This study examines the effect of institutional quality on renewable energy promotion in OECD economies. The study employs annual data from 1980 to 2014 on 18 OECD economies. The robust panel unit root tests show that all the considered variables have a similar order of integration, indicating that they are nonstationary at their levels but stationary at the first‐order differences. The panel cointegration test with structural breaks and cross‐section dependence confirms a long‐run equilibrium association between institutional quality, renewable energy consumption and control variables. The analysis of long‐run estimations displays that better institutional quality makes a unique and substantial contribution to promoting renewable energy consumption. Overall, the study findings offer important policy implications highlighting the importance of institutional quality for the growth of renewable energy and a sustainable world.

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

Considering the importance of political economy in implementing Disaster Risk Reduction (DRR), this research investigates the significance of political economy in the distribution of DRR funding in Bangladesh. The study analysed data from self-reported surveys from 133 members of the sub-district level disaster management committee and government officials working with DRR. Employing the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, we find that political economy factors explain 68% of the variance in funding allocations. We also show that four categories of political economy factors—power and authority, interest and incentives, institutions, and values and ideas—are significantly influential over the distribution of DRR funding across subdistricts of Bangladesh. Our findings offer important policy implications to reduce the potential risks surrounding political economy influences in fund allocation and advance climate finance literature.