Measuring the effects of green technology innovations and renewable energy investment for reducing carbon emissions in China

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 CO2 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 tech-nology 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 CO2 emission, while POP and PI had no obvious reduction effects on CO2 emissions. Different GTIs had the same spatial symbiotic effects on CO2 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.

Automated summary

This paper examines the relationship between green technology innovation and renewable energy with CO2 emissions in China using panel data analysis. The findings reveal that green technology innovation and renewable energy have a negative and substantial impact on CO2 emissions in the long run, but no significant impact in the short run. Additionally, different green technology innovations have similar spatial effects on CO2 emission reduction in the short term.