| 区域创新网络模式研究——以长三角城市群为例 |
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| 引用本文： | 周灿,曾刚,宓泽锋,鲜果.区域创新网络模式研究——以长三角城市群为例[J].地理科学进展,2017,36(7):795-805. |
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| 作者姓名： | 周灿 曾刚 宓泽锋 鲜果 |
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| 作者单位： | 华东师范大学中国现代城市研究中心/城市与区域科学学院,上海 200062 |
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| 基金项目： | 国家自然科学基金项目(41071093, 41371147) |
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| 摘 要： | 网络范式的兴起赋予城市创新模式新的内涵,引起了经济地理学者对不同空间尺度知识流动和创新联系的关注。基于网络视角,以中国知识产权局2014年长三角城市群26个地级市合作发明专利信息为原始数据,借助Ucinet、ArcGIS等分析工具,从本地和跨界多维空间尺度,刻画长三角城市群创新网络结构,测度城市创新网络地位,评价城市创新能力,进而对城市创新模式进行划分。研究表明：①研发密集的大型国有企业、中外合资企业和知名的理工科院校具有较高的知识生产能力,成为长三角城市群创新合作优先链接主体;②长三角城市群重视外部知识获取,跨界网络成为重要的创新合作途径,地理距离对创新合作空间载体选择的制约减弱;③创新网络位置影响知识获取和城市创新,网络视角下的长三角城市群呈现四类创新模式,密集的“本地—跨界”创新网络有助于城市创新。研究结论对长三角城市群不同类型创新模式的优化升级具有一定的参考价值。
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| 关 键 词： | 创新网络 知识流动 创新模式 长三角城市群 |
| 收稿时间： | 2017-01-01 |
| 修稿时间： | 2017-06-01 |
The study of regional innovation network patterns:Evidence from the Yangtze River Delta Urban Agglomeration |
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| Can ZHOU,Gang ZENG,Zefeng MI,Guo XIAN.The study of regional innovation network patterns:Evidence from the Yangtze River Delta Urban Agglomeration[J].Progress in Geography,2017,36(7):795-805. |
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| Authors: | Can ZHOU Gang ZENG Zefeng MI Guo XIAN |
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| Institution: | Center for Modern Chinese City Studies & School of City and Regional Science, East China Normal University, Shanghai 200062, China |
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| Abstract: | In recent years, a large and growing body of scholarly research in economic geography has focused on analyzing the knowledge flows and innovation linkages at different spatial scales. The recognition that the network of both local and trans-local linkages are important for a regional actor’s access to knowledge and resources has pushed scholars to go beyond the traditional local-global dichotomy and adopt a so-called glocal network view of regional innovation. Along with the emergence of network paradigm, the need to better understand regional innovation patterns is widely recognized. From a theoretical perspective, this study sought to consider the linkage between networks, knowledge flows, and innovation patterns. Taking the Yangtze River Delta urban agglomeration as the object and by using Ucinet and ArcGIS tools, we analyzed the intra- and inter-regional innovation network structure and measured the innovation network status based on a unique co-patent dataset issued by the State Intellectual Property Office of China in 2014. The main findings of this study are as follows. (1) Among the firms within the network, State Grid, Jiangsu Electric Power, Zhejiang Electric Power, Shanghai Electric Power, NARI Group, and Sinopec are found to possess the highest rates of centrality. Among the universities, those actors at the center of the network are Zhejiang University, Southeast University, Shanghai Jiaotong University, Donghua University, and East China University of Science and Technology. Prestigious science and engineering universities, large state-owned enterprises, and sino-foreign joint venture enterprises are clearly the most influential actors within the innovation network of the Yangtze River Delta urban agglomeration. Through their structurally central positions they act as the most important bridging and connecting agents in the process of building innovation networks. (2) Recognition of the importance of external sources of knowledge creates an incentive for cities in the Yangtze River Delta urban agglomeration to maintain tight external links. Inter-city level is the main geographical scale of innovation linkages in the Yangtze River Delta urban agglomeration, while geographical proximity is no longer crucial for the formation of collaboration innovation network. (3) The types of network existing within and across regions and the knowledge flow through these networks will impact on regional innovation patterns. From the perspective of intra- and inter-regional innovation networks, the Yangtze River Delta urban agglomeration presents four different types of regional innovation patterns. The successful innovative cities, such as Shanghai, Nanjing, and Hangzhou, are characterized by a dense local network and involved in external links. Weaker innovation lagging regions are likely to possess poor network connections, be it of a local or trans-local nature. Our empirical work suggests that a key driver of regional innovation consists of the capability of actors in a region to access and subsequently utilize both local and trans-local beneficial knowledge. The findings of this study may provide a reference for the optimization and upgrading of intra- and inter-regional innovation networks. |
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| Keywords: | innovation networks knowledge flow city innovation patterns Yangtze River Delta Urban Agglomeration |
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