• Energy Research
• 11. Sustainability close
• UZ close
• COVID-19 close

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.

Abstract The key factor for sustainable development is the integration of sensitive methods for early detection of environmental risk factors for nutritional health with efficient primary prevention against contamination of the food chains as well as deficiency of biologically active trace elements in the human diet. Proper algorithms of laser photostimulation are recommended both for more efficient biological treatment of wastewater and also for a proper amount of essential elements in cultivated plants supplemented by other modern biotechnology for multiplication e.g. vegetables. Laser biotechnology is also recommended for better reclamation followed by enhancement of biomass and bioenergy production in different regions of the world as a contribution to sustainable development and the creation of many green jobs. This eco-innovation supplemented with the complementary biotechnologies e.g. reclamation of semiarid areas, modern apiculture integrated with the cultivation of medical plants, cultivation of algae, aquaculture, energy plantations, reforestation and protection of biodiversity could contribute to better adaptation to climate change and situation connected with COVID-19 pandemic and risk of worsening human health, malnutrition, and hunger.

The aim of the present research is to assess the impact of factors such as welfare, infrastructure, security, and the environment on inbound tourism as well as to develop its forecast. Six proxy indicators of the above-mentioned factors were selected as variables, namely, welfare (real GDP per capita, life expectancy, consumer price index), infrastructure (passenger transportation volume), security (total recorded crimes), and the environment (CO2 emissions). We used a time series-univariate ARIMA model to forecast the inbound tourism in the Republic of Uzbekistan, and applied the ARDL model to assess the impact of lagged real GDP per capita on inbound tourism in both the short and long terms. The results of our research show that security and welfare significantly affect the inflow of foreign tourists in the country, along with the impact of the COVID-19 pandemic crisis, the effects of which are expected to persist beyond 2026.

Food deserts (FD) have attracted attention after the post-COVID-19 pandemic, primarily due to adverse health and other implications of living in areas designated as food deserts. Most studies have focused on various aspects of the impact of food deserts, including the nutritional and health risks of living in FDs. Spatial integration and analysis of the GIS data in food provide a powerful way to expose the issues of creating deserts and how they change over space and time. This study aims to investigate the socioeconomic factors influencing food deserts using geospatial analyses. Guilford, Bladen, and Rutherford Counties in North Carolina were selected as case studies due to their higher percentage of the population with limited healthy food access. This study used open-source data, such as the USDA’s Crop Land Layer (CDL) land cover maps, census data, and the Food Access Research Atlas. This research provides a geostatistical analysis of FDs based on income/expenditure, population, vehicle, and food aid. The study results generally showed that geospatial technologies are vital for investigating FDs. The results will assist policy makers and other responsible agencies in formulating appropriate intervention policies tailored to meet the demands of these counties.