Modeling the impact of the COVID19 outbreak on environment, health sector and energy market – Armeanu – – Sustainable Development

2.1 Prior studies towards COVID-19 and environment

Even if the novel infectious illness critically perils human health and provoke economic downturn, Muhammad et al. (2020) deemed it a Blessing in Disguise due to its constructive effect on environment. Shehzad et al. (2020) noticed that the occurrence of COVID-19 has been considered a crucial evil due to alleviating air contamination. In this regard, Rume and Islam (2020) emphasized that the pandemic enhanced air quality in several cities around the world, reduced greenhouse gas emissions, diminished water contamination and noise, and relieved stress on tourist destinations. For instance, Zhang et al. (2021) claimed that the energy supply and utilization of commercial tourism cities displayed descending trends, which driven lowered carbon releases and improved the ecosystem. As such, there occurred a refurbishment of the environmental setting. The hydrosphere, covering seas, waterways, and oceans have prolonged been experiencing an acute contamination on account of quick sprawl, industrial development, and overutilization, but quarantine measures radically fallen the level of pollution (Facciol et al., 2021). Besides, the closure of industry sectors throughout the lockdown lessened the demand for fossil fuel energy, which in turn improved the atmosphere (Nundy et al., 2021). However, Yang et al. (2021) claimed that the new coronavirus has had both beneficial and harmful indirect effects on the environment. Thus, it was suggested that soil pollution has become more serious than in the past as a result of the elimination of recycling methods in many metropolitan areas and the restriction of sustainable waste management. In this regard, Jia et al. (2021) reasoned that the drop in worldwide carbon emissions are attributable to economic slump, but the pandemic and the variation of oil price have double effects on the sustainable economy. Table 1 summarizes prior literature on the effects of the COVID-19 pandemic on environment.

Kerimray et al. (2020) documented for Almaty, Kazakhstan, a decrease in the PM2.5 concentration by 21% during coronavirus lockdown compared to the matching period in 20182019, as well as a fall in the concentrations of CO and NO₂ by 49% and 35% related to the moment before the isolation, but an increase in O₃ by 15%. For the case of China, Wang and Su (2020) confirmed that COVID-19 diminished the CO₂ releases and the concentration of NO₂ in the air. Likewise, Bao and Zhang (2020) reinforced that the concentrations of pollutant discharges decreased in 44 cities in northern China. With regard to the global financial crisis of 20082009, Peters et al. (2012) highlighted a reduction in CO₂ emissions of 1.3 percent in 2008 and 7.6 percent in 2009 in developed countries, but a 3.4 percent increase in 2010. As well, World Meteorological Organization (2021) noticed that CO₂ emissions from fossil fuels (coal, oil, gas, and cement) soared at 36.64gigatonnes in 2019, before plummeting by 1.98gigatonnes (5.6 percent) in 2020 due to the COVID-19 disease outbreak. In the same vein, Le Quere et al. (2020) reported that by early April 2020, daily global CO₂ releases had reduced by 17 percent relative to the mean 2019 values, while individual country discharges had decreased by 26 percent on average. However, according to Friedlingstein et al. (2021), in 2021, fossil CO₂ releases are expected to rise by 4.9 percent, bringing them back to the similar level as in 2019.

Further, Le Quere et al. (2020) claimed that the economic slump linked with COVID-19 is different, since it is more severely anchored in restricted human conduct. Hence, apart from positive effects on the environment, such as lessened concentrations of NO₂ and PM2.5, clean beaches, and drop of environmental noise level, Zambrano-Monserrate et al. (2020) underlined the enlarged waste, alongside decrease in waste recycling. In the same vein, Mostafa et al. (2021) noticed several adverse ecological effects of COVID-19, such as the rise in medical waste generation, urban and solid garbage creation, less effective solid waste reprocessing activity, as well as an expansion in ozone concentration. Kleme et al. (2020) advised that the disease provoked huge issues in the management of urban solid waste and risky medical waste. Mzoughi et al. (2020) noticed that the response of CO₂ releases to a shock on COVID-19 infections are negative during the entire interval.

Earlier, studies acknowledged certain atmospheric settings that could serve as forecasters of a topmost of disease. Bull (1980) argued that fluctuations in the meteorological conditions are highly significantly associated with shifts in decease rate from pneumonia. Epstein (2001) concluded that the diffusion of West Nile virus in the United States and Europe may be due to the extreme climate circumstances associated to lasting temperature change. According to Yuan et al. (2006), the highest diffuse of SARS occurred at an average temperature of 16.9C, with measured humidity levels of 52.2 percent and a wind speed of 2.8 m/s, indicating that the illness was most likely to occur in the spring in northern China. By means of Spearman correlations, Tosepu et al. (2020) revealed a significant relation between average temperature and COVID-19. However, Irfan et al. (2021) proved that the effect of temperature on COVID-19 is unequal and mixed. For instance, Liu (2022) exhibited for a sample of 295 cities in China that the temperature threshold of 16.92C is the most crucial, with the greatest disease incidence, while propagation diminishes between 28.82C and 50C. For six cities of the Kingdom of Saudi Arabia, Ismail et al. (2022) claimed that high daily infections were detected at temperatures among 23 and 34.5C and humidity levels within 30 and 60 percent, whereas a substantial number of fatalities were identified at temperatures exceeding 28.7C and humidity levels under 40 percent. Xu et al. (2020) established that air quality index is statistically significantly connected with confirmed cases of COVID-19 in several Chinese cities.

2.2 Earlier research regarding COVID-19 and health sector

Besides, triggering human contaminations and fatalities, COVID-19 disease also disorders the stock market. In this regard, Hunjra et al. (2021) reported volatility in the stock indices relating to specific health strategies. He et al. (2020) found that health industry, apart from the manufacturing, information technology, and education strongly reacted to the pandemic in a positive way. Furthermore, according to Harjoto and Rossi (2021), COVID-19 has a favorable impact on the healthcare and telecommunications industries in emerging countries, allowing investors to increase their returns by investing in these sectors. Table 2 reviews earlier literature on the influence of the COVID-19 pandemic on health market.

Further, Carraturo et al. (2020) cautioned that dispersal risk may be amplified in case of a lengthy exposure to polluted ecological sources, the longer interaction with vaporizers formed from sewage or the deficient washing of food and surfaces. Therefore, Leung et al. (2020) argued that wearing masks may avoid the infectious bases. However, the growing demand of shielding kit driven an increase of prices, such as the cost of medical covers raised six times, N95 ventilators three times and gowns twice as much (World Health Organization, 2020). Because enterprises manufacture a huge quantity of units daily, progressively further mask trash has been shaped and must be discharged. Hence, dismissed masks in unsuitable spaces like busses, train stations, hospitals, and streets may cause secondary contaminations (Wang et al., 2020). However, National Center for Immunization and Respiratory Diseases (NCIRD) (2021) declared that individuals may become ill after interacting with infected items (fomites), but the threat is usually deemed minimal. In this regard, based on the review the existing knowledge, Gonalves et al. (2021) confirmed that COVID-19 fomite spread has not been proved. As well, Lewis (2021) reinforced that although the coronavirus that caused the pandemic can be found on door handles and other objects, they are not a primary cause of infection.

Besides, Choo and Rajkumar (2020) noticed that the critical pharmaceutical stock tend to be surpassed as long as common distribution chains, mechanisms, and organizational courses are insufficient for this emergency. Giria and Rana (2020) argued that the creation of completely prepared testing facilities would entail massive capital, proficiency and time, which are narrowed throughout COVID-19 period. Nevertheless, Mishra et al. (2021) emphasized that the pandemic differentiates towards people less fortunate. In this vein, Maxmen (2021) claimed that many people in the global south do not have accessibility to COVID-19 immunizations. This fact is argued by the fact that medical enterprises have marketed all of their available doses to the high-ranking purchasers in the aspirations of getting large revenues (Malpani & Maitland, 2021). Hence, Jecker (2022) asserted that rich and influential governments have a moral obligation to share COVID19 treatments with impoverished countries and to mend ties that have been strained by injustice.

In the fight against the SARS-CoV-2 pandemic, successful clinical trial procedures were launched to speed up the course of treatment aimed at the recovery of COVID-19 affected patients. For instance, remdesivir (Cao et al., 2020) or hydroxychloroquine and azithromycin (Gautret et al., 2020) have been suggested in the cure of coronavirus infection. As such, Chabrire (2021) reported that throughout a phase in which many stock prices plummeted, including those of medical corporations, the stock price of Gilead Sciences, the biopharmaceutical company developing the remdesivir molecule, rose substantially. Ahmed et al. (2021) exhibited the potential benefit of using the medicine ivermectin early in the therapy of adult patients with moderate COVID-19. Anglica Jayk Bernal (2021) documented that in at-risk, unvaccinated adults with COVID-19, timely molnupiravir medication lowered the probability of hospitalization or mortality.

2.3 Previous literature concerning COVID-19 and energy market

The suspension of economic undertakings during the first wave of COVID-19 period entailed earnings losses for many citizens, whereas stay at home guidance has augmented the electricity bill, thus making a substantial economic and financial burden on households (Qarnain et al., 2020). However, COVID-19 is a distinctive reality in wealthy and deprived nations, but they are connected by means of globalization and mankind (FAO, 2020). For instance, because governments restricted households’ purchasing options, prompting them to spend less (Gropp & McShane, 2021), the pandemic triggered a substantial household savings across developed economies, greatly exceeding previous records. According to European Central Bank (2021), in 2020, household savings gathered in surplus of previous levels in Australia, Canada, Japan, the United Kingdom and the United States amounted to an average of 6.7 percent of GDP and 9.5 percent of available revenue. In several economies, such as the United States, Hungary, Estonia, Ireland, and Romania, household bank deposit balances have also increased (OECD, 2020). With reference to developing countries, Bottan et al. (2020) found that the link among job loss and business closure is monotonically decreasing with income prior to the pandemic, exacerbating existing inequality.

The COVID-19 epidemic has also delayed the transition to renewables by disrupting clean energy manufacturing plants, distribution networks, and enterprises (Hosseini, 2020). The falling aggregate level of energy demand has had a detrimental effect for renewable energy generation and carbon trading price schemes (Hoang et al., 2021). As such, Shah et al. (2021) documented for Denmark that pandemic has a multiplier effect along with a harmful impact on renewable energy production. With reference to shocks propagation, Tiwari et al. (2022) found that clean energy transmits the highest value of shocks to other markets. Li and Meng (2022) confirmed that renewable energy stocks are the primary contributors in the analyzed connectedness system. Mensi et al. (2022) reported that return spillovers to the crude oil (gold) market are mostly transmitted through energy (basic resource) sectors. In addition, Nguyen (2020) documented that energy segment experienced the highest abnormal negative returns among other sectors, such as communications, consumer goods, medical assistance, computer technology, and public service. Table 3 summarizes prior findings on the COVID-19 pandemic impact on the energy sector.

Yilmazkuday (2020) showed by means of a structural vector autoregression (VAR) that a weekly upsurge of 1000 in daily COVID-19 cases in the rest of the world generate a cumulative decrease of 0.4% of in crude oil prices, while the COVID-19 cases in China do not have any significant impact on the crude oil prices. Aloui et al. (2020) demonstrated that energy futures markets, namely crude oil and natural gas S&P GS Indexes, respond to COVID-19 shocks. Mzoughi et al. (2020) confirmed through an unrestricted VAR that a positive shock to the number of COVID-19 cases determines a reduction in the price of crude oil, but the adverse reaction of the oil market is temporary. In addition, Khan et al. (2021) found that a negative effect of COVID-19 on energy prices, the adverse impact being larger on the oil prices as contrasted to the natural gas and the heating oil price. Szczygielski et al. (2021) strengthened that COVID-19 related insecurity has a harmful effect on returns in the whole energy markets and caused sharp instability in most nations.

On the contrary, Albulescu (2020) pointed out that the everyday announced cases of coronavirus have a peripheral adverse influence on the crude oil prices in the long term. In the same vein, Maneejuk et al. (2021) revealed that the energy markets perform analogous to both optimistic and adverse COVID-19 shocks.

3.1 Sample and variables

The impact of the pandemic generated by the COVID-19 disease on the world economy is significant, it will manifest itself in the long run and will depend on the intensity of the pandemic manifestation. We used daily data from December 31, 2019 to May 22, 2020, which matches to the first wave of the COVID-19 pandemic. A broad set of factors were selected, including environmental indices on the financial markets, the health care industry, and the oil and energy market. In order to capture COVID-19 pandemic, we included new cases and new deaths of pandemic worldwide as in Le et al. (2021). The selected measures are depicted in Table 4.

3.2 Quantitative analysis strategy

To investigate the impact of the COVID-19 pandemic on the environment, healthcare system, and oil sector, we will implement the autoregressive distributed lag (ARDL) model developed alike Albulescu (2020); Atri et al. (2021); Le et al. (2021); Wang, Li, et al. (2022), as well as the Granger causality test similar to Bourghelle et al. (2021). The ARDL framework has the advantage of allowing us to investigate the short and long run relationships between pandemics and the environment, healthcare, and oil markets. The ARDL specification developed by Pesaran et al. (2001) employs a linear transformation to incorporate short-term adjustments into the long-term equilibrium, using an error correction model.

Because of its ability to use a combination of I(0) and I(1) factors, the ARDL model and the bounds testing methodology are being used. It is critical to select the correct number of lags for the ARDL model, which allows for the investigation of established relationships between variables. Thus, similar to Atri et al. (2021), the Akaike information criteria (AIC) will be evaluated to select the optimal lags for the variables included in the ARDL model.

where, Environment, Health, and Oil_Energy depicts the variables related to each of the three segments, ${}_0$ and are the intercept and the error term respectively, denotes the short-run terms, defines the long-run terms, the error correction term is indicated by ECT, while indicates the adjustment speed, which should be negative and significant in order to validate the long-run relationship.