In terms of today, one may argue, throughout observations from energy literature papers, that (i) one of the main contributors of the global warming is carbon dioxide emissions, (ii) the fossil fuel energy usage greatly contributes to the carbon dioxide emissions, and (iii) the simulations from energy models attract the attention of policy makers to renewable energy as alternative energy source to mitigate the carbon dioxide emissions. Although there appears to be intensive renewable energy works in the related literature regarding renewables' efficiency/impact on environmental quality, a researcher might still need to follow further studies to review the significance of renewables in the environment since (i) the existing seminal papers employ time series models and/or panel data models or some other statistical observation to detect the role of renewables in the environment and (ii) existing papers consider mostly aggregated renewable energy source rather than examining the major component(s) of aggregated renewables. This paper attempted to examine clearly the impact of biomass on carbon dioxide emissions in detail through time series and frequency analyses. Hence, the paper follows wavelet coherence analyses. The data covers the US monthly observations ranging from 1984:1 to 2015 for the variables of total energy carbon dioxide emissions, biomass energy consumption, coal consumption, petroleum consumption, and natural gas consumption. The paper thus, throughout wavelet coherence and wavelet partial coherence analyses, observes frequency properties as well as time series properties of relevant variables to reveal the possible significant influence of biomass usage on the emissions in the USA in both the short-term and the long-term cycles. The paper also reveals, finally, that the biomass consumption mitigates CO2 emissions in the long run cycles after the year 2005 in the USA.

This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.

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We investigate the dynamic relationship between global oil prices, the stock market, and oil and gas stock (FTSE-OG) returns in the UK through a structural vector autoregressive (VAR) framework during the COVID-19 pandemic. The structural VAR results suggest that the impact of structural shocks related to the global oil price on FTSE-OG index returns becomes less important and loses its explanatory power during the pandemic. However, stock market shocks increase their explanatory power in the variations of FTSE-OG index returns.

PurposeThis study aims to measure economic uncertainty in Turkey by a novel economic uncertainty index (EUI) employing principal component analysis (PCA). We assess the impact of Covid-19 pandemic in Turkey with our constructed uncertainty index.Design/methodology/approachIn order to obtain the EUI, this study employs a dimension reduction method of PCA using 14 macroeconomic indicators that spans from January 2011 to July 2020. The first principal component is picked as a proxy for the economic uncertainty in Turkey which explains 52% of total variation in entire sample. In the second part of our analysis, with our constructed EUI we conduct a structural vector autoregressions (SVAR) analysis simulating the Covid-19-induced uncertainty shock to the real economy.FindingsOur EUI sensitively detects important economic/political events in Turkey as well as Covid-19-induced uncertainty rising to extremely high levels during the outbreak. Our SVAR results imply a significant decline in economic activity and in the sub-indices as well. Namely, industrial production drops immediately by 8.2% and cumulative loss over 8 months will be 15% on average. The losses in the capital and intermediate goods are estimated to be 18 and 25% respectively. Forecast error variance decomposition results imply that uncertainty shocks preserve its explanatory power in the long run, and intermediate goods production is more vulnerable to uncertainty shocks than overall industrial production and capital goods production.Practical implicationsThe results indicate that monetary and fiscal policy should aim to decrease uncertainty during Covid-19. Moreover, since investment expenditures are affected severely during the outbreak, policymakers should impose investment subsidies.Originality/valueThis is the first study constructing a novel EUI which sensitively captures the critical economic/political events in Turkey. Moreover, we assess the impact of Covid-19-driven uncertainty on Turkish Economy with a SVAR model.