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This article aims at answering the following questions: (1) What is the influence of age structure on the spread of coronavirus disease 2019 (COVID-19)? (2) What can be the impact of stringency policy (policy responses to the coronavirus pandemic) on the spread of COVID-19? (3) What might be the quantitative effect of development levelincome and number of hospital beds on the number of deaths due to the COVID-19 epidemic? By employing the methodologies of generalized linear model, generalized moments method, and quantile regression models, this article reveals that the shares of median age, age 65, and age 70 and older population have significant positive impacts on the spread of COVID-19 and that the share of age 70 and older people in the population has a relatively greater influence on the spread of the pandemic. The second output of this research is the significant impact of stringency policy on diminishing COVID-19 total cases. The third finding of this paper reveals that the number of hospital beds appears to be vital in reducing the total number of COVID-19 deaths, while GDP per capita does not affect much the level of deaths of the COVID-19 pandemic. Finally, this article suggests some governmental health policies to control and decrease the spread of COVID-19.

Continuous economic growth and the rise in energy consumption are linked with environmental pollution. Demand for health care expenditure increased after the COVID-19 pandemic. This study is interesting in modeling the nexus between public and private health expenditure, carbon dioxide emissions, and economic growth. To this end, the present study analyzed the nexus between public and private health care expenditure, economic growth, and environmental pollution for 36 Asian countries for the period 1991–2017. FMOLS, GMM, and quantile regression analysis confirm the EKC hypothesis in Asia. Besides, FMOLS and quantile regressions reached the reducing effects of government and private health expenditures on CO 2 emissions. While quantile regression results show that public and private health expenditures can mitigate CO 2 emissions; however, these results differ for various levels of CO 2 . Findings of quantile regression show a significant impact of both public and private health expenditures in reducing CO 2 at the 50 th and 75 th quantiles but results are insignificant for the 25 th quantile. Overall, the paper concludes that both government and private health sectors’ expenditures caused CO 2 emissions to decrease in Asia and that the negative impact of the private health sector on CO 2 emissions is greater than that of the government health sector. The concluding remark is that the higher the health spending, the higher the environmental quality will be in Asia. Hence, the health administrators need to increase public and private health expenditures with an effective cost-service and energy-efficient management approach to reach sustainable health services and a sustainable environment in Asia. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-13639-6.

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The novel coronavirus (COVID-19) has tremendously oscillated the global financial markets. Consequently, investors feel pressured to find safe-haven investments during the pandemic crisis. Numerous studies have evaluated bitcoin's safe-haven properties during the COVID-19; however, the present study considered gold as a potential safe-haven for investors of renowned stock markets of Asia, Europe, and the US. The present investigation computed the ratio of gold to bitcoin (Gold_Bitcoin) and compared the safe-haven properties of gold in contrast to bitcoin. The present study analysed the Morlet Wavelet approach and found that most of the time during the COVID-19, gold investments proved to be more beneficial than bitcoin. Remarkably, the findings highlighted that the Gold_Bitcoin ratio increased in higher and lower frequencies combined with CAC40. In the long run, the return on investments in gold increased in contrast to bitcoin returns pooled with DAX30. Also, the Gold_Bitcoin ratio of the US stock market increased during the one-week and one-month cycles of January and August. Likewise, the Hang Seng Index caused the Gold_Bitcoin ratio to rise at a much higher frequency (i.e., the second half of January, the first half of February and April, and the first half of June and August), whereas IBEX35 surged Gold_Bitcoin at a lower frequency (i.e., during January, February, and August). In higher frequency bands, LSE increased the Gold_Bitcoin ratio (i.e., in February and March); nevertheless, Gold_Bitcoin showed a positive connection with FTSEMIB in the one-to-two month's frequency band (i.e., throughout January, February, and August). Interestingly, the returns on the Gold_Bitcoin ratio increased in the SSEC stock market in the high-frequency band (i.e., during March, May, and July 2020).

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