Suitability of regional development based on ecosystem service benefits and losses: A case study of the Yangtze River Delta urban agglomeration, China

Ecological Indicators 107 (2019) 105579

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Suitability of regional development based on ecosystem service benefits and losses: A case study of the Yangtze River Delta urban agglomeration, China Jing Gaoa, Zhaowu Yub,c,

⁎,1

T

, Lucang Wangd,1, Henrik Vejreb

a

School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China Department of Geoscience and Natural Resource Management, Faculty of Science, University of Copenhagen, Copenhagen 1958, Denmark c Key Laboratory of the Coastal and Wetland Ecosystems (Xiamen University), Ministry of Education, PR China d College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, PR China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Suitability of regional development Ecosystem service bundles K-means clustering Multi-scale analysis Yangtze River Delta urban agglomeration

The comprehensive management of ecosystem service benefits and losses provided by landscapes under land-use reorganization is essential for optimizing and regulating landscape patterns and ecological conservation under rapid urbanization. Of the many studies focusing on the quantification and visualization of ecosystem services, few have explored the visualization characteristics of the different scales related to ecosystem services and losses. In this study, we selected one of the most important urban agglomerations in China—the Yangtze River Delta (YRD)—as a case for the analysis of ecosystem service benefits (which include environmental purification, climate and air regulation, and tourism and leisure) and losses (pollen allergies, air pollution, and the heat island effect) and the spatial patterns of both at multiple scales (grid divisions, administrative districts, and functional areas). We found that the value of ecosystem service losses in the YRD, with a net value of approximately −$42.02 × 106, was slightly higher than the value of ecosystem service benefits. The patterns of ecosystem service benefits were consistent with urbanization processes and patterns, with the natural landscape always serving as the main provider of ecosystem service benefits while the spatial pattern of the urbanized landscape determined the ecosystem service losses. Based on K-means clustering using randomly selected objects as an initial cluster center, we divided the YRD into different functional areas (optimized-, key-, and restricted-development zones) within the grid to create divisions with consistent biophysical properties, thereby avoiding the impact of urban administrative boundaries on the results. To facilitate closing the gap between positive and negative ecosystem outcomes in a politically feasible manner, we also identified functional areas in terms of ecosystem service bundles at the administrative district scale. This study obtains quantitative knowledge of functional areas based on ecosystem services benefits and losses, and helps guide sustainable urban agglomeration planning and development.

1. Introduction In general, “ecosystem service benefit” is the benefit that humans receive from ecosystems (Haase et al., 2014). Of course, human activities have led to the loss of global ecosystem services (Bolund and Hunhammar, 1999; Lyytimaki and Sipila, 2009; von Dohren and Haase, 2015), and the term “ecosystem service loss” has been coined to describe the costs that must be paid to relieve the impact of ecosystem degradation on human production and livelihood (Del Toro et al., 2012; Dunn, 2010; Russo et al., 2017), both from natural processes (i.e., floods, earthquakes, toxic gas emissions from natural sources, and

pollen sensitization) (Klimas et al., 2016) and human activities such as the emission of pollutant gases (Lewis et al., 2017). In particular, the high-intensity human activities caused by urbanization lead to changes in landscape type, functions, and structures and alter the quality, quantity, and spatial composition of ecosystem service benefits, resulting in new types of ecosystem service losses (Cortinovis and Geneletti, 2018; Ernstson and Sorlin, 2013). Although the causes and mechanisms of ecosystem service losses remain controversial among researchers, the existence of such losses have been generally recognized (Palta et al., 2016) and the loss of ecosystem services will likely remain an active field of research (Bastian et al., 2012; Haas and Ban, 2018; Ko

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Corresponding author at: Department of Geoscience and Natural Resource Management, Faculty of Science, University of Copenhagen, Copenhagen 1958, Denmark. E-mail address: [email protected] (Z. Yu). 1 Zhaowu Yu and Lucang Wang are the co-first authors. https://doi.org/10.1016/j.ecolind.2019.105579 Received 24 September 2018; Received in revised form 12 July 2019; Accepted 20 July 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Map of the study area (top right is administrative boundaries of the YRD, obtained from remote sensing imagery); top left is administrated boundary of the YRD (administrative districts); bottom right is the national border of China (the red area shows the location of the YRD within China); bottom left shows two typical representative landscape pictures of the YRD).

ecosystem services have focused on quantitative description, assessment and mapping of ecosystem services (Bastian et al., 2012; Byrne et al., 2015), and the analysis of temporal and spatial changes in ecosystem services (Hosaka and Numata, 2016) as well as their socioeconomic influencing factors (Baumgardner et al., 2012). Given the growing problem of environmental pollution and ecological damage, studies have begun to pay more attention to the losses of ecosystem service in urban ecosystems (Haas and Ban, 2018; von Dohren and Haase, 2015). However, although the idea of negative effects on an ecosystem is not new, there has been a lack of systematic research on losses to ecosystem services, particularly in the context of urban agglomeration (Dobbie, 2013; Kuang et al., 2016; Yu et al., 2019a). Urban agglomeration, the highest spatial organizational form of urban development, has become the most salient feature of global urbanization in recent decades (Kyrkilis et al., 2007; Ouyang et al., 2018; Yu et al., 2019b). Urban agglomeration landscapes are generally of three types, namely, artificial, natural, and semi-artificial (Antognelli and Vizzari, 2017). Urbanization and urban agglomeration

and Son, 2018). In urbanized areas, ecosystem service losses appear inevitable and have significant impacts on the linkages between urban (and regional) economies, societies, and environments. The integration of ecosystem service losses into regional development planning is therefore critical to sustainable regional development, particularly in the context of urban agglomerations (Chapin et al., 2000; Kang et al., 2018). Many ecosystem service studies have focused on the evaluation and simulation of different ecosystem service types, including supply (Calvet-Mir et al., 2012; Ernstson and Sorlin, 2013), climate regulation (Fan et al., 2019; Yu et al., 2017), support (Dennis and James, 2016; von Dohren and Haase, 2015), and cultural services (Wei et al., 2015). Methods applied in these studies have included model calculation (Ernstson and Sorlin, 2013), expert input (Xie et al., 2001), and the analysis of ecological footprints (Gomez-Baggethun and Barton, 2013). Urban ecosystems differ significantly from other terrestrial ecosystems because they have special characteristics that are highly disturbed by humans (Forman, 2014; Ko and Son, 2018). Current studies on urban 2

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development has led to the transformation of natural landscapes into semi-artificial and artificial landscapes, leading to problems such as air pollution, biodiversity loss, and the urban heat island (UHI) effect, all of which seriously undermine the ability of urban natural landscapes to provide support services for human survival (Baro et al., 2014; Yu et al., 2018a; Yu et al., 2018b). Although urban ecosystem services have been improved at some local levels, the overall deterioration trend has not been fundamentally inhibited and the degradation of ecosystem functioning has become a prominent problem within urban agglomerations (Del Toro et al., 2012; Jing and Zhang, 2016; Kuriqi et al., 2019; Zhou et al., 2018a). Furthermore, because ecosystems are naturally formed, their boundaries are often inconsistent with administrative boundaries (Firbank et al., 2013; Kuriqi et al., 2019). Accordingly, it is of scientific significance and useful in the optimization and regulation of regional sustainability to conduct a comprehensive assessment of ecosystem service benefits and losses under different landscape components (and configurations) to determine the linkage trade-off between the contribution of ecosystem service benefits and losses between administrative units (Dobbs et al., 2011). The losses of ecosystem services can also be magnified by the high concentrations of population and economic activities within urban agglomerations, (Cuinica et al., 2014; Hong et al., 2017), making it necessary to investigate the relationship between ecosystem service benefits and losses and natural landscapes and human activities during the urbanization process, particularly within urban agglomeration regions with severe human disturbance (Escobedo et al., 2011; Franck et al., 2014; Janhall, 2015). To address the insufficiencies of previous studies and provide policy implications for urban agglomeration regions, this study focused on the following questions: (1) of what economic value are the ecosystem service benefits and losses in the Yangtze River Delta (YRD) urban agglomeration; (2) what are the spatial patterns of various ecosystem service benefits and losses within urban agglomerations; and (3) how can different development zones be divided at multiple scales based on the composition of ecosystem service benefits and losses within urban agglomerations?

Fig. 2. Division of the “central-periphery” circle in the YRD urban agglomeration.

in 2015 (http://earthexplorer.usgs.gov). An object-oriented classification method was used to interpret the TM images using eCognition 8.7 software (eCognition Developer, 2011, http://www.ecognition.com/). The overall accuracy and Kappa Coefficient of the classification were 95.23% and 0.932, respectively. (2) Environmental pollution was assessed using data monitored by the China National Environmental Monitoring Center (http://www.cnemc.cn). (3) Temperature data including the average air temperature and the land surface temperature (LST) were obtained from real-time monitoring (https://tianqi.so.com/ weather/index.html) data and Landsat images. The Kriging Interpolation Model was used to convert the monitoring-point data into surface data relating to UHIs. (4) Socioeconomic data such as population density were obtained from the “Urban Statistical Yearbook of 2015 of China.” The overall methodological flowchart is presented in Fig. 3.

2. Study area and methods 2.1. Study area The YRD urban agglomeration on the eastern coast of China is one of the most developed and populous regions in the country (and the world) (Fig. 1). It contains the 16 prefecture-level cities of Shanghai, Jiangsu, and northern Zhejiang Provinces and has a land area of 0.21 million km2, a population of 150 million, and in 2015 had a gross domestic product (GDP) of 1.81 trillion US dollars, with these factors accounting for 2.2, 18.5, and 11% of the respective national totals. In addition, the YRD urban agglomeration is an important meeting point between the “Belt and Road” system and the Yangtze River Economic Belt, embodying the highest level of China's participation in global competition. Based on these characteristics, a large number of studies have identified the scope of the urban agglomeration and its division into “central-peripheral” circles from the perspective of socio-economic development (Huang and Lu, 2015; Lu et al., 2014; Zhu and Zheng, 2012) (Fig. 2). However, with its rapid economic development and urbanization and its gradual formation into an urban agglomeration, the landscape of the YRD has experienced significant changes in recent years (von Glasow et al., 2013; Wu et al., 2017) and has led to a series of ecosystem service losses such as urban and regional heat island effects and increased air pollution (Sun et al., 2018; Yu et al., 2019a; Zhou et al., 2018a). 2.2. Data collection and analyses The following data were used in this study. (1) Thematic Mapper (TM) image data were used to represent the YRD urban agglomeration

Fig. 3. Overall methodological flowchart. 3

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Table 1 Evaluation indicators of ecosystem service benefits and losses in the YRD urban agglomeration. Indicator types

Definition of index

benefits

Green space absorbs SO2, NOx, and dust from the atmosphere Green space maintains atmospheric chemical composition balances in terms of, e.g., carbon storage and oxygen release Green space regulates regional climate by lowering temperature and increasing precipitation Park green spaces, garden areas, etc., meet the needs of residents for leisure, tourism, and proximity to nature Air contamination reaches a concentration sufficient to endanger human health Wind-borne pollen causes allergic reactions in human body The urban underlying surface has a greater heat absorption rate and a smaller specific heat capacity than undeveloped land; as a result, urban temperatures are higher than contemporaneous suburban temperatures and residents need more air-conditioning cooling

losses

Environmental purification Air regulation Climate regulation Tourism and leisure Air pollution Pollen allergy Heat island effect

Table 2 Calculation of ecosystem service benefit values within the YRD urban agglomeration. Ecosystem service benefits

Formula

Explanation

Environmental purification

Pj = ∑i = 1 Ai Cij

where Pj is the total purification capacity for each gas (kg·hm−2·a−1), i is the type of urban landscape from 1 to 6,

6

j = 1…3 is the type of polluting gas by category, Cij is the purification capacity for gas j in urban landscape type i U= (Table 3), and Ai is the area of each type of urban landscape. 6 where ZC is the total carbon storage capacity of each type of urban landscape i = 1–6 (t·a−1), Ai is the area of urban Zc = ∑i = 1 1.63·Ai ·Bi ·R c landscape type i, Bi is the carbon storage capacity per unit of urban landscape type i (t·hm−2·a−1) (woodland: 7.86, cropland: 5.94, grassland: 4.31, watershed: 0.83, built-up land: 0.21, and unused land: 0.76) (Xu et al., 2015), andRC is the carbon content in CO2, i.e., 27.27% 6 where ZO2 is the total oxygen-release capacity of urban landscape type i = 1 to 6 (t·a−1), Ai is the area of urban ZO2 = ∑i = 1 1.19·Ai ·Bi landscape type i, and Bi is the oxygen-release capacity per unit of urban landscape type i (the same as below) where U is the total value of the carbon storage and oxygen-releasing capacity of all types of urban landscape, Pc is the U = Zc ·Pc + ZO2 ·PO2 value of carbon storage per unit (271.9 yuan·t−1) (Wang et al., 2014), and PO2 is the value of oxygen released per unit (378.6 yuan·t−1) The assessment of climate regulation in this paper is based on the “China Ecosystem Service Value Equivalent Factors Table” determined by Xie et al. (2001), which has been revised based on the actual situation in the YRD to obtain the economic value of climate adjustment per unit area in the YRD urban agglomeration landscape (Table 4) 6 n where U is the total value for tourism and leisure of all types of urban landscape, A is the area of each grid (900 m2), U = ∑i = 1 ∑ j = 1 A ·Vi ·Pj Vi is the value of tourism and leisure per unit (Table 4), and Pj is the normalization coefficient of population density for 3 ∑ j = 1 Vj Pj

Air regulation

Climate regulation

Tourism and leisure

each grid

Table 3 Calculation of ecosystem service loss values within the YRD urban agglomeration. Ecosystem service losses

Formula

Air pollution

V = D PM2.5 ·H ·A ·P PM2.5

Pollen allergies

Heat island effect

Explanation

Cij = f·m·qi ·d·A

E=

I ·J ·(T P−D

− t)

where V is the total cost of controlling pollutants, D PM2.5 is the concentration of PM2.5 (mg·m−3); P PM2.5 is the value of

PM2.5 released per unit (535 yuan·t−1) (Gao et al., 2017), H is the atmospheric altitude, (here, 32 m) and A is the unit grid area (900 m2) where Cij is the pollen allergy cost, f is the incidence rate (0.05) (D'Amato et al., 2007), m is the cost of treating pollen allergies (here, 500 yuan·person−1, based on a consultation with traditional Chinese medicine dermatology experts in the YRD urban agglomeration), qi is the normalized coefficient of pollen concentration for urban landscape type i (woodland: 0.9, cropland: 0.8, grassland: 0.7, watershed: 0.6, built-up land: 0.5, and unused land: 0.4) (Carinanos and Casares-Porcel, 2011), and d is the population density (person·hm−2) where E is the cost value of the heat island effect, I is the price per unit of electricity (0.81 yuan·kw−1·h−1) (Chen et al., 2011), J is the unit of power consumption per area (2.53 kw·h−1·m−2) (Xu et al., 2015), P is the average temperature of the YRD urban agglomeration (30.2 °C), D is the air conditioning set temperature (26 °C), T is the actual temperature of the underlying surface of the city, and t is the average temperature of the suburbs (28.9 °C)

woodland, grassland, watershed, built-up land, and unused land. In general, natural landscape ecosystems can provide positive ecosystem services such as environmental purification, soil and water conservation, climate regulation, air regulation, tourism and leisure, and hydrological regulation. The transition from natural to artificial landscapes, on the other hand, can lead to ecosystem service losses such as air pollution, the spread of pollen allergies, and heat island effects. Based on previous studies (Xiao et al., 2017; Zhou et al., 2018b) and actual publicly available and operable data on the YRD urban agglomeration, four indicators of ecosystem service benefits (environmental purification, climate regulation, air regulation, and tourism and leisure) and three indicators of ecosystem service losses (pollen allergy, air pollution, and the heat island effect) (Table 1) were selected and calculated, as shown in Tables 2 and 3.

Table 4 Quantity of pollutants absorbed per unit area in each type of landscape within the YDR urban agglomeration (kg·hm−2·a−1). Landscape type

Cropland Woodland Grassland Watersheds Built-up land Unused land

Absorbed pollutant type SO2

NOx

Dust

45.00 291.03 21.70 12.00 3.90 5.35

33.30 215.36 16.06 4.60 1.86 3.40

940.00 44300.00 120.00 40.00 58.30 36.70

2.2.1. Evaluation of ecosystem services and losses The landscapes studied within the TRD urban agglomeration were divided into six types of artificial and natural landscapes: cropland, 4

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Table 5 Ecosystem service values per unit area in each type of landscape within the YRD urban agglomeration ($102·hm−2·a−1). ESs

Cropland

Woodland

Grassland

Watersheds

Built-up land

Unused land

Climate regulation Tourism and leisure

5.84 2.77

3.90 1.43

1.94 0.09

1.93 0.03

0 0

0 0

Table 6 Total ecosystem service benefit values by the landscape within the YRD urban agglomeration ($106·a−1). Landscape type

environmental purification

air regulation

climate regulation

tourism and leisure

total

percentage

Cropland Woodland Grassland Watersheds Built-up land Unused land total percentage

3.58 110.36 0.77 2.40 4.96 0.96 123.03 32.36

53.79 58.67 9.90 1.13 0.00 0.56 124.04 32.33

23.21 57.56 5.27 10.41 0.00 0.94 100.21 26.36

0.02 8.42 2.87 16.20 0.00 0.17 32.91 8.66

80.59 235.01 18.81 30.14 4.96 2.64 380.20 100.00

21.20 61.81 4.95 7.63 3.42 0.69 100.00

assigned as the new center of the cluster; (4) the Euclidean distances of all samples were recalculated to obtain k new centers and re-divide all samples into each cluster according to the “principle of nearest distance from center”; (5) steps (3) and (4) were then repeated until all sample centers remained unchanged, i.e., the generated cluster was the same as that formed in the previous round (Table 5).

Table 7 Total ecosystem service loss values by the landscape within the YRD urban agglomeration ($106·a−1). Landscape type

air pollution

pollen allergy

heat island effect

total

percentage

Cropland Woodland Grassland Watersheds Built-up land Unused land total percentage

2.83 2.13 0.96 0.13 24.84 0.19 31.09 7.36

3.52 10.86 0.53 0.19 39.02 0.09 54.22 12.84

71.50 0.00 0.00 0.00 265.27 0.14 336.91 79.80

77.85 12.99 1.50 0.32 329.13 0.42 422.22 100.00

18.44 3.08 0.35 0.08 77.95 0.10 100.00

3. Results 3.1. The value of ecosystem service benefits and losses in the YRD urban agglomeration The total value of ecosystem service benefits in the YRD urban agglomeration was $380.20 × 106, of which woodland contributed the most value (61.81%), followed by cropland (21.20%), watershed (7.63%), grassland (4.95%), and unused land (less than 1%) (Table 6). The high value of the woodland area reflected the fact that it constituted the second-largest landscape type by area (after cropland) while having the highest unit and total values for each ecosystem service. The total value of ecosystem service losses in the agglomeration was $422.22 × 106 (Table 7), of which the contribution of built-up land was as high as 75%, while cropland accounted for about 18%, forestland for 3%, and other landscape types less than 1%. In the study area, built-up land accounted for 38% of the total loss and more than 60% of the total ecosystem service loss.

2.2.2. Composite ecosystem service evaluation and ecosystem service bundle mapping (1) Composite evaluation The composite ecosystem services of an urban landscape can be represented in terms of the difference between services provided and services lost:

C= P− N

(1)

where C is the composite ecosystem service, P is the ecosystem service, and N is the ecosystem service loss.

3.2. Spatial pattern of ecosystem service benefits and losses in the YRD urban agglomeration

(2) Ecosystem service bundles In this study, we used ecosystem service bundles to map the results of ecosystem service benefits and losses (Raudsepp-Hearne et al., 2010). Specifically, we used the prefecture-level city as the statistical unit and expressed the respective bundles as the sums of multiple ecosystem service benefits and losses in the form of a radar map for each region.

The spatial distribution of each individual ecosystem service benefit value in the YRD urban agglomeration was generally consistent with the overall distribution of ecosystem service benefit value. Environmental purification, air regulation, and climate regulation (Fig. 4a–c, respectively), each having high benefit values, were distributed over the ecological protection functional zones, i.e., over the woodlands in northern Zhejiang (Hangzhou, Shaoxing, Ningbo, and Taizhou). The spatial distribution of tourism and leisure values (Fig. 4d) varied widely, reflecting the widely varied distribution and density of tourism attractions. Overall, the spatial distribution of total ecosystem service benefit values in the YRD urban agglomeration was significantly varied (Fig. 5). Specifically, the values in the core area were lower than the in either peripheral or marginal areas, while the ecological green (and blue) spaces around the built-up areas formed a fragmented, plaque-like grid distribution. Spatializing the results of the ecosystem service benefits resulted in a significant correlation between the spatial distributions of ecosystem service benefit values and various landscape

(3) K-means clustering Functional zones based on ecosystem service bundles of benefit and loss data were analyzed and identified using the K-means cluster analysis tool in the R statistical software package (https://www.r-project. org/). The following calculation method was used (Kanungo et al., 2002): (1) k samples were randomly selected to represent the initial mean or center of the clusters to be divided; (2) the Euclidean distances between the remaining samples and each mean were calculated to find the sample with the shortest distance and assign it to the cluster closest to the center; (3) the mean of all samples in each cluster was then 5

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Fig. 4. Spatial pattern of values of each type of ecosystem service benefit in the YRD urban agglomeration.

The spatial distribution of pollen sensitization formed a layered pattern in which sensitization decreased from the centers to the peripheries of the built-up areas (Fig. 6b). The distribution of high-, medium-, and low-loss areas as a result of the heat island effect (Fig. 6c) was roughly

types. The ecosystem service loss results produced high-, medium-, and low-value air pollution service losses with slightly varying distributions, with the high losses primarily distributed in the city centers (Fig. 6a). 6

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bundles were identified (Fig. 9b). Cluster 1 (optimized development zones), characterized by high values of ecosystem service losses but relatively low ecosystem service benefits, was distributed within the core area; examples included Shanghai, southern Jiangsu Province, Hangzhou Bay, etc. Cluster 2 (emphasized development zones) included mixed bundle areas with moderate ecosystem service benefits and losses distributed within the peripheral area; examples included central Jiangsu and Zhejiang provinces and some coastal areas. Cluster 3 (restricted development zones), characterized by high ecosystem service benefit values but relatively low ecosystem service losses, was distributed in marginal areas; examples included some prefecture-level cities in northern Jiangsu and western Zhejiang provinces. 4. Discussion 4.1. Causes of the spatial pattern of ecosystem service benefits and losses in the context of urban agglomeration In general, ecosystem service benefits and losses are closely related to landscape type and spatial structure (Dobbs et al., 2011; Syrbe and Walz, 2012). For instance, it has previously been shown that landscapes such as woodland and water have higher ecosystem service benefit values, while built-up land has higher ecosystem service losses (Dennis and James, 2016; Honey-Roses et al., 2014; Liu, 2014; Yu et al., 2017), findings that are reflected in the results of this study. In addition, we found that the spatial patterns of ecosystem service benefits and losses were consistent with the spatial structure of the overall urban agglomeration. Previous studies have proposed that urban agglomeration spatial structure not only reflects the city interior (in terms of the relation between metropolitan and non-metropolitan areas and industrial and residential areas), but also reflects the inter-city structure (in terms of the relation between core, node, and general cities and hinterland) (Friedmann, 2010; Kuang et al., 2018). For example, major cities (especially core and node cities) are often connected by highly developed complex corridors such as highways and high-speed rail (Xu et al., 2016), which often evolve into an axis (belt) of developmental structures within the urban agglomeration region (Haas and Ban, 2014). Such axes (belts) not only aggregate a large number of population and economic activities but also change the natural landscape pattern and affect the pattern of ecosystem service benefit values (Li et al., 2008). In this manner, the spatial structure of urban agglomeration is an important factor affecting the distribution pattern of ecosystem service benefits and losses. Our results also revealed that ecosystem service losses (air pollution, heat islands, and allergic effects) within urban agglomerations were much higher than in individual cities, which further underpins the importance of determining (explaining) the spatial patterns of ecosystem service benefits and losses. In fact, the results of this study are consistent with a recent study by Yu et al. (2019a), who found that the UHI effect is usually persistent far beyond urban physical boundaries, with temperatures in suburban and rural areas affected by the UHI effect in urban agglomerations. They further proposed that conventional UHI effects manifest as regional heat islands (RHIs) and that the aggregation of RHIs is significant on an urban agglomeration scale. Such patterns might occur because the continuous large-scale distribution of built-up land in urban agglomerations includes isolated woodland, farmland, and water, while individual cities are always surrounded by farmland and woodland (Jiang et al., 2017; Yu et al., 2019b). Based on a clear understanding of the reasons for the spatial pattern of ecosystem service benefit and loss values in an urban agglomeration, the value of ecosystem services can be maximized by protecting the natural ecological landscape of the non-metropolitan areas within the agglomeration through land intensification such as expanding the scope of restricted construction areas (Banzhaf et al., 2017; Kremer et al., 2016; Zhang et al., 2015). It is also necessary to adjust and optimize the spatial structure of land use, particularly in the core and node cities and

Fig. 5. Spatial pattern of total values of ecosystem service benefit in the YRD urban agglomeration.

the same as the distribution of total ecosystem service losses (Fig. 7), with built-up-land most strongly associated with this effect. The overall spatial distribution of ecosystem service losses in the YRD urban agglomeration indicates that the loss values in the core region were higher than in either the peripheral or marginal areas and that regions with high ecosystem service losses were more concentrated overall (Fig. 7).

3.3. Results of cluster analysis at different scales in the YRD urban agglomeration The spatial distribution of the urban agglomeration landscape generally determined the spatial pattern of composite ecosystem service values. Fig. 8 shows that the areas surrounding northern Zhejiang had generally higher composite ecosystem service values than other areas (Fig. 8a). Cluster analysis at the grid level revealed three types of composite ecosystem service grids (Fig. 8b): cluster 1, which was concentrated on the core area of Shanghai and on four other metropolitan elliptical areas, was defined as an optimized development zone; cluster 2, concentrated on the peripheries of the core cities and on elliptical metropolitan areas, was defined as an emphasized development zone; cluster 3, located in the ecological protection barrier (adjacent mountainous areas of Zhejiang Province) and corridor (Yangtze River), was defined as a restricted development zone. The ecosystem service bundle mapping results were also applied to calculate the ecosystem service benefits and losses generated by each prefecture-level city in the YRD urban agglomeration. The results revealed different spatial patterns within each prefecture-level city based on their specific ecosystem service benefits and losses (Fig. 9a). Three types of prefecture-level city with comparable ecosystem service 7

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Fig. 6. Spatial pattern of values of each type of ecosystem service loss in the YRD urban agglomeration.

4.2. Implications of ecosystem service bundle-based functional zoning for regional planning and development

in urban agglomeration corridors. For example, ecological land can be introduced into metropolitan areas and development zones to increase landscape connectivity and minimize the loss of ecosystem services (Davies et al., 2017; de la Barrera and Henriquez, 2017; Kremer et al., 2016).

It has been widely recognized that the integrity of ecosystem services is an important basis for achieving ecological security and 8

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optimized and emphasized development zones are primarily distributed within the core metropolitan areas. This distribution is essentially consistent with the current spatial development planning being applied to the YRD urban agglomeration (Li et al., 2018), suggesting that the strategy is appropriate and can successfully achieve regional sustainable development goals. To be more specific, the YRD urban agglomeration development plan proposes that Shanghai, Hangzhou, Nanjing, and other metropolitan areas should be planned to optimize their development zones, while other cities around the metropolitan area should optimize their development areas (NDRC, 2016). These goals are, for the most part, substantially consistent with the development zoning method proposed in this study. Under current plans, the areas surrounding megacities are classified as ecologically protected and have construction restrictions or prohibitions, which is highly coincident with the restricted development zones identified in this study. Overall, our zoning results are essentially consistent with the actual spatial development plan for the YRD urban agglomeration, demonstrating that this ecosystem service bundle-based development zoning approach can provide useful insights into suitability of regional development along with a scientific basis for cross-administrative cooperation. 4.3. Limitations and further studies In this study, we established indicators of ecosystem service benefits and losses that were used to identify specific divisions within the various development zones of the YRD urban agglomeration. However, there were potential limitations to our work that need to be addressed in future studies. First, the selection of ecosystem service benefit and loss indicators was not comprehensive, and some ecosystem service loss indicators (such as urban floods) deserve further investigation (Barragan and de Andres, 2015; Ma et al., 2016). Second, differences in structure, composition, and location (such as tropical rainforests and temperate deciduous forests) within a given type of urban ecosystem can result in the application of different ecosystem service values to a given landscape (Chi et al., 2015; Han et al., 2014); this also requires further consideration. Finally, we did not fully express spatial differences in ecosystem services within each landscape type; in woodland, for example, there are significant differences among different tree species in terms of carbon sequestration, oxygen release, and absorption of pollutants (Tahar et al., 2017). Future research should therefore consider variations in services caused by differences within a given type of ecosystem.

Fig. 7. Spatial pattern of total values of ecosystem service loss in the YRD urban agglomeration.

regional sustainable development. It is therefore necessary to achieve suitability of regional development through the comprehensive management of multiple ecosystem services (Wang et al., 2017; Wu et al., 2017; Zhang and Wang, 2018). In general, ecosystem services are generated by natural processes and are based on biophysical characteristics. However, for historical and realistic (social economic development and management) reasons, there will be administrative boundaries at all levels within an urban agglomeration region that will fragment the overall ecosystem, thereby diminishing the supply of ecosystem services (Lan et al., 2017; Vejre et al., 2015). Furthermore, the spatial heterogeneity of the landscape requires grid-scale ecosystem service management (Alam et al., 2011). Thus, grid-based and administrative unit-based approaches are key to understanding how multiservices ecosystem interact directly with land users and managers in social processes (Zhang et al., 2016). In this study, we used a K-means clustering method to identify areas with similar ecosystem service bundles and better target the suitability of regional development (Lan et al., 2017; Peng, 2013). Grids and cities were chosen as the spatial units (scales) for cluster analysis, with the grid-scale functional zones based on the clustering results of composite ecosystem services and the city-scale functional zones based on the clustering results of the ecosystem service bundles. The results have implications for effectively combining ecosystem service management with administrative management (highlight the significance of crosscity cooperation in the context of urban agglomeration). Specifically, our K-means clustering-based bundling of ecosystem service benefits and losses revealed three development zones in the YDR urban agglomeration: the restricted development zoning is primarily located in the peripheral non-metropolitan area of the agglomeration, while the

5. Conclusions A comprehensive understanding of the multiple ecosystem service benefits and losses within urban agglomerations remain elusive, and understanding the synergies between different regions and cities is a big challenge in regional development. In this study, we proposed a methodological framework to understand multiple ecosystem service benefits and losses in urban agglomeration regions in the context of their comprehensive effects across landscapes at different scales. Taking the YRD urban agglomeration as an example, our findings suggested the following. (1) Multiple ecosystem service benefits and losses are occurring at the same time, with losses slightly outweighing benefits, resulting in a net value for the YRD of approximately −$42.02 × 106. (2) In general, the value of ecosystem service benefits has declined sharply as a result of the urbanization process, with the natural landscape remaining the primary source of ecosystem services in the YRD urban agglomeration. (3) The value of the composite ecosystem service is highest closest to the periphery of the urban agglomeration, which is consistent with the spatial expansion of the urban agglomeration; the spatial distribution of landscapes within the agglomeration essentially determines the spatial pattern of composite ecosystem service value. (4) Using K-means cluster analysis to produce integrated ecosystem 9

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Fig. 8. Spatial pattern of values of composite ecosystem services in the YRD urban agglomeration.

Fig. 9. Regional linkage distribution pattern.

ecosystem service value pattern and the ecosystem service pattern that is disrupted by administrative boundaries, as well as highlight the importance of cross-city cooperation. The results of this study will aid in enriching the visual assessment of ecosystem service benefits and losses

services and ecosystem service bundles resulted in the designation of three functional zonings on the grid and administrative scales: optimized, emphasized, and restricted development zones. The results of the grid and administrative scale would show the gap between the real 10

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and provide new ideas for optimizing the spatial development of urban agglomerations while suggesting valuable insights for landscape planning in the context of such agglomerations.

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