Exploring the interactive coercing relationship between urbanization and ecosystem service value in the Shanghai–Hangzhou Bay Metropolitan Region

Journal Pre-proof Exploring the interactive coercing relationship between urbanization and ecosystem service value in the Shanghai–Hangzhou Bay Metropolitan Region Rui Xiao, Meng Lin, Xufeng Fei, Yansheng Li, Zhonghao Zhang, Qingxiang Meng PII:

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DOI:

https://doi.org/10.1016/j.jclepro.2019.119803

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JCLP 119803

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Journal of Cleaner Production

Received Date: 16 July 2019 Revised Date:

17 December 2019

Accepted Date: 18 December 2019

Please cite this article as: Xiao R, Lin M, Fei X, Li Y, Zhang Z, Meng Q, Exploring the interactive coercing relationship between urbanization and ecosystem service value in the Shanghai–Hangzhou Bay Metropolitan Region, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/ j.jclepro.2019.119803. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Author Contributions: All authors contributed equally to this work. Rui Xiao, Meng Lin and Qingxiang Meng conceived and designed the experiments. Rui Xiao, Meng Lin and Xufeng Fei analyzed the data. Rui Xiao, Meng Lin, Zhonghao Zhang and Qingxiang Meng wrote the manuscript. Yansheng Li and Xufeng Fei helped improve the manuscript. All authors have read and approved the final manuscript.

Exploring the Interactive Coercing Relationship between Urbanization and Ecosystem Service Value in the Shanghai– Hangzhou Bay Metropolitan Region Rui Xiaoa, Meng Lina, Xufeng Feib,c, Yansheng Lia, Zhonghao Zhangd, Qingxiang Menga*

a

School of Remote Sensing and Information Engineering, Wuhan

University, Wuhan 430079, China; [email protected] (R. Xiao); [email protected]

(M.

Lin); [email protected]

(Y.

Li);

[email protected] (Q. Meng) b

Zhejiang Academy of Agricultural Sciences, No. 198 Shiqiao Road,

Hangzhou 310021, Zhejiang, China ([email protected]) c

Key Laboratory of Information Traceability of Agricultural Products,

Ministry of Agriculture, Hangzhou, China d

Institute of Urban Studies, Shanghai Normal University, Shanghai

200234, China; [email protected] (Z. Zhang) * Correspondence email: [email protected]

1

8907

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Exploring the Interactive Coercing Relationship between

3

Urbanization and Ecosystem Service Value in the Shanghai–

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Hangzhou Bay Metropolitan Region

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Abstract: The rapid expansion of urban areas and intense human activities have exerted serious

6

impacts on the structure and service functions of ecosystems. Correspondingly, due to the limited

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amount of ecosystem service value (ESV), ecological environment is doomed to place restriction on

8

disordered sprawl of urban and extensive growth of economy. A clear understanding of the

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interaction between urbanization and environment is of great significance for formulating regulations

10

of future urban development and eco-environment protection. Taking terrestrial ecosystem services

11

and urbanization as two systems, this research aims at exploring the interactive coercing relationship

12

between urbanization and ESV in the Shanghai–Hangzhou Bay Metropolitan Region (SHB) on the

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system level and subsystem level. First of all, the value coefficient and information entropy methods

14

were employed to estimate the ESV and urbanization level of each city, respectively. Then, a

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coupling coordination degree model (CCDM) was used to reveal the interactive coercing relationship

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between ESV and urbanization. The results showed that the change of land use under urbanization

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caused the degradation of ecosystem service functions in SHB，and the city with the highest

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urbanization level (Shanghai) also endured the worst ESV losses. The overall coupling coordination

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degree (CCD) between urbanization and ESV on the system level displayed an upward trend in SHB.

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However, the CCDs between several ESV subsystems and urbanization subsystems, such as food

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production function and demographic urbanization, had deteriorated in Shanghai, Jiaxing and Ningbo.

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Our study suggested that more consideration should be given to the coordinated and balanced

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development of different ecosystem services and urbanization, intensification of land use, and

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maintenance of ecosystem functions in future urban planning and decision making.

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Key words: Coupling coordination degree model; Shanghai-Hangzhou Bay; Ecosystem service value;

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Urbanization; Interactive coercing relationship

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1. Introduction

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As the foundations for maintaining human survival (Liu et al., 2019), ecosystem services (ESs)

29

refer to life support products (such as raw materials and foods) and services (such as habitats

30

provided) that are directly or indirectly provided by the structure, processes, and functions of

31

ecosystems (Costanza et al., 1998; Wu et al., 2018). Land use changes are the most direct

32

manifestations of human activities' impact on ES (Tuan, 2010). By changing the type of land cover,

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human activities affect the structure, process and function of the ecosystem, which is reflected in

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ecosystem service value (ESV) (Zhao et al., 2004; Mao et al., 2019). Thus, ESV can be used to

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evaluate the potentiality of regional ecosystems for human services and to probe the transformation

36

of the ecological environment caused by human activities. In recent years, researchers have regarded

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ESV as one of the main indicators for evaluating changes in the ecological environment (Wang et al.,

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2019). The quantitative assessment of ESV has become the focus of ecological economics (Yu et al.,

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2018; Zhao et al., 2018; Luo et al., 2018). According to the Millennium Ecosystem Assessment

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(2005), ES is divided into four main categories: provisioning services, regulating services, supporting

41

services and cultural services. In order to measure ES through a unified standard, two approaches

42

including unit value based approach and primary data based approach were employed (Jiang, 2017).

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Primary data based approach used ecological process model and ecological elements data to calculate

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ESV (Guo et al., 2001; Zhang et al., 2018), but it’s pertinence and particularity makes it difficult to

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evaluate ES comprehensively. Based on the relative potential of various ecosystems, unit value based

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approach is comprehensive in evaluating ESV (Costanza et al., 1997; Xie et al., 2003; Xing et al.,

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2018). However, the ascertainment of value coefficient is subjective to a certain extent.

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In recent years, urbanization has become one of the most significant social and economic

49

phenomena, and the world's fastest-growing cities are now located in developing countries,

50

especially in China (Liu et al., 2015; He et al., 2017). The development of cities in China has been

51

phenomenal in the past 30 years(Wu et al., 2011), during which the national urbanization level has

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risen from 17.9% in 1978 to 56.10% in 2015, approximately 1.2% higher than the world average (Lu

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et al., 2011; Wang et al., 2014; National Bureau of Statistics, 2016). As the most active areas of

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human activities, cities, especially those in metropolitan regions, are the regions with the strongest

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forces for natural changes in the ecological environment (Zhang et al., 2015; Zhao et al., 2013).

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According to statistics (CSB, 2016), 68.54% of the fixed asset investment and almost all of the

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foreign capital (98.06%) in China were concentrated in urban agglomerations. Driven by the

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high-intensity investment of domestic and foreign capital, China's urban agglomerations have

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released huge amounts of energy, which resulted in a total gain of 78.78% in GDP output. Meanwhile,

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it also concentrates more than 3/4 of the country's pollution output. In general, the relationship

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between ecological environment and urbanization is interactive (Fang and Jing, 2013; Zhao et al.,

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2017). On the one hand, urbanization brings pressures on the carrying capacity of ecosystems and

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resources through pollutant emissions, which result in ecological problems and even ecological

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disasters (Gendron, 2014; Wu et al., 2018). On the other hand, ecological environment changes also

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influence urbanization through environmental degradation, resource shortage, and so on (Gu, 2002;

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Liu et al., 2018; Xing et al., 2019). The contradiction between environment and human activities

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becomes significant in areas under rapid urbanization (Wang et al., 2016). China's urban

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agglomerations are highly sensitive areas where environmental pollution and deterioration of

69

resourses are serious, which will hinder sustainable development. Therefore, detecting and

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ameliorating the deficiency in the relationships between ecosystems and urbanization of cities (as

71

well as metropolitan regions) have become the focus of scholars worldwide.

72

Current researches on this issue mainly focus on how to describe (e.g., measure the

73

"Environmental Kuznets Curve") and coordinate the relationship between urbanization and

74

ecological environment (Button and Pearce, 1989; Grossman and Krueger, 1995; Diao et al., 2009;

75

Al-Mulali et al., 2016). Moreover, a considerable amount of literature focuses on quantitative

76

analysis of the coupling coordination relationship between urbanization and the ecological

77

environment (Li et al., 2012; Shen et al., 2018). As the metropolitan regions becomes increasingly

78

important economically and socially, research on ecological environment and urbanization of

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metropolitan regions will become one of the new hot spots. In addition, those studies mostly

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regarded urbanization and ESV as two systems, and explored their relationship from the perspective

81

of the whole system, yet few scholars have studied the interaction between the subsystems of

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urbanization and ESV.

83

In view of the above considerations, the objectives of this study were to: 1) identify the

84

spatio-temporal variations of ESV in SHB metropolitan region during 1995 to 2015; 2) assess the

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urbanization level of the SHB metropolitan region during the time period from the perspectives of

86

demography, space, economy and society; and 3) explore the interactive coercing relationship

87

between urbanization and ESV on the system level and subsystem level using CCDM. The results

88

would provide a reference for promoting the coordinated and sustainable relationship between

89

urbanization and ecological environment in urban agglomerations with similar development

90

characteristics.

91

2. Materials and methods

92

2.1 Study area

93

The SHB metropolitan region (118°21′E-122°16′E, 28°51′N-31°53′N) is located on the eastern

94

coast of China, consisting of one core megacity-Shanghai, and other 5 cities (Hangzhou, Ningbo,

95

Shaoxing, Jiaxing and Huzhou) of Zhejiang Province (Fig.1), which are connected through

96

large-scale infrastructure construction. It has a large group of ports and numerous rivers; therefore,

97

its waterway transportation is developed. In 1930s, the prototype of the urban agglomeration had

98

already taken shape. It belongs to the moderate subtropical climate with four distinct seasons. The

99

annual average temperatures are between 15 ° C and 20 ° C. The sunshine duration in the SHB

100

metropolitan region is long, which is conducive to agricultural development.

101

The SHB metropolitan region is one of the demonstration areas of China's reformation and

102

opening up. The convenience and frequency of foreign exchanges have given it outstanding regional

103

advantages, which makes it an important growth pole for economic and social development in the

104

Yangtze River Delta region. In recent decades, the SHB metropolitan region has absorbed large

105

agricultural populations from the central and western China. At the end of 2015, the regional GDP

106

was $769 million (USD), and the total population reached 38.09 million (CSY, 2015). With the

107

population explosion and economic expansion, the demand of built-up land has increased, which

108

leads to the changes of urban spatial structure, as well as the increasingly serious ecological and

109

environmental problems. In this research, the SHB metropolitan region was used as a case study to

110

evaluate the coupling coordination relationships between ESV and urbanization. Therefore, scientific

111

evidence could be provided for the local government to implement policies of sustainable

112

development.

113

Fig.1 Location of the study area

114

115

2.2. Data sources

116

In terms of ESV, this study adopted the spatial distribution data of terrestrial ecosystem types in

117

China (1:100,000), which was provided by the Data Centre for Resources and Environmental

118

Sciences, Chinese Academy of Sciences (RESDC) (http://www.resdc.cn). Based on the 1:100,000

119

scale land use/land cover data obtained by remote sensing interpretation, the spatial distribution data

120

sets of multi-period terrestrial ecosystem types in China, which included information on ecosystem

121

type in forest areas, grasslands, farmlands, water bodies, wetlands, barren land and built-up land,

122

were developed through the identification and research of various ecosystem types in 1995, 2000,

123

2005, 2010 and 2015. Fig. 2 shows the terrestrial ecosystem types in SHB extracted from the national

124

data. The areas of various terrestrial ecosystems are calculated by ArcGIS 10.1.

125

Fig.2 Terrestrial ecosystem types in SHB

126 127

In addition, the main data sources of the yields of crops per unit area and the average prices of

128

crops required for ESV evaluation were statistical data from the China Statistical Yearbook (China

129

National Bureau of Statistics, 1996-2016) and the China Yearbook of Agricultural Price Survey

130

(China National Bureau of Statistics, 1996-2016).

131

We used survey data and statistical data as the main sources of data for the evaluation of

132

urbanization level. The statistical data was obtained from each city’s statistical yearbooks (City

133

Statistics Bureau, 1996-2016) in SHB, the Zhejiang Statistical Yearbook (Zhejiang Statistics Bureau,

134

1996-2016), and the China City Statistical Yearbook (China National Bureau of Statistics,

135

1996-2016). Taking completeness, accuracy and usability into account, we established a database

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from four important aspects based on previous researches (Li et al., 2012; Tang, 2015; Wang et al.,

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2014; Zhou et al., 2018), including demographic urbanization, spatial urbanization, economic

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urbanization and social urbanization (Table 1). Table 1 Index System for urbanization

139 Object level

Criterion level

Index level

Demographic

Population density (person/km )

urbanization

Total population (person)

Urbanization

Built-up area (km ) Spatial Total sown area of crops (km ) urbanization Urban road area (km ) Per capita GDP (CNY) Economic

Total volume of retail sales of the social consumer goods (CNY)

urbanization

Investment in Fixed Assets (CNY) Total export (USD) Number of beds in hospitals and health centres

Social

Collections in public libraries

urbanization

Number of students in colleges and universities Number of patent grants

140

Given the differences in the dimensions and quantities of each selected indicator, the data was

141

normalized before analysis. And all indicators were divided into positive and negative types. The

142

greater the positive indicators were, the better the conditions for the development of urbanization

143

system were (on the contrary, the larger the negative indicators were, the more unfavourable). The

144

following equations were used to transform the indicator into dimensionless values (Li et al., 2012;

145

Wang et al., 2014; Tang, 2015; Liu et al., 2018).

146

Positive indicator:

yij = ( xij - x j min ) / ( x j max - x j min )

(1)

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Negative indicator: yij = ( x j m ax - xij ) / ( x j max - x j min )

(2)

148

2.3 Methods

149

2.3.1 Evaluation of ESV

150

Costanza et al. (1998) first defined the scientific estimation principles and methods of ESV and

151

estimated the 17 ecosystem services of 16 ecosystems in the world by assuming that the ecological

152

service supply and demand curve was a vertical line. Based on this research, many scholars have

153

conducted different degrees of researches on ESV. Considering the insufficient points of previous

154

researches and referring to some of the reliable results, Xie Gaodi employed a method based on a

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questionnaire survey of 200 ecologists in China and made many improvements to Costanza’s

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research methods. He divided the ecosystem service functions into nine categories: gas regulation,

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climate regulation, water conservation, soil formation, waste treatment, biodiversity conservation,

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food production, raw material and entertainment (Xie et al., 2003; Xie et al., 2005; Xie et al., 2008).

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Moreover, Xie formulated the ESV equivalent factor per unit area of Chinese terrestrial ecosystems

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(Table E.1), which referred to the potential capacity of the relative contribution of ecological services

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generated by different ecosystems.

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The economic value of an ESV equivalent factor is equal to 1/7 of the market value of the

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national average grain yield in that year, which means the economic value provided by natural

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ecosystems without human input is 1/7 of the economic value of food production service provided by

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existing farmland per unit area. So based on the average grain yield and the average market price of

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the original grain published by the relevant departments of the country, the economic value of ESV

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equivalent factor per unit area of the SHB (e) is corrected and calculated using Eq. (3). e=

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1 × (P × Y) 7

(3)

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where e is the economic value of food service provided by per unit area of farmland (the economic

170

value of an ESV equivalent factor),

171

area.

172

is the average crop price, and Y is the yield of crop per unit

The ESV per unit area of an ecosystem (E) is determined as follows: E = e×q

173 174

where e is the economic value of the grain production function of the farmland and q is the ESV

175

equivalent factor per unit area.

176

(4)

According to the distribution of ecosystem type, the calculation formula of the overall ESV is: m

n

ESV = ∑∑ Ai Eij

177

(5)

j=1 i =1

is the area of ecosystem type i, and

is the value per unit area of ecosystem service j of

178

where

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ecosystem type i. The ecosystem services include gas regulation, climate regulation, water

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conservation, soil formation, waste treatment, biodiversity conservation, food production, raw

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material and entertainment. Besides, the ecosystems include forest areas, grasslands, farmlands,

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water bodies, wetlands and barren land.

183

2.3.2 Evaluation of urbanization

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There were m sample subjects and n indicators in the urbanization system. The evaluation of the

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urbanization level was calculated by integrating the internal indicators. The linear weighted sum

186

method was used to evaluate urbanization level, with equation as follows: m

n

U = ∑∑ w j yij

187

(6)

i =1 j =1

188

where wj represents the weight of the jth indicator in the urbanization system,

189

the jth indicator.

is the ith sample of

190

The entropy method (Li et al., 2012; Wang et al., 2015) was used to determine the weight of each

191

indicator in the urbanization index system. The weight of each indicator was calculated according to

192

the index information entropy. First of all, the proportion of each normalized sample to the indicator

193

in the urbanization indicator system was calculated: m

pij = yij / ∑ yij

194

(7)

i =1

195

where

200

.

(8):

ej = −

198

199

is the proportion of

Then the entropy of each indicator in the urbanization indicator system was determined by Eq.

196 197

is the ith sample of the jth indicator,

where,

m 1 × ∑ pij × ln pij ln(m) i=1

(8)

is the entropy of each indicator, m is the number of samples for each indicator.

Finally, the weights of the indicators

were given by Eq. (9): n

w j = (1 − e j ) / (n − ∑ e j )

201

(9)

j =1

202

where n is the number of indicator types.

203

2.3.3 The coupling coordination degree model (CCDM)

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To analyse the relationship between ESV and urbanization on a finer level, different ecosystem

205

service functions and urbanization aspects were regarded as subsystems of ESV and urbanization,

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respectively (Fig.3). In the process of urbanization, expansion of built-up land, growth in population

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density, changes in industrial structure, etc., will lead to more resources consumption, environmental

208

pollution, and higher pressure on ecosystem services. By contrast, the services that ecosystems can

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provide are limited and have a binding effect on urbanization. If the demands and destruction of the

210

ecosystem exceed certain limits, ecosystem services would restrict the development of urbanization.

211 212

Fig.3 The subsystems of urbanization and ESV and the interactive coupled relationship between

213

them

214

In this study, the comprehensive evaluation values of the urbanization and ESV were

215

represented by

and

respectively. The CCDM was used to reveal the interactive coercing

216

relationship between urbanization and ESV. We calculated the CCD between the urbanization and

217

ESV both on the system level and the subsystem level.

218

Coupling refers to the phenomenon that two or more systems interact with each other through

219

various interactions, and the coupling degree (CD) describes the extent of interaction between

220

systems or elements. The coupling coordinated degree (CCD) is an index constructed on the basis of

221

the CD to measure the coupling level of integrated system development. In the case of a total of k

222

systems, the coupling degree model (CDM) is given as follows (He et al., 2017):

C = k{(U1× U 2 × … × Uk ) / [∏1≤i , j ≤ k ,i ≠ j U i + U j ]}1/ k

223

(10)

224

where

represents the comprehensive value of system i, C represents the CD of k systems

225

(ranging from 0 to 1), which reflects the strength of the interaction between the systems. When C = 1,

226

the CD of systems is the highest, which means that the systems and their internal elements reach a

227

benign resonance level, and the structure of the integrated system will develop along a more orderly

228

direction. When C = 0, the CD of systems is the lowest, which means that the systems and their

229

internal elements are basically independent, and the integrated system structure will tend to be

230

disordered (Wang et al., 2014).

231

The CDM can reflect the coupling degree of systems, which is of great significance when

232

studying the interactions between urbanization and ecosystems and grasping the order of

233

development. However, the CD is insufficient when the overall efficacy and synergy of the two

234

systems are not considered (Wang et al., 2014). Coordination is a consistent, benign, and mutually

235

reinforcing relationship between systems or within the system, which guarantees the long-term

236

healthy development of the entire system. Therefore, the CCDM of urbanization and ESV was

237

adopted to judge the coordination in systems, and its formula is shown as Eq. (11) and Eq. (12) (Liao,

238

1999):

239

T = aU1 +bU2 (i ≠ j) , and

(11)

240

D = (C×T)1/2 ,

(12)

241

where D represents the CCD between

and

, T reflects the overall effect and level of

and

242

, whereas a and b denote undetermined coefficients which are 0.5 in this research because

243

urbanization and ESV are considered equally important. D ranges from 0 to 1, showing that higher

244

value indicates higher coherence level among subsystems. The coupling coordination level was

245

divided into four different stages with reference to the previous coupling coordination division

246

standard (Tang, 2015), including the low coordination coupling period, the antagonistic period, the

247

running-in period and the high coordination coupling period.

248

When 0≤D<0.2, the coupling coordination relationship between

and

is in the low-level

249

coupling period, which indicates that the coupling effect in the development process is disordered,

250

leading to the unhealthy development of the system. When 0.2≤D<0.5, the coupling coordination

251

relationship between

252

along a harmonious and orderly direction, but the degree of coordination remains low, and the

253

subsystems still cannot promote each other’s development. When 0.5≤D<0.8, the coupling

254

coordination relationship between

255

When 0.8≤D≤1.0, the coupling coordination relationship between

256

coordination coupling period, the relationship is coordinated and optimized, and the development

257

process and coupling effect are harmonious and orderly. To show the difference in the level of

and

is in the antagonistic period, and the coupling effect develops

and

is in the running-in period, which is more optimized. and

is in the high

258

coupling coordination between cities more clearly, these four coupling coordination stages were

259

refined into six specific types, including extreme incoordination (F), moderate incoordination (E),

260

mild incoordination (D), primary coordination (C), good coordination (B), and high-quality

261

coordination (A) (Table 2). Table 2 Coupling Coordination Types

262

Stage

D Value

Specific type

D Value

Interval

High-Level Coupling

0.8-1

High-quality

0.8-1.0

A

Good coordination

0.6-0.8

B

Primary coordination

0.5-0.6

C

Mild incoordination

0.4-0.5

D

Moderate

0.2-0.4

E

coordination Running-in Period

Antagonistic Period

0.5-0.8

0.2-0.5

incoordination Low-Level Coupling

0-0.2

263

3. Results

264

3.1 Estimation of ESV

Extreme incoordination 0-0.2

F

265

Table 3 shows the total ecosystem service value (ESV) and per unit area value of ecosystem

266

service (PESV) of SHB. PESV was used to explore the differences of ESV in the areas of the same

267

size. The results showed that the ESV and PESV in Hangzhou were significantly higher than those in

268

other cities in SHB and were almost 10 times of that in Jiaxing. The ranking of other cities is

269

Shaoxing > Huzhou > Ningbo > Shanghai > Jiaxing, with no significant change from 1995 to 2015.

270

In terms of the ecosystem components of each city, in Hangzhou, Shaoxing, Huzhou and Ningbo,

271

forest ecosystems accounted for the majority of the ecosystem, whereas in Shanghai and Jiaxing,

272

farmland systems occupied the dominant position.

273

Table 3 The total ecosystem service value (ESV) and per unit area value of ecosystem service (PESV) of SHB 1995

2000

2005

2010

2015

ESV

PESV

ESV

PESV

ESV

PESV

ESV

PESV

ESV

PESV

(10 $)

($)

(10 $)

($)

(10 $)

($)

(10 $)

($)

(10 $)

($)

Shanghai

38.65

5842

38.53

5823

33.63

5082

32.11

4852

31.38

4743

Hangzhou

127.79

7593

128.46

7633

128.14

7614

127.90

7600

127.03

7548

Ningbo

52.29

6021

52.21

6013

51.49

5930

51.57

5939

50.56

5823

Jiaxing

13.61

3439

13.66

3454

13.63

3446

13.47

3405

12.92

3267

Huzhou

38.57

6634

39.17

6738

39.34

6767

39.40

6777

39.07

6720

Shaoxing

58.50

7182

58.93

7234

58.17

7141

57.95

7114

57.57

7067

274 275

Fig. 4 ESV changes and ESV change rates of cities during 5 stages

276

Moreover, the ESV change and ESV change rate for each city were obtained by some primary

277

operations of the contents in Table 3 (Fig. 4). Obviously, from 1995 to 2015, there were significant

278

differences in ESV changes among the six cities. Huzhou’s ESV increased during the first three

279

stages due to the expansion of waterbodies and wetlands and then decreased from 2010 to 2015. The

280

ESVs of the other five cities were reduced from 1995 to 2015. In addition, the ESVs of the cities in

281

Zhejiang Province had a fluctuation trend during the time period. The ESVs of Hangzhou, Jiaxing

282

and Shaoxing had increased during the first stage but had decreased from 2000 to 2015, while

283

Ningbo's ESV had increased during the third stage. Besides, from 1995 to 2015, the ESV change and

284

change rate of Shanghai were the largest, declining by more than $700 million (USD) and nearly 20%

285

of ESV in 1995, which was directly related to the fastest urbanization process. Fig. 4 also shows that

286

the ESV changes of Hangzhou, Jiaxing, Huzhou and Shaoxing were relatively small, with the ESV

287

change rate of Hangzhou was the smallest.

288 289

Fig.5 Proportions of various functions to the total ESV in 1995 and 2015

290

Fig. 5 shows the proportions of various ecosystem services to the total ESV of 6 cities in 1995

291

and 2015. Ecosystem service functions that changed more greatly in proportions are highlighted by

292

special markers. In this study, the function with the highest proportion of ESV was defined as the

293

dominant function. From 1995 to 2015, the proportion of various ecosystem service functions of

294

each city changed slightly, and such subtle changes were barely discern in graphics. In 1995, the

295

dominant function was food production in Shanghai and Jiaxing, soil retention in Ningbo, Huzhou

296

and Shaoxing, and water regulation in Hangzhou, respectively. By 2015, water regulation had

297

overtaken soil retention and had become the dominant function of Huzhou, while the dominant

298

functions of other cities had not changed. In addition, from 1995 to 2015, the changes of proportions

299

of various functions in Shanghai and Hangzhou were no more than 0.005, which indicated that the

300

structures of their ecological services function were relatively stable. Jiaxing had the greatest change

301

in its functional structure, and its proportions of food production had decreased by more than 0.01,

302

while the proportions of water regulation and climate regulation had increased by more than 0.01.

303

3.2 Evaluation of urbanization

304

Fig.6 shows the differences of urbanization level in SHB during 1995 and 2015.

Shanghai’s

305

urbanization level was the highest in all the years, ranging from 0.362 in 1995 to 0.973 in 2015.

306

Huzhou’s urbanization level was the lowest in all the years, ranging from 0.121 in 1995 to 0.231 in

307

2015. The urbanization levels for other four cities were in decreasing order of Hangzhou > Ningbo >

308

Shaoxing > Jiaxing in 2015, with corresponding values of 0.575, 0.437, 0.311 and 0.280, respectively.

309

While in 1995, the urbanization levels for these cities were in decreasing order of Hangzhou >

310

Ningbo > Jiaxing > Shaoxing, with corresponding values of 0.362, 0.163, 0.139, 0.126 and 0.124,

311

respectively. It showed that Shaoxing had more rapid urban development during the two decades

312

than Jiaxing.

313 314

Fig.6 Urbanization levels of cities during 1995 and 2015

315

Fig. 7 shows the urbanization levels in subsystem (demographic urbanization, spatial

316

urbanization, economic urbanization and social urbanization). Shanghai's demographic urbanization

317

level was much higher than other cities. The gaps of economic urbanization among the cities in

318

Zhejiang Province were not large in 1995, while significant spatial differences in the economic

319

urbanization level were identified in 2015. For spatial urbanization level, Shanghai and Huzhou were

320

the leaders in 1995, but by 2015, Huzhou's spatial urbanization level was the lowest. For social

321

urbanization level, the situation in 1995 was similar to the economic urbanization, while in 2015,

322

Shanghai was higher than other cities, Hangzhou was the second and Huzhou was the last. The

323

development process of these four subsystems indicated that the spatial urbanization process of each

324

city was fast in the early stage but became slow gradually. In addition, the levels of economic and

325

social urbanization changed little in the early stage, but it changed rapidly in the next few years.

326 327

Fig.7 Levels of different systems of urbanization

328

Note: 1. Shanghai; 2. Hangzhou; 3. Ningbo; 4. Jiaxing; 5. Huzhou; 6. Shaoxing.

329

3.3 The interactive coercing relationship between

330

urbanization and ESV

331

3.3.1 The overall interactive coercing relationship

332

Fig. 8 shows the CCDs between urbanization and ESV and comparison of their levels. The

333

urbanization levels of Shanghai and Jiaxing were higher than the ESV levels, and other cities’

334

urbanization levels were lower than the ESV levels. From 1995 to 2015, the CCDs between

335

urbanization and ESV for Hangzhou were the highest, and for Jiaxing they were the lowest. In

336

addition, from 1995 to 2015, Huzhou and Ningbo had the largest changes in CCDs, which increased

337

by two grades, and other cities’ CCDs increased by one grade.

338 339

Fig.8 the CCD between urbanization and ESV and comparison of their levels

340

3.3.2 The interactive coercing relationship between ESV and subsystems of

341

urbanization

342

343

Fig.9 the CCD of ESV and different urbanization systems

344

Note: DE - Demographic urbanization; EC – Economic urbanization; SP – Spatial urbanization; SO – Social

345

urbanization.

346

Fig. 9 shows the results of CCD between ESV and urbanization subsystem. The CCDs of SO,

347

EC and SP in Hangzhou were higher than other cities in 2015, which were close to 0.9. The CCDs of

348

all the four subsystems in Jiaxing were lower than other cities, which were less than 0.3. The CCDs

349

between ESV and all subsystems of urbanization in Shanghai, Hangzhou, Ningbo, and Shaoxing had

350

all reached the mild incoordination type and above, but the change direction and magnitude of the

351

CCD of each city differed.

352

From the perspective of each city's own development, the CCDs of demographic urbanization

353

and ESV of each city had not changed much, but the change was extremely obvious in terms of the

354

balance of the four systems. In 1995, the disparity in the CCD between urbanization and ESV in

355

Shanghai was conspicuous. With the development of urbanization, the CCD of demographic

356

urbanization (or spatial urbanization) and ESV appeared to be degraded. However, by 2015, the

357

CCDs of ESV and various urbanization systems were similar and relatively balanced. In 2015, the

358

CCDs of demographic urbanization and ESV in Hangzhou, Ningbo, Huzhou and Shaoxing were the

359

lowest among the four systems. The CCD between spatial urbanization and ESV in Huzhou was the

360

largest during 1995 and 2015, while the CCD between economic urbanization and ESV changed the

361

most. Besides, the CCDs of ESV and demographic urbanization, economic urbanization and spatial

362

urbanization in Jiaxing had been degraded.

363

3.3.2 The interactive coercing relationship between urbanization and subsystems

364

of ESV

365 366

Fig.10 Coupling Coordination Degrees of urbanization and different ecosystem service functions

367

Note: GR – Gas regulation; CR – Climate regulation; WR – Water regulation; SR – Soil retention; WT – Waste

368

treatment; BP – Biodiversity protection; FP – Food; RM – Raw material; EC – Entertainment.

369

Fig. 10 shows the CCDs of urbanization and different ecosystem service functions. Hangzhou

370

displayed highest level with all the values more than 0.5, and Jiaxing represented lowest level with

371

all the values less than 0.5. Moreover, the CCDs in SHB were improved in many systems, but the

372

relationship between urbanization and the food production function was reduced in Ningbo and

373

Jiaxing in 1995, 2010 and 2015. Besides, the CCDs between urbanization and most of the various

374

functions in Hangzhou, Ningbo, Huzhou and Shaoxing were relatively balanced, and those of a few

375

functions were lagging, especially the food production function. In addition, the climatic regulation

376

and waste treatment functions for Huzhou and the waste treatment function for Shaoxing were

377

lagging behind. The CCD between urbanization and food production in Shanghai was high, and for

378

raw material, gas regulation and biodiversity protection the CCDs were very low.

379

4. Discussion

380

4.1 Loss of ESV under rapid urbanization

381

The evaluation results of ESV and urbanization level in SHB from 1995 to 2015 showed that

382

the region suffered huge losses of ESV, which was closely related to the process of urban

383

development. Throughout the study period, the 6 cities of SHB had made great progress in the fields

384

of demographic, spatial, economic and social urbanization. However, the speeds and overall levels of

385

urbanization of the cities were quite different, and the subsystem development of urbanization in

386

each city was not balanced. The development of spatial urbanization was earlier than of other

387

systems. In recent years, the rapid urbanization of SHB has led to a sharp increase in built-up land,

388

therefore, high-quality cultivated lands have been eroded, seriously threatening the food and

389

ecological security of this region. As land resources become scarcer, the behindhand pattern of

390

development in the past has been unsustainable (Zhang et al., 2019). The complex and unbalanced

391

urbanization is reflected in the change of ESV. Rapid urbanization has led to a sharp increase in

392

built-up land as well as a sharp decline in cultivated land. The adjustment in industrial structure,

393

especially the increase in aquaculture area year by year, has resulted in the reduction in cultivated

394

land and increase in water area. In the early days, due to government policies, the area of water

395

bodies and density of artificial ditches increased, linking various ecosystem types and facilitating the

396

flows of information, material and energy among patches. As a result, the water regulation function

397

was improved. In Ningbo, a large amount of water areas and wetlands were occupied by cultivated

398

land due to land reclamation, therefore, the ESV was reduced greatly. Sufficient attention should be

399

paid to this problem because coastal wetland ecosystem is one of the most productive natural

400

ecosystems (Sun et al., 2017). During the 20 years of urbanization, the ESVs of the five cities in

401

SHB, except Huzhou, had decreased. In terms of total volume, the ESVs in Shanghai and Ningbo

402

changed the most and were reduced by more than $700 million (USD) and $100 million (USD),

403

respectively. In terms of change rate, Jiaxing surpassed Ningbo and ranked the second. Jiaxing is

404

close to the mega city - Shanghai. The perfect development conditions led to the extraordinary speed

405

of urbanization, which consumed many cultivated lands and ESV. Despite the most severe decline of

406

ESV in Shanghai, the structural change of its ES was the slightest. However, the structure of

407

ecosystem service functions changed greatly. It indicated that the spatial urbanization level of

408

Huzhou was the lowest, which is insufficient to consume the ESV brought by the increase of water

409

area.

410

4.2 Coordination degree between urbanization and ESV

411

Urbanization could influence the ecological environment through population growth, economic

412

development, energy consumption and traffic expansion (Salvati et al., 2018). Conversely, the

413

ecological environment could constrain urban development through population expulsion, capital

414

exclusion, capital competition and policy intervention (Fang et al., 2016). Coordinating the

415

relationship between urbanization and ecological environment is extremely significant for the healthy

416

development of urban agglomerations.

417

The analysis conducted in this study revealed the relationship between urbanization and ESV in

418

SHB and its specific performance. The urbanization and ESV of SHB were regarded as two mutually

419

influential systems. Both the system level and the subsystem level were analyzed. Overall, the

420

ecosystems of Shanghai and Jiaxing, the cities with the highest reduction rate of ESV in the study

421

area, were under greater pressure. Although there were differences between cities, CCDs in each city

422

in the long-term sequence were increasing. However, the complex internal relations of urbanization

423

and ESV in SHB were not always being optimized. First of all, the relationship between the

424

subsystems of urbanization and ESV of cities in SHB was not balanced. What is more remarkable is

425

that the coupling coordination relationship between demographic (or spatial) urbanization and ESV

426

in some cities was imbalanced and degenerate, and the coupling coordination relationship between

427

urbanization and food production functions also deteriorated. As the city with the worst relationship

428

between urbanization and ESV in the study area, Jiaxing exposed some issues in the development

429

over the past 20 years. We advocate that government should adapt to local conditions and policies in

430

accordance with the characteristics of specific urban areas.

431

Urban expansion occupied a large amount of ecological land, and the un-coordination between

432

demographic urbanization and spatial urbanization was a common phenomenon in the study area.

433

This phenomenon has caused varying degrees of damage to the original functional composition of

434

the ecosystem, resulting in the problem of high rate and low quality of urbanization. Due to the

435

limitation of natural geographical conditions, the land use type most often occupied by urbanization

436

is cultivated land. Low land use efficiency, irrational structure, unrealistic land planning, imperfect

437

land use mechanism, and other problems lead to excessive occupation of cultivated land. The area of

438

cultivated land is rapidly reduced, disrupting the ecological process that tends to balance, causing

439

reduced the food production value of the ecosystem. The relationship between food production

440

function and urbanization in many cities is insufficiently coordinated, and the process of their

441

development is very tortuous. Whether China as a populous country can maintain a stable amount of

442

cultivated land is a strategic issue related to food security, which even national security and has a

443

major impact on the food security of the world (Hou et al., 2019). With the development of

444

urbanization, land resources are becoming increasingly scarce, and the extensive development mode

445

in the past has been unsustainable (Deng et al., 2015). In addition, despite the sharp decline of

446

cultivated land in SHB, facility agriculture has gradually emerged, and the extensive use of chemical

447

fertilizers and pesticides has caused agricultural non-point source pollution. Meanwhile, the rapid

448

development of industry has discharged a large amount of industrial wastewater, and the explosion of

449

population has brought abundant domestic sewage (Kong et al., 2018). These have led to pollution of

450

the water and soil environment in the SHB metropolitan region. Therefore, water regulation, waste

451

treatment, and soil retention are also important ecosystem service functions in the region. However,

452

the interactive coercing relationship between these functions and urbanization in SHB were less than

453

satisfactory. There is an urgent need to protect cultivated lands, waters, and woodlands to maintain

454

the functions of ecosystems. In 2015, the Political Bureau of the Central Committee put forward a

455

new concept of "greenization", which refers to an environment-friendly and resource-saving

456

development pattern (Zhang et al., 2019). It is necessary to establish the concept of sustainable

457

development, balance the relationship between urban development and ecological environmental

458

protection, and promote economic development with scientific progress and technological

459

innovation.

460

4.3 Implications

461

Since its reform and opening up, China has developed rapidly, with remarkable

462

accomplishments in industrialization, urbanization and urban-rural integration. However, it is found

463

that the speed of China's spatial urbanization was much faster than that of demographic urbanization

464

(Feng et al., 2019). According to the statistics, the built-up area of 660 cities nationwide increased by

465

5% from 1998 to 2002, while the average annual growth rate of the population was only 1.3%.

466

However, the area of the country's cultivated land was only 1.22 ×108 hm2 in 2008, which was

467

almost equal to the arable land red line of 1.20×108 hm2 (Zhou et al., 2018). Our research also

468

verified the issue mentioned above.

469

More attention should be paid to the quality rather than the speed of such large-scale

470

urbanization (Bai et al., 2018). In terms of urbanization policy in the future, the main mission of SHB

471

is to achieve demographic urbanization with high quality rather than blindly expanding the urban

472

area. The urban construction strategy is to integrate the occupied land and to utilize the existing

473

built-up land intensively rather than occupying the cultivated land or natural ecosystems. Intensive

474

use of land would help to maintain the stability of cultivated land (Zhou et al., 2018). For

475

ecologically fragile cities like Shanghai and Jiaxing where the original ESV was relatively low and

476

where the built-up land had encroached on a large amount of cultivated land, the government should

477

appropriately control the scale of large cities and reasonably determine the boundaries of urban areas.

478

Moreover, developing satellite cities and small towns were suggested in the future urban construction,

479

which indicated that core cities with large radiation effects can promote coordinated regional

480

development for surrounding cities.

481

Urban agglomeration is an important support for regional economic development. Therefore, it

482

is directly related to the quality of regional economy and urbanization (Ye et al., 2018). The

483

development of large urban agglomerations requires the support of ecosystem services in a wide

484

range, and the scale of urbanization is constrained by ecological and environmental carrying capacity

485

(Lu and Chen, et al., 2015). As a highly sensitive and concentrated region of the Yangtze River

486

Economic Belt, the SHB metropolitan region faces severe ecological challenges. In that respect, we

487

propose that the governments or policy makers in SHB metropolitan region should protect the

488

ecological environment and reduce the loss of ESV. Considering the fact that the expansion of water

489

area leads to an increase in ESV at early stages, it is clear that protecting ecosystems with high ESV

490

equivalence factor (such as forests, wetlands and waterbodies) is the most effective way to increase

491

ESV (Chuai et al., 2016). However, when making land use policies, the government should not

492

increase the area of these ecosystems blindly. Human beings need a diverse ecosystem of sundry

493

functions to maintain a healthy life. Moreover, unreasonable land allocation can cause structural

494

imbalances to varying degrees. Therefore, it is important to pay attention to the protection of the

495

original ecological state and the integrity of the ecosystem.

496

4.4 Limitations

497

Based on the study of the spatiotemporal dynamic evolution process and relationship between

498

urbanization and ESV in urban agglomeration, this research emphasized the importance of the

499

coordinated development of urbanization and ESV. By adjusting the value coefficient in accordance

500

with the characteristics of different regions, this framework could also be applied to other regions of

501

the world. However, there were still room for improvement in further research. Many issues related

502

to ESV still need to be further explored, such as the actual form of demand for ecosystem services,

503

the response of ecosystems to human activities, the combined effects of ecosystem services and the

504

importance of its internal structure. Thus, there is an inevitable gap between estimated ESV and its

505

actual value by relying solely on expert decision-making and existing knowledge, which would

506

influence the estimation of ESV in some regions at specific time period. Moreover, the value

507

coefficient method relies entirely on land use patterns. However, unique geographical characteristics

508

of cities, such as topography, may cause differences in the service functions of the same ecosystem

509

(Ye et al., 2018). This limitation can be mitigated by integrating spatial models of biophysical and

510

economic systems (Bryan and Crossman, 2013). In further studies, it is necessary to establish a more

511

detailed and comprehensive ESV model to eliminate these effects. In additions, due to the

512

accessibility of data related to urbanization, this research can only be applied at urban scale, which

513

leads to a relatively rough spatial dimension. Analyzing the interactive coercing relationship between

514

urbanization and ESV from a more detailed spatial scale with the combination of the index

515

dimension, is more conducive to our in-depth exploration of their relationship.

516

5. Conclusions

517

Using SHB metropolitan region as a case study, this research estimated the level of ESV and

518

urbanization, and explored the interactive coercing relationship between ESV and urbanization from

519

the system level and the subsystem level. The results showed that:

520

(1) The forest-rich cities in the southwestern part of the SHB region had relatively high ESVs,

521

while the cities in the central and north-eastern plains were dominated by cultivated land resources,

522

and their ESVs were relatively low. With the development of urbanization, the ESVs of most cities

523

had declined significantly. The rapid urbanization process of Shanghai and Jiaxing had a particularly

524

serious impact on the ecological environment, which was characterized by their ESV decline

525

( ranked first and second respectively). The contradiction between urbanization and ecological

526

environment needed to be solved urgently.

527

(2) The phenomenon prevailed in the study area that the spatial urbanization level is well above

528

other urbanization levels. This problem led to the low quality of urbanization and serious damage to

529

the ecological environment.

530

(3) The relationship between urbanization and ESV for cities in SHB basically became more

531

coupling-coordinated, but the relationship between some subsystems of ESV and urbanization had

532

degraded, among which demographic urbanization and food production function are the major

533

concerns.

534

The SHB metropolitan region is well known as a prominent region where the economy is

535

developing rapidly and it is also a fragile area with increasingly sharp contradictions between people

536

and land.

The exploratory works carried out in this study can also be applied to other rapidly

537

urbanizing at home and abroad, which are not only crucial to the regional sustainable

538

development, but are also conducive to promoting the orderly and effective development of the

539

metropolitan region’s economy.

540 541

Acknowledgement This research was supported by the National Natural Science Foundation of China under Grant

542

41701484 and Grant 41601459.

543

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Highlights Southwestern part of SHB had higher ecosystem service values than northeastern plains. Land use change caused by urbanization led to the degradation of ecosystem service values. The interaction between urbanization and ecosystem service value became more coupling coordinated. Food production function shows a declining tendency in the interaction. There was little progress in the relationship between demographic urbanization and ecosystem service value.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: