RESUMO
Our paper is among the first to measure the potential effects of the COVID-19 pandemic on the tourism industry. Using panel structural vector auto-regression (PSVAR) (Pedroni, 2013) on data from 1995 to 2019 in 185 countries and system dynamic modeling (real-time data parameters connected to COVID-19), we estimate the impact of the pandemic crisis on the tourism industry worldwide. Past pandemic crises operated mostly through idiosyncratic shocks' channels, exposing domestic tourism sectors to large adverse shocks. Once domestic shocks perished (zero infection cases), inbound arrivals revived immediately. The COVID-19 pandemic, however, is different; and recovery of the tourism industry worldwide will take more time than the average expected recovery period of 10 months. Private and public policy support must be coordinated to assure capacity building and operational sustainability of the travel tourism sector during 2020-2021. COVID-19 proves that pandemic outbreaks have a much larger destructive impact on the travel and tourism industry than previous studies indicate. Tourism managers must carefully assess the effects of epidemics on business and develop new risk management methods to deal with the crisis. Furthermore, during 2020-2021, private and public policy support must be coordinated to sustain pre-COVID-19 operational levels of the tourism and travel sector.
RESUMO
The study of the link between production, measured in gross domestic product and CO2 emissions, is a topic under intense research. Carbon emissions are moving together with economic shocks (high synchronicity), particularly at troughs and peaks of a business cycle. This research investigates the influence of economic shocks to carbon emissions. Previous studies do not provide a direct empirical evidence on the impact of economic shocks to carbon emissions that are available. We employ structural vector autoregression to explore the impact of economic shocks on carbon emissions in 20 advanced economies from 1870 to 2016. Our empirical results prove a strong, statistically significant connection between emissions and output with a coefficient of elasticity > 1. We identify a strong empirical link using panel structural vector autoregression between carbon emissions and real GDP growth per capita. Up to 40% of the fluctuations in CO2 emissions is explained by combined economic factors (output, population, oil prices, stock exchange). The findings further indicate that carbon emission is determined by energy policy (energy intensity, carbon intensity, relative costs of renewable energy). Our findings contribute to energy policy management, energy, and business cycle research to inspire novel research on energy cycles.