Disrupting COVID-19 by Improving Environmental Performance

doi:10.21203/rs.3.rs-37522/v1

Download PDF

Research Article

Disrupting COVID-19 by Improving Environmental Performance

https://doi.org/10.21203/rs.3.rs-37522/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

This paper provides evidence that increasing environmental quality can mitigate the spread of COVID-19 by producing natural antibodies. Using recent data from the 2020 Environmental Performance Index for more than 70 countries, our findings validate the effectiveness of lockdown policies in preventing outbreaks. The results are robust across econometric techniques as well as across the environmental proxy variables adopted. We also suggest implementing specific reforms to strengthen ecological performance.

Environmental Economics

Health Economics & Outcomes Research

Macroeconomics

Environmental Policy

Epidemiology

Statistical Epidemiology

COVID-19

EPI

AQI

PM2.5

AIR POLLUTION

ENVIRONMENT

The recent SARS-CoV-2 (COVID-19) outbreak has profoundly disrupted our ingrained habits. IMF (2020) defined this event as a natural calamity natural from an economic point of view, since COVID-19 has so far pushed 71 million people to the extreme poverty threshold. If a second wave ap- pears, that number could reach 100 million. The pandemic triggered the deepest global recession since the Second World War, with a contraction in global gross domestic product of about 5.2 percent in 2020, and with long- run effects on the level of investments and the erosion of human capital stock (WorldBank, 2020).

We believe that mitigating this pandemic calls for a multidisciplinary approach; in this vein, this research builds on several recent environmen- tal studies to investigate whether country-level environmental health plays a role in slowing the virus’s spread. Specifically, this paper analyzed the re- lationship between the country’s environmental performance and COVID-19 diffusion. Some studies highlighted that SARS-CoV-2 propagates through air pollution (e.g., Cui et al., 2003; Conticini et al., 2020; Contini and Costa- bile, 2020; Yongjian et al., 2020; Martelletti and Martelletti, 2020). For instance, Yongjian et al. (2020) documented a direct relationship between daily confirmed COVID-19 cases, the concentration of air pollution, and me- teorological variables in China. Likewise, it is well known that air pollutants affect the upper respiratory tract, reducing the immune defense, so they can be considered a Trojan horse for the virus (Cao et al., 2020). In this regard, Conticini et al. (2020) argued that the Northern regions of Italy are a focal point of the infection as a result of confounding effects, either directly or indirectly related to being one of the most polluted regions of Europe.

In this vein, the present study is the first to track global COVID-19 spread using the 2020 Environmental Performance Index (EPI), a comprehensive indicator evaluating environmental performance according to sustainability targets. Using 32 performance indicators across 11 issue categories, the EPI ranks 180 countries on environmental health and ecosystem vitality (Weldling et al., 2020). Therefore, the EPI index allows us to assess how close countries are to achieving ecological policy targets. Moreover, it constitutes forward guidance to apply the best practices for meeting the UN Sustainable Devel- opment Goals.

All in all, the present study’s most significant contribution derives from adopting different and more extensive country-level ecological performance indicators, going beyond air pollution data to gauge the potential impact of ecological performance on the COVID-19 pandemic. In the remainder of the paper, Section 2 presents the data and variables, Section 3 reports and discusses the results, and Section 4 concludes the paper.

This section presents and comments on the variables in the empirical model. Virus data has been extracted from the Our World in Data (OWID) COVID-19 dataset and covers the period from December 31, 2019 to June 10, 2020. The OWID research teams collect statistics from the European Center for Disease Prevention and Control and post daily updates online.¹

Tables 1 and 2 report the definitions of the variables and their summary statistics, respectively.

COVID-19 is the variable capturing the relevance of the disease in a country; it is defined as the total positive cases per million inhabitants divided by the total number of tests carried out in the country during the sample period. As a consequence, this outbreak measure is not sensitive to the heterogenous government policies implemented around the world to prevent infections. The 2020 EPI index supplies composite metrics on the whole environmental quality of a given country. We assume that the EPI index is not affected by reverse causality issues, since most of the datasets on which this indicator and its sub-inductors were constructed were collected in 2018 or earlier, before the pandemic started (Weldling et al., 2020). Similarly, the EPI team states that the variables are not conditioned by the emissions gathered from the Amazon rainforest fires in 2019. The average EPI score is 55 percent, but the sample includes both top and laggard countries in terms of ecological performance.

Furthermore, to stress test the goodness of our findings, the specifica- tion also focuses on a set of standard environmental performance indicators, such as the pollutant air emissions provided from the EPI dataset. We also adopt three alternative measures of environmental quality, namely air qual- ity (AIR), PMD, and GHP. AIR is a weighted composite index structured as follows: PM₂_.₅ Exposure (55%), Household Solid Fuels (40%), and Ozone Exposure (5%). GHP is another weighted air pollutant indicator, defined as the greenhouse gas (GHG) emissions per capita for the year 2017. This in- dex considers the four main gas pollutants directly related to anthropogenic activities, i.e., CO₂, CH₄, F-gases, and N₂O, which are responsible for cli- mate change and in turn impair both the environment and human health. Conversely, PMD is a relative measure of air pollution, capturing the effect of the exposure to fine air particulate matter smaller than 2.5 microme- ters (PM₂_.₅). Specifically, PMD measures the number of age-standardized, disability-adjusted life years lost per 100,000 persons (DALY rate) owing to exposure to PM₂_.₅ in the previous years (Kyu et al., 2018).

Among the other explicative variables, Latitude is the distance from the equator, calculated as the absolute value of latitude in degrees divided by 90; it captures two distinctive features of the region that should affect the prop- agation of the contagion. First, it defines the climate condition of the area, which captures the seasonal patterns in COVID-19 incidence. In this regard, Hopman et al. (2020) pointed out that as pneumonia infection is climate- sensitive as a result of cultural differences (outdoors social living), either UV radiation or high temperature reduces the virus’s survival time on surfaces. Indeed, Sajadi et al. (2020) found that significant community outbreaks dis- tributed along restricted latitude, temperature, and humidity lines, support- ing the seasonal behavior of human respiratory viruses. Further, Rhodes et al. (2020) documented that countries that lie below 35 degrees North have relatively low mortality rates and low infectious. According to the author, this is also related to the residents’ Vitamin D levels during winter, as Vita- min D is a natural defense from infections. Second, the farther a country is from the equator, the more likely it is to have prominent property rights and high-quality institutions relevant to pollution levels. Therefore, countries with less corruption have lower emission levels, both in terms of CO2 (Hall and Jones, 1999; Cole, 2007; Biswas et al., 2012; Arminen and Menegaki, 2019) and SO2 (Cole, 2007; Leit˜ao, 2010; Biswas et al., 2012), and better overall environmental performance (Lisciandra and Migliardo, 2017). To a large extent, corruption also affects the quality of national health systems.

Imposing lockdowns has relieved the COVID-19 infection outbreaks, flat- tening the contagion curve and saving a lot of human lives. As a consequence,a Government Response stringency index (Stringency ) has been adopted to assess the impacts of government actions to change the curve of the conta- gions. In detail, the index is a composite measure grounded on nine response sub-indicators embracing school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100, where higher values stand for stricter re- sponses (Hale et al., 2020).

The specification also includes other control variables that most likely re- late to the pandemic, namely the GDP per capita in log terms, the population density, and the share of the population that is 70 years and older. This set of control variables is strictly related to facility for spreading the virus, such as the propensity of international travel, active social life, and the demographic factors of the elderly population. At the same time, other factors play an important role in the COVID-19 data. Some countries adopted different poli- cies to address the outbreaks, for example, and developing countries without surveillance systems may face a shadow pandemic. Finally, the specification includes a set of regional dummy variables to control for the heterogenous geographical spread of COVID-19. Because the explicative variables refer to a period (2018 or earlier) before the outbreaks started—the exogenous shocks occurred at the beginning of 2020—this time shift protects the robustness against endogeneity.

This section reports and comments on the empirical results of the two robust regressions, both applied to infer the relationship between the pan- demic and environmental quality indicators. The empirical strategy is based on two robust estimators for cross-sectional data, i.e., quantile regression and the ordinary least square, with robust variance estimates (for more detail, see Berk, 1990; Hamilton, 1992; Koenker and Hallock, 2001).

The results are robust across both the estimators and environmental qual- ity measures, although GHP shows less significance than the other indica- tors. As expected, our findings confirm the novel literature on the effects of pollution as a catalyst of the propagation of the novel coronavirus; better environmental performance is associated with lower contagion rates. Like- wise, air pollution is one way the virus spreads, magnifying the pandemic. Lastly, the coefficient associated with the distance from the equator vali- dates the assumption that COVID-19 is an infection contingent on climate and seasons.

Overall, the present paper provides evidence that may be of interest in identifying “red zones” of virus hotbeds. Moreover, our findings confirm the effectiveness of the lockdown policies in mitigating the contagion. With respect to the other control variables, the associated coefficients are statisti- cally significant, and their signs are in line with the findings highlighted in the literature. Interestingly, the coefficients associated with the continental dummy variable show that the worst-hit pandemic regions lie in European or South America countries; ceteris paribus, Oceanic countries are less prone to COVID-19.

This analysis extends the debates on the relationship between pollution and the COVID-19 pandemic. From a robust regression, some intriguing regularities appear. First, environmental quality is associated with a natural antibody for the virus diffusion. As a consequence, curbing emissions can combat the propagation of SARS-CoV-2. All in all, the empirical evidence seems to reshuffle the contraposition between economic growth and environ- mental performance, underlining that ecological policy cannot be postponed: every action towards sustainability implies more protected health and, in turn, preserves the economic welfare of the country. Therefore, promoting environmental policy is a long-term strategic action since efforts towards sustainability not only represent a weapon to fight climate change but also indirectly induce health and economic stability.

The author certifies that he has no affiliations with or involvement in any organisation or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Arminen, H. and Menegaki, A. N. (2019). Corruption, climate and the energy-environment-growth nexus. Energy Economics, 80:621–634.

Berk, R. (1990). A primer on robust regression. modern methods of data analysis [eds.] j. fox and js long.

Biswas, A. K., Farzanegan, M. R., and Thum, M. (2012). Pollution, shadow economy and corruption: Theory and evidence. Ecological Economics, 75:114–125.

Cao, Y., Chen, M., Dong, D., Xie, S., and Liu, M. (2020). Environmental pollutants damage airway epithelial cell cilia: Implications for the prevention of obstructive lung diseases. Thoracic Cancer, 11(3):505–510.

Cole, M. A. (2007). Corruption, income and the environment: an empirical analysis. Ecological Economics, 62(3-4):637–647.

Conticini, E., Frediani, B., and Caro, D. (2020). Can atmospheric pollution be considered a co-factor in extremely high level of sars-cov-2 lethality in northern italy? Environmental pollution, page 114465.

Contini, D. and Costabile, F. (2020). Does air pollution influence covid-19 outbreaks?

Cui, Y., Zhang, Z.-F., Froines, J., Zhao, J., Wang, H., Yu, S.-Z., and Detels, (2003). Air pollution and case fatality of sars in the people’s republic of china: an ecologic study. Environmental Health, 2(1):1–5.

Hale, T., Webster, S., Petherick, A., Phillips, T., and Kira, B. (2020). Oxford covid-19 government response tracker. Blavatnik School of Government, 25.

Hall, R. E. and Jones, C. I. (1999). Why do some countries produce so much more output per worker than others? The quarterly journal of economics, 114(1):83–116.

Hamilton, L. C. (1992). How robust is robust regression? Stata Technical Bulletin, 1(2).

Hopman, J., Allegranzi, B., and Mehtar, S. (2020). Managing covid-19 in low-and middle-income countries. Jama, 323(16):1549–1550.

Koenker, R. and Hallock, K. F. (2001). Quantile regression. Journal of economic perspectives, 15(4):143–156.

Kyu, H. H., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018). Global, regional, and national disability-adjusted life-years (dalys) for 359 diseases and injuries and healthy life expectancy (hale) for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet, 392(10159):1859–1922.

Leit˜ao, A. (2010). Corruption and the environmental kuznets curve: empir- ical evidence for sulfur. Ecological Economics, 69(11):2191–2201.

Lisciandra, M. and Migliardo, C. (2017). An empirical study of the impact of corruption on environmental performance: Evidence from panel data. Environmental and Resource Economics, 68(2):297–318.

Martelletti, L. and Martelletti, P. (2020). Air pollution and the novel covid- 19 disease: a putative disease risk factor. SN Comprehensive Clinical Medicine, pages 1–5.

Rhodes, J. M., Subramanian, S., Laird, E., and Kenny, R. A. (2020). low population mortality from covid-19 in countries south of latitude 35 degrees north supports vitamin d as a factor determining severity. Alimentary pharmacology & therapeutics, 51(12):1434–1437.

Roser, M., Ritchie, H., Ortiz-Ospina, E., and Hasell, J. (2020). Coronavirus pandemic (covid-19). Our World in Data.

Sajadi, M. M., Habibzadeh, P., Vintzileos, A., Shokouhi, S., Miralles- Wilhelm, F., and Amoroso, A. (2020). Temperature and latitude analysis to predict potential spread and seasonality for covid-19. Available at SSRN 3550308.

Weldling, Z., Emerson, J., de Sherbinin, A., Esty, D., and et al. (2020). 2020 environmental performance index.

WorldBank (2020). Global economic prospects, june 2020.

Yongjian, Z., Jingu, X., Fengming, H., and Liqing, C. (2020). Association between short-term exposure to air pollution and covid-19 infection: Evi- dence from china. Science of the total environment, page 138704.

Due to technical limitations the Tables are available as a download in the Supplementary Files.

tablescovid.pdf

Download PDF

Version 1

posted

You are reading this latest preprint version

Disrupting COVID-19 by Improving Environmental Performance

Status:

Version 1

Abstract

1. Introduction

2. Data and Variables

3. Empirical Results

4. Conclusion

Declarations

References

Tables

Supplementary Files

Status:

Version 1