Evolution characteristics and influencing factors of interurban innovation network structure in the Yellow River Basin

doi:10.3969/j.issn.1004-9479.2025.05.20230456

Abstract

Abstract:

Strengthening cross-regional innovation cooperation is an objective requirement for promoting the ecological protection and high-quality development in the Yellow River Basin. Based on the co-publication data of prefecture-level cities during 2006-2021, inter-city innovation network of the Yellow River Basin was constructed, and then social network analysis method was used to analyze the evolution characteristics of innovation network. After that, the exponential random graph model was used to analyze the influencing factors of interurban innovation network. The results show that: ① The number of co-publication in the Yellow River Basin increases significantly from 2006 to 2021， especially the output of innovation cooperation. The network density and the number of contact paths of network nodes increase significantly to form innovation agglomeration. However， the subject of innovation cooperation is relatively simple， and the enterprise-led deep integration of industry， university and research needs to be strengthened. ② The interurban innovation network in the Yellow River Basin presents a spatial pattern of "dense in the east and sparse in the west" and "dense in the south and sparse in the north"， with the evolution trend of "multi-center and networking". The development is carried out with urban agglomerations as agglomeration areas， and the core cities of urban agglomerations carry out innovative cooperation across urban agglomerations. ③ Provincial capitals and regional central cities such as Xi 'an， Lanzhou， Zhengzhou， Jinan and Qingdao have more connections with other node cities and play a leading and intermediary role in the innovation network. ④ Economic development level， industrial structure， R&D strength， number of innovation institutions and geospatial location have a significant impact on the formation and development of interurban innovation network in the Yellow River Basin. The results can provide policy basis for promoting cross-regional collaborative innovation in the Yellow River Basin.

Key words: innovation network, paper cooperation, Social Network Analysis, exponential random graph model, the Yellow River Basin

摘要：

加强黄河流域跨区域创新合作是推进黄河流域生态保护和高质量发展国家战略的客观要求。立足于地市论文合著数据构建区域创新网络，采用社会网络分析方法和指数随机图模型，系统分析了黄河流域城市创新网络结构的演变特征及影响因素。结果表明：①2006—2021年，黄河流域论文合著数量显著增加，尤其是2019—2021年，创新合作产出急剧增多，网络密度显著提升，网络节点的联系路径增多并形成创新集聚区，但创新合作主体较为单一，以企业为主导的产学研深度融合有待加强。②黄河流域城市创新网络呈现“东密西疏”“南密北疏”的空间分异，具有“多中心、网络化”的演变趋势。创新网络以城市群为集聚区进行发育，同时城市群核心城市开始开展跨城市群的创新合作。③西安、兰州、郑州、济南、青岛等省会城市及区域中心城市与其他节点城市联系紧密，在创新网络中发挥主导和中介作用。④经济发展水平、产业结构、研发实力、创新机构数量、地理空间位置等对于黄河流域城市创新网络的形成和发育具有显著影响。研究结论可为推进黄河流域跨区域协同创新提供决策参考。

关键词: 创新网络, 论文合作, 社会网络分析, 指数随机图模型, 黄河流域

Lin ZHAO, Zhongjun JI, Dianting WU, Xiaotong GAO. Evolution characteristics and influencing factors of interurban innovation network structure in the Yellow River Basin[J]. World Regional Studies, 2025, 34(5): 137-150.

赵林, 汲中俊, 吴殿廷, 高晓彤. 黄河流域城市创新网络结构的演变特征及影响因素[J]. 世界地理研究, 2025, 34(5): 137-150.

Figures/Tables 9

Fig.1 The study area of the Yellow River Basin

Fig.2 Numbers of paper cooperation among cities in the Yellow River Basin

Fig.3 Evaluation index of overall structure feature of interurban innovation networks in the Yellow River Basin

Fig.4 Spatial pattern of interurban innovation networks in the Yellow River Basin

Fig.5 Spatial pattern of degree centrality of interurban innovation networks in the Yellow River Basin

Fig.6 Spatial pattern of intermediary centrality of interurban innovation networks in the Yellow River Basin

Fig.7 Spatial pattern of eigenvector centrality of interurban innovation networks in the Yellow River Basin

Tab.1 Statistics and definitions of the ERGM model

解释变量类型	变量名称	变量含义	解释
网络自组织效应变量	edges	边	基本效应
节点属性效应变量	absdiff	节点异配	若系数为负，属性越相同的节点越倾向发生联结
外生网络效应变量	edgecov(g)	关系属性	若系数为正，两个网络（内、外）关系越倾向于同时发生

Tab.2 Parameter estimation results of ERGM model

变量	拟合系数	标准差	p值
edges	-5.321×10^-1***	5.386×10^-2	0.000 1
absdiff(PGDP)	-1.817×10^-6**	8.694×10^-7	0.036 7
absdiff(IS)	-7.141×10^-3**	3.608×10^-3	0.047 8
absdiff(GOV)	2.347×10^-6***	1.990×10^-7	0.000 1
absdiff(II)	-1.062×10^-4***	3.374×10^-5	0.001 7
edgecov(GP)	8.888×10^-1***	1.161×10^-1	0.000 1
AIC		8027
BIC		8067

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