长江流域资源与环境 >> 2021, Vol. 30 >> Issue (12): 2833-2842.doi: 10.11870/cjlyzyyhj202112003

• 区域可持续发展 • 上一篇    下一篇

长三角知识合作网络的空间格局及影响因素——以合著科研论文为例

戴  靓1,纪宇凡1,张维阳2,3*,曹  湛4,李  洋1   

  1. (1. 南京财经大学公共管理学院,江苏 南京 210023; 2. 华东师范大学 中国行政区划研究中心,上海 200241; 3. 华东师范大学 中国现代城市研究中心,上海 200241;4. 同济大学 建筑与城市规划学院,上海 200092)
  • 出版日期:2021-12-20 发布日期:2022-01-07

Spatial Patterns and Driving Factors of Intercity Knowledge Collaboration Network in Yangtze River Delta:Evidence from Scientific Co-publications

DAI Liang1, JI Yu-fan1, ZHANG Wei-yang2,3, CAO Zhan4, LI Yang1   

  1. (1. School of Public Administration, Nanjing University of Finance and Economics, Nanjing 210023, China; 2. Research Center for China Administrative Division, East China Normal University, Shanghai 200241,China; 3. The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200241,China;4. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)
  • Online:2021-12-20 Published:2022-01-07

摘要: 随着创新发展战略的提出和知识经济的转型,知识流成为探讨城际关系和空间重塑的重要视角,以合著科研论文为媒介的知识合作网络受到关注。基于2015~2019年Web of Science中的论文合著数据,利用爬虫技术,以地级以上城市为节点,构建长三角城市知识合作网络。通过边联系强度、节点中心性和QAP网络回归分析,研究长三角知识合作网络的空间格局及影响机制。结果显示:(1)就空间格局而言,长三角知识流集中于沪宁合杭甬“Z”型发展带上,呈现出“富人俱乐部”现象。南京在长三角的知识生产力和控制力超越上海,居于首位。江苏城市的省内外合作量都较高且均衡,呈现多极发展。安徽城市的省内合作稀疏,主要靠合肥的对外合作发展。浙江的省内联系呈杭州和宁波双核格局,联系强度介于江苏和安徽之间。(2)就影响机制而言,人口规模、研发投入、高校数量对城际知识合作有促进作用,其中高校的影响最突出。距离衰减效应仍然存在,行政边界也有一定程度的阻碍,但不如距离阻尼大。文化邻近性对长三角知识合作影响不显著,而行政等级效应较为明显。

Abstract: Against the backdrop of innovative development strategy and knowledge economy, intercity knowledge flows have become an important perspective to explore intercity relationship and spatial reconstruction. Therefore, knowledge collaboration network based on scientific co-publications has attracted much attention. Based on scientific publication data during 2015-2019 derived from Web of Science, the intercity knowledge collaboration network of the Yangtze River Delta (YRD) was constructed by employing crawler technology, in which nodes represented cities at the prefecture level and above. After that, spatial patterns and influencing factors were analyzed through the lens of dyadic strength, node centrality, and QAP regression. The results have shown that: (1) Knowledge flows in YRD is concentrated in the Z-shape development corridor of Shanghai-Nanjing-Hefei-Hangzhou-Ningbo, presenting obvious “rich-club” phenomenon. Nanjing has the strongest knowledge productivity and control power in YRD. The intercity collaboration between Jiangsu and other cities is relatively high and balanced, showing a multi-polar development. The intercity collaboration in Anhui is sparse, which mainly depends on the development of Hefei’s external collaboration. Zhejiang’s situation lies in between and the intercity collaboration is organized by Hangzhou and Ningbo. (2) Population, R&D, universities have a positive effect on intercity knowledge collaboration, among which universities have the strongest and most significant impact. The distance decay still exists, and administrative boundary could hinder intercity collaboration to some extent, but it’s effect is much smaller than the distance damping. Cultural proximity has no significant impact on knowledge collaboration, whereas administrative hierarchy matters.

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