Asymmetric impact of temperature on COVID-19 spread in India: Evidence from quantile-on-quantile regression approach.

The emergence of new coronavirus (SARS-CoV-2) has become a significant public health issue worldwide. Some researchers have identified a positive link between temperature and COVID-19 cases. However, no detailed research has highlighted the impact of temperature on COVID-19 spread in India. This study aims to fill this research gap by investigating the impact of temperature on COVID-19 spread in the five most affected Indian states. Quantile-on-Quantile regression (QQR) approach is employed to examine in what manner the quantiles of temperature influence the quantiles of COVID-19 cases. Empirical results confirm an asymmetric and heterogenous impact of temperature on COVID-19 spread across lower and higher quantiles of both variables. The results indicate a significant positive impact of temperature on COVID-19 spread in the three Indian states (Maharashtra, Andhra Pradesh, and Karnataka), predominantly in both low and high quantiles. Whereas, the other two states (Tamil Nadu and Uttar Pradesh) exhibit a mixed trend, as the lower quantiles in both states have a negative effect. However, this negative effect becomes weak at middle and higher quantiles. These research findings offer valuable policy recommendations.

Promoting economic and environmental resilience in the post-COVID-19 era through the city and regional on-road fuel sustainability development.

How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles' transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments' effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Assessing Public Willingness to Wear Face Masks during the COVID-19 Pandemic: Fresh Insights from the Theory of Planned Behavior.

Face masks are considered an effective intervention in controlling the spread of airborne viruses, as evidenced by the 2009's H1N1 swine flu and 2003's severe acute respiratory syndrome (SARS) outbreaks. However, research aiming to examine public willingness to wear (WTW) face masks in Pakistan are scarce. The current research aims to overcome this research void and contributes by expanding the theoretical mechanism of theory of planned behavior (TPB) to include three novel dimensions (risk perceptions of the pandemic, perceived benefits of face masks, and unavailability of face masks) to comprehensively analyze the factors that motivate people to, or inhibit people from, wearing face masks. The study is based on an inclusive questionnaire survey of a sample of 738 respondents in the provincial capitals of Pakistan, namely, Lahore, Peshawar, Karachi, Gilgit, and Quetta. Structural equation modeling (SEM) is used to analyze the proposed hypotheses. The results show that attitude, social norms, risk perceptions of the pandemic, and perceived benefits of face masks are the major influencing factors that positively affect public WTW face masks, whereas the cost of face masks and unavailability of face masks tend to have opposite effects. The results emphasize the need to enhance risk perceptions by publicizing the deadly effects of COVID-19 on the environment and society, ensure the availability of face masks at an affordable price, and make integrated and coherent efforts to highlight the benefits that face masks offer.

Energy consumption structural adjustment and carbon neutrality in the post-COVID-19 era.

Since the spread of COVID-19 pandemic all over the world, a significant recession has broken out with no precedent. China has brought up a new voluntary contribution target that achieving carbon neutrality by 2060. How to achieve climate change mitigation targets without heavily hindering economic development is of great importance in the future. In this study, a Markov chain model is employed to forecast primary energy consumption (PEC) structure and verify whether the carbon intensity target would be achieved under three scenarios with different economic growth rates, such as 6.1%, 4.2%, and 2.3%, respectively. A multi-sector dynamic stochastic general equilibrium (DSGE) model is employed to simulate and evaluate economic development, fossil and non-fossil energy consumption, and CO2 emissions under three scenarios using data calibration according to the Markov chain prediction result. The prediction results from the Markov chain show that energy structural adjustment can help us achieve the carbon intensity target of 2030 under both steady and mid-speed development scenarios. As long as the economic growth rate is higher than 4.2%, the carbon intensity target can be achieved mainly through energy consumption structural change, which provides a way to achieve the carbon neutrality target of 2060. The simulation results from the DSGE model show that energy structural adjustment can also smooth the volatility of the economic fluctuation when exogenous stochastic shocks happened.