Spatial Correlation Network Structure and Influencing Factors of Two-Stage Green Innovation Efficiency: Evidence from China

Abstract

With the continuous progress in global sustainable development, green innovation has become the primary driving force for the development of all countries and regions. China has implemented the strategy of constructing a cross-regional green innovation network. As the spatial correlation network structure of green innovation efficiency is complicated, it is necessary to study the change rules of the network structure to coordinate regional green and innovative development. In this paper, the Super-NSBM model is used to calculate the values of two-stage green innovation efficiency of China’s industrial enterprises from 2006 to 2019. Social network analysis is used to explore the rule of changes and causes of the spatial correlation network of two-stage green innovation efficiency. Our findings are as follows. Green innovation efficiency in the two stages presents the relationship of a non-adjacent complex network, and the network of green innovation and R&D efficiency is closely interconnected. Strong hierarchical correlation breaks down when searching for the best spatial configuration relationship. The transformation efficiency of the networked cooperation of green innovation achievements is stable. In the spatial correlation of green innovation and R&D efficiency, Guangdong, Shandong, Beijing, Jiangsu and Zhejiang are at the center of the network. In the spatial correlation of transformation efficiency of green innovation achievements, Shandong, Jiangsu, Guangdong, Henan and Hubei are in the center. The northern coastal areas fall within the scope of green innovation and R&D spillover has and have a close cooperation with the green innovation spillover plate in the southern coastal areas, making green innovation achievements spill over to the Chengdu-Chongqing region and northern region. The cooperation and connection of green innovation activities conform to the rule of geographical proximity. Environmental regulation and marketization are characterized by “hierarchy”, but the economic level is “non-hierarchical”. The government can implement relevant green innovation policies according to local characteristics. Our findings are of great significance to narrow regional green innovation gaps.