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In order to cope with severe environmental crises such as pollution and climate, the Chinese government has put forward the strategic goal of "double carbon" and vigorously developed green emerging industries such as cultural industry. Based on the super efficiency model and the input oriented Malmquist index model, this paper comprehensively measures the innovation efficiency of cultural industry, and selects the panel data of the Yangtze River economic belt from 2015 to 2021 for empirical analysis. In addition, the spatial econometric model is introduced to explore the driving factors affecting the innovation efficiency of cultural industry from the perspective of time and space. The results show that: (1) the overall innovation efficiency of the cultural industry in the Yangtze River economic belt is high, but there is significant spatial heterogeneity. (2) The average Malmquist index of the innovation efficiency of the cultural industry in the Yangtze River economic belt is 1.123, which reflects the continuous improvement of the level of industrial innovation. Environmental factors such as sulfur dioxide emissions in waste gas, investment in industrial pollution control, and per capita GDP have a significant impact on the innovation efficiency of cultural industry in the Yangtze River economic belt. Finally, the paper puts forward policy suggestions to promote the high-quality development of cultural industry from the perspective of technology, policy and environment. [ABSTRACT FROM AUTHOR]

The new energy industry helps to meet the challenges of energy exhaustion and environmental pollution. Based on the panel data from 2015 to 2021, this paper uses the BCC model and Tobit model to construct the empirical measurement of technology innovation efficiency of listed new energy enterprises with three-stage DEA technology and uses the convergence model to test the efficiency difference. The results show that: (1) Using the traditional BCC model, the technical innovation level of new energy enterprises is insufficient, and the overall comprehensive efficiency and pure technical efficiency are 0.220 and 0.302 respectively, which need to be improved. (2) The three-stage DEA model was used to control environmental factors and statistical errors, and the overall efficiency of technological innovation of new energy enterprises increased. The comprehensive efficiency and scale efficiency increased to 0.541 and 0.896 respectively. (3) The technological innovation efficiency of new energy enterprises presents temporal and spatial heterogeneity. The coefficient of variation of technological innovation efficiency of new energy enterprises ranged from 0.084 to 1.000 with significant differences. The technological innovation efficiency of new energy enterprises in eastern, central and western China shows a U-shaped fluctuation. [ABSTRACT FROM AUTHOR]