摘要：It is widely accepted that CO<sub>2</sub> emissions are the primary cause of climate change and environmental destruction. China, the world’s biggest carbon emitter, is the subject of this research. Utilizing the wavelet tools (wavelet correlation, wavelet coherence, multiple wavelet coherence, and partial wavelet coherence), the present study intends to capture the time-frequency dependence between CO<sub>2</sub> emissions and renewable energy, economic growth, trade openness, and energy usage in China between 1965 and 2019. The advantage of the wavelet tools is that they can differentiate between short, medium, and long-run dynamics over the period of study. Furthermore, the study utilized the gradual shift causality test to capture the causal interconnection between CO<sub>2</sub> emissions and the regressors. The findings from Bayer and Hanck showed a long-run relationship among the variables of interest. Furthermore, the findings from the wavelet coherence test revealed a positive relationship between CO<sub>2</sub> emissions and economic growth and energy usage at all frequencies. Although there is a weak negative relationship between renewable energy and CO<sub>2</sub> emissions in the short run, there is no significant co-movement between CO<sub>2</sub> emissions and trade openness. The outcomes of the partial and multiple wavelet coherence also give credence to the outcomes of the wavelet coherence test. Lastly, the gradual shift causality test revealed a one-way causality from energy usage and economic growth to CO<sub>2</sub> emissions. Based on the findings, suitable policy suggestions were proposed.
