Does financial agglomeration promote the green development in China? A spatial spillover perspective

Journal Pre-proof Does financial agglomeration promote the green development in China? A spatial spillover perspective

Huaxi Yuan, Tianshu Zhang, Yidai Feng, Yaobin Liu, Xinyue Ye PII:

S0959-6526(19)32668-X

DOI:

https://doi.org/10.1016/j.jclepro.2019.117808

Article Number:

117808

Reference:

JCLP 117808

To appear in:

Journal of Cleaner Production

Received Date:

10 November 2018

Accepted Date:

26 July 2019

Please cite this article as: Huaxi Yuan, Tianshu Zhang, Yidai Feng, Yaobin Liu, Xinyue Ye, Does financial agglomeration promote the green development in China? A spatial spillover perspective, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.117808

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Journal Pre-proof Does financial agglomeration promote the green development in China? A spatial spillover perspective

Huaxi Yuan a, b , Tianshu Zhangc , Yidai Fenga , Yaobin Liua , Xinyue Yed a School

of Economic & Management, Nanchang University, Nanchang 330031, China

b Department c School d Urban

of Geography, Kent State University, Kent 44240, USA

of Earth Sciences, Zhejiang University, Hangzhou 3100272, China Informatics & Spatial Computing Lab, Department of Informatics, New Jersey Institute of Technology,

Newark 07102, USA

Funding: This work was supported by Major Program of National Social Science Foundation of China (18ZDA047).

* Corresponding author. School of Economic & Management, Nanchang University, Nanchang 330031, China ** Corresponding author. Urban Informatics & Spatial Computing Lab, Department of Informatics, New Jersey Institute of Technology, Newark 07102, USA E-mail addresses: [email protected](Y. Liu), [email protected](X. Ye).

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Does financial agglomeration promote the green development in China: A spatial

2

spillover perspective

3 4

ABSTRACT: The role of financial agglomeration in China's green development has generated

5

many fascinating debates for scholarly research, but few studies have considered environmental

6

effects of financial agglomeration from the spatial spillover perspective. Taking both natural and

7

socio-economic factors into accounts, this paper explores the interaction between financial

8

agglomeration and green development using the panel data of 285 prefecture-level Chinese cities

9

from 2003 to 2015. The results show that both financial agglomeration and green development

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have demonstrated a trend of spatial convergence. Secondly, financial agglomeration can promote

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the green development of both focal and surrounding cities. Thirdly, financial agglomeration is

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conducive to enhance regional green development level in Western China, while only Eastern and

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Central China witness significant spatial spillover effects. Fourthly, the direct effect of financial

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agglomeration on green development at the city level exists, whereas megacities and large cities

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witness a significantly adverse spatial spillover effect. This paper also presents policy

16

recommendations.

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Keywords: Financial agglomeration; green development; heterogeneity; spatial econometric

18

model

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1. Introduction

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As an essential way to solve the contradiction between environmental protection and

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economic growth, China has been pursuing energy efficiency improvement to enhance the

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practice of green development and promote regional coordinated development (Zhang et al., 2014;

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Jiang W, 2016; Meng et al., 2016; Shi et al., 2016; Zeng et al., 2017; Zhao et al., 2018). The

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phenomenal growth of the financial industry in China has advanced theoretical inquiries into the

25

interaction between financial agglomeration and green development. The financial industries tend

26

to influence the practice of green development through agglomeration and diffusion effects as well

27

as the financial functions (Liu et al., 2007). Moreover, due to the accumulation cycle causality

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effect, the impact of financial agglomeration not only exists within the area, but spreads out over

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the neighboring areas through spatial spillover effect (Yu et al., 2017). In July 2013, the Chinese

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State Council promulgated the 13th Five-Year Plan (2016-2020), considering the financial

1

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development as an accelerator of economic restructuring, transformation and upgrading as well as

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the green development.

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Originated from the notions of green economy and sustainable development, most theories

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on green development focus on circular economy, low carbon economy, and ecological economy

35

(Austin, 2017; Liu et al., 2018; Weber and Cabras, 2017). Green development can be defined from

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four perspectives (Table 1): systematic, natural, economic, and sci-tech views. The systematic

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perspective argues that green development is essentially sustainable development (Lv, 2013).

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However, Wang and Zhang (2012) considered green development as blue sky and green land from

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a natural perspective. Shi and Liu (2013) stated that green development equals to economic

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development, while Yang and Gao (2006) defined green development as a process of

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technological revolution from a sci-tech perspective. All efforts described above have explored

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how green development might be associated with the environmental effects, economic impacts,

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and technological changes as an interactive and dynamic process. However, most studies only

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focus on one or a few elements. Hence, this paper tries to probe green development with a more

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comprehensive causality analysis.

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Tab 1

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The comparison of green development concepts Green development

Main theme Comprehensive

Systematic view

development of the economy, society and environment Emphasis on

Natural perspective

environmental protection

Goal

Problems

Promote sustainable

Neglect the cyclical

economic development in

value-added process of

the nature

green development

publications

Lv (2013)

Neglect the dynamic Protect environment and

process of ecological

Wang and Zhang

restrict over-exploitation

environment capacity

(2012)

and resource capacity Promote economic

Economic perspective

Focus on economic

development and solve

Neglect human

growth

social problems by

development

economic means

Sci-tech view

(2013)

Favor green

Focus on green technology

Neglect the application

technology as the

and socio-economic

of diversification driver

driving force

problems

force

2

Shi and Liu

Yang and Gao (2006)

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Financial agglomeration is the accumulation of financial industry in the space. The industrial

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agglomeration is "a group of geographically adjacent, related organizations and institutions,

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located in a certain field that are linked by each other's commonality and complementarity"

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(Porter, 1990). Research has been conducted to examine its measurement, mechanisms, and

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implications (Billings and Johnson, 2016; Ellison and Glaeser, 1997; Shen et al., 2018). Existing

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methods

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Hirschman-Herfindahl index (HHI) and EG index (Cheng, 2016; Hirschman, 1964; Shen et al.,

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2018; Wang et al., 2018). In addition, industrial agglomeration has been regarded as an important

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approach to improve institutional efficiency, national innovation capability, and competitiveness

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(Han et al., 2018; Sellitto et al., 2017). Therefore, based on the law of industrial agglomeration

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and characteristics of financial industry, this paper defines financial agglomeration as the

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accumulative process of the optimization and reorganization of financial industry and its related

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industries giving rise to the establishment of capital, information, innovation and market centers in

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a specific region through the flow of elements.

of

evaluating

financial

agglomeration

mainly

include

location

quotient,

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Revealing the impact of financial agglomeration on green development is a hot topic. The

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current literature mainly illustrates it from three aspects: natural, economic, and sci-tech effects.

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However, financial agglomeration can influence the environment through externalities, thus

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generating natural benefits, i.e. environmental effects. Yan et al. (2016) certificated that there

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exists an inverted U-shaped relationship between credit scale and carbon dioxide intensity whereas

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a U-shaped relationship between FDI scale and carbon dioxide intensity holds in China. Maji et al.

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(2017) employed the co-integration analysis to emphasize that financial agglomeration may result

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in the speeding up of carbon dioxide emissions in both long-term and short-term, leading to the

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environmental deterioration. Financial agglomeration is advantageous to economic development

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through agglomeration effects, thereby bringing about economic effects. It has been confirmed

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that there exists strong correlation between financial agglomeration and economic growth and

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different levels of financial development exert different impacts on economic growth (Fung, 2009;

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Jakob B. Madsen, 2018). Furthermore, verified the fact that financial agglomeration can not only

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assist in bringing along the upgrading of industrial structure but also accelerate the growth of the

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substantial economy through its agglomeration, diffusion effect and financial function(Liu et al., 3

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2007) . Financial agglomeration can be seen as an impetus for innovation through knowledge

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spillover and competition mechanism, thus giving rise to scientific and technological effects.

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Although the role financial development plays on industrial innovation remains controversial, Ho

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et al. (2018) verified that financial deepening enables the promotion of innovation only within a

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fully democratic political system.

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The above research has provided a theoretical basis and framework for this paper.

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Nevertheless, some research gaps exist. Firstly, the indicators are still ambiguous. How to measure

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green development still remains controversial. Second, most studies only examine the impact of

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financial agglomeration from one dimension, but fail to reveal the spatial spillover effects brought

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by financial agglomeration. Third, the spatial heterogeneity of financial agglomeration has largely

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been ignored. Such heterogeneity should be taken into consideration given the close nexus

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between financial agglomeration effect and the scale of places.

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Accordingly, this paper firstly explores whether and how financial agglomeration can affect

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green development based on the theory of agglomeration economy, polarization-trickle-down

91

effect theory and information asymmetry theory. Secondly, we select data of 285 Chinese

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prefecture-level cities from 2003 to 2015 to analyze the direct and spatial spillover effects of

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financial agglomeration on green development. Finally, the impact of financial agglomeration on

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green development is examined from regional and urban heterogeneity perspectives. The research

95

question follow: 1) Can and how financial agglomeration affect China's green development? 2)

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What are the differences in the impact of financial agglomeration on green development due to

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regional and urban size heterogeneity?

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This paper contributes to the literature in three ways. First, the paper provides rich empirical

99

evidences regarding how to use financial agglomeration to promote the green development and

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transformation of economy and society. Second, from the methodological perspective, the

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STIRPAT model is expanded spatially by adopting SDM to examine the impact of financial

102

agglomeration on green development. In this way, the direct effect and spatial spillover effect of

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financial agglomeration on green development are investigated simultaneously, thus avoiding the

104

bias of coefficient caused by space effect omission. Third, this paper adopts the DPSIR model by

105

considering the connotation and causal relationship of green development. 4

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2. The theoretical framework

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2.1. The direct effect of financial agglomeration on green development

108 109

Fig. 1 The mechanism of financial agglomeration influencing green development

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How does financial agglomeration affect local green development (direct effect)? This paper

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analyzes this question from the perspective of the growth, formation and evolution of financial

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agglomeration (Fig. 1):

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(1) The growth of financial agglomeration. To understand the direct effect of financial

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agglomeration on green development, it is necessary to identify the operation mechanism of

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financial agglomeration. Financial agglomeration is the geographical aggregation of financial

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industry and its related industries. Financial structure flexibly allocates capital according to the

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information obtained, thus developing financial market and encouraging regional innovation.

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Since the financial institutions in the central city have strong revenue capacity, the capital and

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information of the surrounding areas continue flowing to the central city, which enjoys abundant

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capital, information, and the market. When the capital reaches a certain level, a strong capital

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center will be formed in the financial agglomeration area. At the same time, as financial

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institutions need to obtain plenty of information to carry out business, the central city turns to be

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the regional information center. The formation of capital and information center will promote the

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business and spatial expansion of financial institutions, attracting more customers to establish the

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market center. Under the action of circular and cumulative causation (Berger, 2008), capital 5

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center, information center, and market center would jointly provide a supportive external

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environment for technological innovation, enabling the formation of innovation centers.

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(2) The formation of agglomeration effect. The emergence of capital center, information

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center, market center and innovation center facilitates the agglomeration effect. Specifically,

130

agglomeration effect can be divided into natural, economic, and technological effects. Natural

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effect refers to that financial agglomeration can improve environmental quality and obtain green

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ecology. Financial industry is generally located in areas with superior geographical location and

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beautiful environment, which has a high requirement for environmental quality and a strong desire

134

for pollution control (Wyckoff, 1989). In addition, the financial industry not only shoulders the

135

responsibility of economic development, but also undertakes the mission of environmental

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protection, which enables financial institutions to improve regional environmental quality by

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adjusting the direction and flow of financial resources. Economic effect indicates that financial

138

agglomeration can bring high-quality economic growth and increase green economic benefits.

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According to the industrial structure evolution theory (Kuznets, 1941), the rapid development of

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the productive services, represented by the financial industry, has led to the exclusion of a large

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number of polluting industries. As a result, the regional industrial structure is moving toward

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cleaner and higher-end industries, achieving win-win results in economic growth and

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environmental protection. Technological effect means that financial agglomeration can foster

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innovation environment, stimulate innovation vitality and develop green science and technology.

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Based on the innovation theory (JosephA, 1946), the spatial accumulation of the financial industry

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will attract the relevant industries to locate in the surrounding area. This not only expands the

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regional market capacity, but also promotes the rapid diffusion of knowledge and technology.

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(3) The transmission of action mechanism. From the perspective of optimal allocation of

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resources, financial institutions can control the flow of capital to release green dividends by

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guiding financial resources to support green industries. From the perspective of industrial structure

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upgrading, financial institutions can optimize regional industrial structure, realize green

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transformation of industrial structure, and improve the quality of regional economic growth by

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financing clean industries and green high-end industries. In addition, the information center

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formed by financial agglomeration can provide abundant market information for business in the 6

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region to improve production efficiency and achieve high-quality development. The innovation

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center formed by financial agglomeration can not only provide a large number of green

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technologies, but also push enterprises to increase innovation input to maintain industrial status,

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thereby forming a benign innovation circle and improving the level of regional green

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development. From the aspects of knowledge spillover accelerating, financial agglomeration

160

gathers not only abundant capital, but also a large number of professional labor force. The

161

formation of information center and market center provides a platform for cooperation and

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exchange between enterprises, which is conducive to promoting the free flow of knowledge

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among different industries and enterprises, so as to improve the innovation level of the whole

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industry and achieve the goal of green development. Because financial institutions already have

165

the green development feature, the agglomeration effect further strengthens such trend.

166

2.2. The spatial spillover of financial agglomeration on green

167

development

168

This paper examines spatial spillover effects from both polarisation effects and trickle-down

169

effect (Fig. 2). The adverse and positive effects on the economic growth of underdeveloped areas

170

from the relatively developed areas are identified as polarisation effects and trickle-down effects

171

respectively (Akinci, 2018; Gil-Alana et al., 2019; Hirschman, 1958). In the process of financial

172

agglomeration, either effect is possible. These effects of financial agglomeration can be identified

173

as follows (Fig. 2):

174

(1) Polarisation effect. Financial agglomeration centers will always grow into the center of

175

capital, information, innovation and market center under the polarisation effects. At the same time,

176

the expansion characteristics of the financial agglomeration centers will easily lead to fierce

177

competitions of the financial centers with different levels and scales. The ones with higher grade

178

and larger scale will snatch the market from lower and smaller ones to expand theirs, resulting in

179

the gradual shrinking of the financial industry in surrounding areas. The Matthew effect will

180

aggravate the spatial agglomeration of the financial industry and gives rise to the imbalanced

181

financial development within the region. That is to say, the financial center relies on strong

182

capital, information, innovation and market to form a financial resource absorption capacity in 7

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neighboring regions, bringing about a higher concentration of financial center resources and a

184

gradual loss of financial resources in neighboring regions. Such center-periphery pattern of

185

financial structure makes progress on the regional green development through the flow of capital

186

and information and thereby leading to the spatial spillover effect.

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(2) Trickle-down effect. The formation of financial agglomeration center accelerates the

188

establishment of branches and investment, disseminating advanced technology and management

189

experience in the neighboring areas through the spillover effect of capital, information,

190

innovation, and market. Moreover, the advance of communication and information technology

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also speeds up such trend. Therefore, the spillover of financial resources as well as the information

192

speeds up the financial development of neighboring areas, acting as the driving force of the green

193

development. Periphery

Periphery

Trickling-down

Polarization

Capital center

Polarization Trickling-down

Financial Information agglomeration center

Market center

Polarization Trickling-down

资本外溢 Innovation center

Periphery

Polarization

Trickling-down

Periphery

194 195

Fig. 2. The spatial spillover mechanism of financial agglomeration on green development

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3. Research methods and data

197

3.1. Research methods

198

3.1.1. DPSIR Model 8

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DPSIR (Driving-force-Pressure-State-Impact-Response) Model is a conceptual model of

200

evaluation index system widely used in the field of environment, aiming to evaluate the impact of

201

human activities on the environment (Lewison et al., 2016). DPSIR model divides the evaluation

202

indexes of natural system into five aspects: driving force, pressure, state, impact and response, and

203

each aspect includes several secondary indexes (Ehara et al., 2018). This model can accurately

204

reflect the interrelationship among various parts of the system, and effectively integrate resources,

205

environment and human health and other elements. It is a framework based on causal organization

206

information and relevant indexes (School of Economic and Resource Management of Beijing

207

Normal University (SERM, 2017). Since green development is a multi-dimensional complex

208

system, how to scientifically measure it is the primary problem to be solved in this paper. This

209

paper attempts to clarify the causal chain of green development system based on DPSIR model, so

210

as to correctly evaluate the level of regional green development (Fig. 3). As a result, this article

211

tries to build the green development index system from five dimensions: green development

212

momentum, green development pressure, green development state, impact of green development

213

and feedback to green development, so as to evaluate the current status and process of green

214

development. The specific steps are as follows.

215

Firstly, z-score is used to standardize the processing of original data: 𝑥∗ =

216 217 218 219 220 221

𝑥―𝑥 𝜎

（1）

Where 𝑥 ∗ , 𝑥 and 𝜎 are the normalized value, mean value and standard deviation of the observed variable x, respectively. Secondly, the weight of each variable is calculated by principal component analysis (PCA), and the weight coefficient is normalized: 𝑚 𝑚 𝑒 𝛽 𝑛 𝛽𝑗 = ∑𝑓 (|𝑅𝑓𝑗| ∙ 𝐶𝑓), 𝐶𝑓 = 𝑓 ∑𝑓 = 1𝑒𝑓, 𝑅𝑓𝑗 = 𝑙𝑓𝑗 𝑒𝑓, 𝑉𝑗 = 𝑗 ∑𝑗 = 1𝛽𝑗（2）

222

Where m is the number of principal components extracted; 𝛽𝑗 indicates the weight

223

coefficient of variable; 𝑅𝑓𝑗 represents the component of the eigen vector of the f-th principal

224

component in the j-th variable; 𝐶𝑓 is the relative contribution rate of variance of the f-th principal

225

component; 𝑒𝑓 refers to the characteristic root of the f-th principal component; 𝑙𝑓𝑗 means the

226

loading value of principal component f on variable j; 𝑉𝑗 is the normalized weight coefficient of

227

the j-th variable; and n is the number of variables in the evaluation index system.

228 229

Finally, the linear weighted sum formula is adopted to calculate the green development index: 9

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233 234 235

𝑛

𝐺𝐷𝐼𝑖𝑡 = ∑𝑗 = 1(𝑉𝑗 ∙ 𝑥𝑖𝑡)

（3）

Where 𝐺𝐷𝐼𝑖𝑡 is the green development level of city i in the period of t, and 𝑥𝑖𝑡 represents the observed value of city i in the period of t.

Fig. 3. Analytical framework for the DPSIR of green development

3.1.2. Spatial autocorrelation analysis

236

Spatial autocorrelation describes the correlation of variables in different spatial locations and

237

is a measure of the aggregation degree of spatial values (Cheng et al., 2018). This paper adopts

238

global spatial autocorrelation to test whether there is spatial autocorrelation among variables.

239

Global Moran's I, Geary's C and Getis are the commonly used indicators to measure global spatial

240

autocorrelation. In this paper, Global Moran's I, which is widely used in existing literatures, is

241

used to describe the degree of spatial autocorrelation of various variables. The calculation formula

242

of Global Moran's I follows (Moran, 1948): 𝑛

243 244 245

𝐼(𝑑) =

∑𝑖 = 1∑

𝑛 𝑊 (𝑥 j ≠ i 𝑖𝑗 𝑖

― 𝑥)(𝑥𝑗 ― 𝑥)

𝑛 𝑛 𝑠2∑𝑖 = 1∑j ≠ i𝑤𝑖𝑗

1

𝑛

𝑛

𝑠2=∑𝑖 = 1(𝑥𝑖 ― 𝑥)2, 𝑥 = 𝑛∑𝑖 = 1𝑥𝑖 𝑍(𝑑) =

𝐼(𝑑) ― 𝐸(𝐼) 𝑉𝐴𝑅(𝐼)

246

Where 𝑥𝑖, 𝑥𝑗 are the attribute value of city i and j; 𝑥 is the average value of its attributes;

247

𝑊𝑖𝑗 indicates the spatial weight matrix, 𝑠2 is the variance; 𝐼(𝑑)means the Moran index at the

248

selected distance d; n is the number of observation areas; Z(d) refers to the test value which is used

249

to test the reliability of the results obtained within the given confidence interval; VAR(I) represents 10

（4） （5）

（6）

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the coefficient of variation, and E(I) is the expected value. When the Global Moran's I is positive,

251

it indicates that the attribute values in the observation area show a spatial clustering trend. When

252

the Global Moran's I is negative, it means that the attribute values of spatial units show a spatial

253

dispersion trend. When the Global Moran's I is zero, it shows that the attribute values of the

254

observation area are independently random distributed.

255

3.1.3. STIRPAT Model

256

This paper employs the impacts by regression on population, affluence and technology

257

(IPAT) model to identify the effect of financial agglomeration on green development. However,

258

the IPAT model only focuses on the impacts of population, affluence and technology on

259

environment, ignoring the effects of other determinants (Ehrlich P R and P., 1971). Therefore,

260

through improving the IPAT model, some scholars have proposed the stochastic impacts by

261

regression on population, affluence and technology (STIRPAT) model. By considering the factors

262

of population, affluence and technological progress, the model can randomly expand other

263

important factors that affect the environment (Dietz and Rosa, 1994). On the basis of theoretical

264

analysis, this paper incorporates financial agglomeration into STIRPAT model to test the impact

265

of financial agglomeration on green development. In order to mitigate the impact of

266

heteroscedasticity, population with large absolute values and affluence are logarithmicalized.

267

Accordingly, STIRPAT model is constructed as follows:

268

𝐺𝐷𝐼𝑖𝑡 = 𝑎 + 𝛽1𝐹𝐴𝑖𝑡 + 𝛽2𝑙𝑛 (𝑃𝑆𝑖𝑡) + 𝛽3𝑙𝑛 (𝑃𝐺𝐷𝑃𝑖𝑡) + 𝛽4𝑇𝐸𝐶𝑖𝑡 + 𝜀𝑖𝑡

269

Where GDI represents the level of green development, which is used to measure

270

environmental impact, the higher the level of green development, the better the environmental

271

quality; FA represents the level of financial agglomeration; PS is the population pressure; PGDP

272

is the degree of affluence and TEC is the technical level; a represents the intercept term and 𝜀𝑖𝑡

273

is the error term.

274

3.1.4. Spatial econometrics model

(7)

275

Agglomeration is a common pattern of industrial spatial layout demonstrating significant

276

spatial correlation characteristics (Martin, 1999). Meanwhile, financial industry and the industrial

277

green development performance in China is spatially dependent (Feng and Chen, 2018). In

278

summary, ignoring the spatial autocorrelation of financial agglomeration and green development

279

might lead to the biased conclusions. LeSage et al. (2009) established the spatial Durbin model

280

(SDM), taking the spatial lag of both the dependent and independent variable into consideration.

281

What’s more, because of the superiority of SDM, we establish a spatial econometric model of 11

Journal Pre-proof 282

industrial agglomeration and environmental pollution in China on the basis of the STIRPAT

283

model, that is: 𝑁

285 286

𝑁

𝐺𝐷𝐼𝑖𝑡 = 𝛼𝑖 +𝜌∑𝑗 = 1𝑊𝑖𝑗𝐼𝑗𝑡 + 𝛽𝑋𝑖𝑡 + 𝜑∑𝑗 = 1𝑊𝑗𝑡𝑋𝑗𝑡 + 𝑈𝑖

284

（8） （9）

𝑈𝑖 = 𝜆𝑊𝜇𝑖 + 𝜀𝑖

Where i, j represent different cities; 𝑊𝑖𝑗 means the spatial weights matrix; 𝑋𝑖𝑡 refers to a

287

vector

of

independent

variables;

288

[𝐹𝐴𝑖𝑡,𝑙𝑛 (𝑃𝑆𝑖𝑡),𝑙𝑛 (𝑃𝐺𝐷𝑃𝑖𝑡),𝑇𝐸𝐶𝑖𝑡 ]; β denotes the regression coefficients for the independent

289

variables; ρ is the spatial autoregressive coefficient for the dependent variable; φ represents the

290

spatial regression coefficients for the independent variables; 𝜆 is the spatial error coefficient.

𝐺𝐷𝐼𝑖𝑡

is

the

level

of

green

development,𝑋𝑖𝑡=

291

For equation (3), if ρ≠0 and φ=0, then equation (3) is a spatial lag panel model (SLPDM)

292

measuring the impact of carbon emissions from neighboring provinces on carbon emissions in the

293

region; if λ ≠ 0 and ρ = 0, then equation (3) is a spatial error panel model (SEPDM) that reflects

294

the influence of factors on the carbon emissions of neighboring provinces other than urbanization

295

level, the total population, wealth level, and technical level; if ρ≠0 and φ≠0 and λ= 0, then

296

equation(3) is a spatial Durbin panel model (SDPM) that not only measures the carbon emissions

297

of neighboring provinces but considers the impact of urbanization level, the total population,

298

wealth level and technical level of neighboring provinces on the carbon emissions of the region.

299

During the practice of modeling process, the model is judged by LR and Wald test.

300

The partial differential decomposition method is used to estimate the regression parameters.

301

Many empirical studies adopt point estimation methods of one or more spatial models to examine

302

spatial spillover effects. Lesage and Pace (2009) argued that the point estimation method might

303

generate bias. The average spillover effect of a regional independent variable on neighboring areas

304

should be observed from the perspective of partial differential decomposition, before conducting

305

the statistical test. Therefore, this paper mainly observes the impact of financial agglomeration on

306

green development through direct and indirect effects. The direct effect is the influence of a local

307

independent variable on the local dependent variable, the indirect effect indicates the influence of

308

a local independent variable on the dependent variables in the adjacent area.

309

3.1.5. Estimation method of the spatial weighting matrix

310

The spatial weight matrix denotes the interdependence of spatial elements. Adjacency or

311

distance is commonly used to identify the degree of spatial interaction between spatial units. The

312

human behaviors and networks have connected many areas tightly across scales, let alone the

313

financial industry, a high-class service industry with high expectation on information transmission 12

Journal Pre-proof 314

(Moss and Townsend, 1999). In summary, this paper uses the economic distance matrix to portray

315

the spatial interaction, hence taking the geographical factors and economic factors into

316

consideration (Zhang et al., 2018).

317

（10）

𝑊 = 𝑊𝑑 ∙ 𝑑𝑖𝑎𝑔(𝑚1 𝑚,𝑚2 𝑚,⋯,𝑚𝑛 𝑚 1

318

Where 𝑊𝑑(𝑊𝑑 = 𝑑𝑖𝑗,𝑖 ≠ 𝑗) is the geographic distance spatial weight matrix denoting the

319

centroid distance among cities, and the latitude and longitude data are obtained from the national

320

geographic information center of China; 𝑚I = ∑𝑡1𝑚𝑖𝑡 (𝑡1 ― 𝑡0 + 1) stands for the mean of the

321

information level in the spatial section i from 𝑡0 to 𝑡1; 𝑚 = ∑𝑖 = 1∑𝑡1𝑚𝑖𝑡 𝑛(𝑡1 ― 𝑡0 + 1)

322

represents the mean of all cities' information levels during the observation period.

323

3.2. Variable selection and data sources

𝑡

0

𝑛

𝑡

0

324

There are two main methods to evaluate green development: index system evaluation and

325

efficiency evaluation. However, index system evaluation has encountered problems such as index

326

duplication or data loss (SERM, 2017; YELP, 2012), while efficiency evaluation has been

327

criticized for fewer factors or defects in efficiency measurement methods. Therefore, by using

328

DPSIR model based on the internal causal chain of green development, thirty-two evaluation

329

indicators are selected from five dimensions of driving force, pressure, state, impact and response

330

(Table 2), thus green development is comprehensively and accurately measured by considering

331

the natural dimension, economic dimension, scientific and technological dimension of green

332

development and its compound function.

333

Referring to existing research methods (Abbasi and Riaz, 2016), the evaluation index system

334

of financial agglomeration is constructed from four dimensions of financial environment, financial

335

scale, financial depth and financial width. Specifically, (1) Financial environment is denoted by

336

city economic aggregate, population size, level per capita economic development and city

337

informatization, characterizing the level of city economic development and information

338

construction. (2) Financial scale is calculated by deposits of the national banking system at

339

year-end as well as the household saving deposits at year-end, representing the financial aggregate

340

and development potential for cities. (3) Financial depth refers to the level of city financial

341

development. It is measured by the share of deposits and loans of city financial institutions to the

342

regional GDP, reflecting the deepening process of regional finance (Shaw, 1973). (4) Financial

343

width represents the number of households and businesses getting the financial services, measured

344

by the scale of deposits and loans per capita financial institutions at the end of the year, the scale 13

Journal Pre-proof 345

of deposits and loans of financial institutions and financial location quotient, representing the

346

vitality of city financial development(Beck et al., 2007) (Table 2).

347

Tab 2

348

The variable data set Type

Evaluation Index System

System Formation

Driving Forces of Green Development

Expenditure for Science and Technology of public finance (10,000 yuan), Labor Productivity in the Secondary Industry(10,000yuan per person), Labor Productivity in the Tertiary Industry(10,000yuan per person), Employees in Scientific Research and Technical Services at Year-end(%)

Pressures of Green Development

Green Development Aggregative Index (GDI)

Financial Agglomeration (FA)

City scale (CS) Population size (PS) Affluence (PGDP) Technology level (TEC)

State of Green Development

Population Density (person/sq.km), the Proportion of Value Added by the Primary Industry (%), the Proportion of Value Added by the Secondary Industry (%), the Proportion of Value Added by the Tertiary Industry (%),the Emission of Sulfur Dioxide Per Unit of GDP (ton/10,000 yuan) ), Industrial Dust Emissions Per Unit of GDP (ton per 10,000 yuan), Industrial Wastewater Discharge Per Unit of GDP (10,000 tons/ yuan), Energy Consumption Per Unit of GDP (kwh/yuan) Per Capita Industrial Sulphur Dioxide Emission(ton per person), Per Capita Industrial Solid Waste Emissions (ton per person), Per Capita Industrial Wastewater Discharges (10,000 tons per person), Total Industrial Output Value Energy Consumption (kwh/yuan), Manufacturing Employed Population Accounts for the Number of Employees at the End of the Year (%), the Number of Buses Owned by City Units (unit/10,000)

Response of Green Development

Percentage of Industrial Sulfur Dioxide Removed (%), Percentage of Industrial Solid Wastes Utilized (%), Percentage of City Wastewater Treatment (%), Attainment Rate of Industrial Wastewater Discharge (%), Rate of City Domestic Harmless Garbage Treatment (%), Local Government Expenditure for Education (10,000 yuan)

Impacts of Green Development

Household Saving Deposits at Year-end (10,000), Per Capita Teacher to Students in Regular Primary Schools(10,000), Per Capita Teacher to Students in Secondary Schools(10,000),Green Covered Area as % of Completed Area(%),Per Capita of Doctors(Licensed Doctors and Assistant Doctors), Per Capita Beds of Hospitals and Health Centers, Per Capita Park Green Area(sq. M)、Per Capita Green Land(sq. M)

Financial Environment

Gross Regional Product (10,000 yuan), Total Population at the Year-end (10,000), Per Capita GDP (yuan/person), Per Capita Subscribes of Internet Services

Financial Scale

Deposits of National Banking System at Year-end (10,000), Loans of National Banking System at Year-end (10,000), Household Saving Deposits at Year-end (10,000)

Financial Depth

Proportion of Deposits and Loans of National Banking System at Year-end to GDP (%)

Financial Width

Proportion of Deposits and Loans of National Banking System at Year-end to Total Population at Year-end of City (%), Proportion of Deposits and Loans of National Banking System at Year-end to Total Areas at Year-end of City (10,000/sq.km), Financial location quotient Population of Districts under City (10,000) Total Population at Year-end of City (10,000) Per Capita GDP (yuan) Unit of Energy Consumption Value(kwh/yuan)

14

of

Industrial

General

Output

Journal Pre-proof Number of internet users per capita (NIU)

Number of Subscribers of Internet Services

349

The data are derived from the China City Statistical Yearbook from 2003 to 2015 and the

350

statistical database of China Economic and Trade Network. The missing years of the data are

351

interpolated with the average of adjacent years. It should also be noted that there exist few missing

352

data in several cities due to the change of administrative divisions. Giving the availability of data,

353

this paper selects panel data of 285 prefectures and above in China such as Chaohu, Bijie,

354

Tongren, Sansha and Lhasa. Descriptive statistics of related variables are shown in Table 3.

355

Tab 3

356

Descriptive statistics of main variables Variables

Observation

Mean

Std. Dev

Min

Max

Unit

GDI

Comprehensive evaluation of DPSIR model

3705

0.00

0.33

-1.07

3.87



FA

Comprehensive evaluation from financial environment, financial scale, financial depth and financial width

3705

0.00

0.81

-0.47

12.15



CS

Population of Districts under city at Year-end

3705

4.56

0.78

2.65

8.13

10 000 persons

lnPS

Household Registered Population at Year-end

3705

15.07

0.71

12.01

18.52

10 000 persons

Per capita GDP

3705

10.02

0.92

5.45

13.91

Yuan per capita

TEC

Unit of Energy Consumption of Industrial General Output

3705

0.06

0.14

0.00

5.98

Kw·h/yuan

NIU

Number of international internet users per capita

3705

0.11

0.17

0.00

3.68

Number of Subscribers of Internet Services

lnPGDP

Definition

357 358

4. Empirical results analysis

359

4.1. The spatial and temporal pattern of financial agglomeration and

360

green development

361

Firstly, this paper constructs the evaluation system of financial agglomeration and green

362

development. The financial agglomeration is depicted from four dimensions: financial

363

environment, financial scale, financial depth and financial width, while the green development is 15

Journal Pre-proof 364

described by driving forces, pressures, states, impacts, and response respectively. Then, this paper

365

measures the financial agglomeration and green development of the 285 cities in China from 2003

366

to 2015 based on the Principal Component Analysis (PCA) and visualizes the results with ArcGIS

367

10.2, since the PCA has its unique advantages in multifactor analysis and is widely used in big

368

data analysis (Granato et al., 2018; Nowicka, 2019).

369 370

Fig. 4. The spatial pattern evolution of financial agglomeration

371

According to Fig. 4, the level of financial agglomeration in China has transformed from the

372

"point-like" to a "chip-like" balanced development. From 2003 to 2015, although the overall

373

financial agglomeration level in China rose rapidly, it is still in a low level stage and lacked

374

medium and high level financial agglomeration. Meanwhile, Fig. 5 shows that with the slow

375

improving speed of green development and a large variance within different areas, the level of

376

green development in the eastern and western areas is slightly higher than that in the central area,

377

showing a spatial convergence. Comparing Fig. 4 and Fig. 5, there is a large overlap of high-level

378

or low-level financial agglomeration areas with high-level or low-level green development areas.

379

High-level areas are located in the eastern areas while the low-level ones are distributed in the

380

central and western regions, which means that there may be some spatial correlation between

381

financial agglomeration and green development, which initially confirms our hypothesis.

16

Journal Pre-proof

382 383 384

Fig. 5. The spatial pattern evolution of green development

4.2. Regression analysis

385

Anselin (1988) stated that everything is inextricably linked to the surroundings, indicating the

386

characteristics of spatial dependence and spillover. Therefore, the introduction of spatial effects in

387

traditional econometric models is favored by many scholars (Chowdhury, 2006; Lee and Jang,

388

2013; Sampson, 2018; Yang and Wong, 2012). This paper employs the global Moran’s I index to

389

excavate the spatial correlation on key variables. According to Table 4, the Moran's I index of the

390

five main variables: GDI, FA, PS, PGDP and TEC have passed the significance test at the 5%

391

significance level, indicating the positive spatial autocorrelation as well as the necessity to taking

392

the spatial effect into consideration.

393

Tab 4

394

Test results of spatial autocorrelation Year

GDI

P-value*

FA

P-value*

PS

P-value*

PGDP

P-value*

TEC

P-value*

2003

0.006

0.083

0.019

0.000

0.018

0.001

0.054

0.000

0.013

0.000

2004

0.011

0.014

0.009

0.015

0.016

0.002

0.044

0.000

0.012

0.002

2005

0.013

0.007

0.013

0.008

0.016

0.002

0.007

0.005

-0.005

0.416

2006

0.010

0.023

0.009

0.023

0.020

0.000

0.028

0.000

0.016

0.002

2007

0.015

0.004

0.010

0.024

0.019

0.000

0.031

0.000

0.008

0.044

2008

0.014

0.005

0.018

0.001

-0.002

0.332

0.017

0.000

0.022

0.000

2009

0.021

0.000

0.016

0.001

0.008

0.026

0.032

0.000

0.027

0.000

2010

0.014

0.006

0.014

0.004

0.010

0.019

0.016

0.001

0.037

0.000

2011

0.008

0.044

0.013

0.002

0.001

0.130

0.009

0.005

0.007

0.069

2012

0.024

0.000

0.019

0.000

0.002

0.178

0.039

0.000

0.027

0.000

2013

0.020

0.000

0.009

0.028

0.003

0.086

0.033

0.000

-0.005

0.432

2014

0.011

0.018

0.014

0.007

0.012

0.013

0.026

0.000

0.028

0.000

2015

0.007

0.056

0.017

0.001

0.016

0.002

0.036

0.000

0.028

0.000

395

To find a proper spatial econometric model, this paper carries out the (Robust) LM test, the

396

Wald test, the LR test and the Hausman test respectively (Table 5). The test results show that both 17

Journal Pre-proof 397

LM and Robust LM are significantly positive at the 1% confidence interval, denoting that the

398

model residuals are spatially dependent. Moreover, the results of Wald test and LR test are also

399

significant at the 1% level, indicating the existence of spatial effects in the independent and spatial

400

variables of the model, thus the spatial Durbin model should be chosen. Additionally, the

401

Hausman results suggest that the fixed effect should also be selected. Based on the above test

402

results, the paper finally selects the spatial Durbin model under the fixed effect to empirically

403

investigate the impact of financial agglomeration on green development.

404

Tab 5

405

Spatial models specification results

406

Test

Statistics

Test

Statistics

LM (lag)test

40.1558***

LR test spatial lag

129.9844***

Robust LM (lag)test

0.1730

Wald test spatial error

42.6977***

LM (error)test

446.9584***

LR test spatial error

69.8066***

Robust LM (error)test

406.9756***

Hausman test

-154.59

Wald test spatial lag

118.0899***

Note: *p < .1, **p < .05, ***p < .01.

407

According to Table 6: (1) The direct impact of focal financial agglomeration on green

408

development is significantly positive at the level of 1%, representing that financial agglomeration

409

can significantly speed up local green development. (2) The indirect impact of focal financial

410

agglomeration on the green development of neighboring areas is significantly positive at the level

411

of 5%, indicating that the development of local financial agglomeration acts as the determinant of

412

local green development, bringing along the green development in surrounding areas. (3) The

413

overall effect of financial agglomeration is significantly positive, implying that financial

414

agglomeration can promote the green development. Notably, the impact of financial

415

agglomeration on green development of focal areas is more significant than that of the

416

surrounding cities, that is, the financial agglomeration within the area is significantly higher than

417

the regional spillover, revealing a prominent "local effect".

418

Tab 6

419

The decomposition of spillover effect at the national level Variables FA

420

Direct effect

Indirect effect

Overall effect

Coefficient

T statistics

Coefficient

T statistics

Coefficient

T statistics

0.257***

44.471

0.062**

2.045

0.319***

10.138

lnPS

-0.012**

-2.132

-0.081

-1.404

-0.093*

-1.668

lnPGDP

0.014***

4.587

-0.023

-1.173

-0.009

-0.451

TEC

-0.196***

-3.147

0.450

1.073

0.255

0.612

Note: *p < .1, **p < .05, ***p < .01.

18

Journal Pre-proof 421

The reason why financial agglomeration exerts a remarkable influence on local green

422

development as well as the neighboring cities can be explained as below: (1) From the perspective

423

of natural effects, because of high expectation on the environment, financial industry prefers to

424

locate in a superior position with exquisite environments (Wyckoff, 1989). In addition, the formed

425

capital and information centers also favor environmental improvement. Actually, the strong

426

pursuit of environmental protection and the ownership of key pollution control resources have

427

made the spatial agglomeration of financial industry conducive to improving the local green

428

development. Due to the flow characteristics of the environment and the enhancement of

429

polarisation effect, the development of financial agglomeration plays a key role on the green

430

development of neighboring cities. (2) From the perspective of economic effects, industrial

431

structure will always evolve from low to high. The rapid development of modern productive

432

service industry led by the financial industry will inevitably cast the polluting industries out,

433

leading to a cleaner and greener industrial structure upgrading. This will not only promote local

434

green development but also provide demonstrations for neighboring cities, thereby improving the

435

environment in local and adjacent areas and bringing about the green economic welfare. (3) From

436

the perspective of technological effects, owning strong industrial relevance, the spatial

437

agglomeration of high-end services, such as the financial industry, will inevitably attract a large

438

number of related industries around. The cluster of productive services, capital centers,

439

information centers, market centers and innovation centers generated by polarisation effects not

440

only enable the improvement of city industrial structure and transformation of regional economic

441

development, but accelerate the rapid flow of knowledge and technology, resulting in the green

442

development of local and neighboring cities.

443

4.3. Regression results analysis of eastern, central and western areas

444

With a vast territory and varying natural environment, the level of economic development in

445

China is different across space. This paper classifies 285 prefecture-level cities into three regions:

446

eastern area, central area and western area. There are 101 cities in the eastern area, 100 cities in

447

the central area, and 84 cities in the western area.

448

According to the regression results in Table 7, there are significant differences among

449

different areas regarding the effects of financial agglomeration on green development: (1) The

450

direct effect of financial agglomeration on green development in the eastern and western areas are

451

significantly positive at the level of 1% while it is not so significant in the central areas. (2) From

452

the perspective of indirect effects, the financial agglomeration in the eastern region does not play a

453

significant role in the green development of adjacent regions. However, in the central areas it is

454

conducive to improving the level of green development in neighboring areas, while it might act as 19

Journal Pre-proof 455

an impediment to the green development of neighboring areas in the western areas. (3) In regards

456

to the total effect, the development of financial agglomeration in both the eastern and central areas

457

can generally promote green development, whereas in the western areas the effect is not

458

significant.

459

Tab 7

460

The decomposition of spillover effect at the regional level East areas

Direct effect

Indirect Effect

Overall effect

Central areas

West areas

Coefficient

T statistics

Coefficient

T statistics

Coefficient

T statistics

FA lnPS lnPGDP TEC FA lnPS lnPGDP TEC FA lnPS lnPGDP

0.039*** -0.065*** -0.018 0.234*** -0.023 0.369 0.054 0.671** 0.319*** -0.093* -0.009

5.205 -3.29 -1.294 2.937 -0.938 1.65 0.397 2.049 0.596 1.329 0.265

-0.015 -0.047*** -0.012 -0.318*** 0.146*** -0.082 0.002 0.263 0.131** -0.129 -0.009

-0.821 -2.941 -1.101 -3.08 2.526 -0.767 0.04 0.703 2.198 -1.193 -0.141

0.232*** -0.008 0.083*** 0.162*** -0.131** -0.124*** -0.011 -0.063 0.101 -0.132 0.072***

12.063 -0.734 7.745 6.319 -2.155 -3.253 -0.211 -0.873 1.58 -3.72 1.375

TEC

0.255*

2.594

-0.055

-0.147

0.01

1.345

461

Note: *p < .1, **p < .05, ***p < .01.

462

4.4. Regression results analysis of different city scales

463

As a high-end service industry, the financial industry is more dependent on the city scale.

464

The larger the city scale is, the more abundant funds, more diversified markets, and more

465

information. In order to test the impact of financial agglomeration on green development among

466

different city scales, the population at the year-end has been selected as the proxy variable to

467

represent the city scale. Due to the uncertainty of administrative divisions in some districts under

468

city, the scale of cities in 2015 has been taken as the criterion to classify the scale of cities. Follow

469

Fu and Hong (2011), the cities can be divided into four categories based on the population at the

470

year-end:

471

inhabitants, Medium city with 500,000 to 1,000,000 inhabitants, and Small city with less than

472

500,000 inhabitants. In sum, there are 53 megacities, 93 large cities, 92 medium cities and 47

473

small cities.

Megacity with more than 2 million inhabitants, Large city with 1 million to 2 million

474

According to the results in Table 8, the impact of financial agglomeration on green

475

development is significantly different among different city scales: (1) The direct effect of financial

476

agglomeration is significantly positive for the green development among different city scales at

477

1% confidence interval, and the order of the impact strength is large cities, megacities, small cities

478

and medium cities from high to low. This indicates that the increase of financial agglomeration is

479

a key player in promoting the local green development, but different city scales show different 20

Journal Pre-proof 480

strength on further improving the level of green development. (2) For megacities and large cities,

481

the indirect effect of financial agglomeration inhibits the improvement of green development in

482

neighboring areas, but such effect is more profound in large cities than megacities. However, the

483

spillover effect of financial agglomeration on the green development in medium and small cities

484

did not pass the significance test, illustrating that for medium and small cities, the negative impact

485

is not so significant. (3) From the perspective of the total effect, despite that financial

486

agglomeration exerts a significant positive impact on the green development of megacities, there

487

is no manifest effect in other three kinds of cities.

488

Tab 8

489

The decomposition of the spillover effect at the city scale Small city Coefficient

Medium city t

statistics

Large city t

Coefficient

statistics

Coefficient

Megacity t

statistics

Coefficient

t statistics

FA

0.147***

2.97

0.129***

4.52

0.257***

14.10

0.215***

22.95

Direct

lnPS

-0.087***

-6.46

0.016*

1.88

-0.039***

-3.85

0.086***

3.94

effect

lnPGDP

0.060***

4.19

0.066***

7.05

0.090***

9.33

0.236***

11.54

TEC

0.123***

4.58

0.414***

5.28

0.788***

11.18

0.716***

3.77

FA

-0.20

-0.77

0.10

0.58

-0.178***

-3.22

-0.071***

-2.86

Indirect

lnPS

0.203**

2.57

0.126*

1.86

-0.329***

-6.07

-0.16

-1.63

effect

lnPGDP

0.326***

3.14

0.03

0.37

-0.03

-0.55

-0.05

-0.56

TEC

0.10

1.15

-0.19

-0.29

-0.11

-0.70

2.349***

2.85

FA

-0.05

-0.19

0.23

1.32

0.08

1.32

0.143***

5.13

Overall

lnPS

0.12

1.52

0.142**

2.12

-0.367***

-6.87

-0.07

-0.76

effect

lnPGDP

0.386***

3.67

0.09

1.27

0.07

1.50

0.181*

1.92

TEC

0.220**

2.53

0.22

0.33

0.674***

4.05

3.065***

3.94

490

Note: *p < .1, **p < .05, ***p < .01.

491

The possible reasons why the impact of financial agglomeration on green development is

492

influenced by the city scale are as follows: (1) Financial agglomeration has brought about

493

abundant pollution control resources and technologies to the local areas. As the center of

494

economic, population and financial agglomeration, the city always owns a large amount of capital,

495

information, advanced technology, management methods and values, stimulating the green

496

development in local areas. However, there exist significant differences in resource capacity, city

497

functions and grades among cities of different scales. Because megacities own wider financial

498

services and resources, the influence of financial agglomeration in large cities is more significant

499

than in megacities. Compared to megacities, the financial agglomeration services in large cities

500

are more concentrated, and the amount of investment is larger. Correspondingly, the effect of 21

Journal Pre-proof 501

financial agglomeration on green development in small cities is greater than that of medium ones.

502

The stronger polarisation effects and polarisation motivation in medium cities might also be a

503

reason contributing to the different impact of financial agglomeration. (2) The scale of cities

504

denotes their status in the developing process. In China, different city scales represent not only the

505

population capacity, but the status of cities including the economic development level, political

506

ranking, governmental power and innovation ability. With abundant resources and information,

507

strong innovation ability and considerable market size, megacities and large cities have the

508

advantages over others on capability and motivation to expand financial agglomeration. This kind

509

of high-level financial agglomeration has a strong polarisation effects which can generate a

510

reinforced "siphon effect" to neighboring areas, thereby hindering the green development of

511

neighboring cities.

512

5. Conclusions and policy recommendations

513

5.1 Conclusions

514

This paper outlines the theoretical framework of financial agglomeration and green

515

development, concluding that financial agglomeration exerts an essential impact on green

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development through natural, economic, scientific, and technological effects. This paper explores

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the direct effect and spillover effect of the financial industry on green development using the panel

518

data of 285 prefecture-level cities in China from 2003 to 2015 by introducing the economic

519

distance matrix into the spatial econometric model.

520

Firstly, the financial agglomeration and green development keep climbing up with a

521

transformation from the "point-like" development towards the "balanced" development based on

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the results of time and space pattern of financial agglomeration and green development in China

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from 2003 to 2015. Furthermore, the level of financial agglomeration and green development in

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the eastern and western areas is higher than that in central areas, showing a clear trend of spatial

525

convergence.

526

Secondly, national level results illustrate that financial agglomeration exerts positive direct

527

effect and spatial spillover effect on green development, and the direct effect coefficient is 0.257

528

and significant at the 1% confidence level. The regression coefficient of the direct effect is much 22

Journal Pre-proof 529

higher than that of the spatial spillover, which is 0.062, stating that the development of the

530

financial agglomeration in focal areas can promote the green development in both focal and

531

adjacent cities.

532

Thirdly, regional level results show that there exist significant differences in the direct effect

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and spatial spillover effect of financial agglomeration on green development among different

534

areas, and the strength and direction of these impacts are affected by the level of regional

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development level. The direct effect of financial agglomeration on green development is

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significant in both eastern and western area, whereas it is not in the central area. The spatial

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spillover effect of financial agglomeration in the central and western area is also significant while

538

it is not in the eastern area.

539

Fourthly, the city scale results indicate that both the direct and spatial spillover effect of

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financial agglomeration on green development vary across scales. In terms of the direct effect,

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there exists a positive impact of financial agglomeration on green development and the order from

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high to low according to the effect strength is large cities, megacities, small cities, and

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medium-size cities. While in terms of the spatial spillover effect, the impact of financial

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agglomeration inhibits the improvement of green development in neighboring areas, with a more

545

apparent effect on large cities and megacities.

546

5.2 Policy recommendations

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Firstly, given that the financial agglomeration acts as the key factor on green development at

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the city scale and the spillover effect of financial agglomeration on green development within the

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area is higher than that inter-areas, it is necessary to build a gradient financial development

550

network. Governments should set targets to construct the world-class financial center and

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furthermore, to form a "multi-center, multi-level" regional financial center according to regional

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characteristics. The aim can only be obtained with the policy support from the governments such

553

as the "national top-level design" and "supply-side reform", as well as the professionals, advanced

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technology and management experiences from developed countries. Since it is unrealistic to solely

555

rely on the impact of the financial centers to promote green development as China because of the

556

vast territories and unbalanced regional development, developing high-level financial centers as

23

Journal Pre-proof 557

well as regional ones jointly to finally form a gradient financial development network is of great

558

importance.

559

Secondly, the impact of financial agglomeration on green development is influenced by the

560

regional developing status with a complex mechanism. As a result, policies and measures should

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be made based on comprehensive identifications and evaluations of individual region before

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allocating resources and impelling the regional green development. Moreover, reform measures

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should be developed and conducted according to the characters of the areas and phases. For the

564

eastern area, it is necessary to utilize its rich resource and become more open to outside to build a

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world-wide financial center with strong radiation capabilities. For the central area, learning and

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introducing the advanced resources and technology from the eastern area so as to make the local

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financial center bigger and stronger is the basis to ultimately strengthen the trickle-down effect in

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surrounding cities. For the western area, the measures to improve the level of local financial

569

agglomeration should be conducted based on the actual situation and the guidance of national

570

policies to establish a green financial system and services that meet the western characteristics

571

thus conducive to the green development.

572

Thirdly, the results show that the impact of financial agglomeration on green development is

573

affected by the city scale and such impact is weakening with the expanding of city scale.

574

Accordingly, identifying the current situation and future development trend of the city scale and

575

adopt related financial methods based on its characteristics is the priority to enable the financial

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agglomeration to effectively promote regional green development. Measures should be made

577

based on different city scales. The above analysis reveals that the direct effect coefficient

578

increases with the expansion of the city scale, indicating that for local cities, continuously

579

improving the level of urbanization and expanding the scale of the city is conducive to enhancing

580

the impact of financial agglomeration on green development. From the perspective of spatial

581

spillover effects, although the indirect effect coefficient of different city scales is negative, the

582

suppression of financial agglomeration on surrounding cities is obviously weakening with the

583

expansion of city scale, demonstrating that the increasing city scale is advantageous to the

584

promotion of financial agglomeration on green development in both focal city and the neighboring

24

Journal Pre-proof 585

ones. Therefore, increasing the city size and urbanization level is of most importance to the green

586

development in China.

587 588 589 590

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