Spatial correlation analysis of low-carbon innovation: A case study of manufacturing patents in China

Abstract

Research on the influence of spatial correlation network structures on low-carbon innovation can inform policymaking in the construction of cross-regional collaboration mechanisms for low-carbon innovation. Based on the data of provincial manufacturing patent applications in China from 2004 to 2017, the primary objective of the proposed approach of social network analysis (SNA) is to empirically investigate the structural characteristics of the spatial correlation network and influencing factors. The process for the method is as follows. (1) The spatial metric (specifically, the European distance) is used to measure the similarity between provinces, and SNA is used to construct the spatial correlation network. The minimum spanning tree (MST) method is applied to verify the effectiveness of the construction method, and (2) the characteristics of the spatial correlation network and influencing factors are analyzed by the quadratic assignment procedure (QAP). The results show the following: (1) China’s low-carbon innovation exhibits a network structure of spatial correlation, and the spatial correlation network density among thirty provinces in China is as high as 0.3483; (2) spatial spillover effects decrease gradually from China’s eastern provinces to the western provinces, and the western and central undeveloped manufacturing provinces mainly receive spillovers from the eastern and central manufacturing provinces; (3) the significant value of the degree of openness is 0.001, indicating that it is the primary influencing factor of the spatial correlation network of China’s low-carbon innovation (the correlation degree of the obtained spatial network significantly increases as the differences in the degree of openness decrease), and the difference of foreign capital utilization and the difference in the degree of openness show a positive correlation. The major focus/achievement described in the paper is that China’s manufacturing exhibit significant characteristics of regional agglomeration and central developed manufacturing provinces, and several eastern provinces exert the primary spatial spillover effect, showing disequilibrium between the eastern and western regions overall. The degree of openness is the main factor that affects the correlation network. This study provides an opportunity for the construction of a cross-regional collaborative mechanism and green innovative development of the spatial correlation network.