Navigating carbon emissions in G-7 economies: a quantile regression analysis of environmental-economic interplay.

This study employs panel data from 1990 to 2020 for the G-7 countries (Canada, France, Germany, Italy, Japan, the UK, and the USA) and employs the examination of heterogeneous slope coefficients and cross-sectional dependence tests as preliminary steps before conducting cointegration analysis and second-generation unit-root tests. This study employs the method of movement quantile regression (MMQR) to analyze long-run and short-run relationships. The findings from the MMQR model indicate that economic growth and imports have a negative impact on consumption-based CO (CCO) emissions, which worsens at higher quantiles. On the other hand, exports, energy efficiency, and renewable energy output (REO) have a positive effect on mitigating CCO emissions, with this effect becoming more pronounced at higher quantiles. Furthermore, the robustness of the results was confirmed through rigorous checks using quantile regression with optimized Markov Chain Monte Carlo techniques, which is a reliable non-parametric approach. These checks consistently demonstrated a significant impact on CCO emissions, thus validating the findings obtained from MMQR. Based on the outcomes, this study recommends that each G-7 nations should make efforts to regulate their CCO emissions by adopting measures that foster ecological equilibrium. Moreover, fostering export-driven sectors, exploring innovative strategies for REO, and improving energy efficiency are crucial measures for effectively tackling CCO emissions within the G-7 countries. The study highlights that renewable energy output (REO) and energy efficiency effectively mitigate CCO emissions at higher quantiles, suggesting the importance of policy measures supporting their development. Additionally, policies targeting import reduction, export promotion, and carbon pricing mechanisms emerge as strategies to curb emissions and foster sustainable development.