金融集聚如何影响绿色发展效率?——基于时空双固定的SPDM与PTR模型的实证分析

doi:10.16381/j.cnki.issn1003-207x.2019.11.007

Abstract

Abstract: The improvement of the impact of financial aggregation on green development efficiency is increasingly prominent from the aspects of capital support, resource allocation efficiency and technological innovation. However, the intensity, scope and pattern of its spatial effect remain to be further investigated. On the basis of space distance attenuation hypothesis and intermediate mechanism hypothesis, with panel data of 272 cities in China from 2003-2014, spatial panel durbin model (SPDM) and panel threshold regression (PTR) are used to explore the spatial spillover effects and its attenuation boundary of the impact of financial aggregation on green development efficiency, and then discuss the intermediate mechanism which leads to a nonlinear relationship between the two. Our empirical results show that:(i) There exists significant spatial dependence in the impact of financial agglomeration on green development efficiency, which shows evident spatial attenuation characteristics. The dense area of negative space spillover is within 300 km, whereas the dense area of positive space spillover is within 400-600 km. (ii) The impact of financial agglomeration on green development efficiency shows the characteristic of "gradient enhancement" under the regulating mechanism of financial aggregation, city size and level of opening-up, while shows the characteristic of "first decrease and then increase" under the constraint of economic development level. Nevertheless, it is worth noting that this promotion is very limited at the present stage.This study not only helps to fully grasp the relationship between financial agglomeration and green development efficiency, but more importantly, provides empirical evidence for government departments to develop regional economic development policies and promote regional green development efficiency.

Key words: financial agglomeration, green development efficiency, spatial panel durbin model, panel threshold regression

CLC Number:

F205

YUAN Hua-xi, LIU Yao-bin, FENG Yi-dai. How does Financial Agglomeration Affect Green Development Efficiency? Empirical Analysis of SPDM and PTR Models Considering Spatio-temporal Double Fixation[J]. Chinese Journal of Management Science, 2019, 27(11): 61-75.

References

[1] 黄解宇, 杨再斌. 金融集聚论:金融中心形成的理论与实践解析[M]. 北京:中国社会科学出版社, 2006.
[2] 王锋, 李紧想, 张芳, 等. 金融集聚能否促进绿色经济发展?——基于中国30个省份的实证分析[J]. 金融论坛, 2017,(9):39-47.
[3] Porteous D J. The geography of finance:Spatial dimensions of intermediary behaviour[M]. Aldershot:Avebury, 1995.
[4] Zhao S X B. Spatial restructuring of financial centers in mainland China and Hong Kong:A geography of finance perspective[J]. Urban Affairs Review, 2003,38(4):535-571.
[5] O'Brien R. Global financial integration:The end of geography[M]. London:Royal Institute of Interational Affairs, 1992.
[6] Ghoul S E, Guedhami O, Ni Y, et al. Does information asymmetry matter to equity pricing? Evidence from firms' geographic location[J]. Contemporary Accounting Research, 2011,30(1):140-181.
[7] 施本植, 许宁, 刘明, 等. 金融集聚对城市绿色经济效率的影响及作用渠道——基于中国249个地级以上城市的实证分析[J]. 技术经济, 2018,37(8):87-95.
[8] 潘彬, 杨鑫, 肖继宏, 等. 民间金融与中国宏观经济——理论机制与实证分析[J]. 中国管理科学, 2018,26(3):51-58.
[9] 严成樑, 李涛, 兰伟. 金融发展、创新与二氧化碳排放[J]. 金融研究, 2016,(1):14-30.
[10] 李红, 王彦晓. 金融集聚、空间溢出与城市经济增长——基于中国286个城市空间面板杜宾模型的经验研究[J]. 国际金融研究, 2014,(2):89-96.
[11] 袁冬梅, 魏后凯. 对外开放促进产业集聚的机理及效应研究——基于中国的理论分析与实证检验[J]. 财贸经济, 2011,(12):120-126.
[12] 于斌斌. 金融集聚促进了产业结构升级吗:空间溢出的视角——基于中国城市动态空间面板模型的分析[J]. 国际金融研究, 2017,(2):12-23.
[13] 何宜庆, 陈林心, 焦剑雄, 等. 金融集聚的时空差异与省域生态效率关系研究[J]. 数理统计与管理, 2017,36(1):162-174.
[14] 葛鹏飞, 黄秀路, 徐璋勇. 金融发展、创新异质性与绿色全要素生产率提升——来自"一带一路"的经验证据[J]. 财经科学, 2018,(1):1-14.
[15] 赫希曼·艾伯特. 经济发展战略[M]. 北京:经济科学出版社, 1992.
[16] 杨亚静. 文化资本、制度环境对区域金融中心的影响[D]. 杭州:浙江大学, 2017.
[17] Ewers M C, Dicce R, Poon J P H, et al. Creating and sustaining Islamic financial centers:Bahrain in the wake of financial and political crises[J]. Urban Geography, 2018,39(1):3-25.
[18] 陈晔婷, 朱锐, 宋志刚, 等. 金融改革对全要素生产率的影响研究——基于五个国家级金融改革试验区的经验数据[J]. 中国管理科学, 2018,26(9):19-28.
[19] 俞颖, 苏慧琨, 李勇. 区域金融差异演进路径与机理[J]. 中国工业经济, 2017,(4):74-93.
[20] 顾乃华. 我国城市生产性服务业集聚对工业的外溢效应及其区域边界——基于HLM模型的实证研究[J]. 财贸经济, 2011,(5):115-122.
[21] 余泳泽, 刘大勇, 宣烨. 生产性服务业集聚对制造业生产效率的外溢效应及其衰减边界——基于空间计量模型的实证分析[J]. 金融研究, 2016,(2):23-36.
[22] Porteous D. The development of financial centres:Location, information externalities and path dependence[J]. Money and the Space Economy, 1999:95-114.
[23] Ingstrup M B, Damgaard T. Cluster facilitation from a cluster life cycle perspective[J]. European Planning Studies, 2013,21(4):556-574.
[24] Jirčíková E, Pavelková D, Bialicdavendra M, et al. The age of clusters and its influence on their activity preferences[J]. Technological & Economic Development of Economy, 2013,19(4):621-637.
[25] 茹乐峰, 苗长虹, 王海江. 我国中心城市金融集聚水平与空间格局研究[J]. 经济地理, 2014,(2):58-66.
[26] Chen K, Chen G. The rise of international financial centers in mainland China[J]. Cities Current Research on Cities (CRoC), 2015,47:10-22.
[27] Zhao S X, Guo N S, Li C L K, et al. Megacities, the world's largest cities unleashed:Major trends and dynamics in contemporary global urban development[J]. World Development, 2017,98:257-289.
[28] 郑强, 冉光和, 邓睿, 等. 中国FDI环境效应的再检验[J]. 中国人口·资源与环境, 2017,27(4):78-86.
[29] Liu Qianqian, Wang Shaojian, Zhang Wenzhong, et al. Does foreign direct investment affect environmental pollution in China's cities? A spatial econometric perspective[J]. Science of The Total Environment, 2018,613-614:521-529.
[30] 陈心颖. 人口集聚对区域劳动生产率的异质性影响[J]. 人口研究, 2015,39(1):85-95.
[31] 孙铁山. 中国三大城市群集聚空间结构演化与地区经济增长[J]. 经济地理, 2016,36(5):63-70.
[32] Ehrlich P R H J P. Impact of population growth[J]. Science, 1971,171(3977):1212-1217.
[33] Anselin L. Spatial econometrics:Methods and models[J]. Studies in Operational Regional Science, 1988,85(411):310-330.
[34] Lesage J P, Pace R K. Introduction to spatial econometrics[M]. Boca Raton:CRC Press, 2009.
[35] Hansen B E. Threshold effects in non-dynamic panels:Estimation, testing, and inference[J]. Journal of Econometrics, 1999,93(2):345-368.
[36] Sun Liyan, Miao Chenglin, Yang Li. Ecological-economic efficiency evaluation of green technology innovation in strategic emerging industries based on entropy weighted TOPSIS method[J]. Ecological Indicators, 2017,73:554-558.
[37] 袁润松, 丰超, 王苗, 等. 技术创新、技术差距与中国区域绿色发展[J]. 科学学研究, 2016,34(10):1593-1600.
[38] Fukuyama H, Weber W L. A directional slacks-based measure of technical inefficiency[J]. Socio-Economic Planning Sciences, 2009,43(4):274-287.
[39] Tao Xueping, Wang Ping, Zhu Bangzhu. Provincial green economic efficiency of China:A non-separable input-output SBM approach[J]. Applied Energy, 2016,171:58-66.
[40] 李健旋, 赵林度. 金融集聚、生产率增长与城乡收入差距的实证分析——基于动态空间面板模型[J]. 中国管理科学, 2018,26(12):34-43.
[41] Gilio L, De Moraes M A F D. Sugarcane industry's socioeconomic impact in São Paulo, Brazil:A spatial dynamic panel approach[J].Energy Economics, 2016,58:27-37.
[42] 王鹤, 周少君. 城镇化影响房地产价格的"直接效应"与"间接效应"分析——基于我国地级市动态空间杜宾模型[J]. 南开经济研究, 2017,(2):3-22.
[43] You Wanhai, Lv Zhike. Spillover effects of economic globalization on CO₂ emissions:A spatial panel approach[J]. Energy Economics, 2018,73:248-257.
[44] Chen W Y, Hu F Z Y, Li X, et al. Strategic interaction in municipal governments' provision of public green spaces:A dynamic spatial panel data analysis in transitional China[J]. Cities, 2017,71:1-10.
[45] Rios V. What drives unemployment disparities in European regions? A dynamic spatial panel approach[J]. Regional Studies, 2017,51(11):1599-1611.
[46] Zhang Qian, Yang Jin, Sun Zhongxiao, et al. Analyzing the impact factors of energy-related CO2 emissions in China:What can spatial panel regressions tell us?[J]. Journal of Cleaner Production, 2017,161:1085-1093.
[47] 余泳泽, 刘大勇, 宣烨. 生产性服务业集聚对制造业生产效率的外溢效应及其衰减边界——基于空间计量模型的实证分析[J]. 金融研究, 2016,(2):23-36.

How does Financial Agglomeration Affect Green Development Efficiency? Empirical Analysis of SPDM and PTR Models Considering Spatio-temporal Double Fixation

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