This study empirically investigates the effect of meat consumption on greenhouse gas emissions (carbon dioxide, methane, and nitrous oxide) in the USA. The impact of meat consumption on greenhouse gas emissions is examined by controlling for economic growth and energy consumption. The empirical analysis finds that all these variables are cointegrated for the long run. Moreover, meat consumption aggravates greenhouse gas emissions. Specifically, meat consumption (except for beef) has a U-shaped relationship with carbon emissions and an inverted U-shaped relationship with methane and nitrous oxide emissions. The causality analysis indicates a unidirectional causality running from meat consumption to greenhouse gas emissions. These empirical findings indicate that the US livestock sector has the potential to become more environmentally friendly with careful policy formulation and implementation.
This paper makes an innovative contribution to the extant literature by analysing the determinants of economic stimulus packages implemented by governments in response to the COVID-19 pandemic. In particular, we explore whether stock market declines observed in many countries can predict the size of COVID-19 stimulus packages. Moreover, we explore whether a country's level of income can augment the underlying relationship between stock market declines and stimulus packages. The findings reveal that a larger stock market decline results in a larger stimulus package; however, this effect is only observed in countries that have an income level greater than the mean and/or median per capita gross domestic product (GDP). Moreover, our results show that monetary policy is more responsive to a stock market decline than fiscal policy. Thus, our results underscore the importance of international donor agencies such as the World Bank and International Monetary Fund (IMF) in supporting less affluent countries in coping with the adverse impacts of the COVID-19 pandemic on their economies.
This paper empirically investigates the effect of carbon emissions on sovereign risk? To answer this question, we use fixed effects model by using annual data from G7 advanced economies, which includes Canada, France, Germany, Italy, Japan, UK and USA, for the period from 1996 to 2014. We employ a novel extreme value theory to measure sovereign risk. The results indicate that climate change (carbon emissions) are likely to increase sovereign risk significantly. We also expand our analysis to some specific sectors, as some of the sectors emit more carbon than others. Specifically, we take top three polluting sectors namely: transportation, electricity and industry and show that they are more likely to increase the sovereign risk. Our results are robust to change in risk measures, estimation in differences and dynamic version of econometric models. Therefore, we have robust consideration that the carbon emissions significantly explain the sovereign risk.
This paper examines the high frequency multiscale relationships and nonlinear multiscale causality between Bitcoin, Ethereum, Monero, Dash, Ripple, and Litecoin. We apply nonlinear Granger causality and rolling window wavelet correlation (RWCC) to 15 min-data. Empirical RWCC results indicate mostly positive co-movements and long-term memory between the cryptocurrencies, especially between Bitcoin, Ethereum, and Monero. The nonlinear Granger causality tests reveal dual causation between most of the cryptocurrency pairs. We advance evidence to improve portfolio risk assessment, and hedging strategies.