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中国南部大湾区科技创新合作网络:一项社会网络分析

Scientific and technological innovation cooperation network of the Greater Bay Area in South China: A social network analysis.

作者信息

Shen Chuyi, Yang Zhenjie, Lam Fat-Iam, Tam Sio Wa, Yang Wenping, Li Yunxin

机构信息

Macao Polytechnic University, Macau, China.

Zhongnan University of Economics and Law, Wuhan, China.

出版信息

PLoS One. 2025 Jul 1;20(7):e0326515. doi: 10.1371/journal.pone.0326515. eCollection 2025.

DOI:10.1371/journal.pone.0326515
PMID:40591554
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12212550/
Abstract

Regional innovation cooperation focused upon either regions within one nation or transnational regions. Different from the discussion in the existing literature, the Guangdong- Hong Kong-Macao Greater Bay Area exists as an exceptional cross-border city-to-city cooperation under "one country, two systems". Based on the social network analysis on co- patents in the Greater Bay Area, this paper aims to investigate the models and features of the innovation cooperation network of science and technology in the Greater Bay Area and the different characteristics of collaboration policy on cross-border knowledge flow before and after 2015. The results show that the innovation network of science and technology in the Greater Bay Area has a loosely structured pattern, with Hong Kong-Shenzhen-Guangzhou as a prominent hub. This structure portrays multiple centers that radiate out to its peripheries. The national policy launched in 2015 reduces the cooperation barriers and promotes the cross-border collaboration. The universities and research institutes possess significant intermediary roles and innovation autonomy in the scientific and technological innovation cooperation network of the Greater Bay Area.

摘要

区域创新合作聚焦于一个国家内部的区域或跨国区域。与现有文献中的讨论不同,粤港澳大湾区是“一国两制”下特殊的跨境城市间合作。基于对大湾区共同专利的社会网络分析,本文旨在探究大湾区科技创新合作网络的模式与特征,以及2015年前后跨境知识流动合作政策的不同特点。结果表明,大湾区科技创新网络结构松散,以香港-深圳-广州为突出枢纽。这种结构呈现出多个中心向外围辐射的态势。2015年出台的国家政策减少了合作障碍,促进了跨境合作。在大湾区的科技创新合作网络中,高校和科研机构具有重要的中介作用和创新自主性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/58c6bd63db57/pone.0326515.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/d8277eae7815/pone.0326515.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/7342d4169fa9/pone.0326515.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/58c6bd63db57/pone.0326515.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/d8277eae7815/pone.0326515.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/7342d4169fa9/pone.0326515.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab30/12212550/58c6bd63db57/pone.0326515.g003.jpg

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本文引用的文献

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Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis.利用社会网络分析研究《医学科学研究杂志》论文的共同作者网络。
J Res Med Sci. 2014 Jan;19(1):41-6.
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Network 'small-world-ness': a quantitative method for determining canonical network equivalence.网络“小世界特性”:一种确定典型网络等效性的定量方法。
PLoS One. 2008 Apr 30;3(4):e0002051. doi: 10.1371/journal.pone.0002051.
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