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| 中国城市知识创新网络的演化特征及其邻近性机制 | |
| 引用本文： | 戴靓,纪宇凡,王嵩,朱青,丁子军.中国城市知识创新网络的演化特征及其邻近性机制[J].资源科学,2022,44(7):1494-1505. |
| 作者姓名： | 戴靓 纪宇凡 王嵩 朱青 丁子军 |
| 作者单位： | 1.南京财经大学公共管理学院,南京 210023 2.东北大学工商管理学院,沈阳 110167 3.中国科学院南京地理与湖泊研究所,南京 210008 |
| 基金项目： | 国家自然科学基金项目(41901189);江苏省自然科学基金项目(BK20190797);江苏省高校自然科学研究项目(19KJB170016) |
| 摘 要： | 在开放式创新模式下,分析中国城市间知识合作创新的网络结构并探讨其背后的邻近性机制对提高城市创新效率、推进国家创新体系建设具有重要意义。本文基于中国285个地级及以上城市间论文合作发表和专利联合申请的截面数据,综合构建了2011年和2019年中国城市知识创新网络,分析其结构演化特征,并采用多元回归的二次指派程序（MRQAP）从邻近性视角探讨其演化机制。结果表明：①2011—2019年中国城市知识创新网络密度增强,择优链接弱化,呈现出多中心发展趋势,合作格局由北京和上海主导转变为北京上海引领与区域中心带动相结合,从而形成多个区域网。②城市间知识合作创新除了受城市经济水平、科教支持力度、行政等级的正向影响外,也受地理、组织、文化、社会、制度邻近的显著促进,邻近性机制对中国城市知识创新网络演化具有较强解释力。③不同维度邻近性对城市知识创新网络的影响是动态的和交互的,过度的地理、社会、认知邻近会阻碍城市间知识合作创新,认知邻近可弥补地理距离,而社会邻近往往伴随着地理邻近。在此基础上,针对中国创新型城市建设和城市协同创新发展提出相关政策建议。 |
| 关 键 词： | 城市网络 知识创新 多维邻近性 非线性 交互性 MRQAP |
| 收稿时间： | 2022-01-20 |
| 修稿时间： | 2022-05-04 |
Evolutionary characteristics and proximity mechanism of intercity knowledge innovation networks in China |
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| DAI Liang,JI Yufan,WANG Song,ZHU Qing,DING Zijun.Evolutionary characteristics and proximity mechanism of intercity knowledge innovation networks in China[J].Resources Science,2022,44(7):1494-1505. | |
| Authors: | DAI Liang JI Yufan WANG Song ZHU Qing DING Zijun |
| Institution: | 1. School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China 2. School of Business Administration, Northeastern University, Shenyang 110167, China 3. Nanjing Institute of Geography & Limnology, CAS, Nanjing 210008, China |
| Abstract: | Against the backdrop of open innovation system, analyzing the structures of China’s intercity knowledge innovation networks and exploring the underlying proximity mechanism are of great significance for improving the efficiency of urban innovation and promoting the construction of the national innovation system. Drawing on the collaborative publications and patents data of 285 cities at the prefecture level and above in China, this study examined the intercity knowledge innovation networks for 2011 and 2019 through summing up publication and patent collaboration networks by weights, and analyzed the structural characteristics and spatiotemporal evolution of the networks. Furthermore, the multiple regression quadradic assignment procedure (MRQAP) model was employed to explore the evolutionary mechanism of the networks from the perspective of proximity. The results show that: (1) The density of China’s intercity knowledge innovation networks increased from 2011 to 2019. The networks presented declined preferential attachment but increased polycentric development. The intercity collaborative patterns transformed from being dominated by Beijing and Shanghai to being led by Beijing and Shanghai and supported by regional centers, forming multiple regional sub-networks. (2) In addition to being positively influenced by urban economic level, technology and education expenditures, and administrative level, intercity knowledge collaboration was also significantly promoted by geographical, organizational, cultural, social, and institutional proximities. The proximity mechanism could well explain the evolution of China’s intercity knowledge innovation networks. (3) The impacts of different proximities on the intercity knowledge innovation networks were dynamic and interactive. Excessive geographical, social, and cognitive proximities could hinder the collaboration between cities. Cognitive proximity could compensate geographical distance while social contacts were frequently located in geographical vicinity. On these bases, policy recommendations were proposed for the construction and coordination of innovative cities. |
| Keywords: | urban network knowledge innovation multidimensional proximity nonlinearity interaction MRQAP |
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