RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2021, Vol. 30 >> Issue (10): 2347-2359.doi: 10.11870/cjlyzyyhj202110004

Previous Articles     Next Articles

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

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.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LI Jian-Bao, HUANG Xian-Jin, MENG Hao, ZHOU Yan, XU Guo-Liang, WU Chang-Yan. Analysis of Cumulative Target Completion Rate of Carbon Intensity in China During the Period of “Twelfth Five-Year”[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2018, 27(08): 1655 .
[2] XIONG Hong-bin, ZHOU Ling-yan. Using PSR-grey Target Model to Assess Ecological and Environmental Impact of Flood Regulation Project At Riparian Zone Around Lake Chaohu[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2018, 27(09): 1977 -1987 .
[3] LI Jia-yi, KUANG Hong-hai, TAN Chao, WANG Pei-pei.  

Spatio-Temporal Characteristics and Ecological Response of Urban Expansion in the Yangtze River Economic Zone [J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2018, 27(10): 2153 -2161 .

[4] TANG Zijun, CHEN Long, QIN Jun, ZHENG Xiang . Numerical Simulation of the Local Flow Field and the Boundary Layer Structure in the Pollution Process in Wuhan[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2018, 27(11): 2540 -2547 .
[5] WANG Dongxiang, ZHANG Yiming, WANG Ruicheng, ZHAO Bingyan, ZHANG Zhiqi, HUANG Xianyu, . Characteristics of Dissolved Organic Matter in Pore Water from the Dajiuhu Peatland, Central China[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2018, 27(11): 2568 -2577 .
[6] WANG Hai-li, HAN Guang-zhong, XIE Xian-jian. Spatiotemporal Pattern Evolvement Based on the DEA Model and Its Driving Factors of Arable Land Utilization Efficiency of the Southwest Region in China[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2018, 27(12): 2784 -2795 .
[7] WANG Cong-cong, WANG Yi-cheng, MA Ren-feng, WANG Jing-min. Impact of Economic Agglomeration on Pollution of Smog Based on Spatial Econometric Model:The Case Study of Yangtze River Delta[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2019, 28(01): 1 -11 .
[8] ZHAO Shu-cheng, ZHANG Zhan-yu, XIA Ji-hong, YANG Jie, SHENG Li-ting, TANG Dan, CHEN Xiao-an, . Phosphorus Adsorption Characteristics of Riparian Soils Surrounding Poyang Lake[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2019, 28(01): 166 -174 .
[9] RUAN Tian, ZHA Qian-yu, YANG Ru, GAO Chao. Effects on Runoff Above the Cuntan Station Area in the Yangtze River Basin Under the 1.5℃ and 2.0℃ Global Warming[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2019, 28(02): 407 -415 .
[10] SHAO Yi-ting, HE Yi, MU Xing-min, GAO Peng, ZHAO Guang-ju, SUN Wen-yi, . Spatiotemporal Variation of Rainfall Erosivity in Qin-Ba Mountains Region[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2019, 28(02): 416 -425 .