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 (2019) 419–427

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Impact of land use change on multiple ecosystem services in the rapidly urbanizing Kunshan City of China: Past trajectories and future projections

T

Ye Wua,b,1, Yu Taoc,1, Guishan Yanga, , Weixin Ouc, , Steven Pueppked, Xiao Sune, Gongtai Chenc, Qin Taoc ⁎

⁎

a

Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China c College of Land Management, Nanjing Agricultural University, Nanjing 210095, China d Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA e Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China b

ARTICLE INFO

ABSTRACT

Keywords: Ecosystem services Land use change Urban expansion Scenario analysis Tradeoff and synergy

Formulation of suitable land use strategies for sustainable management of urban landscapes requires assessment of the potential impact of future urban expansion on ecosystem services. We assessed land use change in the rapidly urbanizing Kunshan City of China during 2006–2016 and projected future land uses in 2030 under three alternative scenarios: Business-As-Usual (BAU), Croplands Protection (CP), and Ecological Restoration (ER). Then we quantified the spatio-temporal variations of six ecosystem services (including crop production, carbon storage, habitat quality, flood regulation, and nitrogen and phosphorus retention) in response to urban land use change from 2006 to 2030 using the InVEST model. We also analyzed temporal variation of the tradeoffs and synergies among multiple ecosystem services throughout the period of study. Our estimates indicate that the urban land of Kunshan increased by 19% over the past decade and will continue to expand by 15% from 2016 to 2030 under the BAU scenario. As a result, crop production, carbon storage, habitat quality, and flood regulation capacity would all decrease tremendously. Crop production would remain stable under the CP scenario owing to the strict protection of croplands, but nitrogen and phosphorus loading would increase by 8%. In contrast, the ER scenario would decrease nutrient loading by over 35% with concomitant benefits to carbon storage, habitat quality, and flood regulation capacity. However, crop production would decrease dramatically under the ER scenario, primarily due to the transition of large areas of croplands to ecological zones. Although croplands were the major sources for nitrogen and phosphorus loading in Kunshan during the years 2006–2016, urban land would become the major source of pollution under all future scenarios. Crop production and habitat quality were not significantly correlated during the years 2006–2016, while they would be positively correlated under the BAU and CP scenarios. This implies that croplands would become increasingly important in providing habitats as urban land continues to expand by replacing ecological land from 2016 to 2030. We propose four major land use strategies, including compact urban growth, croplands protection, reforestation to build greenways, and wetlands restoration in the riparian areas of drinking water sources to improve ecosystem services in Kunshan.

1. Introduction

as of 2000 (Seto et al., 2012). Much of this expansion is likely to occur in eastern China, creating an 1800-km coastal urban corridor from Hangzhou to Shenyang (Seto et al., 2012). This monumental increase in urban land cover is one of the primary drivers of habitat loss and ecosystem degradation in the rapidly urbanizing regions (He et al., 2014). There is a large body of literature on urban expansion and its impact on ecosystem services in China. Most current emphasis is on major

The past several decades have ushered in rapid urban expansion across the globe. Indeed, remotely sensed images confirm that urban land area worldwide increased by 58,000 km2 between 1970 and 2000 (Seto et al., 2011). This trend is likely to continue and accelerate in the coming decades. It is forecasted that by 2030, urban land cover will increase by 1.2 million km2, nearly tripling the global urban land area

Corresponding authors. E-mail addresses: [email protected] (Y. Wu), [email protected] (Y. Tao), [email protected] (G. Yang), [email protected] (W. Ou). 1 Co-first author of this work. ⁎

https://doi.org/10.1016/j.landusepol.2019.04.022 Received 31 October 2018; Received in revised form 12 April 2019; Accepted 14 April 2019 Available online 24 April 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.

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urban agglomerations (e.g., Beijing-Tianjin-Hebei and the Yangtze River and Pearl River Deltas) and Eastern Chinese megacities with over 5 million residents: Beijing (Peng et al., 2017), Tianjin (Long et al., 2014), Shanghai (Chen et al., 2019), Nanjing (Li et al., 2016), Hangzhou (Su et al., 2012), Wuhan (Zhang et al., 2018), Guangzhou (Ye et al., 2018), Shenzhen (Li et al., 2010), etc. Some past studies rely on a land cover-based approach, such as ecosystem service value per unit area, to quantify changes in ecosystem services in response to urban expansion (Cai et al., 2017; Wang et al., 2018a; Zhou et al., 2018). Others combine land cover data with biophysical models, such as the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to measure ecosystem services (Tao et al., 2015; Lyu et al., 2018). The most commonly investigated ecosystem services include food production, carbon storage, water yield, nutrient retention, soil conservation, air purification, and habitat quality (Xu et al., 2016; Zhang et al., 2017). For instance, He et al. (2016) assessed the potential impact of urban expansion on regional carbon storage in Beijing from 1990 to 2030 by linking the LUSD-urban and InVEST models. They found that regional carbon storage would decrease in response to future urban expansion at a greater rate than in the past two decades. Zhang et al. (2019) also simulated the urban expansion and ecosystem services dynamics in Beijing from 2013 to 2040 under different development scenarios. They concluded that losses of critical ecosystem services would be significantly lower under a scenario to conserve ecosystem services than those under the business-as-usual scenario. Nevertheless, due to the uncertainty of future development policies and the complexity of urban expansion in different regions, assessing the impact of future urban expansion on ecosystem services remains challenging. It is possible to quantify the tradeoffs and synergies among ecosystem services in urban landscapes (Zhao et al., 2018), but it remains unclear how these correlations will vary temporally in response to future urban expansion. Relatively little is known about rapid growth of small to mediumsized Chinese cities, where the focus has been on the impact of past urban expansion on ecosystem services (Tao et al., 2018; Qiao et al., 2019). Here we pose the following three research questions about the future expansion of such cities: (1) How will critical ecosystem services change over space and time under future development scenarios in the rapidly urbanizing landscape? (2) What are the temporal variations of

the tradeoffs and synergies among multiple ecosystem services in response to urban expansion? (3) What are appropriate land use strategies (e.g., the spatial arrangement of different land covers) to reduce the impact of urban expansion on ecosystem services? We take the rapidly urbanizing Kunshan City as a test case. First, we developed three land use change scenarios for Kunshan in 2030 based on local land use planning and regulation policies. Then the spatiotemporal variations of six critical ecosystem services were quantified from 2006 through 2030 using the InVEST model. We also analyzed the correlations between these ecosystem services over the period of study and propose management measures for improving future ecosystem services for Kunshan. 2. Materials and methods 2.1. Study area The City of Kunshan (120°48´21˝–121°09´04˝E, 31°06´31˝– 31°32´36˝N) lies in the Yangtze River Delta (YRD) region of China, and is only 50-km to the west of Shanghai (Fig. 1). It covers an area of 931 km2. The elevation in Kunshan increases slightly (from 2.8 m to 6 m) from the northeast to the southwest. The city is characterized by a humid subtropical climate, with a mean annual temperature of 15 °C and an average annual precipitation of 1064 mm. The rainy season is from May to September. Kunshan has experienced rapid economic and population growth since the turn of the century. The total population has more than doubled, from less than one million in 2006 to over two million in 2016, and it is projected to reach three million by 2030 according to the Urban Master Planning Document of Kunshan (2016–2030). The proportion of urban residents increased from 50% to 70% during the years 2006–2016. The Gross Domestic Product (GDP) of Kunshan also increased notably over the same period, from 100 billion to 300 billion yuan (Jiangsu Statistical Yearbook, 2017). In fact, Kunshan was the first county-level city in China to yield more than 300 billion yuan of GDP. The per capita GDP of Kunshan reached 180 thousand yuan in 2016, the highest in Jiangsu Province. Rapid urbanization and economic growth of Kunshan over the past decade have been accompanied by increasing environmental concerns. For instance, the combination of an ever-increasing population and a

Fig. 1. Location of the study area. The enlarged map to the right shows the Landsat TM image of Kunshan acquired on September 18, 2006 (R:G:B = 7:5:1). 420

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decreasing area of croplands has reduced the cultivated area per capita in Kunshan over the past years. Thus in 2016, cultivated area per capita in Kunshan was only 200 m2, far less than the threshold value of 500 m2 recommended by the United Nations (FAO, 2011). The surface water quality in Kunshan has been degraded by non-point source pollution from urban areas and croplands such that only 64% of the surface water in Kunshan was in good quality (i.e., III level or above) in 2016.

only occupy ecological land. Under the Ecological Restoration (ER) scenario, ecological land parcels in 2016 are protected from urban expansion such that new urban land parcels (as in the land use planning map) can only occupy croplands. The ER scenario also requires that croplands within the 30-m buffer of roads and water bodies are replaced with woodland and wetlands, respectively (Sun et al., 2018b). 2.3. Methods to quantify ecosystem services

2.2. Land use change and future projections

We identified six important ecosystem services for Kunshan: crop production, carbon storage, habitat quality, flood regulation, and nitrogen and phosphorus retention. All six were quantified using the InVEST model (Sharp et al., 2018), which uses land use maps and biophysical data to analyze and predict the supply of ecosystem services for a region (Nelson et al., 2009). This model is most useful when simulating how future alternative scenarios may impact ecosystem services (Zheng et al., 2016).

2.2.1. Land use change in Kunshan from 2006 to 2016 The 30-m grid maps of land use in Kunshan for the years 2006, 2011, and 2016 were produced by the Land and Resources Bureau of Kunshan using land survey data. We reclassified the original land use data into 14 categories: high intensity urban, medium intensity urban, low intensity urban, land for mining, railroad, primary road, secondary road, paddy field, irrigated field, non-irrigated field, grassland, woodland, wetlands, and open water (Fig. 2).

2.3.1. Crop production Crop production was estimated for paddy fields, irrigated fields, and non-irrigated fields based on the average grain crop yield per hectare (≈7.0 t/ha) in Kunshan over the years 2006–2016 (Suzhou Statistical Yearbook, 2017). This number was further adjusted with the normalized NDVI value (ranging between 0 and 1) of the year 2006 to produce a crop yield map for each grid cell.

2.2.2. Future land use projections in Kunshan We projected future land uses in Kunshan in 2030 under three alternative development scenarios primarily based on the land use planning map (2016–2030) produced by the Land and Resources Bureau of Kunshan (Fig. 2). According to the land use planning map, under the Business-As-Usual (BAU) scenario, medium-intensity urban land parcels would continue to expand from 2016 to 2030 and occupy both cropland and ecological land. Under the Croplands Protection (CP) scenario, all the croplands in 2016 are protected from urban expansion such that new urban land parcels (as in the land use planning map) can

2.3.2. Carbon storage Carbon storage was estimated as the total amount of aboveground vegetation biomass. This is because carbon stored in vegetation is more

Fig. 2. Land use maps of Kunshan from 2006 to 2030 under the Business-As-Usual (BAU) scenario, the Croplands Protection (CP) scenario, and the Ecological Restoration (ER) scenario. 421

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likely to be impacted by land use change in the short term, while carbon stored in soil can have a much slower response to changes in land use (Hutyra et al., 2011). The carbon density of each land use type was derived from the local field study (Table S1, Chuai et al., 2011, 2013). Carbon density values were further adjusted with the normalized NDVI value of the year 2006 to represent spatial variations of carbon density within each land use type.

We obtained data on average annual precipitation and annual reference evapotranspiration of Kunshan during the years 2006–2016 from the Resource and Environment Data Cloud Platform (www.resdc.cn). The soil depth data was derived from the Harmonized World Soil Database (HWSD). We also downloaded the 30-m resolution Digital Elevation Model (DEM) from the China Geospatial Data Cloud (www.gscloud.cn). The plant evapotranspiration coefficient and root depth for different land use types were derived from previous studies (Table S3, Sun and Li, 2017; Hu et al., 2018).

2.3.3. Habitat quality Habitat quality is a proxy for biodiversity conservation and provides an estimate of the habitat and vegetation across a landscape that would support an ecosystem (Sun et al., 2018a). The InVEST model quantifies habitat quality based on the relative impact and sensitivity of the habitat to threats, the distance between habitats and sources of threats, and the location of protected areas (Sharp et al., 2018). The impact irxy of threat r from grid cell y on the habitat in grid cell x can be represented using the following equations:

d xy

irxy = 1

2.3.5. Nitrogen and phosphorus retention Eutrophication, caused by excess nutrient inputs, principally nitrogen and phosphorus from nonpoint source runoff from agricultural or urban landscapes, is one of the most serious and stubborn surface water impairment problems (Qiu and Turner, 2013). In Kunshan, increasing nonpoint source nitrogen and phosphorus are the major threats to surface water quality. We used the annual nitrogen and phosphorus loading from each 30-m grid cell as the proxy for the nutrient retention service provided across the landscape. Higher nitrogen and phosphorus export values reflect lower nutrient retention capacity. The InVEST model calculates the actual amount of nitrogen and phosphorus exported from upstream grid cell x that eventually reaches the downstream water bodies (Expx) with the following equation:

if linear

dr max

2.99 d xy if exponential dr max

irxy = exp

where dxy is the linear distance between grid cells x and y and dr max is the maximum effective distance of the threat. In this study, exponential decay was used for threat of urban and mining land, and linear decay was used for threat of roads and croplands. The total threat level Dxj in a grid cell x with land use type j is calculated as follows: R

Yr

r R r=1

Dxj = r =1 y=1

ry·irxy r

x

X

Expx = ALVx

ALVx = HSSx polx

Sjr

where polx is the export coefficient for grid cell x, and HSSx is the hydrologic sensitivity score for grid cell x. In this study, the nitrogen and phosphorus export coefficient and retention efficiency for different land use types were derived based on previous studies (Table S3, Wen et al., 2011; Li et al., 2013; Wang et al., 2017).

x

Yu

U

2.3.6. Total ecosystem services The Total Ecosystem Services (TES) index was developed in this study to reflect and quantify the total supply of multiple ecosystem services in the region. The TES index was calculated as follows: n

Yx Ymin

AETx Px

= log

where U Yu is the sum of the water yield of grid cells along the flow path above grid cell x.

TESj =

i

Sij

i=1

where FRx indicates the flood regulation capacity of grid cell x, Yx is the average annual water yield from grid cell x, Ymax and Ymin are the maximum and minimum water yield from any grid cells in the landscape, respectively. Larger water yield from grid cell x indicates lower capacity of grid cell x to regulate water flow. Annual water yield can be quantified using the InVEST model. The model is based on the principle of water balance and uses the average annual precipitation (Px) and actual annual evapotranspiration (AETx) to calculate the annual water yield (Yx) in each grid cell as follows:

Yx = 1

¯w

where λx is the runoff index for grid cell x, and ¯w is the mean runoff index in the watershed.

2.3.4. Flood regulation Flood regulation service is defined as the capacity of a land parcel to retain stormwater runoff. We used annual water yield as the inverse indicator to measure flood regulation as follows:

Ymax Ymax

x

HSSx =

Dxj )

where Hj is the habitat suitability of land use type j. The habitat score ranges from 0 to 1, where 1 indicates the highest habitat quality. The habitat suitability of different land use types, the maximum effective distance and relative impact of threats, and the sensitivity of the habitat to threats were obtained based on previous studies (Table S2, Chen et al., 2016; Xu et al., 2018).

FRx =

Ry )

where ALVx is the adjusted nitrogen and phosphorus export from grid cell x, Ry is the retention efficiency of each downstream grid cell y, and X represents the number of downstream grid cells.

where ry indicates intensity of the threat within the cell y, ωr indicates the relative impact of threat r, βx indicates the level of accessibility in grid cell x, and Sjr indicates the sensitivity of land use type j to threat r. The habitat quality Qxj of land use type j is finally calculated as follows:

Qxj = Hj (1

(1 y=x+1

where TESj is the total value of multiple ecosystem services in year or scenario j, ωi is the weight assigned to the ith ecosystem service, Sij is the standardized value for the ith ecosystem service in year or scenario j, and n is the number of ecosystem services evaluated. To ensure that different ecosystem services can be added together, we used two separate equations to standardize the value of ecosystem services. The positive indicators, such as crop yield, the amount of carbon stored, habitat quality score, and flood regulation capacity, were standardized as follows:

Px

Sx = 422

Ex Emin Emax Emin

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(0.15), flood regulation (0.25), nitrogen retention (0.15), and phosphorus retention (0.15). These weights are designed to take Kunshan’s unique local situation into account. The terrain, for example, is characterized by large areas of lowland, which are vulnerable to floods during the rainy season. Therefore, nutrient retention and flood regulation are considered to be the two most important ecosystem services in the region. The climatic, terrain, and soil conditions in Kunshan are suitable for growing crops, and so it is also important to protect croplands and crop production services from rapid urban expansion. Due to its flat terrain, Kunshan has large areas of wetlands but few woodlands, making it more important and feasible for this region to protect wetlands as important habitats than to sequester carbon by woodlands. 3. Results 3.1. Actual and projected land use change in Kunshan between 2006 and 2030

Fig. 3. Actual land use change in Kunshan from 2006 to 2016 and projected change between 2016 and 2030 under three alternative development scenarios.

As shown in Figs. 2 and 3, the proportion of urban land (including roads) in Kunshan increased from 39% in 2006 to 46% in 2016, representing a 19% increase in the total area of urban land over the past decade. The total area of croplands decreased by 17% over the same time period, but the extent of open water remained relatively stable. The extent of urban land expansion between 2016 and 2030 is projected to grow by 15%, 7%, and 3%, respectively, under the BAU, CP, and ER scenarios. In contrast, the extent of croplands is projected to decrease by 19% and 61% under the BAU and ER scenarios, respectively, but remain about the same under the CP scenario. The total area of woodlands and wetlands is projected to decrease by 13% under the BAU and CP scenarios, but increase by 45% under the ER scenario.

where Sx is the standardized value (ranging between 0 and 1) for grid cell x, Ex is the original ecosystem service value for grid cell x, Emax and Emin are the maximum and minimum value of any grid cells in the landscape, respectively. The negative indicators, such as nitrogen and phosphorus loading, were standardized as follows:

Sx =

Emax Emax

Ex Emin

For all these six ecosystem services, a standardized value of 1 indicates the best performance, while a standardized value of 0 indicates the worst performance. We evaluated the relative importance of each ecosystem service for calculating the TES index and assigned weights to the services as follows: crop production (0.20), carbon storage (0.10), habitat quality

3.2. Actual and projected changes in ecosystem services in Kunshan between 2006 and 2030 As shown in Figs. 4 and 5, the total crop yield, carbon storage,

Fig. 4. The actual supply of multiple ecosystem services in Kunshan from 2006 to 2016 and the projected supply from 2016 to 2030 under three alternative development scenarios. Supplementary data for the spatial maps of these six ecosystem services are in Figs. S1–S6. 423

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nitrogen and phosphorus loading in Kunshan from 2006 to 2030 (Fig. 6). The total nitrogen and total phosphorus exported from urban land increased by about 60% from 2006 to 2030 under the BAU scenario, while the nutrients exported from croplands decreased by about 40% during the same period. Although croplands were the major sources for nitrogen and phosphorus loading during the years 2006–2016, urban land would become the major source of pollution under all future scenarios, especially the ER scenario, under which about 70% of the nutrients would be exported from urban land. Due to the transition of croplands to woodland and wetland buffers along roads and water bodies under the ER scenario, the combined amount of nutrients exported from urban and croplands would decrease by 9% and 72%, respectively, between 2016 and 2030. The trend for TES in Kunshan was generally negative over the years 2006–2016 due to rapid urban expansion (Fig. S7). It is predicted to continue from 2016 to 2030 under all future scenarios but with important differences. Thus TES would decrease to a larger extent and with greater intensity under the BAU scenario than it would under either of the other two scenarios. Due to the restricted urban expansion and the creation of ecological buffer zones under the ER scenario, the TES would actually increase and intensify over almost the entire footprint of Kunshan. Pockets of increase are also evident under the other scenarios, but they are sharply restricted in space and intensity, and they are offset by other pockets of significant decrease, particularly under the BAU scenario (Fig. 7).

Fig. 5. Changes in multiple ecosystem services in Kunshan from 2016 to 2030 under three alternative development scenarios.

habitat quality, and flood regulation capacity in Kunshan decreased by 19%, 8%, 12%, and 11%, respectively during the years 2006–2016. These decreases were primarily caused by rapid urban encroachment onto both croplands and ecological land over the past decade. The total crop yield was projected to remain stable from 2016 to 2030 under the CP scenario, but decrease by 20% under the BAU scenario. Under the ER scenario, the total crop yield was projected to decrease by an even larger 60%. Carbon storage was projected to decrease from 2016 to 2030 by 13% and 6% under the BAU and CP scenarios, respectively, but increase by 16% under the ER scenario. Similarly, habitat quality was projected to decrease by 11% and 7% under the BAU and CP scenarios, respectively, but increase by 5% under the ER scenario. Flood regulation capacity in Kunshan was also projected to decrease by 10% and 6% under the BAU and CP scenarios, respectively, but remain stable from 2016 to 2030 under the ER scenario. The total nitrogen and total phosphorus loading in Kunshan decreased by 4% and 10%, respectively during the years 2006–2016 (Fig. 4). Under the BAU scenario, the total nitrogen loading was projected to increase slightly from 2016 to 2030 by 2%, while the total phosphorus loading would remain stable. The total nitrogen and total phosphorus loading were both projected to increase from 2016 to 2030 by 8% under the CP scenario, but decrease dramatically by 35% and 40%, respectively under the ER scenario (Fig. 5). In aggregate, urban and croplands contributed more than 95% of

3.3. Tradeoffs and synergies among multiple ecosystem services We performed spatial correlation analysis between pairs of ecosystem services to characterize their relationships in Kunshan over the period from 2006 to 2030 (Table 1 and Tables S4–S8). Carbon storage, habitat quality, and flood regulation were positively correlated with each other throughout the period of study. Crop production was positively correlated with carbon storage and flood regulation, indicating the high potential of croplands in Kunshan to sequester carbon and regulate stormwater runoff. In contrast, crop production had a strongly negative relationship with nitrogen and phosphorus retention, due to the intensive nutrient loading from croplands. Nutrient retention was also negatively correlated with carbon storage but positively correlated with habitat quality.

Fig. 6. The total nitrogen and total phosphorus exported from urban and croplands in Kunshan from 2006 to 2016 and projected future export under three future development scenarios. 424

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Fig. 7. Predicted changes in Total Ecosystem Services (TES) from 2016 to 2030 under alternative development scenarios. Supplementary data for the spatial maps of TES during the years 2006–2030 are in Fig. S7.

Table 1 Correlation analysis between pairs of ecosystem services in Kunshan in the year 2016. Supplementary data for the correlation analysis in the years 2006, 2011, and 2030 under different scenarios is in Tables S4–S8. Crop production Crop production Carbon storage Habitat quality Flood regulation Nitrogen retention Phosphorus retention

1

Carbon storage 0.338 1

***

Habitat quality

Flood regulation **

0.074 0.105* 1

0.241 0.278** 0.485*** 1

Nitrogen retention −0.450 −0.081 0.205** 0.135* 1

***

Phosphorus retention −0.499*** −0.128* 0.141* 0.029 0.980*** 1

* p < 0.1. ** p < 0.05. *** p < 0.01.

In general, the correlations between pairs of ecosystem services in Kunshan were consistent throughout the period of study, but there were two exceptions. Crop production was not correlated with habitat quality over the years 2006–2016, but these two ecosystem services would be positively correlated under the BAU and CP scenarios. This implies that croplands will become increasingly important in providing habitats as urban land continues to expand and occupy ecological land in the future. In addition, the positive but weak relationship between flood regulation and nutrient retention from 2006 to 2016 would continue through 2030 under the BAU and CP scenarios but become strongly positive under the ER scenario, primarily due to the transition of croplands to woodland and wetlands.

nutrient export in Beijing decreased due to the replacement of croplands by urban land. In contrast, nutrient export in Atlanta increased because of the conversion of forest land to urban land. We also found decreased crop production, carbon storage, habitat quality, and flood regulation capacity in Kunshan. However, nitrogen and phosphorus export decreased in Kunshan from 2006 to 2016, a period during which urban expansion mainly replaced croplands. As urban land continues to expand at the expense of ecological land, the InVEST model predicts that nutrient export would increase from 2016 to 2030 under the BAU and CP scenarios but be significantly reduced under the ER scenario.

4. Discussion

Although our analysis confirms that continued rapid urban expansion will have negative impacts on the supply of multiple ecosystem services in Kunshan, the three scenarios also provide a basis for decision making that with high probability would lead to different outcomes. Should the current conditions not change, the BAU scenario predicts a number of environmental outcomes that are usually deemed undesirable. Crop production, carbon storage, habitat quality, and flood regulation capacity would all decrease tremendously from 2016 to 2030, with concomitant slight increases in nitrogen loading. The other two modules offer alternatives, should environmental preservation be a future priority. Thus the total amount of nitrogen and phosphorus exported to the water bodies of Kunshan could be limited, other ecosystem services enhanced, and croplands protected under the CP scenario. The tradeoff is reduced urban expansion compared to business as usual. Environmental benefits are accentuated even more under the ER scenario, but the tradeoff is more sharply reduced urban expansion and significant loss of croplands.

4.2. Land use strategies for improving ecosystem services in Kunshan

4.1. Modeling ecosystem services in expanding urban areas The impacts of urban expansion on ecosystem services are known to be site-specific. On the one hand, rapid urban expansion can result in decreased supply to the landscape of multiple ecosystem services—food production, carbon storage, habitat quality, air purification, and water conservation (Zhang et al., 2017). The replacement of forest land by urban land is often the main cause for the simultaneous losses of these ecosystem services (Wang et al., 2018b; Xie et al., 2018). On the other hand, the conversion of unused bare land and croplands to urban land can lead to beneficial effects such as stabilization of sand and reduction of soil erosion (Lyu et al., 2018; Sun et al., 2018b). Sun et al. (2018a, 2018b) investigated nutrient retention by using the InVEST model to calculate nitrogen and phosphorus export in Beijing and Atlanta, two fast-growing cities in China and the US. Over the past three decades, 425

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These tradeoffs are issues that are beginning to face Kunshan’s urban planners as they chart out a future for the growing city. We propose four major land use strategies that could be used as guidelines by those seeking to improve ecosystem services in Kunshan. First and most importantly, more compact urban growth should be promoted through intensive use of current urban land resources. This will reduce the total expanded urban area and the negative influence of urban expansion on multiple ecosystem services. Second, croplands with high productivity, which account for 60% of the total area of croplands as of 2016, should be strictly preserved from urban expansion to maintain crop production in the landscape. Third, trees should be planted along primary roads to weaken their barrier effect on species migration and increase connectivity among important habitats (Kong et al., 2010). This will help the Kunshan government achieve its goal of raising forest cover by 40% and doubling the total length of greenways by 2030. The increase in forest cover will also enhance carbon storage and sequestration in the region. Finally, wetlands should be rehabilitated by replacing croplands in riparian areas that serve as sources of drinking water—Kuilei Lake, Yangcheng Lake, and the Miaojing River. This will reduce non-point source pollution and protect surface water quality, and provide flood regulation effect (Sun et al., 2018a).

landscape functions in response to rapid urban expansion (Peng et al., 2019). The results of such analyses will provide a solid scientific basis for integrated development planning and zoning to reconcile economic development with environmental protection, and thus to realize sustainable development for the rapidly urbanizing regions (Peng et al., 2016). 5. Conclusions In this study, we assessed the spatial and temporal variations of multiple ecosystem services in response to rapid urban expansion in Kunshan between 2006 and 2016. Three alternative scenarios were investigated to explore future changes in ecosystem services between 2016 and 2030 under different land use strategies. According to our estimates, the urban land of Kunshan increased by 19% over the past decade. It would continue to expand by 15% from 2016 to 2030 under the BAU scenario. As a result, crop production, carbon storage, habitat quality, and flood regulation capacity would all be significantly compromised. Crop production would remain stable under the CP scenario owing to the strict protection of croplands. In contrast, crop production would decrease dramatically by 60% under the ER scenario, primarily due to the transition of large areas of croplands to ecological land. Total nutrient loading in Kunshan would increase by 2% and 8% under the BAU and CP scenarios, respectively, while it would decrease by over 35% under the ER scenario. Based on these findings, we propose four major land use strategies, including compact urban growth, croplands protection, reforestation, and wetlands restoration to improve ecosystem services in Kunshan.

4.3. Limitations and future perspectives Some limitations always exist in projecting future land uses, and this is true for our analysis of Kunshan. For instance, all cropland areas as of 2016 are strictly protected from future urban expansion under the CP scenario. In reality, this policy may be too rigid and impractical, given the pressures to permit future urban growth. It thus may be more feasible to protect only the croplands with high productivity. Under the ER scenario, croplands within the 30-m buffer zones of all levels of roads and all sizes of water bodies are replaced with ecological land. This results in a huge, 61% loss of croplands, an area nearly three times larger than losses under the BAU scenario. To achieve a better balance between ecological restoration and croplands protection, it may be necessary to protect only the croplands along primary roads and drinking water sources. It is important to recognize that limitations in all models, including InVEST, can lead to inaccuracies and uncertainties in ecosystem services assessment. InVEST estimates crop production and carbon storage on the simple basis of crop yield and carbon storage per hectare of each land use type. Although the results were further corrected using the NDVI value of each grid cell, the variations of crop production and carbon storage within each land use type could not be precisely assessed in this study. The habitat quality module is also limited, because it assumes that all threats are additive. In most cases, however, the collective impact of multiple threats is much greater than their individual sums (Sharp et al., 2018). Results from the water yield and nutrient flow modules should also be interpreted with caution. The former is based on annual averages and thus neglects the sub-annual patterns of overland flow. Future studies should consider the seasonal variability of precipitation, in particular assessment of the capacity of the landscape to regulate peak flow during the rainy season. The nutrient retention module is sensitive to variation in the nutrient loading and retention values of different land use types (Sun et al., 2018b), which means that errors in these empirical parameter values will have a large effect on predictions. Finally, it must be recognized that we determined the weights of individual ecosystem services for calculating the TES index subjectively. Expert judgments were used, but there is potential for introduction of bias into the final results. In spite of the above limitations, our study establishes for the first time a broad framework of reference for those charged with managing future land use in Kunshan. In the future, it is important to include not only ecosystem services/functions, but also socio-economic functions (e.g., residential support) to more comprehensively assess the dynamics, tradeoffs, and drivers of multiple

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