Green innovation, resource price and carbon emissions during the COVID-19 times: New findings from wavelet local multiple correlation analysis

This paper investigates how oil price, COVID-19, and global energy innovation can affect carbon emissions under time- and frequency-varying perspectives. We contribute to the literature by being the first research to document the relationship between these variables in the short and long run (dynamically) at different frequencies in a multivariate context, thus providing a more detailed picture of the forces driving CO2 emissions. For this purpose, we use a novel methodology, i.e., the wavelet local multiple correlation (WLMC) recently developed by Polanco-Martínez et al. (2020). The results provide fresh evidence of long-run asymmetric dynamic correlations, highlighting how the oil price plays a key role in the dynamics of CO2 emissions. Moreover, we find that, during the long period, there is a strong negative co-movement between CO2 and the global energy innovation index, i.e., more investment in clean energy induces less emission. Supported by our findings, this research suggests crucial policy implications and insights for the governments worldwide in their efforts to revive their economies amidst the pandemic and environmental uncertainties.