The Spatial–Temporal Evolution of China’s Automobile Manufacturing Cluster Network and Its Influencing Factors

The global cluster networks (GCN) theory has improved the theory concerning industrial clusters. This study attempts to conduct empirical research on the spatial and temporal evolution of China’s automobile industry using GCN. By using cooperative applications for patent data and buyer–supplier data between cluster firms, a 203 × 203 inter-cluster association network was constructed. The main findings are as follows. First, the scale and density of the networks increased from 2000 to 2012. R&D cooperation sub-networks became increasingly localized, forming communities composed of several local clusters. The number of community buyer–supplier sub-networks was relatively stable, while the linkages between communities gradually increased. Second, cluster networks existed at various spatial scales. The spatial scale of the R&D cooperation sub-networks changed from a macro to micro scale, and the buyer–supplier sub-networks expanded to a larger spatial scale. Third, the intra-industry division of labor was an important factor for cluster networks. Among them, vertical division within the industry promoted R&D cooperation sub-networks and buyer–supplier cooperation. The horizontal division only affected buyer–supplier cooperation. Buyer–supplier cooperation significantly affected R&D cooperation. The impact of local specialization on R&D cooperation was not robust.