长江流域资源与环境 >> 2021, Vol. 30 >> Issue (10): 2347-2359.doi: 10.11870/cjlyzyyhj202110004

• 区域可持续发展 • 上一篇    下一篇

长三角专利转移网络的时空演化研究

付晓宁1,2,孙  伟1,闫东升3   

  1. (1. 中国科学院南京地理与湖泊研究所,中国科学院流域地理学重点实验室, 江苏 南京 210008;2.中国科学院大学,北京100049;3.河海大学公共管理学院,江苏 南京 211100)
  • 出版日期:2021-10-20 发布日期:2021-11-05

Spatial-Temporal Evolution of the Yangtze River Delta Patent Transfer Network

FU Xiao-ning1,2 , SUN Wei1,YAN Dong-sheng1,3   

  1. (1. Nanjing Institute of Geography & Limnology, Key Laboratory of Watershed Geography Science, Chinese Academy of Sciences, Nanjing 210008, China;2. University of Chinese Academy of Science, Beijing 100049, China;3. Public Administration School,Hohai University, Nanjing  211100, China)
  • Online:2021-10-20 Published:2021-11-05

摘要: 基于专利转移数据,采用空间分析、社会网络分析等多种方法,刻画长三角41个城市2010~2018年专利转移网络时空格局演变特征,并采用空间计量方法探讨城市创新联系的影响因素。结果表明:(1)长三角专利产出和输出均呈现明显的空间极化效应,但专利吸纳的“核心—边缘”特征逐渐弱化;(2)城市专利转移网络呈现显著扩张态势,多中心网络化格局凸显,且空间依赖性逐步增强;专利转移的路径选择具有地理临近特征,且路径创造与路径依赖并存;(3)以专利转移数据表征城市创新联系强度,基于空间计量模型的实证研究发现,人力资本、产业发展、基础设施和政府支持等是影响城市创新联系的关键因素,且这一过程存在显著为正的空间溢出效应。通过分析专利转移网络的时空演变及其驱动因素,研究不仅完善了创新联系的动态测度方法,也可以为长三角制定科学政策、提升创新能力、构建科技创新共同体提供参考。

Abstract: Based on the patent transfer data, the evolutionary characteristics of the patent transfer network in 41 cities in the Yangtze River Delta from 2010 to 2018 are depicted by using various methods such as spatial analysis and social network analysis, and the influencing factors of city innovation connection are discussed by using spatial measurement method. The results show that :(1)Since 2010, the innovation capability of the Yangtze River Delta has gradually increased. The concentration of patent output is still concentrated in space, but it has gradually changed from Shanghai as the core to a trend of agglomeration in Shanghai, Suzhou, Hangzhou, Nanjing and other cities. The spatial pattern of patent absorption has developed from polarization to balanced development, especially the “core-periphery” pattern with Shanghai as the center has gradually weakened. (2) The urban patent transfer network has shown a significant expansion trend, the multi-center network pattern is prominent, and the spatial dependence is gradually increasing; the path selection of patent transfer has the characteristics of geographical proximity, and path creation and path dependence coexist, peripheral cities enhance their own innovation capabilities by attracting the resource advantages of core cities to establish R&D centers. (3) Using patent transfer data to characterize the strength of urban innovation linkages, empirical study based on spatial measurement found that human capital, industrial development, infrastructure and government support are the key factors affecting the connection of urban innovation, and this process has a significant positive spatial spillover effect. R&D personnel can increase the level of urban innovation output and promote the flow of innovative elements; the technology service industry promotes urban innovation links by improving the communication efficiency between innovation subjects; the upgrading of infrastructure accelerates the flow of elements between cities and significantly improves the efficiency of innovation links; As the promoter of the innovation environment, the government promotes the innovative development of cities through the formulation and implementation of policies. By analyzing the temporal and spatial evolution of the patent transfer network and its driving factors, it not only improves the dynamic measurement method of innovation connection, but also provides a reference for formulating scientific policies in the Yangtze River Delta, improving innovation capabilities, and building a technological innovation community.

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