Spatial Dynamics of Intercity Technology Transfer Networks in China's Three Urban Agglomerations: A Patent Transaction Perspective

被引:41
作者
Liu, Chengliang [1 ,2 ,3 ]
Niu, Caicheng [2 ]
Han, Ji [3 ,4 ,5 ]
机构
[1] East China Normal Univ, Inst Global Innovat & Dev, Shanghai 200062, Peoples R China
[2] East China Normal Univ, Sch Urban & Reg Sci, Shanghai 200241, Peoples R China
[3] East China Normal Univ, Inst Ecochongming, Shanghai 200062, Peoples R China
[4] East China Normal Univ, Shanghai Key Lab Urban Ecol Proc & Ecorestorat, Shanghai 200241, Peoples R China
[5] East China Normal Univ, Sch Ecol & Environm Sci, Shanghai 200241, Peoples R China
基金
中国国家自然科学基金;
关键词
technology transfer; patent transaction; spatial dynamics; urban agglomerations; social network analysis; China; CIRCULAR VISUALIZATION; KNOWLEDGE SPILLOVERS; ABSORPTIVE-CAPACITY; INTERNAL MIGRATION; INNOVATION; PROXIMITY; GEOGRAPHY; COLLABORATIONS; POLYCENTRICITY; CENTRALITY;
D O I
10.3390/su11061647
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Technology transfer has become a vital pipeline for acquiring external knowledge. The purpose of this paper is to portray the spatial dynamics of intercity technology transfer networks in China's three urban agglomerations based on patent right transaction data from 2008 to 2015. The integration of social networks and spatial visualization is used to explore spatial networks and influencing variables of the networks. The results demonstrate that Beijing, Shanghai, and Shenzhen are emerging as hubs in the three urban agglomerations. The spatial distributions of degree and weighted degree are significantly heterogeneous and hierarchical. The larger cities play the role of a knowledge and technology incubator, highly related to their economic scale, research and development (R&D) input, and innovation output. The evolution of intercity technology linkages is driven by the networking mechanisms of preferential attachment, hierarchical and contagious diffusion, path dependence, and path breaking. Moreover, we found that the geographical proximity and technology gaps are determinants of the strength of intercity technology linkages. As a result, it has been discovered that the network in the Beijing-Tianjin-Hebei agglomeration is organized in a tree network, while the Yangtze River Delta features a polycentric network and the Pearl River Delta has multi-star characteristics.
引用
收藏
页数:24
相关论文
共 75 条
[51]  
[刘承良 Liu Chengliang], 2018, [地理学报, Acta Geographica Sinica], V73, P1462
[52]   Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis [J].
Liu, Chengliang ;
Wang, Jiaqi ;
Zhang, Hong .
MARITIME POLICY & MANAGEMENT, 2018, 45 (01) :89-104
[53]   The Effect of Geographical Proximity on Scientific Cooperation among Chinese Cities from 1990 to 2010 [J].
Ma, Haitao ;
Fang, Chuanglin ;
Pang, Bo ;
Li, Guangdong .
PLOS ONE, 2014, 9 (11)
[54]   R&D collaborations and the role of proximity [J].
Marek, Philipp ;
Titze, Mirko ;
Fuhrmeister, Clemens ;
Blum, Ulrich .
REGIONAL STUDIES, 2017, 51 (12) :1761-1773
[55]   Proximity, networking and knowledge production in Europe: What lessons for innovation policy? [J].
Marrocu, Emanuela ;
Paci, Raffaele ;
Usai, Stefano .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2013, 80 (08) :1484-1498
[56]   Knowledge spillovers in Europe: A patent citations analysis [J].
Maurseth, PB ;
Verspagen, B .
SCANDINAVIAN JOURNAL OF ECONOMICS, 2002, 104 (04) :531-545
[57]  
National Bureau of Statistics, 2016, CHIN CIT STAT YB
[58]   The structure and evolution of ICT global innovation network [J].
Nepelski, Daniel ;
De Prato, Giuditta .
INDUSTRY AND INNOVATION, 2018, 25 (10) :940-965
[59]   Mapping urban networks through inter-firm service relationships: The case of China [J].
Pan, Fenghua ;
Bi, Wenkai ;
Lenzer, James ;
Zhao, Simon .
URBAN STUDIES, 2017, 54 (16) :3639-3654
[60]   Circular visualization of China's internal migration flows 2010-2015 [J].
Qi, Wei ;
Abel, Guy J. ;
Muttarak, Raya ;
Liu, Shenghe .
ENVIRONMENT AND PLANNING A, 2017, 49 (11) :2432-2436