Modelling the impact of uncertainty on sectoral GHG emissions in Saudi Arabia using the causality-in-quantiles and quantile-on-quantile approaches

The purpose of this research is to investigate the implications of policy uncertainty on sectoral GHG emissions in Saudi Arabia. The research performs a battery of advanced quantile-based methodologies, namely the quantile unit root test (QAR), causality-in-quantiles (CiQ) test, and the Quantile-on-Quantile regression (QQR) as the reference model, while the quantile regression (QR) is carried out to check the robustness of the findings. The results of the CiQ test indicate a statistically significant causal relationship at different quantiles between uncertainty and emissions, with a particular emphasis on the industrial and transportation sectors. On the other hand, the QQR suggests that the effects of uncertainty on emissions exhibit asymmetry and are contingent upon both the quantile order and the economic sector. Furthermore, uncertainty increases GHG emissions in all sectors, with the exception of agriculture. The results particularly demonstrate that when uncertainty is low, it can increase environmental deterioration by raising GHG emissions, especially in sectors with higher levels of pollution. Finally, the environmental repercussions stemming from uncertainty are more conspicuous in the industrial sector. The results from the quantile regression analysis provide empirical support for the robustness of the QQR. The research findings offer significant policy recommendations for policymakers.