Analysis of the spatial association network structure of China’s green innovation efficiency

Abstract

Improving the green innovation efficiency (GIE) is the main approach to high-quality development in China. Identifying the spatial distribution and influencing factors of GIE is conducive to sustainable and balanced regional development. In this research, the slack based measure-data envelopment analysis model (SBM-DEA) is used to measure the GIE of 30 provinces in China from 2013 to 2023. The gravity model and social network analysis methods are used to explore the spatial correlation of GIE in the provinces. The influencing factors of GIE are analyzed by the Tobit model. The results show that: (1) During the study period, the GIE of China’s provinces presented a high level, the spatial accumulation characteristics were obvious and the spatial correlation showed a complex network structure; (2) The association network is still in the primary stage, and the network strength and network association degree are low; (3) Provinces with strong innovation ability do not affect the green innovation of other provinces. In addition, these regions have obtained higher innovation linkage benefits from other provinces; (4) The amount of patent, education and government support have a significant positive impact on GIE, while total energy consumption and the secondary industry’s share of the gross domestic product have a significant negative impact on GIE. This study will reveal the characteristics of green innovation behavior of China from a new perspective, and provide a scientific basis for the formulation of green innovation behavior and related policies of China.