The Evaluation, Dynamic Evolutionary Characteristics and Influencing Factors of Green Innovation Efficiency in China

Abstract

Green innovation efficiency (GIE), which reflects the relationship between inputs and outputs that consider environmental impacts, is critical to China’s realization of green and sustainable development. In view of the differences in economic development and resource endowment across provinces, it is necessary to measure and evaluate the GIE in a rational manner. In this paper, we study the evaluation, dynamic evolutionary characteristics, and influencing factors of GIE for 30 provinces in China over the period 2011-2022, using the super-SBM undesirable model, the spatial Markov chain model, and the geographically and temporally weighted regression model, respectively. Results show that the eastern region has a significantly higher GIE than the other regions, followed by the central region. Also, the state of provincial GIE in China is affected by the level of neighboring regions. GDP per capita, the marketization index, and industrial structure promoted GIE, while R&D expenditure, import and export trade, and the digital financial inclusion index showed negative results. There is also spatiotemporal heterogeneity in the effects of individual factors on GIE. Thus, a one-size-fits-all policy for GIE is highly unsuitable for the realities of individual provinces in China. We propose a number of targeted and differentiated policy recommendations that can be implemented.