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Regarding this, our study aims to scrutinize the impact of geopolitical oil price uncertainty on clean energy stocks by controlling the influences of economic policy uncertainty, gold, natural gas, and coal prices. For that purpose, the study utilized monthly data from July 2007 to September 2020 and employed a machine learning method, namely kernel-based regularized least squares approach. Empirical analysis reveals that geopolitical oil price uncertainty and coal prices have a nonlinear positive effect on clean energy stock prices. It is also found that the impact of global economic policy uncertainty, gold, and natural gas prices on clean energy stock prices is nonlinear and negative. The implication signifies that clean energy stock prices are hampered by economic policy uncertainty, and gold, and natural gas prices, thus hindering the development of clean energy sources. Similarly, for the robustness of the study, the quantile regression approach and findings reveal similar outcomes to that of the KRLS model. Based on these findings, policy implications that potentially aid renewable energy investments are put forward. 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