| Literature DB >> 34519987 |
Muhammad Khalid Anser1, Abdelmohsen A Nassani2, Khalid Zaman3, Muhammad Moinuddin Qazi Abro2.
Abstract
The study's objective is to examine the relationship between COVID-19 cases, environmental sustainability ratings, and mineral resource rents in a large cross section of 97 countries. The emergence of novel coronavirus 2019 (COVID-19) enlarges its magnitude across the international borders and damages social, economic, and environmental infrastructure with a high rate of human death tolls. The mineral resources are also devastated, which served as a primary raw input into the production system. The adverse effects of the COVID-19 pandemic on the environment and mineral resources are studied in a large panel of countries and found that mineral resource rents and population growth improve environmental sustainability rating (ESR). In contrast, an increase in coronavirus cases decreases the rating scale across countries. Further, mineral resources first decrease along with increased COVID-19 cases due to strict government policies, including the mandatory shutdown of economic institutions. Further, mineral resource rents increase later because of resuming economic activities in many parts of the world. The high rate of population growth is another important factor that negatively affects mineral resources across countries. Through impulse response and variance decomposition estimates, an exacerbated coronavirus cases and population growth would likely negatively affect ESR and mineral resources. In contrast, COVID-19 recovered cases will likely play a more significant role in securing mineral resources over time. Therefore, the global mineral resource conservation policies and improving ESR are highly needed during the COVID-19 to keep the significant economic gains in unprecedented times.Entities:
Keywords: COVID-19 pandemic; Environmental sustainability rating; Innovation accounting matrix; Markov switching approach; Mineral resources; Population growth
Mesh:
Year: 2021 PMID: 34519987 PMCID: PMC8438285 DOI: 10.1007/s11356-021-16259-2
Source DB: PubMed Journal: Environ Sci Pollut Res Int ISSN: 0944-1344 Impact factor: 4.223
Environment and natural resource degradation during the pandemic recession
| Ganguly et al. ( | India | Air pollutants, i.e., PM10, NO2 | Lockdown measures and public health | Lockdown measures improve air quality indicators and public health in a country |
| Gonzalez-Perez et al. ( | Latin America and the Caribbean region | Environmental degradation, financial debt, income inequality, governance indicator | Sustainable development | Socioeconomic and environmental factors exacerbate coronavirus cases in a region |
| Miller-Rushing et al. ( | USA | Resource conservation strategies | Resource policies | COVID-19 pandemic creates difficulty in managing economic and natural resources, which need to be managed through sustainable resource policies |
| Selvaranjan et al. ( | Six different countries | Plastic pollution | Face masks | The high use of face masks to prevent coronavirus disease led to increased plastic pollution across countries |
| Patterson Edward et al. ( | India | Coastal environmental health | Lockdown measures | The lockdown measures improved the life of coastal environmental species in a country |
| Ambika et al. ( | India | Health risks, environmental risks | Social distancing, lockdown | The strict compliance of coronavirus SOPs helps to reduce environmental and health risks in a country. |
| Mell and Whitten ( | UK | Natural environment, green financing | Lockdown, health risks, urban planning | Post-COVID-19 strategies, including urban planning and sustainable reforms, would be helpful to move forward towards global prosperity |
| Anser et al. ( | 17 countries | Financial development | Environmental reforms | Environmental reforms would be helpful to reduce carbon damages to tackle coronavirus cases across countries |
List of countries
| USA, Brazil, Russia, India, Spain, Peru, Chile, Iran, Mexico, Pakistan, Turkey, Saudi Arabia, South Africa, Canada, Colombia, China, Egypt, Sweden, Argentina, Ecuador, Indonesia, Ukraine, Portugal, Oman, Philippines, Poland, Panama, Bolivia, Dominican Republic, Afghanistan, Romania, Ireland, Armenia, Nigeria, Kazakhstan, Japan, Austria, Honduras, Guatemala, Ghana, Azerbaijan, Serbia, Algeria, Cameron, Morocco, Malaysia, Uzbekistan, Australia, Finland, Senegal, North Macedonia, Tajikistan, Ethiopia, Guinea, Gabon, Kyrgyzstan, Bulgaria, Mauritania, Hungry, Bosnia Herzegovina, Greece, Thailand, Costa Rica, Croatia, Albania, Cuba, Nicaragua, Mali, Madagascar, Sri Lanka, Slovakia, Zambia, New Zealand, Sierra Leone, Tunisia, Malawi, Jordan, Niger, Cyprus, Burkina Faso, Uruguay, Georgia, Rwanda, Chad, Mozambique, Uganda, Eswatini, Liberia, Jamaica, Togo, Zimbabwe, Tanzania, Surinam, Vietnam, Guyana, Namibia, Burundi |
Figure 1Research framework of the study Source: Author’s extraction
Descriptive statistics
| Mean | 2.778 | 2.545 | 88315.23 | 3543.902 | 47912.24 | 68572175 |
| Maximum | 4 | 26.21 | 2637077 | 128437 | 1093456 | 1.44E+09 |
| Minimum | 2.5 | 5.73E-05 | 170 | 1 | 109 | 586594 |
| Std. dev. | 0.456 | 4.699 | 317794.5 | 14887.46 | 148094.8 | 2.10E+08 |
| Skewness | 1.319 | 2.783 | 6.468 | 7.096 | 5.228 | 5.798 |
| Kurtosis | 3.321 | 11.549 | 48.83 | 57.04 | 33.246 | 37.168 |
Markov switching regression estimates for Eq. 1
| (MRENT)t-1 | 0.000194 | 0.009921 | 0.019532 | 0.9844 |
| ln(CASES)t | −0.675729 | 0.188888 | −3.577408 | 0.0003 |
| ln(SQCASES)t | 0.041041 | 0.008831 | 4.647178 | 0.0000 |
| ln(DEATH)t | 0.067331 | 0.048721 | 1.381974 | 0.1670 |
| ln(POP)t | −0.015267 | 0.033225 | −0.459507 | 0.6459 |
| Constant | 3.841027 | 1.059784 | 3.624348 | 0.0003 |
| ln(SIGMA) | −1.373673 | 0.108982 | −12.60464 | 0.0000 |
| (MRENT)t-1 | −0.379500 | 0.304902 | −1.244661 | 0.2133 |
| ln(CASES)t | −9.999763 | 4.036975 | −2.477043 | 0.0132 |
| ln(SQCASES)t | 0.632328 | 0.237299 | 2.664691 | 0.0077 |
| ln(DEATH)t | −1.832940 | 1.285365 | −1.426008 | 0.1539 |
| ln(POP)t | −2.582335 | 0.859351 | −3.004982 | 0.0027 |
| Constant | 96.44001 | 20.82994 | 4.629874 | 0.0000 |
| ln(SIGMA) | 1.443525 | 0.141919 | 10.17149 | 0.0000 |
| ln(RECOV)t | −0.124071 | 0.085810 | −1.445879 | 0.1482 |
| P11-C | 0.760610 | 0.337127 | 2.256154 | 0.0241 |
| P21-C | 0.288126 | 0.440182 | 0.654562 | 0.5127 |
| Mean dependent var | 2.573028 | S.D. dependent var | 4.718024 | |
| S.E. of regression | 4.692239 | Sum squared resid | 1673.300 | |
| Durbin-Watson stat | 1.716064 | Log likelihood | −147.6864 | |
Figure 2U-shaped relationship between mineral resources and COVID-19 cases Source: Author’s estimation
Cross-sectional regression analysis for Eq. 2
| Dependent variable: ln(ESR) | ||||
|---|---|---|---|---|
| Variable | Coefficient | Std. error | Prob. | |
| C | 0.525812 | 0.154894 | 3.394666 | 0.0010 |
| ln(MRENT) | 0.014358 | 0.005206 | 2.758031 | 0.0070 |
| ln(CASES) | −0.041120 | 0.007655 | −5.371830 | 0.0000 |
| ln(POP) | 0.052231 | 0.011121 | 4.696650 | 0.0000 |
| 0.290821 | ||||
| Adjusted | 0.267944 | |||
| 12.71252 | ||||
| Prob( | 0.000000 | |||
IRF estimates for Eq. 1
| June 2021 | −0.457 | −0.499 | 0.122 | −0.770 | −0.142 |
| July 2021 | 0.920 | −0.055 | −0.527 | −0.234 | 0.586 |
| August 2021 | −0.332 | −0.307 | 0.016 | −0.130 | 0.019 |
| September 2021 | 0.166 | −0.116 | 0.018 | 0.023 | 0.093 |
| October 2021 | −0.095 | −0.142 | 0.023 | 0.012 | −0.032 |
| November 2021 | 0.047 | −0.082 | −0.020 | 0.032 | 0.026 |
| December 2021 | −0.032 | −0.092 | −0.019 | 0.010 | 0.013 |
| January 2022 | 0.003 | −0.080 | −0.021 | 0.014 | 0.026 |
| February 2022 | −0.017 | −0.083 | −0.012 | 0.011 | 0.016 |
IRF estimates for Eq. 2
|
| ||||
|---|---|---|---|---|
June 2021 | −0.071740 | −0.025133 | −0.098481 | 0.008475 |
July 2021 | 0.064463 | 0.022951 | −0.051719 | −0.032975 |
| August 2021 | −0.005293 | −0.013243 | −0.036844 | −0.014966 |
| September 2021 | 0.019292 | 0.012209 | −0.016941 | −0.007531 |
| October 2021 | 0.001992 | −0.004981 | −0.014548 | −0.004503 |
| November 2021 | 0.005832 | 0.005001 | −0.008381 | −0.002144 |
| December 2021 | 0.001504 | −0.001718 | −0.007014 | −0.002160 |
| January 2022 | 0.002216 | 0.001906 | −0.004245 | −0.001065 |
| February 2022 | 0.000913 | −0.000541 | −0.003341 | −0.001029 |
Note: ESR shows environmental sustainability rating, MRENT shows mineral rents, CASES shows COVID-19 registered cases, and POP shows population
VDA estimates
|
| ||||||
|---|---|---|---|---|---|---|
| June 2021 | 4.440 | 95.544 | 1.266 | 0.075 | 3.009 | 0.103 |
| July 2021 | 4.609 | 92.663 | 1.190 | 1.378 | 3.051 | 1.716 |
| August 2021 | 4.633 | 92.214 | 1.619 | 1.365 | 3.099 | 1.700 |
| September 2021 | 4.638 | 92.125 | 1.678 | 1.363 | 3.094 | 1.737 |
| October 2021 | 4.642 | 92.034 | 1.770 | 1.364 | 3.091 | 1.739 |
| November 2021 | 4.643 | 91.996 | 1.801 | 1.365 | 3.094 | 1.742 |
| December 2021 | 4.644 | 91.958 | 1.839 | 1.366 | 3.093 | 1.742 |
| January 2022 | 4.645 | 91.924 | 1.869 | 1.368 | 3.093 | 1.744 |
| February 2022 | 4.646 | 91.892 | 1.901 | 1.368 | 3.092 | 1.745 |
VDA estimates for Eq. 2
|
| |||||
|---|---|---|---|---|---|
|
| |||||
| June 2021 | 0.465636 | 95.20243 | 0.291341 | 4.473104 | 0.033124 |
| July 2021 | 0.474617 | 93.47830 | 0.514262 | 5.492858 | 0.514581 |
| August 2021 | 0.476494 | 92.75578 | 0.587461 | 6.047575 | 0.609181 |
| September 2021 | 0.477401 | 92.56706 | 0.650630 | 6.150553 | 0.631757 |
| October 2021 | 0.477674 | 92.46304 | 0.660760 | 6.236283 | 0.639920 |
| November 2021 | 0.477814 | 92.42372 | 0.671326 | 6.263395 | 0.641559 |
| December 2021 | 0.477876 | 92.40080 | 0.672444 | 6.283320 | 0.643435 |
| January 2022 | 0.477905 | 92.39174 | 0.673953 | 6.290449 | 0.643854 |
| February 2022 | 0.477919 | 92.38671 | 0.674042 | 6.294967 | 0.644280 |