Search

In recent years, there is a widespread belief among researchers and academicians that Bitcoin usage is imposing an additional burden on the environment inducing climate change. Although several studies have focussed on issues related to the energy consumption of the basic cryptocurrencies, an open question remains regarding the environmental depiction of Bitcoin. By resorting to Bayesian analysis and quantile cointegrated vector autoregression (CQVAR), this study seeks to disentangle the driving forces that shape the carbon footprint of Bitcoin. The sample used in the empirical analysis consists of a daily panel dataset covering 50 countries over the period 2016–2018. The empirical findings corroborate a causal effect between the use of Bitcoin and its underlying carbon dioxide emissions generated by the increasing energy load. The CQVAR is associated with positive marginal posterior means for most of the covariates of the model across all the estimated quantiles. In contrast, there is a negative and statistically significant relationship between Bitcoin miner's revenue and carbon emissions, uncovering a multimodal distribution pattern of the marginal posterior densities which is stronger at higher than in lower quantiles. This finding suggests that the lower (higher) miner's Bitcoin revenues, the more abrupt (gradual) the effect on environmental degradation. Therefore, a sustainable energy strategy focussing on the penetration of renewable energy sources along with the use of energy‐efficient mining hardware will alleviate the carbon footprint of Bitcoin. [ABSTRACT FROM AUTHOR]

The scope of this study is to unravel possible convergence clubs regarding the occupancy rates of the hotel industry in the US states in the aftermath of the first COVID-19 pandemic wave. For this reason, the underlying paper resorts to the application of the generic convergence algorithm developed in Phillips and Sul (2007) for a sample of 50 US states over the daily period ranging from 01.12.2019 to 26.07.2020. The empirical analysis supports the identification of two primary convergence clubs consisting of an equally distributed number of regions (states). However, the two clubs can be merged into one after the implementation of the Phillips and Sul (2009) methodology, revealing that the first pandemic wave has eliminated any distinct (economic) characteristic between the different US states. JEL codes: L10, L80, R10. [ABSTRACT FROM AUTHOR]

Although financial development is recognized as a vital determinant of countries' economic growth path, many empirical studies fail to further isolate the role of socioeconomic indicators on accelerating growth. This study attempts to fill this gap by examining the statistical significance and the behaviour of several socioeconomic indicators on economic growth. We apply parametric (System GMM estimators) and semi‐parametric techniques along the lines of Baltagi and Li. Annals of Economics and Finance, 2002, 3, 103–116 on a panel data set of 19 Eurozone countries over the period 1995–2017. We test for non‐linear effects on economic growth for three banking indicators (domestic credit, non‐performing loans, and banking capitalization). In contrast to the related literature, our findings provide sufficient evidence of non‐linear relationships between several aspects of financial development and economic growth. Our results survive robustness checks implying significant policy implications for policymakers and regulators in their effort of balancing banking development with a resurgence in economic growth within the EU periphery. [ABSTRACT FROM AUTHOR]