| Literature DB >> 33939085 |
Muhammad Khalid Anser1, Bushra Usman2, Shabir Hyder3, Abdelmohsen A Nassani4, Sameh E Askar5, Khalid Zaman6, Muhammad Moinuddin Qazi Abro4.
Abstract
The study's objective is to evaluate the impact of environmental sustainability rating, financial development, changes in the price level and carbon damages on the new COVID-19 cases in a cross-sectional panel of 17 countries. The study developed two broad models to analyse the relationship between the stated factors at the current level and forecast level. The results show that improvement in the environmental sustainability rating and financial efficiency reduces the COVID-19 cases, while continued economic growth and changes in price level likely to exacerbate the COVID-19 cases across countries. The forecast results suggest the U-shaped relationship between COVID-19 cases and carbon damages controlling financial development, price level and environmental sustainability rating. The variance decomposition analysis shows that carbon damages, environmental sustainability rating and price level changes will largely influence COVID-19 cases over the next year. The soundness of economic and ecological regulated policies would be helpful to contain coronavirus cases globally.Entities:
Keywords: COVID-19 pandemic; Carbon damages; Environmental sustainability rating; Financial development, price level; Robust least squares regression
Mesh:
Substances:
Year: 2021 PMID: 33939085 PMCID: PMC8089134 DOI: 10.1007/s11356-021-13873-y
Source DB: PubMed Journal: Environ Sci Pollut Res Int ISSN: 0944-1344 Impact factor: 4.223
Prominent epidemics, outbreaks and pandemics
| Pathogen | Year | Cases/mortality | Geographical location | References |
|---|---|---|---|---|
| Influenza (Spanish flu) | 1918–1920 | 100 million deaths out of 500 million cases | China to worldwide | Saunders-Hastings and Krewski ( |
| Influenza (Asian flu) | 1957–1958 | 2 million deaths | China to worldwide | |
| HIV/AIDS | 1960–to date | 32 million deaths out of 75 million cases | Africa to worldwide | WHO ( |
| Cholera | 1961–to date | 29,000 deaths out of 5 million per year | South Asia to worldwide | WHO ( |
| Influenza (Hong Kong flu) | 1968–1969 | 2 million deaths | China to worldwide | SinoBiological ( |
| SARS | 2002–2003 | 800 deaths out from 8000 | China to 37 countries | Chesak ( |
| Influenza (Swine flu) | 2009–2010 | 6 million deaths | Mexico to worldwide | Bloom and Cadarette ( |
| Ebola | 2014–2016 | 11,325 deaths out from 28,600 | West Africa | CDCP ( |
| Zika | 2015–to date | No confirms deaths | Brazil and America | Partlow ( |
| Dengue | 2016 | 38,000 deaths out of 100 million | Worldwide | Institute for Health Metrics and Evaluation ( |
| Coronavirus (COVID-19) | 2019–2020 | 26,495 deaths out of 571,678 cases in four months | China to worldwide | WHO (2020) |
Current literature on different communicable diseases (including COVID-19) and healthcare expenditures
| Authors | Country | Communicable diseases | Causes/symptoms | Consequences | Prevention | Medication |
|---|---|---|---|---|---|---|
| Fukuda et al. ( | Japan | Hepatitis C virus (HCV) | Liver failure | Healthcare expenditures increases in age groups. | The need for economical and effective drug therapies would be beneficial for HCV patients. | Oral and injectables are available for HCV-infected people. |
| Ward et al. ( | US | HIV management | HIV is a sexually transmitted disease, while it further spread with infected blood and breastfeeding. It damages the immune system that affects the quality of life of the patient. | The HIV patients could bear not only the cost of the therapy while it has associated with some other toxicities, including cardiovascular disease, kidney issues and osteoporosis. | The life expectancy can increase with the associated cost of the therapy. | There is no such cure rate of HIV patients while symptomatic treatment is given to the patients to increase life expectancy. |
| Njau et al. ( | Romania | Measles and rubella | Rashes, fever, lymph nodes, flu, headache, red eyes, etc. | The cost of measles and rubella outbreaks was US$9.9 million, among which measles and rubella per cost of patients were around the US$439 and US$132, respectively. Further, the result indicates that about 36% of households could not afford this high viral cost, thus have to borrow it from other sources. | Routine vaccination would be helpful to reduce the economic burden. | MMR vaccine primarily used for this viral disease. |
| Pedrazzoli et al. ( | A general survey across countries | Tuberculosis | TB is more prone due to poverty, lack of knowledge, income and financial issues. | The economic consequences are apparent, which includes reduced labour supply, low labour productivity, less income and household resilience. | TB DOTS programme, patient-centred TB services and free medicines given to the needy people would help cure this disease. | The four-drug therapy primarily used in the first phase then decrease up to three or two medicines. It is around 6 to 8 months of medication treatment that is curable. |
| Albuquerque et al. ( | Brazil | Zika virus | Children are affected mainly by the Zika virus, leading to cognitive impairment, epilepsy, visual problems and arthrogryposis. | The low priority areas, marginalized population and inability to afford healthcare prices affected mainly by the Zika virus. | Frequent healthcare visits and regular follow-up with the physicians would positively prevent the Zika virus. | There is no specific vaccine and medicine; thus, it mainly treats it through symptomatic medication. |
| Kum et al. ( | Sierra Leone | Ebola virus | Unexplained haemorrhaging is the main symptom. | The disease negatively affects the country’s budget due to the affected countries’ food and mining business disclosure. | Clinical care and the patient’s immune response would mainly prevent it from this disease. | The FDA approves the Ebola vaccine rVSV-ZEBOV. |
| Bai et al. ( | China | COVID-19 | Viral pneumonia resulted in the outbreak of coronavirus. | Fever, cough, body pain and respiratory problems are common symptoms. | Social distancing suggests prevention. | There is no such vaccine or medicine for this viral infection. Self-isolation and quarantine hospitals/places recommended. |
| Grasselli et al. ( | Italy | COVID-19 | The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lead to COVID-19. | Treatment that does not respond to atypical pneumonia may lead to COVID-19. | Intensive care units build up and allocated for COVID-19 patients. | Set up local procedures for the triage of patients with respiratory issues. |
| Adalja et al. ( | US | COVID-19 | SARS-CoV-2 lead to COVID-19. | Healthcare workers are mainly in danger to expose directly to COVID-19 patients. | The need for a proper healthcare system is required to confront this disease. | Diagnostic testing, local hospitals and clinics need to move quickly forward to tackle the disease. |
| Murthy et al. ( | General survey | COVID-19 | The SARS, the Middle East respiratory syndrome and different severe influenza, including A(H7N9) and A(H1N1), are the integral components of this infectious disease. | Older patients (median age ≈ 60 years) are affected mainly by this virus, while milder illnesses found in children. | Increase urine intensity, lung-protective ventilation and reduced lung inflation are recommended for possibly minimizing the severity of this disease. | An early antibiotic for symptomatic treatment suggested following some other healthcare guidelines to confront this virus; however, there is no such specific vaccine/medicine until yet launched to reduced mortalities. Precaution is the only medicine. |
Fig. 1Rise in new infected COVID-19 cases. Source: Worldometer (2020, dated 19th October 2020
Fig. 2Research framework. Source: Self-extract
Descriptive statistics
| Methods | NEW | CDAM | CPI | ESR | GDPPC | MS |
|---|---|---|---|---|---|---|
| Mean | 1356 | 2.607 | 3.174 | 3.117 | 13,371.53 | 77.672 |
| Maximum | 9138 | 5.618 | 10.578 | 4 | 57,071.17 | 197.017 |
| Minimum | 2 | 0.613 | 0.382 | 3 | 1116.358 | 17.831 |
| Std. Dev. | 2472.148 | 1.722 | 3.044 | 0.281 | 17,593.98 | 48.125 |
| Skewness | 2.177 | 0.686 | 1.150 | 2.249 | 1.478 | 0.990 |
| Kurtosis | 6.913 | 2.108 | 3.237 | 6.925 | 3.763 | 3.352 |
Note: NEW shows new infected cases, CDAM shows carbon damages, CPI shows inflation, ESR shows environmental sustainability rating, GDPPC shows per capita GDP and MS shows money supply
Robust least squares regression estimates for Eq. (1)
| Dependent variable: ln(NEW) | ||||
|---|---|---|---|---|
| Variable | Coefficient | Std. Error | z-Statistic | Prob. |
| C | 29.004 | 5.460 | 5.311 | 0.000 |
| ln(ESR) | − 16.703 | 3.487 | − 4.789 | 0.000 |
| ln(GDPPC) | 0.888 | 0.244 | 3.626 | 0.000 |
| ln(CPI) | 0.814 | 0.280 | 2.899 | 0.003 |
| ln(MS) | − 3.095 | 0.448 | − 6.895 | 0.000 |
| Robust statistics | ||||
|
| 0.526 | Adjusted | 0.369 | |
| Rw2 | 0.939 | Adjust Rw2 | 0.939 | |
| AIC | 44.014 | SIC | 51.055 | |
| Rn2 | 85.478 | Prob(Rn2) | 0.000 | |
Note: NEW shows new infected cases, CPI shows inflation, ESR shows environmental sustainability rating, GDPPC shows per capita GDP and MS offers money supply
Diagnostic test estimates for Eq. (1)
| Variables | Variance inflation factors (VIF) | Other tests |
|---|---|---|
| ln(ESR) | 1.135 | JB test: 0.468 Prob. Value: (0.791) |
| ln(GDPPC) | 1.313 | Autocorrelation LM test: 0.565 Prob. Value: (0.585) |
| ln(CPI) | 1.171 | Heteroskedasticity test: 1.205 Prob. Value: (0.358) |
| ln(MS) | 1.120 | Ramsey RESET test: 1.242 Prob. Value: 0.239 |
Note: CPI shows inflation, ESR shows environmental sustainability rating, GDPPC shows per capita GDP and MS offers money supply
Fig. 3Model stability tests for Eq. (1). Source: Author’s estimate
Robust least square regression estimates for Eq. (2)
| Dependent variable: ln(NEWF) | ||||
|---|---|---|---|---|
| Variable | Coefficient | Std. Error | z-Statistic | Prob. |
| C | 29.002 | 1.032 | 28.089 | 0.000 |
| ln(CDAM) | − 0.585 | 0.181 | − 3.215 | 0.001 |
| ln(SQCDAM) | 0.298 | 0.135 | 2.199 | 0.027 |
| ln(ESR) | − 12.623 | 0.646 | − 19.528 | 0.000 |
| ln(CPI) | 0.612 | 0.052 | 11.778 | 0.000 |
| ln(MS) | − 2.352 | 0.117 | − 20.096 | 0.000 |
| Robust statistics | ||||
|
| 0.880 | Adjusted | 0.826 | |
| Rw2 | 0.994 | Adjust Rw2 | 0.994 | |
| AIC | 20.294 | SIC | 28.889 | |
| Rn2 | 1511.871 | Prob(Rn2) | 0.000 | |
Note: NEWF shows new forecast infected cases, CDAM shows carbon damages, SQDAM shows a square of CDAM, CPI shows inflation, ESR shows environmental sustainability rating, GDPPC shows per capita GDP and MS shows money supply
Diagnostic test estimates for Eq. (2)
| Variables | VIF | Other tests |
|---|---|---|
| ln(CDAM) | 7.158 | JB test: 0.941 Prob. Value: (0.624) |
| ln(SQCDAM) | 9.143 | Autocorrelation LM test: 1.646 Prob. Value: (0.245) |
| ln(ESR) | 1.202 | Heteroskedasticity test: 0.581 Prob. Value: (0.713) |
| ln(CPI) | 1.235 | |
| ln(MS) | 2.347 |
Note: CDAM shows carbon damages, SQDAM shows a square of CDAM, CPI shows inflation, ESR shows environmental sustainability rating, GDPPC shows per capita GDP and MS shows money supply
Fig. 4Model stability tests for Eq. (2). Source: Author’s estimate
Variance decomposition estimates
| Variance decomposition of ▲ln(NEW) | |||||||
|---|---|---|---|---|---|---|---|
| Months | SE. | ▲ln(NEW) | ▲ln(CDAM) | ▲ln(CPI) | ▲ln(ESR) | ▲ln(GDPPC) | ▲ln(MS) |
| January 2021 | 2.654876 | 99.44022 | 0.434700 | 0.025601 | 0.002212 | 0.083785 | 0.013486 |
| February 2021 | 2.659966 | 99.07438 | 0.689012 | 0.043968 | 0.033965 | 0.099237 | 0.059434 |
| March 2021 | 2.660194 | 99.06047 | 0.696863 | 0.046784 | 0.036024 | 0.099241 | 0.060616 |
| April 2021 | 2.660198 | 99.06023 | 0.696866 | 0.046926 | 0.036051 | 0.099290 | 0.060632 |
| May 2021 | 2.660198 | 99.06022 | 0.696872 | 0.046927 | 0.036052 | 0.099294 | 0.060639 |
| June 2021 | 2.660198 | 99.06021 | 0.696873 | 0.046927 | 0.036052 | 0.099294 | 0.060639 |
| July 2021 | 2.660198 | 99.06021 | 0.696873 | 0.046927 | 0.036052 | 0.099294 | 0.060639 |
| August 2021 | 2.660198 | 99.06021 | 0.696873 | 0.046927 | 0.036052 | 0.099294 | 0.060639 |
| September 2021 | 2.660198 | 99.06021 | 0.696873 | 0.046927 | 0.036052 | 0.099294 | 0.060639 |
| Variance decomposition of ▲ln(NEWF) | |||||||
| Months | SE. | ▲ln(NEWF) | ▲ln(CDAM) | ▲ln(CPI) | ▲ln(ESR) | ▲ln(GDPPC) | ▲ln(MS) |
| February 2021 | 2.259445 | 99.56116 | 0.145534 | 0.167805 | 0.101744 | 0.023752 | 2.25E-08 |
| March 2021 | 2.259966 | 99.54227 | 0.158234 | 0.172984 | 0.101747 | 0.024764 | 2.26E-08 |
| April 2021 | 2.259984 | 99.54074 | 0.158641 | 0.174017 | 0.101820 | 0.024786 | 2.26E-08 |
| May 2021 | 2.259985 | 99.54066 | 0.158651 | 0.174058 | 0.101841 | 0.024786 | 2.26E-08 |
| June 2021 | 2.259985 | 99.54066 | 0.158654 | 0.174059 | 0.101842 | 0.024786 | 2.26E-08 |
| July 2021 | 2.259985 | 99.54066 | 0.158654 | 0.174059 | 0.101842 | 0.024786 | 2.26E-08 |
| August 2021 | 2.259985 | 99.54066 | 0.158654 | 0.174059 | 0.101842 | 0.024786 | 2.26E-08 |
| September 2021 | 2.259985 | 99.54066 | 0.158654 | 0.174059 | 0.101842 | 0.024786 | 2.26E-08 |
Note: ▲ shows the first difference, ln shows natural logarithm, CDAM shows carbon damages, SQDAM shows a square of CDAM, CPI shows inflation, ESR shows environmental sustainability rating, GDPPC shows per capita GDP and MS shows money supply