This paper aims to examine the symmetric and asymmetric conditional impacts of policy uncertainty on CO2 emissions within Gulf Cooperation Council (GCC) economies.
The study uses second-generation techniques, specifically designed to control cross-sectional dependence, heterogeneity and unobserved common factors. Additionally, bootstrap quantile regression is used to identify the environmental impacts of policy uncertainty across different levels of environmental deterioration (low, moderate and high). The method of moments quantile regression (MMQR) and Bayesian quantile regression are implemented to confirm the reliability of the findings. Finally, a Leave-One-Country-Out analysis is conducted to address potential size-related bias.
The empirical investigation reveals heterogeneous linkages between policy uncertainty and environmental quality. The pooled mean group estimator reveals that policy uncertainty lowers emissions. The bootstrap quantile regression shows that in nations with high initial CO2 emissions, a 1% change in uncertainty results in a 0.14% to 0.24% fall in emissions. The asymmetric analysis further indicates that positive and negative policy uncertainty shocks are inversely related to CO2 emissions; however, the emissions response is stronger to declines in policy uncertainty than to increases. The MMQR and Bayesian quantile regression results strongly confirm these findings. Once Saudi Arabia is excluded from the sample, emissions in other countries become more sensitive to uncertainty at the upper quantiles, while the environmental repercussions of uncertainty at the lower and medium quantiles become statistically significant.
The findings provide policymakers with actionable guidance to design effective strategies to curb environmental degradation amid policy uncertainty in the GCC.
