Unlocking the potential of foreign direct investment to reduce energy intensity.

This study examines the potential of foreign direct investment (FDI) to reduce energy intensity in developing countries between 1996 and 2019. Using a generalized method of moments (GMM) estimator, we investigated the linear and non-linear impact of FDI on energy intensity through the interaction effect of FDI and technological progress (TP). The results reveal that FDI has a positive and significant direct effect on energy intensity, whereas the energy-saving effect is evident through energy-efficient technology transfers. The strength of this effect depends on the level of technological progress in developing countries. The outcomes of the Hausman-Taylor and dynamic panel data estimations corroborated these research findings, and disaggregated data analysis by income groups also provided similar results, signifying the validity of the results. Based on the research findings, policy recommendations are formulated to improve the ability of FDI to reduce energy intensity in developing countries.

Testing oil price volatility during Covid-19: Global economic impact.

This paper is occasioned by the current events in the crude oil markets throughout the Covid pandemic time. The study analyzes the evolving nature of crude oil cost unpredictability caused by the variations that influence the crude sector throughout the current contagion. Every day's dataset is within the first month of 2020 and December 30, 2021 were measured by applying VAR and GARCH models. The results corroborate that the current contagion has adverse effects on the crude sector, primarily in two ways. It resulted in the headwinds for demand and cut international demand for crude oil, increasing uncertainty for major advanced and developing nations. Next, it resulted in output headwinds as the pandemic caused hydrocarbons conflicts among the leading crude supplying countries. The two headwinds seem to have caused the more than necessary crude unpredictability. Moreover, it was found that the United States output, total requirements, and crude-leaning demand shocks adversely affect the supply unpredictability of the United States and the extractive sectors. The findings depict that crude price instability responded significantly to the contagion caused by crude headwinds. Specifically, the study recorded the effect of uncertainty because of these headwinds beyond financiers' concerns about crude price instability. This study indicates that spillovers do not have meaningful forecast data, igniting critical debates concerning the relevance of the spillover indicator for predicting at minimal sampling occurrence.

Asymmetric impact of pandemics-related uncertainty on CO2 emissions: evidence from top-10 polluted countries.

The recent COVD-19 pandemic has been a major shock, affecting various macroeconomic indicators, including the environmental quality. The question of how the pandemics-related uncertainty will affect the environment is of paramount importance. The study analyzes the asymmetric impact of pandemic uncertainty on CO2 emissions in top-10 polluted economies (China, USA, India, Russia, Germany, Japan, Iran, South Korea, Indonesia, and Saudi Arabia). Taking panel data from 1996 to 2018, a unique technique, 'Quantile-on-Quantile (QQ)', is employed. CO2 emissions are used as an indicator of environmental quality. The outcomes define how the quantiles of pandemic uncertainty impact the quantiles of carbon emissions asymmetrically by providing an effective paradigm for comprehending the overall dependence framework. The outcomes reveal that pandemic uncertainty promotes environmental quality by lowering CO2 emissions in our sample countries at various quantiles. However, Japan shows mixed findings. The effect of PUN on CO2 is substantially larger in India, Germany, and South Korea and lower in Russia and Saudi Arabia. Furthermore, the magnitude of asymmetry in the pandemic uncertainty-CO2 emissions association differs by economy, emphasizing that government must pay particular caution and prudence when adopting pandemics-related uncertainty and environmental quality policies.

The asymmetric nexus between air pollution and COVID-19: Evidence from a non-linear panel autoregressive distributed lag model.

The emergence of a new coronavirus (COVID-19) has become a major global concern that has damaged human health and disturbing environmental quality. Some researchers have identified a positive relationship between air pollution (fine particulate matter PM2.5) and COVID-19. Nonetheless, no inclusive investigation has comprehensively examined this relationship for a tropical climate such as India. This study aims to address this knowledge gap by investigating the nexus between air pollution and COVID-19 in the ten most affected Indian states using daily observations from 9th March to September 20, 2020. The study has used the newly developed Hidden Panel Cointegration test and Nonlinear Panel Autoregressive Distributed Lag (NPARDL) model for asymmetric analysis. Empirical results illustrate an asymmetric relationship between PM2.5 and COVID-19 cases. More precisely, a 1% change in the positive shocks of PM2.5 increases the COVID-19 cases by 0.439%. Besides, the estimates of individual states expose the heterogeneous effects of PM2.5 on COVID-19. The asymmetric causality test of Hatemi-J's (2011) also suggests that the positive shocks on PM2.5 Granger-cause positive shocks on COVID19 cases. Research findings indicate that air pollution is the root cause of this outbreak; thus, the government should recognize this channel and implement robust policy guidelines to control the spread of environmental pollution.

Purely data-driven exploration of COVID-19 pandemic after three months of the outbreak

It has been three months since the novel coronavirus (COVID-19) pandemic outbreak. Many research studies were carried to understand its epidemiological characteristics in the early phase of the disease outbreak. The current study is yet another contribution to better understand the disease properties by parameter estimation of mathematical SIR epidemic modeling. The authors use Johns Hopkins Universitys dataset to estimate the basic reproduction number of COVID-19 for representative countries (Japan, Germany, Italy, France, and Netherlands) selected using cluster analysis. As a by-product, the authors estimate transmission, recovery, and death rates for each selected country and carry statistical tests to see if there are any significant differences.