多尺度视角下中国新能源汽车产业创新空间格局及网络特征

创新活动与创新网络的多尺度空间模式是创新经济地理学关注的焦点。论文选择在知识分类上兼具隐性和显性知识特征的新能源汽车产业作为研究对象,利用来自国家知识产权局的相关专利数据,分析了2001—2015年中国新能源汽车产业独立创新活动和合作创新网络在“区域—省域—市域”的多尺度空间特征,并对其进行空间分类。结果显示：① 创新活动在城市层面最具解释力度,中国新能源汽车产业技术创新活动主要集中在东部和北部沿海地区,市域尺度上的创新活动存在较大差异;② 内部创新网络和外部创新网络在区域层面最具解释力度,创新网络集中在东部和北部沿海地区,市域尺度上的创新网络变异明显;③ 在不同尺度上,创新网络与创新活动之间存在不同程度的差异。地方创新环境与创新主体性质是造成上述格局差异的重要原因。仅停留在空间上的分析很难深入解释创新网络格局,应从更微观的创新主体层面探究创新结网的过程和机理。此外,创新活动和创新网络分别具有“多尺度结构性嵌套”和“多尺度能动性交换”特征,未来研究应对两者之间的差别给予更多关注。

Multilevel spatial patterns and network characteristics of China's new energy vehicle industrial technological innovation

Multilevel spatial models of innovation activities and innovation networks have been the focus of researchers. To provide more direct evidence for multilevel spatial model of innovation, we chose the new energy vehicle industry (NEV industry) as an example, because the knowledge in NEV industry is tacit and codified, which means it would be with a significant multilevel feature. This study used the patent data of 2004 to 2018 from the State Intellectual Property Office (SIPO) to analyze the multilevel spatial characteristics of China's NEV industry innovation from 2001 to 2015 because the patents in China would be published 18 to 24 months after the application and there is a time lag of almost 3 years between innovation input and output. We used the Theil coefficient to understand the multilevel structure, the coefficient of variation (CV) and Gini coefficient to clear the variation and difference of innovation at multiple levels, and the local Moran's I, Getis-Ord $G_{i}^{*}$, and natural breaks (Jenks) to map the spatial agglomerations and clustering. The results show that: 1) At the municipal level resides the strongest explanation of innovation activities and they are mainly agglomerated in the eastern coastal region (ECR) and northern coastal region (NCR), and the distribution of innovation activities are greatly different at the municipal level. 2) The regional level has the strongest explanatory power of internal and external innovation networks and they are mainly clustered in the ECR and NCR, and the distribution of innovation networks are quite different at the municipal level. 3) The distributions of innovation networks and innovation activities are significantly different at different spatial levels. Based on this reality, we point out that the formation mechanism of innovation activities and innovation networks are different. The spatial pattern of innovation activities is affected by regional innovation system, and the innovation networks are the result of actors' choices. We argue that it is difficult to explain the spatial characteristics of innovation networks only from the spatial perspective. Therefore, it is necessary to examine the formation process of innovation networks from a more micro level. Because the municipal level is the most important place for innovation activities and the regional level is the key area for innovation networks, as the Theil coefficient shows, we actually provide direct evidence for the multilevel structural nesting feature of innovation activities and the multilevel initiative interaction feature of innovation networks. It would be necessary for researchers to discuss the differences between them in the future.