This study examines whether renewable energy adoption unconditionally contributes to economic outcomes in developing countries, or whether it can, under certain institutional conditions, impede economic progress. Analyzing panel data from 45 developing countries (2000–2020) using Driscoll–Kraay standard errors, we find a statistically significant negative association between an increasing renewable energy share and both GDP growth and GDP per capita. Crucially, institutional quality plays a pivotal moderating role. While stronger institutions, such as control of corruption and rule of law, mitigate negative impacts on per capita income and employment, they paradoxically appear to exacerbate the negative effect on overall GDP growth, suggesting a more transparent internalization of transition costs in better-governed environments. Furthermore, our findings demonstrate significant heterogeneity across income levels: low-income countries experience positive economic effects from renewable energy adoption, whereas lower-middle-income countries face more pronounced negative impacts. These results challenge the uniformly positive narrative of green growth, underscoring that the energy transition involves significant economic trade-offs critically dependent on a nation's institutional framework and developmental stage. Policymakers must adopt context-specific strategies to navigate these complexities effectively.

This study investigates the dynamic and asymmetric connectedness between four crude oil benchmarks (Brent, WTI, INE, Murban) and three climate risk indexes (Physical Risk Index, Transition Risk Index, and U.S. Climate Policy Uncertainty Index). Addressing a critical gap in the literature, which often relies on linear models and average connectedness, we employ the quantile-on-quantile connectedness method to capture non-linear, asymmetric, and state-dependent spillovers, particularly under extreme market conditions. Our analysis reveals that climate risk indexes are predominantly net receivers of shocks from oil markets, with connectedness intensifying sharply during periods of market stress, political conflict, or sudden climate events. The findings highlight that systemic risk is significantly elevated at extreme quantiles, demonstrating that linear models may substantially underestimate true systemic risk during critical junctures. Methodologically, this research demonstrates the efficacy of quantile-on-quantile connectedness in revealing tail-risk effects. Empirically, it provides the most comprehensive comparison to date of connectedness across diverse crude oil benchmarks and climate risk indexes. The results offer crucial insights for investors seeking resilient portfolios, and for policymakers and regulators in designing macro-prudential oversight frameworks that recognize the non-linear and state-dependent nature of climate-financial contagion, emphasizing the need for flexible policies and continuous monitoring.