Research on the Structural Features and Influence Mechanism of the Low-Carbon Technology Cooperation Network Based on Temporal Exponential Random Graph Model

Abstract

China actively promotes cross-regional low-carbon technology cooperation to improve low-carbon technologies and remove technological barriers to sustainable development. In this process, a cross-regional low-carbon technology cooperation network (LCTCN) has been developed and evolved. To help China rationalize the allocation of innovation resources and promote the cross-regional exchange of low-carbon technologies, we measured the LCTCN using low-carbon technology co-patents from 2011 to 2020. We investigated changes in the network structure using social network analysis. In addition, we examined the endogenous structures and exogenous factors that influence the formation of cooperation relationships in the network using a time exponential random graph model (TERGM). We came to the following conclusions: (1) The LCTCN develops toward complexity, showing prominent characteristics of spatial imbalance, heterogeneity, and core-periphery. (2) Among the endogenous structural variables, the coefficient of geometrically weighted degree (Gwdegree) is significantly negative, suggesting that regions within LCTCN tend to form partnerships with already well-connected regions. On the other hand, a positive coefficient of geometrically weighted dyad shared partner statistic (GWDSP) suggests that regions tend to link in multiple ways to each other. (3) Among the exogenous variables, the coefficient of the digital economy is significantly positive. As a result, for every level of digital economy development in a region, the probability of establishing low-carbon technology cooperation between that region and other regions increases by 87.39%. (4) External openness and geographical proximity can also facilitate establishing partnerships. The formation of low-carbon partnerships in the network results from a combination of endogenous structures and exogenous variables.