Green innovation marks the high-tech industries under green development background, which needs governmental supports. Disagreement exists in the research of the relation between governmental supports and green innovative efficient, and insufficient studies on regional differences and mechanism. This paper, based on China’s provincial panel data of high-tech industries, uses DEA model to measure their 2009 to 2020 green innovative efficiencies of China’s 28 provinces, autonomous regions and municipalities, and applies the Global Moran’s Index to check the spatial autocorrelation of the green innovative efficiency of high-tech industries. Mixed fixed effects spatial lag model is employed to analyze the impacts of governmental supports on high-tech’s green innovative efficiencies, and the robustness test is carried out using the method of variable replacement. The regression analysis on the impacts of government supports on green innovation efficiency of high-tech industries has been carried out in the eastern, central and western regions of China to assess internal mechanism of governmental supports. China’s average values of green innovative efficiency of high-tech industries has risen to 0.611 from 0.511 during 2009 to 2020, with eastern higher the national average, and central and western lower. According to the Moran index test, the green innovation efficiency of China’s high-tech industry shows strong positive spatial correlation under the spatial weight matrix from 2009 to 2020. Governmental supports show an outstanding inverted U-shaped relation with green innovative efficiency of high-tech industries, which is positively contributed by environmental regulations, economic performance and foreign direct investment. Impacts of governmental supports vary with eastern, central and western. Governmental supports cast a directive role on green innovative efficiency, displaying an obvious inverted U-shaped relation with enterprises’ research & development investments. This paper presents suggestions that governments make appropriate subsidy range according to high-tech’s actual situation, with supportive policies varying with regions, industries and eco-environments.

This paper, based on high-tech industrial panel data of 11 provinces along Yangtze River Economic Zone during 2007 to 2016, establishes indexes marking technical innovation efficiency from perspective of input/output, and uses ultra-efficiency DEA model and panel threshold model to study the influence of governmental allowance and financing concentration on technical innovation efficiency. Research indicates the average technical innovation efficiency of high-tech industry is 1.193, with outstanding variance among the up-, middle- and down-stream, high in both up- and down-stream and low in middle-stream. There is an obvious three threshold effect between governmental allowance and technical innovation efficiency. Influence intensity and direction of governmental allowance on innovation efficiency vary with financing threshold ranges.