Turbulence and Transformation: When Global Shocks Drive Major Emerging Economies' Climate Progress

This article investigates the complex relationships between multiple uncertainty dimensions and carbon dioxide emissions in India and China, the world's two largest emerging economy emitters responsible for approximately 45% of global CO2 emissions. Employing monthly data from January 2000 to December 2022, we utilize wavelet coherence analysis and the novel wavelet quantile regression method to study how economic policy uncertainty, climate policy uncertainty, trade policy uncertainty, energy‐related uncertainty, and geopolitical risk simultaneously influence emissions trajectories. The study results reveal heterogeneous and time‐varying relationships that challenge conventional linear assumptions about uncertainty–emissions dynamics. The analysis reveals that identical uncertainty types can yield opposing effects on emissions varying upon temporal scales and historical contexts. For instance, economic policy uncertainty in India exhibited strong positive correlations with emissions during 2000–2016; nevertheless, it inverted to negative associations from 2017 to 2022. The wavelet quantile regression results indicate that uncertainty effects are most obvious at extreme emission quantiles, suggesting heightened impacts during crisis periods or rapid structural transitions. Geopolitical events including the 2008 financial crisis, US–China trade conflict, and COVID‐19 pandemic created distinct uncertainty–emission patterns across both nations. These findings offer crucial insights for policymakers managing climate transitions amid global uncertainties, suggesting that well‐designed policy frameworks can leverage uncertainty to accelerate clean energy adoption while maintaining economic stability.