行业知识基础视角下城际创新网络模拟与邻近机制——以长三角城市群为例

从知识创造动态与经济绩效2个维度量化识别行业知识基础，基于修正引力模型构建2005—2019年长三角城市群异质性知识基础行业城际创新网络，采用社会网络分析法与二次指派程序（QAP）探究不同类型知识基础行业创新网络的结构演化与邻近机制，结果显示：1）行业知识基础可划分为解析型、象征型、解析-综合型、综合-解析-象征型和综合-象征-解析型5种类型。2）解析型网络呈双核、其他网络呈多核结构；解析型、解析-综合型和综合-解析-象征型网络核心城市行政或经济中心指向明显，子群内中心城市联系紧密，象征型和综合-象征-解析型网络核心城市变动大，子群内联系等级不明显；解析型网络结构日益“紧密化”，象征型呈现“松散—紧凑—松散”、解析-综合型呈现“紧凑—松散—紧凑”的演变态势，综合型知识基础主导的网络结构较稳定。3）地理邻近对除解析型以外的4种网络影响显著为正，认知与技术邻近对5种网络影响均显著为正，制度邻近对解析型、解析-综合型网络影响显著为正；地理与技术邻近交互对5种网络影响均显著为正，地理与认知邻近交互对解析型、象征型和解析-综合型网络影响显著为正。

Inter-City Innovation Network Simulation and Proximity Mechanism from the Perspective of Industrial Knowledge Bases: A Case Study on the Yangtze River Delta Urban Agglomeration

Currently, there is less focus on "knowledge heterogeneity" among different innovation subjects in inter-city innovation network research. Knowledge bases theory has gradually become a new entry point from the perspective of "knowledge heterogeneity" in regional theory and practice research. In this study, we explore the structural characteristics and proximity mechanism of inter-city innovation networks formed by heterogeneous knowledge-based industries, using the Yangtze River Delta Urban Agglomeration as a case study. We theorize knowledge creation logic as having two dimensions, that is, knowledge creation dynamics and economic performance, and develop a framework with a quantitative method to identify the typology of industrial knowledge bases. We use modified gravity models to construct inter-city innovation networks of heterogeneous industries, divided into different knowledge bases in the Yangtze River Delta urban agglomeration from 2005 to 2019. To clarify how these inter-city innovation networks evolve and what effects proximity has on evolution, we conduct social network analysis and implement the Quadratic Assignment Procedure (QAP). The results show the following: (1) Industrial knowledge bases are divided into five types, namely, analytical, symbolic, analytical-synthetic, synthetic-analytical-symbolic, and synthetic-symbolic-analytical. Therefore, innovation networks are also defined as these types. (2) The analytical network outlines a dual-core structure while other network structures are multi-core; the analytical, analytical-synthetic, and synthetic-analytical-symbolic networks are evidently oriented by core cities with administrative or economic functions; the central cities of the subgroups are closely connected, while the core cities of symbolic and synthetic-symbolic-analytical networks vary greatly, and weak connections appear within these subgroups; for the analytical, symbolic, analytical-synthetic, and synthetic knowledge base-dominated network structures, their evolution trends are increasingly compact, "loose-compact-loose", "compact-loose-compact", and relatively stable, respectively. (3) Inter-city innovation links are facilitated by geographical proximity, except for analytical networks. Cognitive proximity and technological proximity have a significant positive impact on innovation links. Institutional proximity contributes to the formation of analytical and analytical-synthetic networks. The interaction between geographical and technological proximity has a significant positive effect on innovation networks. In the analytical, symbolic, and analytical-integrated networks, the interaction of geographical proximity and cognitive proximity induces cities to contact each other. Theoretically, through China's practical application scenarios, this study quantitatively identifies the combination form of industry knowledge base for the first time and further deepens the connotation of knowledge bases theory. Simultaneously, it also addresses the deficiency of attention to the "knowledge heterogeneity" of the innovation subject in research on inter-city innovation networks. Practically, it provides a new perspective for optimizing the allocation of innovation resources for different types of knowledge-based subjects in the Yangtze River Delta urban agglomeration, improving the efficiency of inter-city knowledge circulation, building a differentiated regional innovation system, and formulating innovation governance strategies.