Spatio-Temporal Evolution and Driving Mechanism of Green Innovation in China

Abstract

Sustainable development has become a global consensus, and green innovation is the key to promoting transition to sustainable development. The study on green innovation contributes to develop and implement green innovation policies. This paper investigates the spatio-temporal characteristics and driving mechanism of green innovation 2009–2019 in China from the perspective of economic geography based on a variety of methods such as GIS tools and Geodetector, in two dimensions of green innovation power (GIP) and green innovation growth ability (GIGA). The findings show that (1) The GIP and GIGA in China continue to increase, with obvious decreasing gradient characteristics from eastern to central and western China, extreme polarization, and obvious spatial aggregation, and the high-value regions show a change from coastal and riverine distribution to coastal distribution, with Shandong and Yangtze River Delta as the centers of high-value regions. (2) The power of the 18 driving factors on green innovation varies widely across time, and the 7 factors such as green area in urban completed area and investment in urban environmental infrastructure facilities are super interaction factors. Besides, the 5 variables of innovation input, foreign connection, economic environment, market environment and environmental regulation have different driving forces on green innovation, suggesting that the driving mechanism has changed in different periods. (3) Core factors of GIP were identified as R&D intramural expenditure and R&D personnel equivalent; important factors were identified as 5 factors such as R&D intramural expenditure in high-tech industry and FDI. Core factors of GIGA were identified as R&D intramural expenditure and added value of financial industry; important factors were identified as 4 factors such as R&D intramural expenditure in high-tech industry and GDP. (4) The 31 provinces in China were classified into 4 types of policy areas by BCG model, and proper policy suggestions were put forward. The research methods and conclusions of this paper can provide reference for green innovation policy optimization in China and other countries under similar conditions.

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