Given the dire state of global warming, it is critical to investigate the elements that influence carbon emissions intensity and to precisely monitor progress in carbon emissions intensity growth in order to meet the aim of lowering CO2 emissions. This research explores the association among renewable energy and non-renewable energy consumption, the urban population, research and development expenditure, technological innovation, and carbon emissions intensity in China using annual time series data over the period 1990 to 2019. The Dynamic ARDL simulation technique was utilized to investigate the long-run and short-run correlations between renewable and non-renewable energy consumption and CEI. The results suggest that there is strong evidence of a long-run correlation between the variables. The findings indicate that in the long-run, renewable energy and non-renewable energy consumption, and research and development expenditure have a positive influence on CEI by 0.27%, 0.75%, and 0.21%, whereas the urban population has a negative influence by 2.31%, respectively. However, the urban population and technological innovation have positively affected the short-run CEI by 12.17% and 0.23%, respectively. Policies should focus on continuous investment in renewable energy sources, clean energy innovation, improving energy efficiency, forest restoration, and carbon neutrality initiatives to lessen the environmental extreme pressure associated with CO2 emissions.
Keywords: Carbon emissions intensity; China; DYNARDL; Non-renewable energy consumption; Renewable energy.
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