中国城市知识复杂性的空间特征及影响研究

doi:10.11821/dlxb201808003

摘要/Abstract

摘要：

学者和政策制定者一致认为“知识”是推动经济长期增长的关键因素之一,且并非所有知识都具有相同价值。然而,经济地理学一直致力于计量知识的投入和产出,而忽视评估知识质量。为此,首先借鉴Hidalgo等的“双峰网络模型”构建了知识复杂性的测度模型,并根据中国知识产权局1986-2015年间的14349355条专利记录,依照122项专利分类识别中国城市的技术结构。将中国城市知识复杂性的分布、集聚特征和演化过程地图化,以探索知识的空间分布及其复杂性的关联。结果表明：① 具有最复杂技术结构的城市未必是那些专利申请率最高的城市,说明强调知识的数量与质量并重,有助于科学衡量中国知识的生产格局;② 知识复杂性在中国呈不均匀分布,其空间集聚显著,演化过程显示热点区域“南下”态势明显;③ 专利转移数据进一步证明,知识复杂性越高的城市,其知识的空间粘性越大,越不易流动,因此将区域知识结构从低复杂度转化为高复杂度将有助于地区重塑自身优势。

关键词: 知识复杂性, 知识流动, 双峰网络模型, 专利, 城市, 中国

Abstract:

Knowledge is one of the key drivers of long-term economic growth. Additionally, it is widely accepted that not all knowledge has the same value. Nevertheless, too often in the field of economic geography and cognate, we have been obsessed with counting knowledge inputs and outputs rather than assessing the quality of knowledge produced. To fill this gap, this article focuses on the knowledge complexity and its significant role in regional innovation. To begin with, based on the "bimodal network models" of Hidalgo and Hausmann, designed to measure the complexity of knowledge, the article investigates 14349355 pieces of patent records from the State Intellectual Property Office of P.R.China to identify the technological structure of Chinese cities in terms of the patent classes between 1986 and 2015. Furthermore, the article explores the evolution of knowledge complexity in Chinese cities and how the spatial diffusion of knowledge is linked to complexity using visualizing tools such as Gephi and ArcGIS. The results show that: (1) The cities with the most complex technological structures are not necessarily those with the highest rates of patenting, which suggests that the emphasis on both quality and quantity will definitely contribute to a better understanding of the distribution of knowledge production in China; (2) Knowledge complexity is unevenly distributed across China and the evolution process shows that the hot spots are obviously southward, indicating the characteristics of geographical agglomeration and the great differences between zones; (3) More complex patents are less likely to be transferred than those with less complexity in terms of the data of patent transference and the pattern is also verified by correlational analysis, Linear Probability Model and logistic regression approach of STATA. Therefore, upgrading regional knowledge structure from low complexity to high complexity will contribute to reshaping regional advantages respectively.

Key words: knowledge complexity, bimodal network model, knowledge flow, patents, cities, China

张翼鸥,谷人旭. 中国城市知识复杂性的空间特征及影响研究[J]. 地理学报, 2018, 73(8): 1421-1432.

ZHANG Yiou,GU Renxu. The geography of knowledge complexity and its influence in Chinese cities[J]. Acta Geographica Sinica, 2018, 73(8): 1421-1432.

参考文献

[1]	Schumpeter J.Capitalism,Socialism,Democracy. New York: Harper and Row, 1942.
[2]	Corrado C A, Hulten C R.How do you measure a "technological revolution"? The American Economic Review, 2010, 100(2): 99-104.http://pubs.aeaweb.org/doi/10.1257/aer.100.2.99
[3]	Romer P M.Increasing returns and long-run growth. Journal of Political Economy, 1986, 94(5): 1002-1037.https://www.journals.uchicago.edu/doi/10.1086/261420
[4]	Dicken P.Global Shift: Mapping the Changing Contours of The World Economy. London: SAGE Publications Ltd, 2007.http://www.bokus.com/bok/9781593854379/global-shift-mapping-the-changing-contours-of-the-world-economy/
[5]	Pavitt K.R&D, patenting and innovative activities: A statistical exploration. Research Policy, 1982, 11(1): 33-51.http://linkinghub.elsevier.com/retrieve/pii/0048733382900051
[6]	Kogler D F, Rigby D L, Tucker I.Mapping knowledge space and technological relatedness in US cities. European Planning Studies, 2013, 21(9): 1374-1391.http://www.tandfonline.com/doi/abs/10.1080/09654313.2012.755832
[7]	Boschma R, Balland P A, Kogler D F.Relatedness and technological change in cities: The rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010. Industrial and Corporate Change, 2014, 24(1): 223-250.
[8]	Maskell P, Malmberg A.The competitiveness of firms and regions: 'Ubiquitification' and the importance of localized learning. European Urban and Regional Studies, 1999, 6(1): 9-25.http://journals.sagepub.com/doi/10.1177/096977649900600102
[9]	Asheim B T, Gertler M S.The geography of innovation: Regional innovation systems//The Oxford handbook of innovation. 2005.
[10]	Hidalgo C A, Hausmann R.The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 2009, 106(26): 10570-10575.http://www.pnas.org/cgi/doi/10.1073/pnas.0900943106
[11]	Tamer C S, Calantone R J, Zhao Y.Tacit knowledge transfer and firm innovation capability. Journal of Business & Industrial Marketing, 2003, 18(1): 6-21.http://www.emeraldinsight.com/doi/full/10.1108/08858620310458615
[12]	Castaldi C, Frenken K, Los B.Related variety, unrelated variety and technological breakthroughs: An analysis of US state-level patenting. Regional Studies, 2015, 49(5): 767-781.http://www.tandfonline.com/doi/full/10.1080/00343404.2014.940305
[13]	Fleming L, Sorenson O.Technology as a complex adaptive system: Evidence from patent data. Research Policy, 2001, 30(7): 1019-1039.http://linkinghub.elsevier.com/retrieve/pii/S0048733300001359
[14]	Balland P A, Rigby D.The geography of complex knowledge. Economic Geography, 2017, 93(1): 1-23.https://www.tandfonline.com/doi/full/10.1080/00130095.2016.1205947
[15]	Fleming L, Sorenson O.Technology as a complex adaptive system: Evidence from patent data. Research Policy, 2001, 30(7): 1019-1039.http://linkinghub.elsevier.com/retrieve/pii/S0048733300001359
[16]	Jaffe A B, Trajtenberg M.Patents, Citations,Innovations: A Window on the Knowledge Economy. Cambridge: MIT Press, 2002.http://onlinelibrary.wiley.com/doi/10.1002/asi.10248/full
[17]	Quatraro F.Knowledge coherence, variety and economic growth: Manufacturing evidence from Italian regions. Research Policy, 2010, 39(10): 1289-1302.http://linkinghub.elsevier.com/retrieve/pii/S0048733310001952
[18]	Everett M G, Borgatti S P.The dual-projection approach for two-mode networks. Social Networks, 2013, 35(2): 204-210.http://linkinghub.elsevier.com/retrieve/pii/S0378873312000354
[19]	Rigby D L.Technological relatedness and knowledge space: Entry and exit of US cities from patent classes. Regional Studies, 2015, 49(11): 1922-1937.http://www.tandfonline.com/doi/full/10.1080/00343404.2013.854878
[20]	Feldman M P, Audretsch D B.Innovation in cities: Science-based diversity, specialization and localized competition. European Economic Review, 1999, 43(2): 409-429.http://linkinghub.elsevier.com/retrieve/pii/S0014292198000476
[21]	Lv Lachang, Xie Yuanyuan, Huang Ru.The comparison of innovation ability hierarchy of three megalopolises in China. Human Geography, 2013, 28(3): 91-95.
[21]	[吕拉昌, 谢媛媛, 黄茹. 我国三大都市圈城市创新能级体系比较. 人文地理, 2013, 28(3): 91-95.]http://www.cnki.com.cn/Article/CJFDTotal-RWDL201303017.htm
[22]	Li Guoping, Wang Chunyang.Spatial characteristics and dynamic changes of provincial innovation output in China: An investigation using the ESDA. Geographical Research, 2012, 31(1): 95-106.
[22]	[李国平, 王春杨. 我国省域创新产出的空间特征和时空演化. 地理研究, 2012, 31(1): 95-106.]
[23]	Wang Junsong, Yan Yan, Hu Shuhong.Spatial pattern and determinants of Chinese urban innovative capabilities base on spatial panel data model. Scientia Geographica Sinica, 2017, 37(1): 11-18.
[23]	[王俊松, 颜燕, 胡曙虹. 中国城市技术创新能力的空间特征及影响因素: 基于空间面板数据模型的研究. 地理科学, 2017, 37(1): 11-18.]
[24]	Anselin L.Interactive techniques and exploratory spatial data analysis. Geographical Information Systems: Principles, Techniques, Management and Applications, 1999, 1: 251-264.
[25]	Zhou Mi, Sun Liyang.The transfer of patent right,characteristics of spatial network and the way for regional collaborative innovation in the area of Beijing, Tianjin and Hebei. Studies in Science of Science, 2016, 34(11): 1736-1743.
[25]	[周密, 孙浬阳. 专利权转移, 空间网络与京津冀协同创新研究. 科学学研究, 2016, 34(11): 1736-1743.]http://d.wanfangdata.com.cn/Periodical/kxxyj201611016
[26]	Hagedoorn J, Cloodt M.Measuring innovative performance: Is there an advantage in using multiple indicators? Research Policy, 2003, 32(8): 1365-1379.http://linkinghub.elsevier.com/retrieve/pii/S0048733302001373