地理科学 ›› 2021, Vol. 41 ›› Issue (8): 1380-1388.doi: 10.13249/j.cnki.sgs.2021.08.009
收稿日期:
2020-06-29
出版日期:
2021-08-25
发布日期:
2021-10-11
作者简介:
王庆喜(1976−),男,湖南邵阳人,教授,博导,主要从事技术创新与区域经济发展研究。E-mail: wqx1976@zjut.edu.cn
基金资助:
Received:
2020-06-29
Online:
2021-08-25
Published:
2021-10-11
Supported by:
摘要:
以浙江90个县级城市(城区)为对象,基于大数据专利文本挖掘析取城市间合作申请专利数,构建创新网络,采用空间网络模型和负二项回归模型分析2007—2017年浙江城市创新网络的网络结构、时空演化及创新合作强度的影响机制。研究发现:① 浙江城市创新网络规模逐渐扩张,“小世界性”显著,通达性较好,整体呈以杭州湾区为核心的“网络局部化、辐射中心化”特征,等级层次性清晰;② 浙江城市创新合作强度与经济规模、教育水平、政策支持、技术势差、城市行政等级、技术邻近性、边界相邻效应显著正相关且受地理距离约束,认知邻近性和制度邻近性与创新合作间分别呈“U”型和倒“U”型曲线关系,网络效应更多在整体网层面上促进了创新合作。在特定省份县级城市层面探讨了如何加强城市创新网络协同效应,以促进地方城市间的创新联系。
中图分类号:
王庆喜, 胡志学. 多维邻近下浙江城市创新网络演化及其机制研究[J]. 地理科学, 2021, 41(8): 1380-1388.
Wang Qingxi, Hu Zhixue. Urban Innovation Network of Zhejiang From the Perspective of Multidimensional Proximities[J]. SCIENTIA GEOGRAPHICA SINICA, 2021, 41(8): 1380-1388.
表 1
2007—2017年创新整体网络结构性指标
年份 | 关系数 | 节点数 | 网络密度 | 网络直径 | 平均度数 | 平均路径长度 | 平均聚类系数 |
2007 | 64.0 | 47.0 | 0.016 | 7(10) | 2.73 | 2.81 | 0.13 |
2008 | 101.0 | 62.0 | 0.025 | 6(10) | 5.51 | 2.75 | 0.15 |
2009 | 109.0 | 64.0 | 0.027 | 7(10) | 5.16 | 2.67 | 0.14 |
2010 | 132.0 | 72.0 | 0.033 | 6(10) | 6.73 | 2.62 | 0.16 |
2011 | 109.0 | 63.0 | 0.027 | 6(11) | 7.67 | 2.66 | 0.22 |
2012 | 149.0 | 75.0 | 0.037 | 5(8) | 12.87 | 2.46 | 0.21 |
2013 | 159.0 | 80.0 | 0.039 | 6(7) | 17.13 | 2.60 | 0.17 |
2014 | 199.0 | 82.0 | 0.049 | 5(7) | 21.13 | 2.57 | 0.23 |
2015 | 249.0 | 85.0 | 0.061 | 5(6) | 29.27 | 2.47 | 0.23 |
2016 | 255.0 | 85.0 | 0.063 | 5(6) | 34.22 | 2.38 | 0.25 |
2017 | 310.0 | 89.0 | 0.077 | 5(6) | 40.62 | 2.43 | 0.27 |
表 2
创新合作强度负二项回归模型估计结果
变量类别 | 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 | 模型7 |
经济发展水平 | pergdp1 | 0.313*** | 0.225** | 0.041* | ||||
pergdp2 | 0.875*** | 0.712*** | 0.445*** | |||||
教育水平 | edu1 | 0.124*** | 0.100** | 0.111*** | ||||
edu2 | 0.106*** | 0.093*** | 0.054** | |||||
政策支持 | policy1 | 0.005*** | 0.002 | 0.005*** | ||||
policy2 | 0.006*** | 0.005*** | 0.002* | |||||
地理邻近性 | gDistance | −2.5e−6*** | −4.75e−6*** | −1.02e−6** | ||||
border | 0.236** | 0.272** | 0.251** | |||||
认知邻近性 | cDistance | −2.127*** | −1.867*** | −1.122*** | ||||
cDistance2 | 2.051*** | 1.655*** | 1.468*** | |||||
制度邻近性 | sDistance | 0.671*** | 0.399* | 0.343* | ||||
sDistance2 | −1.700*** | −1.052*** | −1.122*** | |||||
技术邻近性 | tDistance | 0.910*** | 0.513*** | 0.327** | ||||
网络效应 | NETstruc | 2.457*** | 2.178*** | 1.871*** | ||||
NETchar | 0.034* | 0.011*** | 0.085* | |||||
控制变量 | grade | 0.369*** | 0.603*** | 0.359*** | 0.013* | |||
differ | 5e−4*** | 9e−4*** | 7e−4*** | 4e−4*** | ||||
常数项 | −4.191*** | −0.321* | −2.813*** | −4.004*** | −1.091*** | −0.321*** | −0.386*** | |
Loglikelihood | −4930.0 | −5186.0 | −4875.0 | −4887.0 | −4978.0 | −4771.0 | −4691.0 | |
Wald chi2(6) | 622.0 | 129.0 | 553.0 | 717.0 | 587.0 | 786.0 | 953.0 | |
Hausman P | <1e−4 | <1e−4 | <1e−4 | <1e−4 | <1e−4 | <1e−4 | <1e−4 |
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