The heterogeneity can greatly influence the green innovative development of industrial enterprises. Based on the stochastic frontier analysis model of heterogeneity, this paper measures the green innovative efficiency of industrial enterprises in China for the time period of 2008 to 2017, and concludes the following results: (a) in the production function estimation, the R&D expenditure of industrial enterprises is the main positive factor in influencing green innovation, while the energy consumption the main negative factor. The effects of R&D talents and environment quality are not obvious at both stages. (b) The overall green innovation efficiency of industrial enterprises is only 0.2981 at R&D stage with an efficiency loss of 0.7019, and the residual efficiency of green innovation is 0.9966 with persistent efficiency as 0.2991. The overall green innovation efficiency of industrial enterprises is only 0.3930 at new product sales stage with an efficiency loss of 0.607, and the residual efficiency of green innovation is 0.8196 with persistent efficiency as 0.4783. (c) In the sample period, there appears to be an apparent decreasing of green innovative efficiency level from R&D stage to new product sales stage. Besides, the distribution of both overall efficiency and persistent efficiency tend to disperse, and there are great differences among years which are expanded at different stages. (d) Certain "club convergence" exists in overall efficiency and persistent efficiency of green innovation. The structural problem at R&D stage is the main factor in influencing the green innovation overall efficiency. The residual factors such as time effect at new product sales stage affect new product transformation. In order to increase green innovation efficiency, considering development level of selves, regions can establish a "club" to set up an efficient and sharable patent transfer platform, and reduce new product transformation loss.
Keywords: Club convergence; Green innovation efficiency,; Industrial enterprises,; Stochastic frontier analysis of heterogeneity,.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.