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This paper uses panel data on renewable energy in China. Thus, the current analysis fills the gaps by examining the relationship between green technology innovation and renewable energy and CO 2 emissions from 1990 to 2018. The current study examines all concerns connected to panel data analysis with advanced panel estimators, such as cross-sectional dependence, stationarity, variation in slope parameters, and structural break. Our findings show that test results reveal that green technology innovation and renewable energy have a negative and considerable influence on CO2 emissions in the long run. Moreover, the short-run relationship of green technology innovation, on the other hand, is not significant—as evidenced by the findings of robustness tests such as AMG and CCEMG. Other empirical results revealed that GTI, REN and NEN significantly reduced local CO 2 emission, while POP and PI had no obvious reduction effects on CO 2 emissions. Different GTIs had the same spatial "symbiotic effects" on CO 2 emission reduction in the short term, showing positive spatial spillover reduction effects. Lastly, we conclude that the use of green technology innovation has positive externalities. Some policies to assist green innovation technologies and renewable energy resources have been suggested for the Chinese government to enact. • This paper uses panel data on renewable energy in China. • The current study examines all concerns connected to panel data analysis with advanced panel estimators. • The short-run relationship of green technology innovation is not significant. • We conclude that the use of green technology innovation has positive externalities. [ABSTRACT FROM AUTHOR]