https://orcid.org/0000-0002-7820-4850
Retraction
The PLOS One Editors retract this article [1] because it was identified as one of a series of submissions for which we have concerns about potential manipulation of the publication process, peer review integrity, and authorship. These concerns call into question the validity and provenance of the reported results. We regret that the issues were not identified prior to the article’s publication.
HC did not agree with the retraction. SC, DW, and MS either did not respond directly or could not be reached.
23 Oct 2025: The PLOS One Editors (2025) Retraction: Business credit network characteristics and impact on green economy efficiency: Evidence from the Greater Bay Area around Hangzhou Bay of China. PLOS ONE 20(10): e0334774. https://doi.org/10.1371/journal.pone.0334774 View retraction
Figures
Abstract
In regions where the development of formal finance is relatively lagging behind, commercial credit has partially replaced the role of formal finance and facilitated the development of the private economy and even the country, thus making commercial credit an important entry point for understanding and promoting sustainable economic development. Taking the Hangzhou Bay Greater Bay Area as a case study, based on the City Business Credit Environment Index (CEI) from 2015 to 2019, we examine the characteristics of business credit networks using social network analysis and discuss the impact of business credit on urban green economy efficiency heterogeneity by drawing on spatial econometrics. The study confirms that the structure of business credit networks in the Hangzhou Bay Greater Bay Area tends to be dense, the network density and number of connections show growth, the spatial network structure is taking shape, and the strength of spatial connections among cities has increased. Hangzhou, Shaoxing, Jiaxing and Shanghai are at the centre of the network and play a radiation-driven role. The business credit network in the Hangzhou Bay Greater Bay Area is characterised by self-stability and has evolved from a multi-centre to a single centre. Business credit is negatively correlated with the efficiency of the green economy in the Hangzhou Bay Area, which is a departure from the Chinese "financial development paradox". In terms of heterogeneity, the relationship remains consistent for port cities and open coastal cities in general, while the effect is less pronounced for cities above sub-provincial level. The study concludes that, with the high-quality economic development of the Hangzhou Bay Greater Bay Area, the Chinese "financial development paradox" does not exist in the region at this stage, which also highlights the need to accelerate the construction of a Chinese-style modernisation theory and practice system.
Citation: Chen H, Chen S, Wang D, Shen M (2023) RETRACTED: Business credit network characteristics and impact on green economy efficiency: Evidence from the Greater Bay Area around Hangzhou Bay of China. PLoS ONE 18(4): e0284019. https://doi.org/10.1371/journal.pone.0284019
Editor: Ricky Chee Jiun Chia, Universiti Malaysia Sabah, MALAYSIA
Received: December 30, 2022; Accepted: March 21, 2023; Published: April 20, 2023
Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This research was funded by the Major Special Project for Philosophical and Social Science Research of the Ministry of Education of China (no. 2022JZDZ009).
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
China’s economy has shifted from high growth to green and high quality development, and improving the efficiency of the green economy is key. However, in the early years of reform and opening up, China’s legal and financial systems lagged behind in their efforts to achieve rapid economic development. Some studies suggest that the reason for China’s ’financial development paradox’ is that commercial credit has, to some extent, taken over the role of formal finance [1]. Does the Chinese ’financial development paradox’ still exist at this stage for developed coastal regions such as the Hangzhou Bay Area? How does the social network nature of commercial credit affect the efficiency of cities’ green economies? How can business credit systems be improved to better contribute to sustainable economic development on a larger scale? These are the questions that will be the focus of this paper.
In terms of the theoretical logic of the impact of business credit on the efficiency of urban green economies, on the one hand, the promotion of business credit to enhance the regional business environment may be more conducive to the innovative development of green-friendly enterprises, which in turn will improve the efficiency of urban green economies. Firstly, by seeking to be included in the ’red list’ of environmental credit, green enterprises may gain a better position in terms of market access, access to resources, project support and regulatory flexibility, which will have a positive impact on their green production efficiency. Secondly, under the reputation mechanism, companies with a good environmental label are more likely to be recognised by their associates and the public in the market, and can better address the financing constraints they face through the commercial credit route. Finally, in China’s specific national context, green enterprises are more encouraged by the government, and those in charge of them gain political affiliation by seeking positions as deputies to the National People’s Congress and members of the Chinese People’s Political Consultative Conference in order to better understand development policies, reorient their businesses and improve their production capacity, which has the potential to increase the efficiency of the green economy at the regional level. On the other hand, in the context of the accelerated development of China’s social credit system, the increased external binding force of commercial credit compliance may lead to a trade-off between polluting enterprises that choose to relocate across regions for fear of being included in a list of serious environmental defaulters and subject to joint cross-sectoral credit sanctions, which could lead to inter-regional changes in green economy efficiency. In the absence of national social credit regulations, the degree of environmental credit regulation varies from province to province, and under the "pollution sanctuary" effect, some enterprises may choose to locate in provinces with less stringent environmental regulations on balance, which will have a profound impact on the efficiency of the green economy in both the inbound and outbound regions, given the tendency of industries to cluster. This will have a profound impact on the efficiency of the green economy in both the inbound and outbound regions.
The established literature focuses on the impact of business credit on the efficiency of the green economy from two perspectives. The first perspective is to analyse the impact of the external business environment, such as the institutional system and business environment, on corporate innovation. For example, Xu Hao and Feng Tao (2018) study argues that the institutional environment, typified by the legal environment, plays an important role in the costs, benefits and behavioural decisions of firms’ micro-innovation activities [2]. Based on World Bank Doing Business data, Xia Houxue et al. (2019) find that an improved regional business environment has a more pronounced positive impact on innovation of rent-free firms [3]; the second perspective is to analyse the possible impact of differentiated financing instruments and structures on firms’ micro-innovation and regional sustainable development from the perspective of financing constraints. Overall, a robustly functioning financial credit market plays an important role in the implementation of regional innovation strategies [4]. Given that innovation does not happen overnight and often takes a long time, differentiated financial instruments are adaptable in promoting regional innovation [5], especially for basic, high technology R&D investments [6]. Although there is a lack of systematic research on how commercial credit affects the efficiency of the green economy, some work has been done on the relationship from the periphery, both from a macro and micro perspective respectively, in the two areas mentioned above.
However, given the spatial spread and interaction of information, the evaluation of the business credit status of a given city and its possible impact on the efficiency of the green economy cannot only be analysed locally, but also in the context of its own business credit status and the reception of business credit from other regions, i.e. in the context of a network formed by links with other regions, and as a new tool to explore economic As a new tool for exploring economic networks, the social network analysis method is based on a "relational" perspective and can better reflect the relative position and network characteristics of a given region in a regional network (Serrano & Boguñá, 2003) [7], which is unique in the study of social phenomena and structures. In fact, many scholars in China and abroad have conducted numerous studies using the SNA method.
From overseas studies, Feng Wang et al. (2018) [8] explored the structure and effects of sub-provincial carbon emission networks based on Chinese provincial carbon emission data from 2008–2014, drawing on social network analysis (SNA) and quadratic allocation procedure (QAP). Xintao Li et al. (2019) [9] used the Beijing-Tianjin-Hebei city cluster as a case study, based on social network analysis (SNA) Danning Zhang et al. (2021) [10] used SNA to establish a macro-, meso- and micro-level indicator system for China’s coworking industry, which provides an idea to better measure the status of coworking. Yanling Zhi et al. (2022) [11] used a super-efficient relaxation model (SBM) to measure China’s provincial-level The SBM is based on the vector autoregressive (VAR) Granger causality test and social network analysis (SNA) to analyse the spatial correlation of AWUE between provinces and to explore the transmission mechanism of AWUE spillover effects across provinces. Elena Calvo-Gallardo et al. (2022) [12] evaluate the underlying network of innovation systems based on SNA. positive role, an attempt to advance innovation system research in the field of modelling, simulation and performance assessment. Xiaoqiong Liu et al. (2022) [13] used social network analysis to study the responsibility and obligations of different cities and contributors in water environment management in the context of economic development in the Yangtze River Delta region accompanied by growing environmental problems.
From domestic studies, Kang Wei (2012) [14] and Zhu Zhengwei et al. (2013) [15] used social network analysis (SNA) to analyse the possible network structure features during the dissemination of sudden public opinion. Xiao Jianzhong et al. (2012) [16] explored the structure and development of trade networks in China’s natural gas market by drawing on SNA. Yanwen Shi et al. (2015) [17] used SNA to analyse the overall network and internal structure of innovation in the Shouguang vegetable and Yanling flower and tree clusters as a case study. Yang Chen et al. (2017) [18] analyzed the international service trade network and influencing factors of Asia-Pacific economies based on social network analysis method. Li Hangfei et al. (2018) [19] used SNA to study the position of mainland China in the international trade pattern and explored its impact on Taiwan’s economic development. Pan et al. (2019) [20] adopted a social network analysis approach to profile the spatial interactions and internal structures of cities along the G60 science and innovation corridor based on city data from 2009, 2013 and 2017. Zhao Lei et al. (2021) [21] analyzed the structure and dynamic changes of the global electronic information manufacturing trade network based on SNA. Hao Shuai et al. (2022) [22] constructed WEF nexus system efficiency evaluation indicators and discussed the spatial network structure of WEF nexus system efficiency at the provincial level in China using the SNA method.
In summary, all existing studies are somewhat one-sided, either analysing the implementation paths, inter-provincial spatial interactions and possible impacts of business credit on green development efficiency at the macro level, or studying the possible mitigating effects of business credit on corporate financing constraints at the micro level. The article is therefore based on the study of the Hangzhou Bay region. Therefore, this article uses the Hangzhou Bay Area as a case study to examine the effect of business credit on urban green economic efficiency based on a reasonable measurement of business credit network characteristics. At the methodological level, Social Network Analysis (SNA) is used to analyse the network characteristics of the Hangzhou Bay Greater Bay Area, and spatial econometrics is applied to explore the possible role of business credit on green economic efficiency in the Hangzhou Bay Greater Bay Area.
The reason for choosing China as the background for this study is the special nature of China’s economic development since the reform and opening up, one of the important phenomena is the simultaneous existence of lagging formal financial development and high economic growth, i.e. the Chinese "financial development paradox", which has provided important insights for other developing countries in the world, especially those with insufficient formal financial development, to explore how to adjust their financial policies to develop their economies. This is an important insight for other developing countries around the world, especially those with insufficient formal financial development, to explore how to adjust their financial policies to develop their economies. Indeed, one of the assumptions that explains the Chinese ’financial development paradox’ is that commercial credit has replaced formal finance and contributed to the quality of economic development, but is this assumption still valid in contemporary China? Therefore, it is particularly urgent to examine the role of commercial credit in green economic efficiency from a spatial perspective, using the Hangzhou Bay Area as a Chinese case study, especially after 45 years of reform and opening up. The answers to these questions will help to further answer the question of the dynamic impact of business credit on economic development.
The marginal contributions and innovations of this paper compared to existing studies are: firstly, although some scholars have paid attention to commercial credit, most of them have conducted case studies or regional comparisons on the specific content of policy formulation, introduction and implementation, but lacked attention to the characteristics of commercial credit networks, which is the entry point of this paper; secondly, taking the Hangzhou Bay Greater Bay Area as the research object, this paper attempts to examine whether there is a Chinese "financial development paradox" in the developed coastal regions of China from a meso level. Secondly, the study takes the Hangzhou Bay Greater Bay Area as an object of study, and attempts to verify whether there is a Chinese "financial development paradox" in developed coastal areas of China, and examines the heterogeneity of cities in different locations, with different traditions of openness and political status, in order to better reflect the reality of economic development. Finally, a more scientific spatial econometric model is used to assess the possible impact of business credit on the efficiency of urban green economies, taking into account the competing resource factors in the spatial context of cities.
2. Characteristics of the business credit network in the Hangzhou Bay Greater Bay Area
The spatial network model is constructed based on the modified gravity model after the improvement of the traditional gravity model. The network density model and the network centrality model measure the overall spatial network situation and the local spatial network situation of the business credit in the Hangzhou Bay Greater Bay Area respectively, and combined with the time series, the dynamic spatial network evolution of the business credit in the region can be grasped globally.
2.1 A model for building a business credit space network in the Hangzhou Bay Greater Bay Area
2.1.1 Gravitational model.
At a given geospatial scale, different cities interact through commercial credit exchanges to build an interactive network structure. Drawing on the gravitational model of physics, a basic gravitational model of urban economic linkages is developed.
In Eq (1), Pij represents the economic attractiveness of city i to j, Pi, Pj and Gi, Gj represent the population and economic volume of cities i and j respectively, units in million and billion respectively,and Dij represents the geographical distance between the two cities. In this paper, the geographical distance of each city in the Hangzhou Bay Greater Bay Area is measured by the shortest distance from the administrative centre of each city in Baidu Map,units in kilometres. Since this formula only measures the single linkage between cities and does not take into account the two-way linkage between cities, in order to measure more scientifically the actual characteristics of the spatial structure of business credit in the Greater Bay Area around Hangzhou Bay, the traditional gravitational model is appropriately improved and a new gravitational model is constructed.
In Eq (2), Rij represents the influence of city i’s business credit on region j’s business credit. Gi and Gj represent city i’s and j’s business credit respectively, as measured by the "China City Business Credit Environment Index (CEI)", which is regularly released by the China City Business Credit Environment Index Group. aij represents city i’s contribution to Rij, as measured by city The study is based on the Hangzhou Bay Area, which is the largest bay area in China. The study population is cities above prefecture level in the Hangzhou Bay Greater Bay Area, and the latest data on the business credit environment is only updated to 2019. Given the research needs and data availability, the study interval is set at 2015 to 2019.
2.1.2 Network density model.
Network density characterises the spatial interaction of business credit between cities and is positively correlated with the closeness of city links, as measured by the ratio between the ’actual number of links’ and the ’theoretical maximum number of links’ between cities, with the equation is, ρ for network density, L for the total number of relationships that actually exist and N for the number of cities.
2.1.3 Network centrality model.
One of the most typical tools for measuring the local characteristics of a network is centrality analysis, which measures the central position of a particular city in the overall network and involves two main types of indicators: point centrality, which is divided into point-in centrality and point-out centrality, and point-out centrality, which characterises the ability of a particular city to receive business credit influence from other cities, and point-out centrality, which characterises the ability of a particular city to influence other cities in the Greater Bay Area. The equation is described as The equation is described as
(3)
(4)
In Eqs (3)(4), Cin and Cout represent the point-in centrality and point-out centrality respectively, Qij and Qji represent the strength of connection between two nodes i(j) and j(i) cities respectively, and N represents the number of cities studied.
Unlike point degree centrality, which represents the concentration of nodes in a social network, point degree centrality potential measures the central tendency of a social network. The equation is described as
(5)
In Eq (5), C represents the point degree centrality potential, Cmax represents the social network centrality maximum, and Ci represents the city centrality of each node.
2.2 Empirical study
1. Analysis of the overall network density of business credit in the Hangzhou Bay Greater Bay Area. Taking the seven cities of Shanghai, Hangzhou, Ningbo, Huzhou, Jiaxing, Shaoxing and Zhoushan within the spatial scope of the Hangzhou Bay Greater Bay Area as the object of study,the NetDraw function of Ucinet software was used to draw a visual network structure of business credit in the Hangzhou Bay Greater Bay Area. From 2015 to 2019, the structure of the business credit network in the Hangzhou Bay Greater Bay Area gradually tends to become denser, indicating that the strength of the spatial connection of each city’s business credit is increasing. As an important part of the Yangtze River Delta city cluster, the Hangzhou Bay Greater Bay Area is in a special period of transition from a county economy to a metropolitan area economy. With the joint construction of the Yangtze River Delta regional credit system since 2004, the exogenous and endogenous transaction costs that prevent the specialised division of labour in the green industry from being refined have been decreasing, which further drives the integration of business credit in the Hangzhou Bay Greater Bay Area and the spatial The strengthening of dependency.
Based on the Ucinet software to measure the network density of business credit in the Hangzhou Bay Greater Bay Area and collated, the results are reported in Table 1. After processing the matrix with the cut-off values of mean, 1, and 3 respectively, it is found that the network density and the number of associations of business credit in the Hangzhou Bay Greater Bay Area show an overall increase from 2015 to 2019. In terms of network density, it increased from 0.3469, 0.5238 and 0.0714 in 2019 to 0.4048, 0.5476 and 0.0952 in 2019, respectively, an increase of 0.0579, 0.0238, 0.0238, and 16.69%, 4.54% and 33.33% year-on-year, respectively, over the four years. This indicates that the spatial connection between cities in the Hangzhou Bay Greater Bay Area is strengthening and the spatial network structure is gradually taking shape. In the context of accelerating the construction of a unified national market, the spatial interaction and reliance on business credit in the Hangzhou Bay Greater Bay Area is also increasing along with the increasing improvement of the incentive mechanism of the social credit system system and the innovative application of advanced information technology in a wider scope and wider area.
2. Analysis of the centrality of the business credit network in the Hangzhou Bay Greater Bay Area. In order to reflect the changing position in the business credit network of different regions in the Greater Bay Area, the network point-out degree and network point-in degree of each city were measured, and the results are shown in Table 2. Overall, there is no significant change in the ranking of the cities, both in terms of network point-out degree and network point-in degree. This indicates that the business credit network of the Hangzhou Bay Greater Bay Area has certain self-stability characteristics. From the perspective of network point-out degree, the regional cities of Hangzhou, Shaoxing, Jiaxing and Shanghai have always been at the top of the list, with the average commercial credit point-out degree of the four cities reaching 17.449, 13.781, 10.022 and 8.159 in 2015 and 2019 respectively, leading the way in the business credit environment of the Greater Bay Area. From a network point of entry perspective, Hangzhou, Shaoxing and Jiaxing also ranked high, indicating that the social network nature of business credit determines that while it drives the development of business credit in other cities in the Greater Bay Area, it is also potentially influenced by the spillover of business credit from other cities.
3. In order to characterize the regional linkages as a whole, the network centrality potential of business credit spatial linkages in the Greater Bay Area around Hangzhou Bay from 2015 to 2019 was calculated (Table 3), and the results show that both the network point-out degree and point-in degree centrality potentials showed some growth from 2005 to 2019. In terms of point-out degree central potential, it increased from 17.166% in 2015 to 17.371% in 2019. This indicates that the business credit of the Hangzhou Bay Greater Bay Area has experienced an evolutionary path from polycentric to monocentric, the influence of the central cities in the Greater Bay Area is converging, and the business credit interaction among the seven cities in the Greater Bay Area is gradually increasing, which is closely linked to the national strategy of integrated and high-quality development of the Yangtze River Delta. From the point entry degree central potential, from 14.697% in 2015 to 15.560% in 2019, under the construction strategy of the four major metropolitan areas of Zhejiang Province [Zhejiang Province focuses on the development of the Hangzhou Metropolitan Area, Ningbo Metropolitan Area, Wenzhou Metropolitan Area and Jinyi Metropolitan Area. Of these, the Hangzhou metropolitan area covers the municipalities of Hangzhou, Huzhou, Jiaxing and Shaoxing and focuses on the development of the digital economy and finance; the Ningbo metropolitan area includes the municipalities of Ningbo, Zhoushan and Taizhou and is primarily utilised for the development of the port economy and manufacturing industry. As the geographical area of the Hangzhou Bay Greater Bay Area includes most of the Hangzhou metropolitan area and the Ningbo metropolitan area, the high-quality development of the Greater Bay Area economy is inextricably linked to the implementation of Zhejiang’s provincial metropolitan area strategy.], with the continuous improvement of transportation, communication and other infrastructures, the cities in the Greater Bay Area around Hangzhou Bay not only have frequent commercial credit interaction with the outside world, but also the interaction between cities has increased, which is important for implementing the new development concept, building a new development This is crucial to implementing the new development concept, building a new development pattern, accelerating the construction of a unified domestic market and creating an international first-class regional business environment.
3. Impact of business credit on green economy efficiency in the Greater Bay Area around Hangzhou Bay
3.1 Model design and description of variables
Drawing on Li and Lin (2017) [23] methodology, a benchmark model of the impact of business credit on urban green economy efficiency was constructed as follows.
Where gtfpit denotes the green total factor productivity of city i in year t, networki t denotes the spatial network characteristics of business credit in city i in year t, as specifically measured by network point-out degree and network point-in degree, respectively, αi and αt characterise the fixed effects at the regional and temporal levels, respectively, and Zit represents the regional level control variables.
For the explanatory variables, the results of the GML index measure of the SBM model were used to characterise the green economic efficiency of the city, following OH (2010) [24] research. In this regard, the mathematical expression of the SBM model is
(7)
The SBM model is based on the assumption of constant size, denotes the slack in inputs, desired and undesired outputs, and the p objective function value denotes the decision unit efficiency value.
The mathematical expression for the GML index is
(8)
The GML index can be decomposed into technical efficiency (EC) and technical progress (TC). ,
denote the efficiency values of the decision unit in period t and period t+1 in period t, respectively.
In the measurement of urban green economy efficiency, the selection of input indicators includes: (1) capital input. The physical capital stock of the city is selected for measurement. Due to the lack of corresponding statistical indicators, this paper adopts the method of Shan, Haojie (2008) [25], using the data of fixed asset investment flow in each city, and using 2010 as the base period for deflating, in which the depreciation rate is set at 10.96%. (2) Labour input. The total number of employees in the secondary and tertiary industries was selected as the indicator of labour force in a particular city. (3) Resource and energy inputs. Total water supply and social electricity consumption were chosen as the indicators for measuring resource and energy inputs in the economic development of the city respectively. (4) Desired output. Considering the level of economic development and the quality of life of urban residents as the main indicators of desired output, the real gross regional product and the greening coverage of built-up areas were selected as the proxy variables for the above two indicators respectively. (5) Non-desired output. Industrial wastewater emissions, industrial soot emissions, industrial SO2 emissions and PM2.5 concentrations were mainly selected as indicators to measure the pollution situation in the process of urban economic development. From the kernel density plot (Fig 1), although the measured green economic efficiency of the Yangtze River Delta cities shows a right-skewed distribution overall, it is not significantly different from the normal distribution (Normal distribution), and the measured results generally meet the statistical requirements.
For the control variables, following Taskin, Zaim (2001) [26] et al, the control variables of ownership structure (X1), level of economic development (X2), industrial structure (X3), degree of openness to the outside world (X4), degree of government intervention (X5), advanced level of labour market (X6), level of science and technology innovation (X7) and degree of environmental regulation (X8) were selected for inclusion in the model for econometric estimation. According to the new institutional economics and the environmental Kuznets curve, ownership structure and the level of economic development are important factors affecting the efficiency of urban green economies. Government intervention and the degree of environmental regulation have an impact on green total factor productivity under the goal of a peak carbon neutral policy. The "pollution sanctuary" hypothesis implies that the external linkages and industrial structure characteristics of a region cannot be ignored when talking about green development. The level of science and technology innovation plays a role in the way a region develops and sustains a green economy. In addition, labour market conditions, which involve the potential for division of labour, may also have an impact on the efficiency of urban green economies.
The ownership structure (X1) is measured by the share of private and self-employed workers in the total number of employees, the level of economic development (X2) is characterized by GDP per capita, the industrial structure (X3), the degree of openness to the outside world (X4), the degree of government intervention (X5) and the level of scientific and technological innovation (X7) are measured by the value added of the secondary industry, the amount of foreign capital actually used in the year, the general budget expenditure of the local government and the share of R & D internal expenditure in the regional GDP, respectively. &D internal expenditure as a proportion of regional GDP, the degree of advanced labour market (X6) is measured by the number of university students per 10,000 people in cities, and the degree of environmental regulation (X8) is measured by the centralised treatment rate of urban sewage treatment plants. In summary, the inclusion of institutions, factors and environment as control variables is used to mitigate the problem of missing explanatory variables.
However, the magnitude of the effect of business credit on the efficiency of the green economy of a city is more pronounced in the context of the construction of a unified national market, where the influence of provincial administrative fragmentation on the development of the market economy is gradually weakening and the green development of cities is increasingly influenced by neighbouring cities, especially when the integrated development of the Yangtze River Delta has been elevated to a national strategy, and the spatial interaction of business credit will become more pronounced. Spatial correlation tests on the core variables confirm these suspicions. In 2019, the global univariate Moran indices for business credit and green economic efficiency were 0.4044 and 0.3960, respectively, and the global bivariate Moran index for business credit and green economic efficiency was 0.3990 (Table 4), confirming a more significant spatial correlation between urban green economic efficiency and in the implementation effect of business credit on green economic efficiency.
In view of the fact that the regional development process is not only influenced by its own endowments, but also increasingly by the role of geographically adjacent regions, SLM (spatial lag model) and SEM (spatial error model) are considered for the study of the relationship between business credit and green economic efficiency. Among them, the SLM expression is.
ρ and W represent the spatial regression coefficients, the n × n spatial weight matrix, respectively, characterising the effect of neighbouring regional observations on local green economic efficiency and whether the regions are adjacent to each other in turn (in rook neighbourhoods, W takes 1 when adjacent and 0 otherwise). Wit measures the effect of spatial distance on regional green economic efficiency.
λ denotes the spatial error coefficient, which measures the direction and strength of the effect of green economic efficiency in neighbouring regions on local observations. In comparison with SLM, SEM spatial dependence is manifested in the error term, which reflects the effect of the dependent variable error shock on local observations in neighbouring regions. u denotes the random error vector showing a normal distribution.
In addition, considering the research needs and data availability, the analysis was conducted for the cities of Shanghai, Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing and Zhoushan, which are involved in the Hangzhou Bay Greater Bay Area, and the study period is from 2015 to 2019. The input and output data were obtained from the China City Statistical Yearbook, the statistical bulletins of various provinces and cities and the EPS data platform. The PM2.5 concentration data of different cities were obtained from the satellite remote sensing data published by the National Aeronautics and Space Administration of the United States, and the 1:4 million Chinese basic geographic information data provided by the National Basic Geographic Information Centre were cropped to obtain the average PM2.5 concentration values of the cities in the past years. For missing data, the interpolation method was used to process the data.
3.2 Baseline analysis
Based on spatial correlation tests of the core variables, spatial econometric methods were used to test the possible impact of business credit on urban green economy efficiency. The estimation results are reported in models (1)-(3) in Table 5, with OLS, SLM and SEM denoting ordinary least squares models, spatial lag models and spatial error models respectively. Combining the magnitude of Log-likelihood, AIC and SC values, model (3) was chosen to analyse the overall impact of commercial credit on the efficiency of the urban green economy. The results show that the coefficient of the impact of business credit on the green economic efficiency of cities in the Hangzhou Bay Greater Bay Area is -0.0006, which indicates that overall business credit has a relatively significant negative relationship with urban green economic efficiency. This is a deviation from the Chinese "financial development paradox", which attributed the role of commercial credit as a substitute for formal finance to a certain extent. In fact, commercial credit, by its very nature, is bound to a greater extent by informal rules such as reputation and tripartite guarantees, and in the process of increasing the size of commercial credit, the risks associated with it increase non-linearly due to the existence of social networks, which adversely affects the efficiency of the green economy. In fact, compared to formal finance, which has lower financing costs, enterprises that use commercial credit as their main source of funding have lower overall and sustainable operational capacity and are less concerned with environmental protection and green development, which are not conducive to the efficiency of urban green economies.
3.3 Regional heterogeneity analysis
Combining the magnitude of Log-likelihood, AIC and SC values, models (6), (9) and (12) were selected to carry out further analysis. First, the treatment effect coefficient of business credit on green total factor productivity is -0.0005 for both port cities and open coastal cities, while the effect of business credit on green economic efficiency is insignificant for cities above sub-provincial level. At this stage, important port cities are generally in an important window of transition from industrial to post-industrial societies, where accelerated population mobility further leads to the collapse of traditional interpersonal interaction and modern interpersonal interaction based on the rule of law becomes increasingly mainstream, under which the realistic market demand for credit rules gradually increases, while commercial credit can neither be regulated by an effective credit system nor is it difficult to provide an This deviates from the demand for credit that accompanies the green and high-quality development of cities, which has a more significant negative impact on the efficiency of the green economy. Secondly, due to the phased nature of green development, there is a complementary relationship between commercial credit and formal finance when formal finance is underdeveloped, and a substitution relationship between commercial credit and lower-cost formal finance when formal finance is more mature. For open coastal cities, the role of commercial credit as a back-up capital channel for business operations has diminished due to the improvement of the market system, the decline in the cost of the financing system and the development of formal finance, and the explanatory power of the Chinese "financial development paradox" from the perspective of commercial credit has diminished, especially after the US sub-prime mortgage crisis in 2008. In particular, after the US sub-prime crisis in 2008, the volatility, limited scale and cost overlap of commercial credit have determined its limited contribution to the green transformation of enterprises, which, coupled with the contagiousness of credit risk across regions, has led to a negative impact of commercial credit on the efficiency of the green economy. Finally, over the past 40 years of reform and opening up, the gap between prefecture-level cities and cities above sub-provincial level in terms of capital, manpower and technology has gradually narrowed due to spatial spillover of institutions, technology and resources. Against the backdrop of the national social credit system, prefecture-level cities should strengthen policy refinement and support in a broader and deeper context, such as improving public credit information systems and building comprehensive financial services platforms, which can, on the one hand, increase the investment intensity of cities and reduce exogenous transaction costs for enterprises to access information, and on the other hand, focus on anti-corruption and government integrity. On the other hand, the building of political integrity with a focus on anti-corruption has further reduced endogenous transaction costs in the market and weakened the importance of political connections in the process of corporate financing.
4. Conclusions and policy recommendations
Based on the City Business Credit Environment Index (CEI) from 2015 to 2019, the article takes the Hangzhou Bay Greater Bay Area as a research case, measures the characteristics of business credit networks using social network analysis, and examines the impact of business credit on green economic efficiency heterogeneity using spatial econometrics. It was found that (1) the spatial connection and dependency of business credit among cities in the Hangzhou Bay Greater Bay Area gradually increased, and the structure of business credit network gradually formed and tended to be dense, and had certain self-stability characteristics. Hangzhou, Shaoxing, Jiaxing and Shanghai are at the centre of the business credit network, playing an exemplary and leading role as well as being influenced by potential spillovers from other regions. (2) Business credit and urban green economy efficiency are generally negatively correlated. As commercial credit is more influenced by informal rules such as reputation and third-party guarantees, after a certain scale of development, along with the non-linear expansion of the network, the instability and vulnerability of commercial credit emerge, which is not conducive to enhancing the efficiency of the urban green economy, implying the Chinese "paradox of financial development" formed since the reform and opening up. This implies that the Chinese "paradox of financial development", which has been developed since the reform and opening up, no longer has realistic explanatory power for the high-quality development of the green economy in the Greater Bay Area around Hangzhou Bay. (3) Due to the improvement of the market system, the decline in the cost of the financing system and the development of formal finance, modern human interaction under the rule of law has become mainstream, and there is a substitution relationship between commercial credit and lower-cost formal finance in important ports and open coastal cities, and the characteristics of unstable commercial credit, limited scale and superimposed costs determine its role in green total factor productivity. (4) Sub-provincial cities have focused on anti-corruption and building a "pro" and "clean" government-enterprise relationship, weakening the possible influence of political connections on financing constraints. The effect on the efficiency of the urban green economy is not obvious.
Possible limitations of the study include the following three points: Firstly, due to the marked differences in the development of different regions in China, the analysis of the economic effects of business credit using the Hangzhou Bay Greater Bay Area as a case study may be under-representative and unable to provide a full description of the actual situation in other places. Secondly, the study period is from 2015 to 2019, which cannot show the situation of the characteristics of business credit networks and the potential impact on economic efficiency over a longer period of time such as since the reform and opening up. Thirdly, it only focuses on commercial credit, ignoring the relationship between formal finance and commercial credit as either substitutes or complements. If formal finance and commercial credit were combined to carry out the corresponding analysis, it would be more comprehensive to show the mechanism of the impact of commercial credit on China’s high-quality economic development.
The policy recommendations in this paper are: firstly, to improve the supporting system for commercial credit regulation, and prevent the possible cross-territory transmission of commercial credit risks. To address the lack of a system for regulating commercial credit, strengthen the establishment of rules and regulations, enhance the development resilience of commercial credit, promote the transformation of commercial credit from a "soft constraint" to a "hard constraint", and continuously improve the matching of commercial credit to the efficiency of the green economy. Secondly, we should accelerate the introduction of comprehensive national legislation on social credit, promote the standardised application of social credit in the government, market and society, and strengthen the effective synergy between commercial credit and formal finance. In response to the problem of institutional congestion in the regulation of commercial credit, the use of administrative resources should be reasonably regulated to reduce undue administrative intervention in the market allocation of resource factors. Once again, it is important to take into account national and regional characteristics, and to introduce and implement differentiated credit measures based on regional heterogeneity to promote the unification of connotative innovative development and outward synergistic development, and to view the role of commercial credit dynamically according to its stage characteristics, giving full play to its unique advantages and tapping into its positive contribution to the efficiency of the green economy. Finally, the study has not found evidence of a Chinese ’financial development paradox’ in the Hangzhou Bay Area, which inspires us to take a more critical approach to building a theoretical system that is compatible with Chinese modernisation. Against the backdrop of China’s economic development, it is a realistic proposition for the academic community to create, improve or discard theories of commercial credit in order to enhance theoretical guidance and practical relevance. It is important to oppose both the "fetishism" of copying and applying theories, as well as the closed-mindedness of closed doors. Instead, it is important to deeply grasp the reality of research and judge the applicability of existing theories involving business credit, and to strengthen the integration of theory and practice, which will greatly benefit the overall green economic efficiency of the region and thus the profound transformation of the Chinese economy.
References
- 1. Allen F., Qian J. and Law M. Finance and Economic Growth in China[J]. Journal of Financial Economics, 2005, 77,57–116.
- 2. Hao Xu, Tao Feng. Does institutional environment optimization help drive technological innovation?—An empirical analysis based on a dynamic inter-provincial spatial panel in China[J]. Financial and Economic Research,2018,44(04):47–61.
- 3. Houxue Xia, Qingmei Tan, Junhong Bai. Business environment, corporate rent-seeking and market innovation-Empirical evidence from a survey of Chinese firms’ business environment[J]. Economic Research,2019,54(04):84–98.
- 4. NANDA and R. Rhodes-Kropf M. Financing Risk and Innovation[J]. Social Science Electronic Publishing, 2014, 11–13.
- 5. Alessandrini Pietro and Fratianni Michele. Dominant Currencies, Special Drawing Rights and Supernational Bank Money[J]. World Economics, 2009,10(4), 45–68.
- 6. Czarnitzki D. and Hottenrott H. Financial Constraints: Routine Versus Cutting Edge R&D Investment[J]. Journal of Economics & Management Strategy, 2011, 20(1),121–157.
- 7. Serrano M. Á. and Boguñá M. Topology of the World Trade Web[J]. Physical Review E, 2003, 68:634–646.
- 8. Wang Feng, Gao Mengnan, Liu Juan, and Fan Wenna.The Spatial Network Structure of China’s Regional Carbon Emissions and Its Network Effect[J].Energies, 2018,11(10): 2706.
- 9. Li Xintao, Feng Dong, Li Jian, and Zhang Zaisheng. Research on the Spatial Network Characteristics and Synergetic Abatement Effect of the Carbon Emissions in Beijing-Tianjin-Hebei Urban Agglomeration[J]. Sustainability,2019,11(5): 1444.
- 10. Zhang Danning, Yan Ming, Wang Haowen, and Li Weiwei. 2021. Sustainable-Development Measurement of China’s Coworking Industry Using Social-Network Analysis Method[J].Sustainability 13(11): 5902.
- 11.
Zhi, Yanling, Fan Zhang, Huimin Wang, Teng Qin, Jinping Tong, et al. Agricultural Water Use Efficiency: Is There Any Spatial Correlation between Different Regions?
- 12. Calvo-Gallardo Elena, Nieves Arranz, and Juan Carlos Fernandez de Arroyabe. Contribution of the Horizon2020 Program to the Research and Innovation Strategies for Smart Specialization in Coal Regions in Transition: The Spanish Case[J].Sustainability,2022,14(4): 2065.
- 13. Liu Xiaoqiong, Wang Xu, Lu Feiyu, Liu Shuai, and Chen Kunlun. Evaluation of the Governance Efficiency of Water Environmental Governance Efficiency in Yangtze River Delta from the Perspective of Multivariate Synergies[J]. International Journal of Environmental Research and Public Health,2022,19(4): 2347.
- 14. Wei Kang. SNA-based identification of key nodes of public opinion on the network of emergencies: an example of the "7–23 moving train accident"[J]. Journal of Public Management,2012,9(03):101–111+127–128.
- 15. Zhengwei Zhu,Jia Shi. Research on the mechanism of risk perception difference formation in major engineering projects—a case study based on SNA[J]. China Administration,2013(11):106–112.
- 16. Jianzhong Xiao, Chao Hu, Xiaolin Wang, Jinhua Cheng. Study on the evolution of China’s natural gas market and its security[J]. China Population-Resources and Environment,2012,22(12):143–153.
- 17. Yanwen Shi, Erling Li, Xiaojian Li, Fan Lou, Xiaoyan Chen. SNA-based analysis of innovation network and knowledge flow in agricultural industry clusters—An example of Shouguang vegetable industry cluster and Yanling flower and tree industry cluster[J]. Economic Geography,2015,35(08):114–122.
- 18. Chen Yang, Haipeng Wang, Qingxiao Han. Structural characteristics of international service trade networks and their influencing factors identification based on SNA approach—empirical evidence from the Asia-Pacific region[J]. International Business (Journal of the University of International Business and Economics),2017(06):65–75.
- 19. Hangfei Li, Suqiong Wei, Songlin Chen, Shaobin Wei, Jialiang Zhao. Analysis of China Taiwan’s "New Southbound Policy" from the perspective of economic and trade—based on SNA and VAR methods[J]. Geoscience,2018,38(01):67–77.
- 20. PAN Jiadong, BAO Haibo, ZHOU Xuewu. Research on economic linkages of city clusters along the G60 science and innovation corridor based on SNA[J]. Zhejiang Journal,2019(05):73–83.
- 21. Lei Zhao, Suqiong Wei, Xiaojun You. Study on the evolutionary characteristics and mechanism of global electronic information manufacturing trade network based on SNA[J]. World Geography Research,2021,30(04):708–720.
- 22. Hao Shuai, Sun Cai-Zhi. A study on the efficiency of China’s water-energy-grain nexus system based on network DEA and SNA model[J]. Geographical Research,2022,41(07):2030–2050.
- 23. Li J., and Lin B.…Does Energy and CO2 Emissions Performance of China Benefit From Regional Integration?[J]. Energy Policy, 2017,101:366–378.
- 24. OH D. A global Malmquist-Luenberger productivity index[J]. Journal of Productivity Analysis,2010,34(3): 183–97.
- 25. Haojie Shan. Re-estimation of capital stock K in China:1952–2006[J]. Quantitative Economic and Technical Economics Research, 2008,25(10):17–31.
- 26. Taskin F., and Zaim O. The Role of International Trade on Environmental Efficiency: A DEA Approach[J]. Economic Modelling, 2001.18(1):1–17.