中国新能源汽车产业创新网络特征及其多维邻近性成因

随着知识经济的发展和创新驱动发展战略的实施,创新网络及其多维邻近性机制已成为政府和学者共同关注的焦点.利用Ucinet软件,从区域、主体性质等维度,刻画了2009-2014年产业快速发展时期的中国新能源汽车专利创新网络特征.运用PLS-SEM模型,探究全国及不同区域创新网络的多维邻近性影响过程及原因.研究发现:①与中国...

Characteristics and proximities mechanism of China's new energy vehicle industry innovation network

In the context of the knowledge-based economy and innovation-driven development strategy, innovation networks and their multidimensional proximity mechanisms have attracted increasing attention from governments and economic geographers. However, knowledge about their regional differences remains limited. Using Ucinet software, the authors describe the technological innovation network characteristics of the new energy vehicle (NEV) industry in China from 2009 to 2014, which corresponds to the period of rapid development of vehicle ownership. The authors use partial least squares structure equation modelling to explore the impacts of multidimensional proximities from national and regional perspectives. The study found that: (1) Unlike innovation, activities are primarily concentrated in metropolitan areas and three major urban agglomerations. The innovation networks of the NEV industry in China are primarily concentrated in the Beijing-Tianjin-Hebei and Yangtze River Delta regions; the innovation networks in the Pearl River Delta region are significantly weaker. Additionally, regions with poor innovation could achieve more innovation cooperatively. (2) For the network characteristics, the national innovation network presents a “core-edge” structure, and the State Grid is the most important innovation actor of the network. In the Beijing-Tianjin-Hebei region, large state-owned enterprise groups control the network which has a prominent “core-edge” structure, the most active collaborative innovation, and a high degree of network extroversion. Regarding the innovation network in the Yangtze River Delta, the types of actors in the region are diversified, the “core-edge” structure is prominent, the intensity of cooperative innovation is high, and the degree of network extroversion is relatively high. In the Pearl River Delta, private enterprises are the principal actors in the network, the regional network structure is loose, cooperative innovation is limited, and the network is highly extroverted. (3) For regional proximity mechanisms in the Beijing-Tianjin-Hebei region, the Grid Group strongly promoted the formation of cross-regional networks. In the Yangtze River Delta, the State Grid and large regional enterprises have influenced the formation of networks, and abundant knowledge sources have promoted cooperation. In the Pearl River Delta, the impact of geographical proximity on the cooperation and innovation in private enterprises is small, and knowledge demand has promoted cross-regional cooperation. Based on these results, we present some policy recommendations in industrial and regional dimensions.