中国城市网络中心性的影响因素及形成机理——基于上市公司500强企业网络视角

利用2016年中国上市公司500强企业网络数据和隶属联系模型构建中国城市网络,在解析城市网络中心性结构特征的基础上,定量识别与测度了中国城市网络中心性空间分异的影响因素,在“行为—结构—绩效”分析框架下探索性解析了城市网络中心性的形成机理。研究发现：城市出度中心性体系呈现“香港-北京”双核结构特征,中介度体系呈现以北京为首位城市的首位分布特征,入度中心性体系则呈现多核心结构特征;市场潜力、关键资源、区位条件和经营环境等城市属性特征对于城市网络中心性具有显著正向影响,不同维度中心性特征对于不同类型影响因素的敏感性存在差异;择优选择、偏好依附和网络邻近是中国城市网络中心性发育的动力机制,这将进一步强化具有传统优势城市的网络地位。在网络发展环境下,中国城市化政策和城市治理体系需要作出相应调整,中国政府必须积极应对网络环境下的城市间发展差距,并在更大空间尺度上推动城市之间的网络合作。

Determinants and mechanisms of degree centrality in the urban network in China: A study based on corporate networks of the largest 500 listed companies

This article aims to analyze the determinants and mechanisms of network power and position of cities for developing urban network theory and improving urbanization policy in China. Data on headquarter and branch locations of the largest 500 listed companies in China in 2016 were subjected to ownership linkage model to approximate the urban network, resulting in a 294×294 valued urban network. Three conclusions are drawn: 1) Outdegree centrality presents a dual core spatial structure, betweeness has a primate distribution, and indegree centrality is a multi-core system. These three measurements of centrality have a strong spatial correlation, and the cities with high centrality values are densely concentrated in the traditional urban agglomerations. 2) City attributes such as market potential, key resources, location condition, and business environment have a significant positive impact on the centrality of urban network, and the sensitivity of different centrality characteristics to various types of influencing factors is different. 3) Selecting superior actors, preferential attachment, and geographical proximity are the dynamic mechanisms in the expansion of urban network space in China, which will further strengthen the power of cities with traditional advantages. The Chinese government must actively respond to the possibly increasing gap between cities under the network environment and promote the cooperation between cities on a larger spatial scale.