{1}{{\text{R}}_{\text{i}\text{j}}}}\right)\)	(6)

\({\text{C}\text{C}}_{\text{i}}=0.6\times {\text{s}\text{h}\text{a}\text{d}\text{e}}_{\text{i}}+0.2\times {\text{E}\text{T}\text{I}}_{\text{i}}+0.2\times {\text{a}\text{l}\text{b}\text{e}\text{d}\text{o}}_{\text{i}}\)	(7)
\({\text{E}\text{T}\text{I}}_{\text{i}}=\frac{{\text{K}}_{\text{c}}\times {\text{E}\text{T}}_{0}}{{\text{E}\text{T}}_{\text{m}\text{a}\text{x}}}\)	(8)
\({\text{G}\text{A}}_{\text{i}}={\text{p}\text{i}\text{x}\text{e}\text{l}}_{\text{a}\text{r}\text{e}\text{a}}\times \sum _{\text{j}\in \text{d} \text{r}\text{a}\text{d}\text{i}\text{u}\text{s} \text{f}\text{r}\text{o}\text{m} \text{i}}{\text{g}}_{\text{j}}\)	(9)
\({\text{C}\text{C}}_{\text{p}\text{a}\text{r}\text{k}\text{i}}=\sum _{\text{j}\in \text{d} \text{r}\text{a}\text{d}\text{i}\text{u}\text{s} \text{f}\text{r}\text{o}\text{m} \text{i}}{\text{g}}_{\text{j}}\times {\text{C}\text{C}}_{\text{j}}\times {\text{e}}^{\left(\frac{-\text{d}(\text{i},\text{j})}{{\text{d}}_{\text{c}\text{o}\text{o}\text{l}}}\right)}\)	(10)
\({\text{H}\text{M}}_{\text{i}}=\left\{\begin{array}{c}{\text{C}\text{C}}_{\text{i}}, if {\text{C}\text{C}}_{\text{i}}\ge {\text{C}\text{C}}_{\text{p}\text{a}\text{r}\text{k}\text{i}} or {\text{C}\text{A}}_{\text{i}}<2ha\\ {\text{C}\text{C}}_{\text{p}\text{a}\text{r}\text{k}\text{i}}, otherwise\end{array}\right.\)	(11)

\({\text{F}\text{M}}_{\text{i}}=1-\frac{{\text{R}}_{\text{i}}}{{\text{P}}_{\text{m}\text{a}\text{x}}}\)	(12)
\({\text{R}}_{\text{i}}=\left\{\begin{array}{c}\frac{{\left(\text{P}-0.2{\text{I}}_{\text{i}}\right)}^{2}}{\text{P}+0.8{\text{I}}_{\text{i}}}, P>0.2{\text{I}}_{\text{i}}\\ 0 , P\le 0.2{\text{I}}_{\text{i}}\end{array}\right.\)	(13)
\({\text{I}}_{\text{i}}=\frac{25400}{{\text{C}\text{N}}_{\text{i}}}-254\)	(14)

\(\text{S}=\sum _{\text{x}}^{\text{N}-1}\sum _{\text{y}=\text{x}+1}^{\text{N}}\text{s}\text{g}\text{n}({\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}})\)	(17)
\(\text{s}\text{g}\text{n}({\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}})=\left\{\begin{array}{c} 1 , {\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}>0\\ 0 , {\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}=0\\ -1, {\text{E}\text{S}}_{\text{y}}-{\text{E}\text{S}}_{\text{x}}<0\end{array}\right.\)	(18)
\(\text{V}\text{a}\text{r}\left(\text{S}\right)=\frac{\text{N}(\text{N}-1)(2\text{N}+5)}{18}\)	(19)
\(\text{Z}=\left\{\begin{array}{c}\frac{\text{S}}{\sqrt{\text{V}\text{a}\text{r}\left(\text{S}\right)}}, S>0\\ 0 , S=0\\ \frac{\text{S}+1}{\sqrt{\text{V}\text{a}\text{r}\left(\text{S}\right)}}, S<0\end{array}\right.\)	(20)

\(\varvec{\beta }\)	\(\mathbf{Z}\)	Trend features of ESs	Trend types of ESs
\({\beta }>0\)	\(\left\|\text{Z}\right\|>2.58\)	Extremely significant increase	Increase***
	\(1.96<\left\|\text{Z}\right\|\le 2.58\)	Significant increase	Increase**
	\(1.65<\left\|\text{Z}\right\|\le 1.96\)	Sub-significant increase	Increase*
	\(\left\|\text{Z}\right\|\le 1.65\)	Weak increase	Increase
\({\beta }=0\)	\(\left\|\text{Z}\right\|\)	No relationship	No relationship
\({\beta }<0\)	\(\left\|\text{Z}\right\|\le 1.65\)	Weak decrease	Decrease
	\(1.65<\left\|\text{Z}\right\|\le 1.96\)	Sub-significant decrease	Decrease*
	\(1.96<\left\|\text{Z}\right\|\le 2.58\)	Significant decrease	Decrease**
	\(\left\|\text{Z}\right\|>2.58\)	Extremely significant decrease	Decrease***