The spatial threshold effect and its regional boundary of financial agglomeration on green development: A case study in China

Journal of Cleaner Production 244 (2020) 118670

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

The spatial threshold effect and its regional boundary of financial agglomeration on green development: A case study in China Huaxi Yuan a, c, 1, Yidai Feng a, e, *, Jay Lee b, c, **, 2, Haimeng Liu d, Ruzi Li a a

School of Economic & Management, Nanchang University, Nanchang, 330031, China College of Environment and Planning, Henan University, Kaifeng, Henan, 475001, China c Department of Geography, Kent State University, Kent, 44240, USA d Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, 100101, China e Department of City and Regional Planning, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 June 2019 Received in revised form 18 September 2019 Accepted 29 September 2019 Available online 1 October 2019

Financial agglomeration is a key approach to the green transition of China’s economy and society. However, few studies have examined the spatial threshold effect on the regional boundary of financial agglomeration. In this paper, the mechanism of the spatial threshold effect is analyzed with spatial panel Durbin model and panel threshold regression (SPDM_PTR) model over 272 prefectural-level cities in China from 2003 to 2014. The results show: (1) Financial agglomeration plays an important role in promoting local green development. (2) The spatial spillover effect progresses in stages. When the agglomeration level exceeds 1.949, the spatial spillover effect of high-level financial agglomeration is significantly stronger as opposed to medium- and low-levels. (3) The regional boundary of the spillover effect changes with the financial agglomeration level, with 1480 Km for high-level, 860 Km for mediumlevel and 700 Km for low-level financial agglomeration. These findings suggest concrete evidence for developing policies for further encouraging green development of regional financial agglomerations. © 2019 Published by Elsevier Ltd.

Handling editor: Zhifu Mi Keywords: Financial agglomeration Green development Spatial threshold effect Regional boundary SSBM model SPDM_PTR model

1. Introduction Rapid industrialization and urbanization have made developing countries suffer from serious environment pollution, which threatens both sustainable economic development and people’s wellbeing. In 2012, 3.7 million people died because of air pollution, 88% of them from developing countries (World Health Organization, 2014). As the largest developing country and the second largest economy in the world, China is facing a severe environmental crisis. China’s economic loss caused by environmental pollution increased from 3.05% of Gross Domestic Products

* Corresponding author. ** Corresponding author. College of Environment and Planning, Henan University, Kaifeng, Henan, 475001, China E-mail addresses: [email protected] (H. Yuan), [email protected] (Y. Feng), [email protected] (J. Lee). 1 School of Economic & Management, Nanchang University, Research interests: industrial agglomeration and green development, 999 Xuefu Rd., Nanchang 330031, Jiangxi, China. 2 Department of Geography, Kent State University, Kent, OH, 44242e0001, USA. https://doi.org/10.1016/j.jclepro.2019.118670 0959-6526/© 2019 Published by Elsevier Ltd.

(GDP) in 2004 to 3.3% in 2013 (Chinese Academy of Environmental Planning, 2004; 2013), and 1.2 million people died from outdoor air pollution every year (Organisation for Economic Co-operation and Development, 2014). Nevertheless, the long-recognized conflicting positions between economic development and environmental protection are not without possible compromises. Albrizio et al. (2017) found it was possible to implement strict environmental policies to boost the growth in industrial productivity, which is similar to the ~ anes et al. (2017). Yang et al. (2019) research finding of DeschA believed as long as the annual average concentration level of fine particulates in the Beijing-Tianjin-Hebei region was controlled to below 35 mg per cubic meter, reducing fine particulate matters (PM2.5) by one unit could reduce the annual medical expenditure by $9.2 billion dollars, which equivalent to 1.5% of China’s annual medical expenditure (Barwick et al., 2018). In addition, Sepehri and Sarrafzadeh (2019) confirmed that novel technologies can be used to realize the green transformation of economy and society. The Fifth Plenary Session of the Eighteenth Central Committee of the Communist Party of China defined green development as an

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Abbreviations Symbol Meaning CPC Communist Party of China PTRM Panel threshold regression model SPDM Spatial Panel Durbin Model SPLM Spatial panel lag model SPEM Spatial panel error model SPDM_PTRM Spatial Panel Durbin Model & Panel threshold regression model VIF Variance Inflation Factor STIRPATM Stochastic impacts by regression on population, affluence and technology model SSBM Super Slack Based Measure CAEP Chinese Academy of Environmental Planning OECD Organization for Economic Co-operation and Development CEIS China Economic Information Service

important approach to the green transformation of China’s economy and society as well to the healthy development of mankind. The core of green development is to protect environment while developing economy. Green development depends on favorable external conditions. For example, it has special requirements for regional capital supply and financial structure. When these requirements are met, there could be spatial agglomerations formed, which are important for financial industries. In recent years, China has promoted the establishment of over 30 financial centers, including Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin, Wuhan, and other large and medium-sized cities. Three of the world’s top ten international financial centers are in China so far (China Economic Information Service (CEIS), 2018). In July 2013, the Chinese government upgraded the policy for adjusting economic structure for green development into a national strategy. The policy now encourages regions to promote green development through building advanced financial centers. Financial agglomeration refers to the spatio-temporal dynamic coordination, allocation, and combination of financial resources per regional conditions, the growth and development of financial industry, and the formation of financial agglomeration complex in a certain regional space (Ye et al., 2018). Financial industry is a high value-added green industry, thus has the attribute of green development (Gabriel and Rosenthal, 2013; Stephan Schmidheiny, 1998). It can support green development by adjusting the direction of the economic growth and improving the efficiency of capital allocation. In addition, it can also promote regional greenness by supporting technological innovation. If developing financial agglomeration is desirable, there are several questions that need to be addressed: Can the geographical agglomeration of financial industry affect green development? What are the characteristics and regularity of its influencing process? The answers to the above questions not only provide a new path for the green transformation of China’s economic and social development in the new era, but they also provide rich empirical evidence for financial agglomeration to support green development in other developing countries (Fu and Geng, 2019; Pieretti and Bourgain, 2006b; Wu et al., 2018; Ye et al., 2018). This paper fills the gap in research on green development in the following aspects. First, we suggest and demonstrate a highly feasible way to theoretically and empirically analyze the spatial threshold effect of a financial agglomeration and its attenuation

boundary while paying attention to its economic and social effects as well as the environmental benefits. Second, we propose and construct an SPDM_PTR model for modeling both the threshold effect and spatial spillover effect to support the investigation of these effects’ financial agglomeration on green development. This modeling approach avoids separating spatial and temporal effects in the coefficient deviation. Third, different from existing studies which only use radial or angular SBM models, we adopted an improved Super Slack Based Measure (SSBM) model to evaluate the status quo of China’s green development to effectively solve the problems of overestimation and disability of non-proportional adjustment. 2. Literature review Although financial agglomeration and green development are both important topics in academic research, few studies have examined both financial agglomeration and green development at the same time. To that end, this paper expounds the research status of financial agglomeration and green development from two aspects of research content and research methods. In terms of research content, existing literature mainly focuses on the causes, types, and effects of financial agglomeration. On the causes of financial agglomeration: information flows are an important condition in the formation of financial agglomeration. Porteous (1999) used samples from Montreal, Toronto, Sydney, and Melbourne to analyze how information flows affected the formation of financial agglomerations, suggesting that path dependence effect, which means that the current financial agglomeration level is subject to the influence of the previous level, was the fundamental reason for the sustained development of financial agglomeration. As opposed to that, Corbridge (1994) and Martin (1999) found that the information asymmetry and the changes in information hinterlands are the direct reasons for the weakening of financial agglomeration. Park (1982) believed that scale economy is the essence of financial agglomeration, the cooperation among banks, the sharing of infrastructure among financial institutions, and the rapidity of information communication within a region are decisive factors affecting the formation of a financial agglomeration. Beyond these factors, residents’ income level, existing bank branches and their spillover effects, residents’ financial literacy, and financial culture can also be main factors leading to the formation of spatial agglomerations of financial institutions (Leyshon and Thrift, 1997). With regard to the types of financial agglomeration: According to the extents of financial centers’ influence, financial agglomerations can be divided into three types: regional, national, and global (Daly, 1984). If referring to the characteristics of spatial layout and the classification of manufacturing clusters, financial agglomerations can be further divided into Marshallian new industrial districts, hub-and-spoke, satellite industrial platforms, and stateanchored districts (Markusen, 1996). Pandit and Cook (2003) classified the financial agglomeration in Britain as the Bristol financial cluster in southwest England being a satellite industrial platform cluster, greater London being a hub-and-spoke cluster, and Edinburgh/Glasgow in south Scotland being close to a hub-and-spoke cluster. Finally, concerning the effects of financial agglomeration: Although there is not yet any agreement on the relationship between financial development and economic growth, the financial industry undoubtedly has a crucial impact on a region’s economic development. Given this, the effects of financial agglomeration have become an important research field. Pieretti and Bourgain (2006a) used industrial data of Luxembourg and confirmed that there are significant agglomeration forces among financial

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

intermediaries as well as among financial institutions, business services, and computer industries. Such agglomerations played an important role in maintaining Luxembourg’s banking status. To explore how financial agglomerations affected industrial structure, Yu (2017) provided empirical evidence for the effect of external scale economy, effect of resource configuration optimization, effect of networked economy, effect of having innovation incentives, and having circulation and accumulated effects in China’s regional economies. In terms of research methods, existing studies generally used Panel Threshold Regression models (PTRM) and Spatial Panel Durbin Models (SPDM) to investigate the spatial spillover effect of financial agglomeration and its threshold (Table 1). Chang (2015) employed a PTRM to study the impact of financial agglomerations on the environment. The study contended that private and state credits increased energy consumption in low-income countries, while stock market trading augmented energy consumption in high-income countries. In a similar way, Ruiz (2018) utilized a dynamic PTRM to test the influence of financial agglomerations on economic growth, the result indicated that the countries exceeding the threshold value set in the study grew faster than those of other countries. Yue et al. (2019) found that financial agglomerations had a significant non-linear relationship with energy consumption of countries in transition. Ye et al. (2018) discovered that financial agglomerations not only promoted the growth of local populations and economic urbanization, but they also affected the urbanization process of neighboring areas. Wang et al. (2019a,b) and Xie and Pan (2018) confirmed that financial agglomeration did have spatial spillover effects after applied a Spatial Panel Durbin Model (SPDM) to examine the influence of financial agglomeration on regional economic growth. Also using SPDM, Yuan et al. (2019a) investigated the spatial spillover effect of financial agglomerations on China’s green development but failed to reveal the threshold effect between the two. The above studies adopted the threshold model or spatial econometric model to test the threshold effect or spatial spillover effect of financial agglomerations. However, by doing this, the asymmetry in time and spatial dependence of financial agglomerations have been artificially separated, which may lead to the bias of the estimated coefficient (Richardson, 1976). In addition, the spillover effect of a financial agglomeration is not without a spatial boundary. Referring to the market friction theory, Gehrig (2000) proved that the financial activities that were sensitive to information flows were geographically clustered and the financial activities that were not sensitive to information flows were geographically dispersed. Information externalities and asymmetric information were also important factors that not only shaped information hinterlands and financial centers, but also affected the spatial spillover of financial agglomeration. It should be noted, however that, not all types of information can be transmitted over a long distance at a constant cost. Krugman

3

(1991) believe that financial agglomeration is often limited by geographical distance in the process of spillover. There also exist studies that believed that the formation of financial agglomerations is a self-reinforcing process with obvious path dependence characteristics. That is, a financial agglomeration is continuous and cumulative on the time dimension. Its effect in the initial period would increase exponentially with the passage of time (Bardsley et al., 2018; Martin and Sunley, 2006). Under the influence of geographical friction of information transmission and local preferences, financial agglomerations are likely local agglomerations (Zhao et al., 2004). The development of modern information technology has reduced the impact of geographical distance, but many financial businesses still require face-to-face communication. Accordingly, the spatial spillover effect of financial agglomerations tended to be influenced by geographical distances between financial institutions and show regional boundaries (Zhang, 2014). Even though existing studies have extensively examined and discussed financial agglomerations, there are still issues to be explored: (1) The environmental benefits brought by financial agglomerations have been largely ignored. Few studies have directly discussed the complex relationships between financial agglomerations and green development. (2) The threshold effect and spatial spillover effect of financial agglomerations, which are supposed to be interdependent and inseparable in time and space, have been artificially separated in most existing studies, leading to conclusions deviating from what were observed in reality. (3) The phenomenon that spatial spillover effect could be spatially bounded has not been fully researched. 3. Conceptual framework 3.1. The mechanism of threshold effect 3.1.1. The scale effect of financial agglomeration The scale effect of financial agglomeration refers to that the effect that an agglomeration of financial institutions and their related industries in central cities have on generating returns to increasing scales (Fig. 1). The geographical proximity and industries nearby financial institutions can promote cross-regions and crossindustries cooperation between financial institutions, reduce financing cost, and boost the technology spillover of knowledge. Accordingly, enterprises can obtain economies of scale and scopes of economies in the process of financial agglomeration. Consequently, that also increases the proportion of “good output” (or green output) and achieves the goal of pollution control (Park, 1982). 3.1.2. The composition effect of financial agglomerations The composition effect of financial agglomerations represents the promotion of regional green development through upgraded industrial structure (Fig. 1). The upgrading of industrial structure

Table 1 Input-output factors definition and descriptive statistics. References

Sample

Time period

Methodology

Regional boundary

Chang (2015) Ruiz (2018) Yue et al. (2019) Ye et al. (2018) Wang et al. (2019a,b) Xie and Pan (2018) (Yue et al., 2019)

53 countries 116 economies 21 transitional countries YREB in China BTH in China 30 Chinese provinces 285 Chinese cities

1999e2008 1991e2004 2006e2015 2006e2016 2007e2016 2009e2015 2003e2015

PTR Model PTR Model PTR Model SPDM SPDM SPDM SPLM

Not Not Not Not Not Not Not

Note: YREB is China’s Yangtze River Economic Belt; BTH is BeijingeTianjineHebei region in China; SPLM is Spatial panel lag model.

examined examined examined examined examined examined examined

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Fig. 1. The mechanism of the threshold effect of financial agglomeration on green development. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

needs a favorable system environment and a large amount of capital investment. Buera et al. (2011) believed that financial agglomeration was conducive to the collection and processing of information of different investment opportunities, and promoted the allocation of funds from projects with low productivity to projects with high productivity, so as to optimize the industrial structure. Along this line of thoughts, industrial structure could be adjusted through the upgrading and rationalization of industrial structure to drive green development (Wang et al., 2019a,b), thus increasing “good output” (i.e., industrial output that does not cause any negative impacts to environment) and achieving the goal of pollution control.

3.1.3. The technique effect of financial agglomeration The technical effect of financial agglomerations means that financial agglomerations can promote green development by stimulating technological innovations in a region (Fig. 1). Information centers and capital centers can provide funding support and information assurance for technological innovations. Financial agglomeration centers not only have strong technical will, but also can promote technology and drive the upgrading of the regional industrial structure through financial functions such as capital support, resource allocation, and enterprise supervision. This is to realize the growth of “good output” from industries and pollution control (Yu, 2018). Nevertheless, the improvement of technological level lays a foundation for the expansion of production scale and production capacity of enterprises. It may lead to a substantial increase in resources and energy input, causing serious environmental problems and the increase of “bad output".

3.1.4. The network effect of financial agglomeration The network effect of financial agglomerations indicates that financial agglomerations can optimize the allocation of resources through a financial network (Fig. 1). With such allocation, it is possible to improve or maintain regional environmental quality. According to the transaction costs theory and the characteristics of knowledge spillover, the network effect mainly came from two aspects (Churen Sun, 2018). On the one hand, financial agglomeration network could reduce the costs of financial transactions, information collection, communication, and sharing of enterprises. Such cost reduction could have additional benefits brought by cooperating networks, formed a knowledge sharing mechanism, and accelerated knowledge spillover. On the other hand, financial networks provided a platform which not only helped enterprises to establish a mutually trusting and cooperating mechanism, but also boosted the cooperation and exchanges among enterprises. This could restrain the opportunism tendency in enterprise management, lessen the supervision cost of financial institutions, thereby increasing the proportion of “good output” thus helping to control environmental pollution.

3.2. The mechanism of spatial spillover effect and its regional boundary 3.2.1. Why is there spatial spillover effect of financial agglomeration on green development Polarization effect: Cities functioning as financial centers could become regional capital centers, information centers, innovation centers, and channel centers through polarization effect as described in Fig. 2. The expansion of financial agglomeration tended to result in fierce competition among financial centers of

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5

Periphery

Periphery Trickling-down

Polarization

Capital center

Polarization Trickling-down

Financial Information agglomeration center

Market center

Polarization Trickling-down

Innovation center

Periphery

Polarization

Trickling-down

Periphery

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

different sizes and levels. High-level and large-scale financial centers would crowd out low-level and small-scale financial centers, leading to the gradual shrinking of the financial industry in surrounding (peripheral) areas of financial centers. In addition, the expansion of the core financial district would accelerate the spatial imbalance within a financial spatial agglomeration. When the financial centers became overcrowded it often began the processes of generating spatial spillover effects, which are the trickle-down effect discussed below. Trickle-down effect: Financial resources could set up branches and increase investments in surrounding areas through capital overflow, information overflow, innovation overflow and channel overflow mechanisms as shown in Fig. 2. These were to drive the financial development of peripheral areas. The development of modern communication and information technology also provided conditions for the spillover of financial resources and information from centers to peripheries.

preferences. As credence goods, the production and trading of financial products involve intensive and complex contractual arrangements. In order to reduce transaction costs and risks, financial institutions tend to cooperate with local enterprises or those who they are familiar with. This tendency brings about the longdistance attenuation of spatial spillover effect of financial agglomeration (Fig. 2). Restrictions imposed by local protectionism. For promoting local economic development, government often reallocates financial resources by intervening in local financial activities. In addition, since the financial sector is a big taxpayer in China, governments at all levels tend to provide protection to retain tax revenue locally. Accordingly, under the restriction of local protectionism, the spatial spillover effect presents the characteristic of regional boundary (Fig. 2) (Yuan et al., 2019b).

4. Methods, variables and data 3.2.2. Why is there regional boundary of the spatial spillover effect Remote attenuation of non-standardized information. Nonstandardized information refers to some invisible information that cannot be clearly coded. It will cause information loss in the process of long-distance transmission. A lot of basic financial services such as pre-loan investigation and post-loan management are highly dependent on non-standardized information. However, in China, where the market economy is not perfect, many middle and small-sized enterprises have the defects of opaque statements and insufficient financial information. This forces financial practitioners to collect and process information through informal channels such as interpersonal relationships and social networks to make lending decisions and to supervise enterprises. Thereby, the distance attenuation of non-standardized information will cause the regional boundary of spatial spillover effect to exist (Fig. 2). Industry preference. The unique characteristics of financial industry makes financial agglomerations to have strong local

4.1. Methods 4.1.1. Benchmark model Based on a Stochastic impacts by regression on population, affluence and technology model (STIRPATM), which has been widely used in the field of environmental economy. The model can not only examine the impact of population and economic level and technological progress on the environment, but also be randomly expanded according to the specific situation of the country (Zhou et al., 2018). Therefore, this paper incorporates the variables of financial agglomeration and green development into the model, which are given as:

Iit ¼ lnai þ a1 Fit þ a2 lnPit þ a3 lnAit þ a4 lnTit þ lnεit

(1)

where a is the coefficient, i means the city, t refers to the time; I, F, P, A, T are green development, financial agglomeration, population

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size, affluence and technology progress respectively; ε indicates the random disturbance term.

feedback effect, leading to biased estimations of regression coefficients and making it difficult to accurately measure the impact of explanatory variables on explained variables (Elhorst, 2010). Some researchers proposed that the average spillover effect of a region on adjacent regions when the explanatory variable changes in a region can be derived through partial differential matrix decomposition. With statistical tests, it is possible to provide a solid theoretical basis for measuring spatial spillover effect (Li et al., 2018; You and Lv, 2018). In order to interpret the impact of changes in explanatory variables on a region and its adjacent regions more clearly, Lesage and

4.1.2. Panel threshold regression (PTR) model PTRM as proposed by Hansen (1999) can be used to estimate the threshold value of our model. It can also test the significance of endogenous threshold effect. This paper adoptes the PTRM to test the threshold effect of financial agglomeration on green development by using the level of financial agglomeration as the threshold variable:

Iit ¼ lnai þ b1 Fit þ b2 lnPit þ b3 lnAit þ b4 lnTit þ b5 Fit $IðFit  l1 Þ þ b6 Fit Iðl1 < Fit < l2 Þ // þ bnþ1 Fit $Iðln1 < Fit  ln Þ þ bnþ2 Fit  IðFit > ln Þ þ lnεit

where l1 ; l2 , ln1 , …; ln represent the threshold value to be estimated; I () indicates the index function.

Pace (2009) defined the influence of explanatory variables on explained variables in a local region as the direct effect and the effect in neighboring regions as the indirect effect (i.e., spillover effect). The sum of the two effects was defined as the total effect, which represented the average influence of the explanatory variables on the explained variable. Based on this, this paper analyzed the influence of financial agglomerations on green development through direct effect and spillover effect. Our SPDM can be rewritten as:

4.1.3. Spatial panel Durbin model (SPDM) Due to the strong regional externality of financial agglomerations, the fact that financial agglomerations in different cities could influence each other should be considered in our analysis. In general it is necessary to add the spatial effect on any traditional econometric model when analyzing the spatial spillover effect of a financial agglomeration. An SPDM contains both the spatial lag term of explained variable and explanatory variables. It could be used to correct the problems of biased estimates of model coefficients caused by missing values. When the true data-generating process was a SPLM or a spatial panel error model (SPEM), an SPDM could still obtain unbiased estimation coefficients (Balta-Ozkan et al., 2015). Hence, by spatially expanding the STIRPATM, we

Iit ¼ ð1  rWÞ1 þ ð1  rWÞ1 ðX4 þ WX qÞ þ ð1  rWÞ1 ε

W12 qk 4k « WN2 qk

established an SPDM that considered the spatial spillover effect of financial agglomeration and green development:

Iit ¼ a þ r

N X j¼1

Wij Iit þ 4Xit þ q

N X

Wij Xit þ ci þ mt þ εit

(3)

j¼1

where r means the influence of local green development on the green development of neighboring areas; X includes financial agglomeration, population size, affluence and technology progress; W is the spatial weight matrix; ci indicates the individual fixed effect; ut is the time fixed effect; εit represents the random error term. When applying spatial econometric models to investigate the influence of explanatory variables on explained variables, the changes in local explanatory variables would loop back to the local region through influencing explanatory variables in neighboring regions. The conventional point estimation cannot capture this

(4)

The partial differential equation matrix could be obtained by taking the derivative of the k-th explanatory variable as the independent variable:

2

3 vI1 vIN 2 / 6 vX 7 4k 6 1k vXNk 7   6 7 6 W21 qk vI vI 1 6 / ¼6 « « « 7 7 ¼ ð1  rWÞ 4 « vX1k vXNk t 6 6 7 4 vIN 5 vIN WN1 qk / vX1K vXNk

(2)

/ / « /

3 W1N qk W2N qk 7 7 5 « 4k

(5)

By observing the partial differential decomposition matrix, a unit change in an explanatory variable would lead to changes in the explained variable of this unit, thus generates direct effects. The explained variables of adjacent area units would also change, resulting in a spatial spillover effect. Direct effect and spatial spillover effect correspond to diagonal and non-diagonal elements in the matrix, respectively. The sum of the matrix would be the total effect. 4.1.4. Spatial panel Durbin model_ panel threshold regression model (SPDM_PTRM) To account for the fact that the threshold effect and the spatial spillover effect of financial agglomerations on green development are inseparable in time and space, this paper established a combined model. We named this model SPDM_PTRM. This model considered both the threshold effect and the spatial effect based on the construction principle of PTRM and SPDM. This is for

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investigating the spatial threshold effect of financial agglomerations on green development. The specific steps of calculation in this modeling process are: (1) PTRM was used to identify the threshold effect of financial agglomerations on green development. If a financial agglomeration had double thresholds for green development l1 and l2 (l1 < l2 ). When the financial agglomeration was less than l1 , dummy variable d1 would be 1, otherwise 0; when the financial agglomeration was greater than l1 and was less than l2 , the dummy variable d2 would be 1, otherwise 0; when the financial agglomeration was greater than l2 , the dummy variable d3 would be 1, otherwise 0. (2) Dummy variables d1 , d2 and d3 were adopted to construct interaction terms with the variables of financial agglomeration respectively in Eq (6) and identify the regime to which the sample belongs. (3) SPDM was employed to investigate the differences in the influence of financial agglomerations on green development under the constraint of variable thresholds. Considering the shortcomings of conventional point estimation, this paper applied partial differential decomposition method in calculating the direct effect and spatial spillover effect of financial agglomerations on green development. Therefore, the expression of SPDM_PTR model is:

Iit ¼ a þ r

N X

7

4.2. Variable selection 4.2.1. Green development (I) There are two main methods for evaluating green development. The first is an evaluation method of an index system, which can comprehensively measure green development, but is easily affected by subjective factors in the process of index selection and weight distribution (Wu et al., 2018). The second method is data envelopment analysis (DEA), which has the benefit of better depicting the core characteristics of green development which is achieving higher economic output and less environmental pollution with less resource input. DEA is powerful in measuring the efficiency of decision-making unit (Charnes et al., 1978). Conventional DEA models are mostly radial or angular, which do not fully consider the problem of slack of input or output. Therefore, it may be impossible to evaluate the efficiency with non-expected output. But with the introduction of non-radial and non-angular slack based measure (SBM), the slack variable could be directly put into the objective function, which not only solved the problem of inputoutput relaxation, but also put forward the method for estimating the efficiency with non-expected output (Tone, 2001). In this paper, non-radial and non-angle super slack based measures (SSBM) were adopted to evaluate the green development level of 285 Chinese cities. Assuming that m types of inputs Xit ¼ ðx1it ; x1it ; /xmit Þ2Rþ m were used in the t period of city i, n

Wij Iit þ t1 lnPit þ t2 lnAit þ t3 lnTit þ t4 Fit $d1 ðFit  l1 Þ þ t5 Fit $

j¼1

d2 ðl1 < Fit < l2 Þ// þ tnþ1 Fit $dnþ1 ðln1 < Fit  ln Þ þ tnþ2 Fit $ dnþ2 ðFit > ln Þ þ p1 WlnPit þ p2 WlnAit þ p3 WlnTit þ p4 WFit $d1 ðFit  l1 Þ þp5 WFit $d2 ðl1 < Fit < l2 Þ// þ pnþ1 WFit $dnþ1 ðln1 < Fit  ln Þ þpnþ2 WFit $dnþ2 ðFit > ln Þ þ ci þ mt þ εit

where t; p represent the regression coefficient and spatial lag coefficient of financial agglomeration, population factor, wealth degree, and technical factor respectively. Other variables are defined as before. 4.1.5. Spatial weighting matrix (W) Modern information and communication technologies have strong inter-regional links at all levels of the globe. As a high-end service industry, financial industry has high requirements for the convenience and sensitivity of information transmission (Zhao et al., 2004). Accordingly, this paper employed the product of the information technology level of two cities and the inverse ratio of the square of the distance between the two cities to depict the influence of information technology on financial agglomeration and green development. The level of information technology was measured by the number of per capita Internet users in the cities, or

Wij ¼

Pi Pj d2ij

(7)

where dij was the geographical distance between city i and city j calculated according to the longitude and latitude of the city; Pi and Pj represented the level of information technology development of city i and city j respectively.

(6)

types of expected outputs Y dit ¼ ðY d1it ; Y d1it ; /Y dnit Þ2Rþ n and g types of non-expected outputs Y uit ¼ ðY u1it ; Y u1it ; /Y unit Þ2Rþ g were produced. In addition, all input and output elements were required to be non-0. The weights of observed values of city i were wti for all time t. By incorporating environmental technology into the model, the production possibilities set in t period are:

Pt ¼

n

i i  X X  Xit ; Y dit ; Y uit xjit  wti xjit ; cj; ydkit  wti ydkit ; i¼1

ck; yurit 

i X

i¼1

o wti yurit ; cr; wti  0; ci

(8)

i¼1 P P If wti ¼ 1, it denotes that the production technology was i¼1 under the condition of variable return to scale (VRS), otherwise is constant returned to scale (CRS). The sequential SBM of green development efficiency of region i in t period under resource and environment constraints can be expressed as:

Pm sjt 1 1m j¼1 xjt0 " # r*0t ¼ min d Pg sgrt P skt n 1 1 þ nþg k¼1 yd þ r¼1 yg kt0

rt0

(9)

8

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Table 2 Input-output factors definition and descriptive statistics.

Inputs

Outputs

8 > > > > > > > > > > > < s:t:

Definition

Observation

Mean

Standard deviation

Minimum

Maximum

Unit

Label force Capital stock Energy consumption Desirable: real gross domestic product Undesirable: environmental pollution index

3264 3264 3264 3264 3264

5.60eþ04 0.468 4.80eþ05 10.931 0.000

8.80eþ04 0.688 7.90eþ05 15.664 0.809

897.750 0.041 1020 0.093 0.434

1.30eþ06 9.543 8.10eþ06 180.982 15.689

Million people Million yuan 104Kilowatt-hour Million yuan e

xjt0 ¼

P X

wti xjit

þ

s jt

i¼1

ydkt0 ¼

P X

wti ydkit  sdkt

i¼1

> > > > > > > > > > > :

g

yrt0 ¼

P X

(10) g

wti yurit þ srt

i¼1

s jt

> 0; sdkt

g > 0; srt

> 0; wti

>0

where s represents the slack variables of input and output. r* is the d objective function which is strictly decreasing with respect to s jt , skt g * and srt , and 0  r  1. The evaluated unit was efficient if and only g d if r* ¼ 1 and s jt ¼ skt ¼ srt ¼ 0, otherwise it was inefficient and there was a need to improve the input and output. r* refers to green development efficiency. Input-output variables are shown in Table 2. The environmental pollution index is referred to Du and Li (2019) and Feng et al. (2019). 4.2.2. Financial agglomeration (F) It is common to apply spatial Gini coefficient (Ellison and Glaeser, 1994), location entropy index (Xiao et al., 2018), concentration ration of industry (Zheng and Lin, 2018), HerfindahlHirschman index (Mitchell, 2019), Ellison-Glaeser index (Wang et al., 2018) and Duranton-Overman index (Duranton and Overman, 2005) to describe the level of a financial agglomeration. Due to China’s vast territory and large differences between provinces and cities, the use of location entropy index was beneficial in eliminating the impact of different regional sizes, so as to accurately reflect the spatial distribution of financial agglomeration. For this reason, referring to existing literature (Du and Zhang, 2018), this paper selected the number of financial practitioners to measure the level of financial agglomeration through location entropy index. The calculation formula was:

Fist ¼

Eist =Eit ; Qst =Qt

where Fist represented the agglomeration level of s industry in t period of city i,; Eist reflected the number of employees of s industry in t period of city i,;Eit referred to the number of employees in all industries in t period of city i,;Qst was the number of employees of s industry in t period nationwide; Qt meant the number of employees in all industries in t period nationwide. The higher the location entropy index was, the higher the industrial agglomeration level would be, and vice versa. 4.2.3. Population size (P) The expansion of population could be an important cause of environmental problems. The increase in population often led to the continuous rise of consumption demand, the enlarging of production scale, and the soaring consumption of energy and

resources. It may also lead to the increase in environmental pollution. Moreover, at the beginning of industrialization, the comprehensive quality of population would be low, and the awareness of environmental protection would be weak, which further aggravated environmental pollution. Accordingly, in this paper, the total urban population at the end of the year is adopted to depict the impact of population on green development (Zhang et al., 2019). 4.2.4. Affluence (A) With the rise of people’s income level, the scale, level, and structure of consumption would change accordingly, thus affecting the environment. Specifically, the increase in consumption brought by the increase of population scale would inevitably result in the expansion of social production scale, which would lead to more consumption of resources and energy. With this consideration, per capita GDP was adopted in this paper as a measure for the impact of affluence on green development (Jiang et al., 2018). Specifically, inflation-adjusted 2003 GDP index was used. 4.2.5. Technology progress (T) The environmental impact of each unit of consumption or production depends not only on population and affluence, but also on production technology. Technological progress could be a doubleedged sword, which provides the conditions for development and production as well as causing ecological damage and environmental pollution. In this paper, the influence of technological progress on green development was represented by energy consumption per unit of GDP, following Cheng et al. (2017). 4.3. Data sources In order to avoid the impact of drastic changes in administrative divisions of prefecture-level cities in China before 2003 and to maintain the consistency and the integrity of samples, this paper selected the panel data of 272 prefecture-level cities from 2003 to 2014 to investigate the complex relationship between financial agglomerations and green development. The data were mainly from China Urban Statistical Yearbook (2004e2015). The missing data of some cities or years were supplemented by an interpolation method, and the cities with serious data deficiency were eliminated. To eliminate the impact of inflation over time, the GDP index was used to deflate all price variables based on 2003. Descriptive statistics of main variables are shown in Fig. 3. According to the boxplots of the variables, the original values of I and F basically accord with the normal distribution characteristics and there are no serious abnormal values. The original values of P, A, and T deviate from the normal distribution (skewed distribution), and the data are mainly skewed to the lower values. Therefore, P, A, and T all are logarithmized in this paper so to make the data more stable and reduce the influence of heteroscedasticity. To verify whether there was serious collinearity among variables, correlation coefficient analysis and Variance Inflation Factor (VIF) test were carried out on main variables in this paper (Table 3). It turned out that the major variables were not highly correlated.

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

9

Fig. 3. Boxplots for the variables I, F, P, A, T.

Table 3 Correlation analysis and VIF test.

I F P A T

VIF

I

F

P

A

T

e 1.01 1.01 1.01 1

1 0.0679 0.0259 0.2696 0.2454

1 0.005 0.0164 0.0311

1 0.0551 0.0404

1 0.0553

1

Meanwhile, the result of VIF test showed that VIF values were all less than 10, which meant that there was no serious multicollinearity between explanatory variables. 5. Empirical results 5.1. The analysis of spatial threshold effect This section analyzes the differences of the influence of financial agglomeration on green development in the process of time-space separation and time-space unification. This section also highlights the advantages of an SPDM_PTRM, which considered both threshold effect and spatial spillover effect. First, the threshold effect and spatial spillover effect of financial agglomerations on green development were estimated respectively by a PTRM and an SPDM from the perspective of time-space separation, as treated in the existing literature. Then an SPDM_PTRM was used to re-estimate the spatial threshold effect of financial agglomeration on green development. 5.1.1. The threshold effect of financial agglomeration on green development Based on the theoretical analysis, there was a significant nonlinear relationship between financial agglomeration and green development. A PTRM was applied to test the threshold effect of financial agglomeration on green development. According to results shown in Table 4, the single threshold and double threshold of financial agglomerations both passed the 5% significance test, indicating that there existed significant double thresholds of financial agglomerations on green development. That is, financial agglomerations had a nonlinear correlation with green development, thus traditional linear model could not accurately estimate the influence. Considering heteroscedasticity, the paper adopted feasible generalized least squares (FGLS) to estimate the regression

coefficient of financial agglomeration on green development. In order to further analyze the threshold effect of financial agglomerations on green development, this paper defined cities with F < 1.128 as low-level financial agglomerations, cities with 1.128  F  1.949 as medium-level financial agglomerations, and cities with F > 1.949 as high-level financial agglomerations. According to Table 5, it can be seen that: (1) When the financial agglomeration level was lower than the threshold value of 1.128, the regression coefficient of a financial agglomeration on green development was 0.074 and significant at the 1% confidence level, which meant financial agglomeration significantly promoted local green development. (2) When the financial agglomeration level was between the threshold value of 1.128 and 1.949, the estimated coefficient was 0.111 and passed the significance test of 1%, indicating the promoting effect of a medium-level financial agglomeration was stronger than that of aa low-level financial agglomeration.

Table 4 Threshold test of financial agglomeration.

Single threshold Double threshold Triple threshold

F-value

P-value

Critical value 1%

5%

10%

12.285** 12.153** 4.501*

0.018 0.017 0.142

15.093 14.680 14.241

7.329 8.170 8.580

4.728 5.546 5.725

Note: (1) The P-value and critical value are obtained by using Bootstrap to repeatedly sample 3264 times. (2) Fixed effect model is adopted. (3) *, ** and *** respectively mean significant at the levels of 10%, 5% and 1%.

Table 5 Threshold value and parameter estimation. Variable

Coefficient

t-value

F < 1.128 1.128  F  1.949 F > 1.949 lnP lnA lnT Constant

0.074*** 0.111*** 0.212*** 0.459*** 0.598*** 0.979*** 0.810***

3.220 5.970 7.200 3.380 9.190 12.670 6.930

10

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

Fig. 4. The spatial evolution pattern of China’s green development (2003e2014).

(3) When the financial agglomeration level was higher than the threshold value of 1.949, the regression coefficient was 0.212 and significant at the 1% confidence level, representing that the promoting effect of a high-level financial agglomeration was the strongest. It is obvious that there are three typical stages in the process of financial agglomerations influencing green development. The higher the level of a financial agglomeration is, the stronger the role of the financial agglomeration in promoting green development would be. This is also similar to the result estimated by Shi et al. (2018). 5.1.2. The spatial spillover effect of financial agglomeration on green development Before the empirical analysis, ArcGIS10.2 was used to visualize the spatial evolution patterns of China’s green development status from 2003 to 2014. These maps allow us to better identify the spatial correlation of green development. According to Fig. 4, from 2003 to 2014, China’s regions with high levels of green development gradually migrated from the eastern coastal cities to the inland areas in the central and western regions. Overall, there was an obvious trend of agglomeration and diffusion of green development levels between regions, which showed that green development had an obvious spatial dependence. It is often a prerequisite for spatial econometric analysis to distinguish whether variables have spatial autocorrelation and the characteristics they may have. Accordingly, the global Moran’s index was applied to measure the levels of spatial autocorrelation in core variables (Table 6). The results demonstrate that I, C, P, A, and T basically passed the significance test, and the values of Moran’s index were significantly positive as a whole. This denoted that the variables of green development, financial agglomeration, population size, affluence, and technology progress all have spatial autocorrelation, and generally show a spatial agglomeration trend. However, the analysis still needs to test whether an SPDM was suitable to be used to estimate the spatial spillover effect of financial agglomerations on green development. First, the spatial

lag effect and spatial error effect were discerned by LM (Robust) test. The results in Table 7 revealed that the spatial lag effect and spatial error effect both passed the significance tests through LM test and Robust LM test. That is, the SPLM and the SPEM were applicable to study the spatial spillover effect of financial agglomeration on green development. Secondly, Wald test and LR test were utilized to find out that

Table 6 Spatial autocorrelation test. Year

I

C

P

A

T

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

0.005 0.011 0.017* 0.009 0.011 0.01 0.013* 0.01 0.009 0.013* 0.016* 0.03** 0.026** 0.029** 0.026**

0.037** 0.03** 0.039** 0.037** 0.034** 0.025** 0.003 0.021* 0.019* 0.036 0.04 0.021* 0.021* 0.05 0.022**

0.02* 0.021* 0.021* 0.021* 0.022* 0.02* 0.019* 0.019* 0.019* 0.018* 0.017* 0.017* 0.017* 0.018* 0.018*

0.032** 0.032** 0.029** 0.026** 0.024** 0.022** 0.019* 0.02* 0.02* 0.02* 0.02* 0.019* 0.019* 0.019* 0.019*

0.013 0.012 0.008 0.02** 0.003 0.003* 0.019** 0.03** 0.032** 0.047*** 0.049*** 0.056*** 0.056*** 0.055*** 0.051***

Table 7 Identification test of spatial panel econometrics model. Test

Statistics

P-value

LM(lag)test Robust LM(lag)test LM(error)test Robust LM(error)test Wald test spatial lag Wald test spatial error LR test spatial lag LR test spatial error Hausman test

7.2299 5.5559 25.9524 24.2783 93.10 34.1246 88.6859 47.7818 37.70

0.007 0.018 0.000 0.000 0.000 0.000 0.000 0.000 0.000

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

whether the SPDM, the more general spatial econometric model, was fit for use. According to the results, the SPDM could not be degenerated into the SPLM or the SPEM, indicating that the SPDM was the most suitable regression model. In addition, Hausman test results showed that fixed effect model was more applicable than random effect model to estimate the impact of financial agglomeration on green development. Therefore, this paper employed the SPDM with fixed effect to empirically test the spatial spillover effect of financial agglomeration on green development. Based on the above analysis, this paper employed an SPDM with fixed effect to verify the hypothesis that financial agglomeration had spatial spillover effect on green development as well as further analyzing the influence of financial agglomerations on green development, and adopted partial differential matrix to explain the estimated results (Table 8). The following results can be obtained from Table 8: (1) The coefficient of the direct effect of financial agglomerations on green development was 0.092 at the 1% significance level, denoting that financial agglomerations were helpful to promote local green development. (2) The coefficient of the spatial spillover effect was 0.4 that passed the significance test at 0.1 level, which meant that the improvement of financial agglomeration level was conducive to promoting the green development of neighboring regions. The study of Yuan et al. (2019a) also reached a similar conclusion. The above analysis shows that financial agglomerations had significant positive spatial spillover effect on green development, which strongly supports the research hypothesis outlined above. 5.1.3. The spatial threshold effect of financial agglomerations on green development It can be seen from the above analysis that financial agglomerations had both threshold effect and spatial spillover effect on green development. Conventional empirical analysis often artificially disconnected the two effects, resulting in the deviated estimation results. Accordingly, an SPDM_PTRM was applied to investigate the spatial threshold effect of financial agglomeration Table 8 Estimation results of SPDM. Variable

Direct effect

Spillover effect

F

0.092*** (4.67) 0.003 (-0.40) 0.083*** (10.81) 0.074*** (11.47)

0.400* (2.34) 0.623** (2.40) 0.243 (-1.44) 0.678 (-1.53)

lnP lnA lnT

Table 9 Estimation result of SPDM_PTR model. Variable

F  1.128 1.128 < F  1.949 F > 1.949 lnP lnA lnT

Direct effect

Spillover effect

SPDM_PTR

SPDM

SPDM_PTR

SPDM

0.001 (0.02) 0.039* (1.79) 0.202*** (5.87) 0.004 (0.46) 0.068*** (8.24) 0.079*** (11.57)

0.092***(4.67)

1.422* (-1.94) 1.759*** (-2.62) 2.337*** (3.34) 0.453* (1.78) 1.163*** (-2.93) 0.055 (0.12)

0.400*(2.34)

0.003 (-0.40) 0.083*** (10.81) 0.074*** (11.47)

0.623** (2.40) 0.243 (-1.44) 0.678 (-1.53)

11

on green development. An SPDM and a PTRM were also used to compare results (Table 9). From the results of total effect estimation of the SPDM_PTRM: When the agglomeration level was lower than 1.128, in the PTRM, the estimated coefficient of financial agglomeration on green development was 0.074 and significant at the level of 1%, while the coefficient of the SPDM_PTRM was 1.421 and significant at the level of 10%. When the agglomeration level was between 1.128 and 1.914, the financial agglomeration coefficients of the PTRM and the SPDM_PTRM were 0.111 and 1.720 respectively, and both passed the significance test at 1% level. When the agglomeration level exceeded the threshold value of 1.949, the regression coefficient of the SPDM_PTRM was 2.539 and significant at the level of 1%, while that of the PTRM was 0.111 and significant at the level of 1%. The financial agglomeration coefficient of the SPDM (without time effect) was 0.493 and significant at the level of 1%. The above analysis shows that, the PTRM, which tests the threshold effect of financial agglomerations on green development in time, has estimated coefficients in the same direction as that by the SPDM_PTRM. However, it overestimates the impact of financial agglomerations on green development for ignoring the spatial effect. On the other hand, the SPDM, which examines the spatial spillover effect of financial agglomeration on green development in space, fails to take into account the non-linear impact, thus cannot accurately reflect the disparate impact of financial agglomeration on green development. Since time effect and spatial effect both exist in the relationship between financial agglomerations and green development, ignoring any of them will lead to the inability to obtain accurate estimation results. As opposed to this, the SPDM_PTRM with time-space consistency can accurately reveal the influence of financial agglomerations on green development. The results of the partial differential equation matrix of the SPDM_PTRM show (Table 9): (1) From the perspective of direct effect: When the financial agglomeration level was lower than 1.128, the regression coefficient of financial agglomerations on green development was 0.001, which did not pass the significance test. This means that financial agglomerations could not drive the local green development at low-level financial agglomeration. When the financial agglomerations level was between the threshold values of 1.128 and 1.949, the regression coefficient was 0.039 and significant at the 10% confidence level, indicating that in the medium stage of financial agglomerations, the improvement of financial agglomeration level was conducive to promoting local green development. When financial agglomerations exceeded the second threshold value of 1.949, the regression coefficient was 0.202, passing the significance test of 1%. At this time, the regression coefficient value of financial agglomerations is the largest and the most significant, which means that at the stage of high-level financial agglomerations, financial agglomerations had the strongest and most significant promoting effect on local green development. (2) From the perspective of spatial spillover effect: When the financial agglomeration level was less than 1.128, the regression coefficient of financial agglomerations on green development was 1.422 and significant at the 10% confidence level. That is to say, low-level financial agglomerations would inhibit the green development of adjacent areas. When the financial agglomeration level was between the threshold value of 1.128 and 1.949, the regression coefficient was 1.759, which passes the significance test of 1%, denoting that medium-level financial agglomerations were

12

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

more unfavorable to the improvement of the green development of neighboring regions than low-level. When the financial agglomerations surpassed the second threshold value of 1.949, the regression coefficient was 2.377 and significant at the 1% confidence level, which means that, in highlevel financial agglomeration stage, financial agglomeration had a significant positive spatial spillover effect on adjacent areas. 5.2. The analysis of regional boundary of spatial spillover effect In order to test the hypothesis that the influence of financial agglomerations on green development would have a spatial attenuation boundary. By referring to the method mentioned in existing literature, this paper cut off the geographical distances between cities according to the attenuation law of geographical distance (the farther the distance, the smaller the spatial spillover effect). Specifically, suppose the distance interval between two cities is [dmin ; dmax ], and g is the progressive distance from dmin to dmax , when dij  d, the element of geographical unit is the ratio of the product of the informatization level of two cities to the square of the inverse distance of two cities. When dij < d, on the other hand, the element of the geographical unit in the matrix is 0. This method can remove cities within the distance of d from the spatial weight matrix, so to allow a better observation of the longdistance attenuation change of the spatial spillover effect of financial agglomeration on green development (Halpern and € zy, 2007): Murako

Wd = d ¼ dmin ; dmin þ g; dmin þ 2g; //; dmax where Wd ¼ [Wij， d]

NN

is the spatial weight matrix, and

dij  d (12) dij < d

F<1.128

(1) The attenuation boundary of the spatial spillover effect of low-level financial agglomerations was about 700 km, and the dense area was within 200 km for the spatial spillover effect. Specifically, the coefficient of the spatial spillover effect of financial agglomerations on green development fluctuates within the ranged from 1.449 to 1.068. The spatial correlation coefficients fluctuated around 1.4 within 200 km. The range of 200e700 km was the fast decreasing region of spatial spillover effect, where the spatial correlation coefficient attenuated from 1.499 to 1.068. Over 700 km, the spatial spillover effect showed a random fluctuation state, which was mainly due to the reduction of spatial units in the weight matrix. (2) At the medium-level financial agglomeration stage, within 360 km was the dense area of the spatial spillover effect, there was a relatively obvious decline after exceeding 860 km. Specifically, the coefficient of the spatial spillover effect of financial agglomerations fluctuated between 0.967 and 2.093. Within 360 km was the dense area with spatial spillover effect and its spatial autocorrelation coefficient

1.128 F 1.949

F>1.949

3.400 3.100 2.800 2.500 2.200 1.900 1.600 1.300 1.000 0.700 0.400 0.100 -0.200 -0.500

20 60 100 140 180 240 280 320 360 400 520 560 600 640 680 720 760 800 840 880 920 960 1000 1040 1080 1120 1160 1200 1240 1280 1320 1360 1400 1440 1480 1600

Spatial spillover effects

Wij;

8 Pi Pj > > < 2 dij d¼ > > : 0

(11)

Based on the spatial weight matrix of threshold distances, an SPDM_PTR model was adopted to re-estimate the spatial spillover effect of financial agglomerations on green development. Among the 272 cities, the shortest distance was 17 km from Anyang to Hebi. Therefore, the initial value of the threshold distance space weight matrix was set as 20 km, and the stepping distance was also set as g ¼ 20km, which lasted until 1,600 km. Secondly, the coefficients of spillover effect and t statistics of financial agglomeration obtained under different distance thresholds were recorded. Finally, the coefficients of spatial spillover effect were visualized (Fig. 5) to allow us to investigate the attenuation boundary of spatial spillover effect of financial agglomerations on green development. As shown in Fig. 5, with the increase in distances, the spatial spillover effect of financial agglomerations at different levels show significant changes:

-0.800

Distance

-1.100 -1.400 -1.700 -2.000 -2.300 Fig. 5. Attenuation process of spatial spillover effect of financial agglomeration.

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

fluctuates around 1.7. Within 360e860 km range was the area where the effect of spatial spillover effect decreases rapidly, and the spatial autocorrelation coefficient dropped from 1.708 to 0.967. When the distance was greater than 860 km, the spatial spillover effect of financial agglomerations showed the characteristics of random fluctuations. Compared with the effect at the low-level financial agglomeration stage, the inhibition effect of financial agglomerations on green development in this stage was stronger and more significant, and the fluctuation range of the coefficient of spatial spillover effect was larger. (3) The dense area of the spatial spillover effect of high-level financial agglomerations was within 580 km, and after 1480 km, the effect presented as random fluctuation. Specifically, the regression coefficient of spatial spillover effect fluctuated within the range of 0.19e3.168. Within 580 km was a dense area of spatial spillover, and the coefficient of spatial spillover effect fluctuated around 2.2. 580e1480 km was the region where the effect decreased rapidly, and the coefficient value declined quickly from 3.168 to 0.802. After exceeding 1480 km, the spatial correlation coefficient showed a random fluctuation state, which was significantly different from the stage of low-level and medium-level financial agglomerations. 6. Discussion 6.1. Research findings (1) Financial agglomerations can significantly promote local green development, and the higher the level of a financial agglomeration, the stronger its promoting effect on local green development would be. (2) There are stage differences in the influence of financial agglomerations on green development of neighboring regions. Low-level financial agglomerations and medium-level financial agglomerations would hinder the green development of neighboring areas. However, once entering the highlevel financial agglomeration stage, financial agglomerations can significantly promote the green development of neighboring areas. (3) The spatial spillover effect of financial agglomerations on green development shows regional boundary, which is restricted by the level of financial agglomerations, and the higher the level of a financial agglomeration, the wider the scope of spatial spillover. The regional boundary of high-level financial agglomerations is 1480 km, while the influence range of medium-level and low-level financial agglomerations is 860 km and 700 km respectively.

6.2. Policy implications Based on the above discussion, we suggest that the following methods be used to promote green development through encouraging financial agglomerations: (1) The spatial distribution pattern of “leading financial centers drive, multilevel financial centers supplement” should be formed. As the driving effect of financial agglomerations on local green development increases with the level of financial agglomerations, it is necessary to promote the construction of high-level financial centers by building a group of internationally competitive financial centers as well as

13

establishing different levels of financial centers in secondary regions as a supplement. First, government should support the transformation of Hong Kong, Shanghai, and Beijing into well-known international financial centers through policies and systems based on the layout of global financial centers and the advantages of domestic central cities, so as to ensure the formation of iconic and competitive financial centers in China. Second, as the “night watchman” of the market, government should overcome the diminishing returns on marginal scale brought by negative externality of agglomeration through system design and policy instruments. Third, different levels of financial agglomeration should be built in the secondary regions to meet the needs of both urban financial services and highlevel financial centers, hence to form a nationwide dense network of financial centers driven by leading financial centers and supplemented by multilevel financial centers. (2) Differentiated financial agglomeration strategies should be formulated. Since only high-level financial agglomeration can form significant positive spatial spillover to the green development of neighboring areas, it is necessary to scientifically identify the stage of financial agglomerations. Then, according to the geographical location and the development situation of economic and society of the city, financial agglomeration strategies suitable for its development direction should be formulated, and policy intervention should be adopted to avoid or alleviate the phenomenon of financial exclusion. Third, in order to give full play to the supporting function of spatial spillover effects and overcome the negative influence caused by low-level financial agglomerations, cities with low-level financial agglomerations should integrate financial resources, standardize service functions and business scope, actively integrate into the national network of financial centers, and enhance the centrality of nodes. In doing so, the level of financial agglomerations can be raised, thereby conducive to utilizing the advantages of regional financial agglomeration. (3) Financial market with internal and external linkage to realize regional integration should be constructed. Because the regional boundary of the spatial spillover effect of a financial agglomeration is restricted both by its development level and administrative boundary, besides improving the financial agglomeration level, it is also important to solve the problems caused by administrative division through internal reform and external cooperation. On the one hand, the government should make full use of the market mechanism, and boost the promoting effect of financial agglomerations on green development by deepening the reform of financial system and allowing the free flow of financial resources across regions, so as to realize the optimal allocation of financial resources in a larger scope. On the other hand, governments at all levels should strive to promote the integration of regional financial market, remove the obstacles of administrative boundary on the spatial spillover effect of financial agglomerations, therefore overcome the adverse effect of administrative constraints by formulating transscale regional cooperation strategies and setting up regional cooperation and coordination institutions.

6.3. Limitation and future studies As in other scientific research, findings from this study are also limited to certain conditions. On the one hand, since this study only

14

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

focuses on agglomerations of financial industry and does not conduct comparative study between that and other industries, the conclusions of this paper cannot be used to guide the relationship between agglomerations of other industries and green development. On the other hand, this study only selects Chinese samples for empirical test, and does not choose regions with huge differences from China like developed countries or backward regions for comparative study, which also leads to certain limitations. There are also other aspects needing further study. First, because the evolutionary mechanism and characteristics of agglomeration of different industries are significantly different, we need to further study the spatial threshold effect of different industrial agglomerations to extend the benefits of green development to the greatest extent (Wang et al., 2018). Second, since different countries have different levels of financial agglomerations (Shi et al., 2018; Ye et al., 2018), we also need to select samples on a larger scale in the future, especially in different countries, in order to draw more general scientific conclusions through comparative analysis (Otsuka et al., 2014). Third, the SPDM_PTRM needs to be applied to other study

SSBM model is used to accurately evaluate the status of green development. Finally, SPDM_PTRM is built to empirically test the spatial threshold effect of financial agglomerations on green development and identify the spatial spillover boundary and its differences in the process of financial agglomeration transformation. This study indicates that financial agglomerations have a significant spatial threshold effect on green development, which has obvious difference under the distance constraint. Funding This work was supported by Major Program of National Social Science Foundation of China (18ZDA047); China Scholarship Fund of Study Abroad Program (201806820047); The tutorial class of training project for young Marxists in Jiangxi (19QM22); National Natural Science Foundation of China (41801164). The authors would also like to thank reviewers for commenting on this paper.. Appendix A

Table A Estimation result of Spatial econometric model Variable

SPDM

F F  1.128

0.092***

SPDM_PTR

0.003 (-0.35) 0.082*** (10.26) 0.075*** (11.20)

0.000 (-0.00) 0.039* (1.82) 0.198*** (5.44) 0.004 (0.46) 0.068*** (8.12) 0.078*** (11.53)

878.156 1792.313 1901.945 3264 272

854.629 1761.258 1919.616 3264 272

1.128 < F  1.949 F > 1.949 lnP lnA lnT

Log L AIC BIC Obs. Number of cities

Variable

SPDM

W*F

0.384*** (2.61)

SPDM_PTR

W*F  1.128

1.313** (-2.44) 1.616*** (-3.77) 2.139*** (5.66) 0.423** (2.02) 1.070*** (-4.52) 0.042 (0.10) 854.629 1761.258 1919.616 3264 272

1.128 < W*F  1.949 W*F > 1.949 W*lnP

0.585*** (2.86) 0.233 (-1.53) 0.627 (-1.57) 878.156 1792.313 1901.945 3264 272

W*lnA W*lnT Log L AIC BIC Obs. Number of cities

related to economic phenomena (Han et al., 2018) to explore their spatial threshold characteristics. 7. Conclusion First, this paper provides theoretical analysis for studying the spatial threshold effect and its spatial attenuation boundary of financial agglomerations on green development. Second, improved

Appendix B

Table B Spatial spillover coefficient of financial agglomeration with distance Distance

F < 1.128

T value

1.128 < F < 1.949

T value

F > 1.949

T value

Distance

F < 1.128

T value

1.128 < F < 1.949

T value

F > 1.949

T value

20 km 40 km 60 km 80 km 100 km 120 km 140 km 160 km 180 km 200 km 240 km

1.429* 1.422* 1.428* 1.428* 1.427* 1.425* 1.431* 1.431* 1.430* 1.415 1.431*

1.94 1.94 1.94 1.94 1.94 1.93 1.94 1.94 1.94 1.32 1.94

1.766*** 1.759*** 1.764*** 1.765*** 1.766*** 1.765*** 1.772*** 1.770*** 1.765*** 2.093** 1.761***

2.62 2.62 2.62 2.62 2.62 2.62 2.62 2.62 2.62 2.26 2.62

2.344*** 2.337*** 2.342*** 2.343*** 2.344*** 2.343*** 2.351*** 2.348*** 2.343*** 3.168*** 2.338***

3.34 3.34 3.34 3.34 3.34 3.34 3.34 3.34 3.34 2.78 3.34

840 km 860 km 880 km 900 km 920 km 940 km 960 km 980 km 1000 km 1020 km 1040 km

0.465 0.392 0.167 0.013 0.293 0.261 0.282 0.254 0.009 0.068 0.299

0.89 0.78 0.37 0.03 0.68 0.59 0.63 0.6 0.02 0.18 0.75

1.177** 0.967** 0.450 0.350 0.688* 0.747** 0.723* 0.685* 0.347 0.266 0.697**

2.41 2.16 1.25 1.11 1.88 1.98 1.88 1.9 1.1 0.9 2.01

1.598*** 1.437*** 0.922*** 0.852*** 1.205*** 1.273*** 1.246*** 1.167*** 0.849*** 0.837*** 1.249***

3.25 3.16 2.59 2.7 3.25 3.28 3.12 3.16 2.7 2.83 3.49

H. Yuan et al. / Journal of Cleaner Production 244 (2020) 118670

15

Table B (continued ) Distance

F < 1.128

T value

1.128 < F < 1.949

T value

F > 1.949

T value

Distance

F < 1.128

T value

1.128 < F < 1.949

T value

F > 1.949

T value

260 km 280 km 300 km 320 km 340 km 360 km 380 km 400 km 500 km 520 km 540 km 560 km 580 km 600 km 620 km 640 km 660 km 680 km 700 km 720 km 740 km 760 km 780 km 800 km 820 km

1.414* 1.412* 1.448 1.413* 1.449** 1.415* 1.364* 1.380* 1.271* 1.399** 1.357* 1.418* 1.422* 0.593 1.117* 1.214* 1.228* 1.106* 1.068* 0.904 0.937 0.712 0.750 0.125 0.381

1.92 1.93 1.43 1.93 1.98 1.95 1.9 1.9 1.83 1.97 1.92 1.95 1.94 0.86 1.78 1.81 1.92 1.74 1.74 1.56 1.63 1.3 1.37 0.27 0.75

1.747*** 1.749*** 2.012** 1.716*** 1.736*** 1.708*** 1.679*** 1.710*** 1.572** 1.646*** 1.607** 1.710*** 1.742*** 1.122** 1.445** 1.492** 1.480*** 1.435** 1.429** 1.312** 1.324** 1.148** 1.166** 0.715** 1.119**

2.6 2.61 2.32 2.59 2.61 2.6 2.58 2.58 2.52 2.59 2.55 2.6 2.6 2.01 2.57 2.49 2.59 2.52 2.57 2.5 2.57 2.34 2.37 2.05 2.37

2.326*** 2.326*** 3.133*** 2.292*** 2.309*** 2.279*** 2.246*** 2.279*** 2.097*** 2.185*** 2.112*** 2.217*** 2.227*** 1.746*** 1.829*** 1.966*** 1.893*** 1.898*** 1.837*** 1.728*** 1.765*** 1.596*** 1.592*** 1.151*** 1.496***

3.33 3.34 2.85 3.32 3.35 3.35 3.33 3.32 3.28 3.34 3.28 3.29 3.26 2.76 3.29 3.22 3.32 3.3 3.31 3.3 3.39 3.23 3.22 2.99 3.21

1060 km 1080 km 1100 km 1120 km 1140 km 1160 km 1180 km 1200 km 1220 km 1240 km 1260 km 1280 km 1300 km 1320 km 1340 km 1360 km 1380 km 1400 km 1420 km 1440 km 1460 km 1480 km 1600 km 1800 km

0.454 0.362 0.112 0.003 0.389 0.271 0.234 0.063 0.111 0.190 0.510* 0.542** 0.221 0.205 0.270 0.078 0.150 0.031 0.153 0.182 0.520** 0.017 0.186 0.048

1.08 0.88 0.29 0.01 1.18 0.83 0.66 0.23 0.41 0.73 1.92 2.09 0.85 0.81 1.04 0.29 0.6 0.12 0.65 0.78 2.34 0.06 0.89 0.27

0.828** 0.622* 0.532 0.602* 0.251 0.001 0.568* 0.163 0.018 0.084 0.008 0.052 0.380* 0.304 0.232 0.580** 0.425** 0.518** 0.427** 0.387** 0.357** 0.275 0.401** 0.355**

2.27 1.79 1.58 1.92 0.96 0 1.87 0.81 0.08 0.43 0.04 0.26 1.76 1.49 1.08 2.43 2.04 2.56 2.27 2.1 2.16 1.19 2.55 2.5

1.391*** 1.223*** 1.128*** 1.206*** 0.813*** 0.503** 1.136*** 0.419** 0.554*** 0.671*** 0.509*** 0.506*** 0.913*** 0.840*** 0.749*** 1.110*** 0.912*** 1.035*** 0.926*** 0.968*** 0.190 0.802*** 0.065 0.132

3.65 3.38 3.24 3.75 3.19 2.14 3.67 2.24 2.85 3.54 2.67 2.77 4.38 4.28 3.65 4.67 4.57 5.11 4.92 5.29 1.2 3.3 0.43 0.93

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