key: cord-0798793-aiy931sh authors: Ozkan, Aydin; Ozkan, Gulcin; Yalaman, Abdullah; Yildiz, Yilmaz title: Climate risk, culture and the Covid-19 mortality: A cross-country analysis date: 2021-01-19 journal: World Dev DOI: 10.1016/j.worlddev.2021.105412 sha: 8380918044af0180a883e94c7d7707ae66b16f89 doc_id: 798793 cord_uid: aiy931sh Why have some countries done significantly better than others in fighting the Covid-19 pandemic? Had some countries been better prepared than others? This paper attempts to shed light on these questions by examining the role of climate risk and culture in explaining the cross-country variation in the Covid-19 mortality, while controlling for other potential drivers. In our analysis, we consider climate risk, readiness to climate change and individualism as main indicators reflecting the climate and culture status of individual countries. Using data from 110 countries, we find that the greater the climate risk; the lower the readiness to climate change and the more individualistic the society, the higher the pandemic mortality rate. We also present a series of sensitivity checks and show that our findings are robust to different specifications, alternative definitions of the mortality rate; and different estimation methods. One policy implication arising from our results is that countries that were better prepared for the climate emergency were also better placed to fight the pandemic. Overall, countries in which individuals look after each other and the environment, creating sustainable societies, are better able to cope with climate and public health emergencies. The Covid-19 crisis is unquestionably the greatest global public health emergency, at least since the influenza pandemic of 1918. As the virus has been transmitted across the globe with alarming pace and catastrophic consequences, exposing significant cross-country variation in the mortality rates, several key questions have emerged. Why have some countries done significantly better than others in fighting the Covid-19 pandemic? Were some countries more exposed than others to the virus? Had some countries been better prepared against such a health crisis than others? In this paper, we attempt to shed some light on these questions, by exploring the links between the Covid-19 crisis, climate change and culture. Scientists have long established deep links between climate change and pandemics (Colwell, 1996; Epstein, 2002; Harvell et al., 2009; Semenza & Menne, 2009; Altizer et al., 2013; Ficetola & Rubolini, 2020) . Specifically, climate change is known to drive wild-life closer to people, resulting in extensive interactions between wild animals, domestic animals and humans. This, in turn, paves the way for viruses that are harmless in wild animals to be transmitted to humans with deadly consequences. (Kamradt-Scott, 2012; Vora, 2012) . Indeed, around 75 percent of all major diseases affecting humans, including Aids, Sars, H5N1 avian flu and the H1N1 flu now originate in animals (USAID). It is also widely agreed that frequent extreme weather events have played a major role in the range and spread of diseases, increasingly turning them into epidemics and pandemics (Loevinsohn, 1994; McMichael, 2015; Tosepu et al., 2020) . Since the outbreak of the pandemic early in 2020, there has been much discussion on how learning from the coronavirus would help tackle the impending climate crisis (Jacobsen, 2020; World Economic Forum, 2020) . It is argued that responding to the pandemic can teach policymakers important lessons towards fighting other global threats, including climate change. Increasing national and international resilience against future epidemics and pandemics is highlighted as required preparation towards future climate change (Djalante, Shaw, & DeWit, 2020) . In this paper, we approach this issue in reverse order by exploring whether countries that are vulnerable to climate risk suffered more in the face of the Covid-19 pandemic. We hence have more specific questions to consider, namely (1) had countries been better prepared for climate change, would they have fared better in their fight against the Covid-19 pandemic? (2) Did cultural traits such as individualism that are closely linked to climate action, play any role in the human cost of the Covid-19? To establish whether a country's standing with regard to climate change and its cultural traits impact its fight against the pandemic, we examine the link between climate risk, culture and the Covid-19 outcomes. Our choice of metric for the pandemic outcomes is the Covid-19 mortality rate -defined as the number of pandemic related deaths as a proportion of the total number of cases -widely referred to as the case-fatality ratio. The relationship between climate conditions and mortality has long been recognized; through the impact of both extreme hot and cold weather increasing mortality (Curriero, 2002; Deschenes & Greenstone, 2011; Barreca, 2012) ; through warmer climates and spread of diseases (Altizer et al., 2013) ; and through changes in weather patterns, adaptation and mortality (Deschenes & Greenstone, 2011; Hajat et al., 2014) . Given such significant role of climate change in health outcomes, and particularly in mortality, one would expect climate risk to play a significant role in influencing the Covid-19 fatality rate. Indeed, Ma et al. (2020) documents the effect of temperature and humidity on the Covid-19 mortality in Wuhan province, China, the pandemic has originated in. Similarly, Araujo & Naimi (2020) and Ficetola & Rubolini (2020) establish how the spread of the coronavirus is likely to be impacted by climate. 1 Yet, there is no formal analysis of climate risk as a potential factor underlying the cross-country variation in Covid-19 mortality rates. To the best of our knowledge, this is the first paper formally establishing the link between climate risk, culture and the Covid-19 mortality rate. 2 While it is true that a definitive cross-country comparison is difficult until after the full set of data are available -naturally after the pandemic is all but eradicated -there is now sufficient information enabling us to examine the link between climate risk, the preparedness of countries and the Covid-19 mortality rates. As is widely observed, the great majority of the worstaffected countries appear to have passed the peak of first wave of pandemic in terms of both the number of cases and the number of deaths, allowing comparability of the fatality rates (see, for example, Jinjarak, et al., 2020) . 3 This is clearly seen in Figure 1 that displays the seven-day rolling averages of the number of deaths and the number of cases over January-May 2020, for 25 countries with highest number of deaths. 4 Another difficulty in cross-country comparisons of mortality rates arises from potential biases in reporting fatalities and the number of infections. The former is caused by under-reporting and the latter arises from the wide variation in testing ability across countries. Using the case-fatality rate is likely to moderate this bias given both the numerator and the denominator are likely to be lower than their true unobserved values. 1 Recent work has also documented the link between climate indicators such as weather and temperature changes and the spread of Covid-19 using country-level data for: Brazil (Prata et al., 2020) ; China (Shi et al., 2020) ; USA (Bashier et al., 2020) ; Spain (Briz-Redon and Serrano-Aroca, 2020) and Turkey (Sahin, 2020) . 2 Clear parallels between the Covid-19 crisis and the climate emergency gave rise to a proliferation of studies drawing lessons from the former for the latter (Buscher et al., 2021; Botzen et al., 2020; Herrero & Thonton, 2020; Howarth et al., 2020; Klenert et al., 2020; and Manzanedo & Manning, 2020) . It was argued that both types of crisis are less costly to prevent than cure and hence the early action is key (Klenert et al., 2020 and Manzanedo & Manning, 2020) ; climate emergency should be treated with the same urgency as the Covid-19 pandemic (Herrero & Thornton, 2020) ; and behavioural changes are essential for future climate action (Botzen et al., 2021; Howarth et al., 2020) . 3 Excess mortality, the difference between number of deaths in excess of the historical average over the same period would be a better measure of mortality, yet this data is only available for a small number of countries, and for restricted periods at present (FT, 27 May, 2020) . We utilize the available data on excess death as part of our robustness analysis in Section 4. 4 It should be noted that several countries have witnessed a notable increase in the number of cases beyond our sample period, especially picking up from September 2020 -widely referred to as the second wave of the pandemic. While our analysis therefore focuses on the initial phase of the pandemic, most of the worsthit countries in the first wave were also hit hard in the second wave, allowing us to generalize our findings. 0 2000 4000 01feb2020 01apr2020 01jun2020 USA 0 1000 01feb2020 01apr2020 01jun2020 Brazil 0 500 1000 01feb2020 01apr2020 01jun2020 Spain 0 1000 01feb2020 01apr2020 01jun2020 UK 0 1000 01feb2020 01apr2020 01jun2020 Italy 0 1000 01feb2020 01apr2020 01jun2020 France 0 100 200 01feb2020 01apr2020 01jun2020 Germany 0 50 100 01feb2020 01apr2020 01jun2020 Turkey 0 200 01feb2020 01apr2020 01jun2020 India 0 200 01feb2020 01apr2020 01jun2020 Peru 0 200 01feb2020 01apr2020 01jun2020 Canada 0 200 01feb2020 01apr2020 01jun2020 China 0 50 01feb2020 01apr2020 01jun2020 Chile 0 200 400 01feb2020 01apr2020 01jun2020 Mexico 0 200 400 01feb2020 01apr2020 01jun2020 Belgium 0 200 01feb2020 01apr2020 01jun2020 Netherlands 0 200 01feb2020 01apr2020 01jun2020 Ecuador 0 50 100 01feb2020 01apr2020 01jun2020 Sweden 0 1 01feb2020 01apr2020 01jun2020 Singapore 0 20 40 01feb2020 01apr2020 01jun2020 Portugal 0 100 01feb2020 01apr2020 01jun2020 Switzerland 0 5 10 01feb2020 01apr2020 01jun2020 UAE 0 100 01feb2020 01apr2020 01jun2020 Ireland 0 20 40 01feb2020 01apr2020 01jun2020 Indonesia 0 10 20 01feb2020 01apr2020 01jun2020 Colombia Figure 1a . 7-day rolling average of number of deaths, Jan-May 2020 In addition, as is also noted in Atkeson (2020) , the difficulty in estimating Covid-19 casefatality rate at the earlier stages of the pandemic is significantly alleviated as large scale testing becomes available, as has been the case at the time of recording of our data in late May, 2020. Notwithstanding these arguments, we re-estimate our model using different dates and an alternative definition of mortality -the number of deaths per capita -as part of our robustness tests. We also provide an informal analysis of our key relationships using excess death data, which are available only for a limited number of countries. Brazil 0 10000 01feb2020 01apr2020 01jun2020 Spain 0 10000 01feb2020 01apr2020 01jun2020 UK 0 10000 01feb2020 01apr2020 01jun2020 Italy 0 10000 01feb2020 01apr2020 01jun2020 France 0 10000 01feb2020 01apr2020 01jun2020 Germany 0 5000 01feb2020 01apr2020 01jun2020 Turkey 0 10000 01feb2020 01apr2020 01jun2020 India 0 5000 01feb2020 01apr2020 01jun2020 Peru 0 2000 01feb2020 01apr2020 01jun2020 Canada 0 5000 01feb2020 01apr2020 01jun2020 China 0 5000 01feb2020 01apr2020 01jun2020 Chile 0 2000 4000 01feb2020 01apr2020 01jun2020 Mexico 0 2000 01feb2020 01apr2020 01jun2020 Belgium 0 500 1000 01feb2020 01apr2020 01jun2020 Netherlands 0 1000 2000 01feb2020 01apr2020 01jun2020 Ecuador 0 1000 01feb2020 01apr2020 01jun2020 Sweden 0 500 1000 01feb2020 01apr2020 01jun2020 Singapore 0 1000 01feb2020 01apr2020 01jun2020 Portugal 0 500 1000 01feb2020 01apr2020 01jun2020 Switzerland 0 1000 01feb2020 01apr2020 01jun2020 UAE 0 1000 01feb2020 01apr2020 01jun2020 Ireland 0 1000 01feb2020 01apr2020 01jun2020 Indonesia 0 1000 01feb2020 01apr2020 01jun2020 Colombia Figure 1b . 7-day rolling average of number of cases, Jan-May 2020 Our findings yield some important insights. First, we establish that climate risk has been a major determinant of the Covid-19 mortality. That is, countries that face a greater climate risk are found to have suffered a higher mortality. This is a robust result that prevails whether we measure climate risk on past outcomes such as deaths and income losses from weather related events in the past, or on current exposure and capacity to respond to climate change. Second, we also find that greater preparedness to climate risk is consistently associated with a lower Covid-19 mortality rate. Third, we find that collectivist societies are associated with consistently lower fatalities in the face of the Covid-19. Finally, our results suggest that public health capacity in terms of both health expenditures and number of hospital beds; the share of the elderly population; and economic resilience are important factors in fighting a pandemic. We present a range of robustness checks and show that our findings on the relationship between climate risk, culture and the mortality rate is remarkably consistent across all specifications. By formally establishing the link between climate-related risks and the Covid-19 mortality, this paper contributes to several strands in the literature. First, by quantifying additional costs of climate risks and inaction, we contribute to the debate on climate change. Second, by identifying climate and culture as additional sources of the cross-country variation in the pandemic-related mortality, we contribute to the fast-developing literature on the Covid-19 and efforts to develop our understanding of pandemics. Our findings clearly point to the crucial need for investment in both climate action and public health infrastructure as key lessons from the Covid-19 crisis, and hence towards better preparation for similar disasters in future. The rest of this paper is organized as follows. Section 2 outlines our empirical methodology. Section 3 describes the data and the variables used in the analysis. Section 4 presents and discusses our empirical findings, including a set of robustness checks. Finally, Section 5 concludes. To examine the link between climate risk, culture and the Covid-19 mortality rate, we estimate the following specification: where DR is death rate and i denotes country. In our benchmark case, DR corresponds to the cumulative number of deaths as a ratio of the cumulative number of cases, as recorded on the 26 th of May, 2020 (data on other dates are used as part of the robustness checks in Section 4). in equation (1) index, Stringency. In addition, denotes controls for time-invariant regional characteristics. In this section, we briefly discuss data on variables that are our focus in this paper -climate risk, readiness and individualism -and other controls containing factors that are also likely to influence the pandemic-related mortality. We also provide the reasoning for including each variable in our analysis. Full definitions of the variables used in the analysis and the sources of data are given in Table A1 in the Appendix A. Death rate -Our choice of mortality rate, DR, is the ratio of the number of confirmed Covid-19 deaths to the total number of confirmed cases -widely referred to as the case-fatality rate (DR1). It is a measure of how deadly the pandemic is in individual countries. As part of our robustness checks, we also use crude fatality rate (DR2), defined as the number of deaths per 100,000 of the population, as well as excess death, given by the number of deaths over and above the historical average over the same period. The analysis of excess death rate is provided only descriptively due to limited data availability. We utilize three separate climate-related indicators; Climate Risk; ND-Gain; and Readiness. Climate Risk -This is a country-specific measure of climate risk defined as the scale of damage from extreme weather-related events in the past, such as storms, floods, heat waves. Climate Risk is a combined measure incorporating four indicators; number of deaths; number of deaths per 100, 000; total losses of income; and total income losses per unit of GDP. The resulting index reflects the vulnerability of a country against similar events, and as such is branded as climate risk and is measured for the 1999-2018 period. ND-Gain -This measure, provided by the Notre Dame Global Adaptation Initiative, is a composite index combining an extensive set of both vulnerabilities to and resilience against climate change and other global challenges, quantifying the net readiness to climate change. Its components reflect exposure to climate change and contain country-specific measures of dependence on sectors susceptible to climate emergencies; capacity of adaptation; economic ability to adapt in the face of climate change; institutional readiness for adaptation; and social readiness incorporating factors such as infrastructure, education and innovation adaptation actions. ND-Gain is calculated as net readiness and is normalized to lie between [0-100], with a higher score indicating a greater readiness and/or lower vulnerability to climate change. Readiness -This measure, a component of ND-Gain index, combines economic, governance and social adaptability in the face of climate change, hence indicating how prepared individual countries are against the climate emergencies. Individualism -A widely used measure of cultural traits is the degree of individualism, reflecting the prevailing cultural worldviews. Individualism is defined as the strength of 'preference for a loosely-knit social framework in which individuals are expected to take care of only themselves and their immediate families', with a higher score indicating a greater degree individualism. (Hofstede, 2001) . Before we formally establish the impact of climate change and culture variables on the pandemic mortality rate, we present the bivariate relationships between mortality and Climate Risk; and mortality and Individualism, respectively in Figure 2 . The clustering of countries with high climate risk corresponding to high pandemic-mortality rate is clearly visible from Clearly, mortality rates during a public health emergency such as the Covid-19 are likely to be closely linked with public health capacity. We incorporate two controls as measures of the public health infrastructure, namely the level of health expenditure expressed as a percentage of total public spending, Health Exp; and the number of hospital beds available per 100,000 people in the population, Hospital Beds. Both measures feature widely in the existing work on pandemics including the Covid-19 episode, with the number of hospital beds consistently emerging as a key driver of mortality (see, for example, Morens & Fauci, 2007; Favero, 2020; Sussman, 2020) . Two other control variables relate to population characteristics. The age distribution of population has already been established as an important risk factor in the fight against the Covid-19 (see, for example, Dowd et al., 2020; Vincent & Taccone, 2020) . We use Pop65, the ratio of those aged 65 and above as a proportion of total population, to capture the role of population age in mortality rates. Countries with higher population density are also known to be at greater risk in the face of an infectious disease as the transmission of the virus is much faster in densely populated areas (Rocklöv & Sjödin, 2020) . To capture this effect, we use a density proxy, Density, defined as the number of people per square kilometre. In addition, we control for economic conditions prevailing at the time the country in question is hit by the pandemic by using both the latest pre-Covid-19 GDP growth rates, GDP Growth, and GDP per capita figures, GDP per capita. Finally, we also control for the regional effects that are common for countries in the same region, by incorporating regional dummy variables for seven distinct regions following the World Bank classification, as reported in Appendix A. We control for the policy response to the pandemic as a potential determinant of the resulting fatalities, through the use of Stringency Index (see, for example, Acemoglu et al., 2020; Anderson et al., 2020; Lin & Meissner, 2020) . This index taken from the Coronavirus government response tracker at the University of Oxford, reflecting the number and strictness of government policies in response to the pandemic. The attributes in the index include containment and closure policies; economic policies and healthcare responses such as testing regime; and emergency investment into healthcare. (1). Column (1) reports our baseline specification that only includes the control variables, with estimated results mostly in line with predictions. Specifically, we find a negative and significant relation between the Covid-19 mortality rate (DR1) and GDP Growth, and GDP per capita, suggesting better outcomes for countries with greater economic resilience. In contrast, the percentage of population aged 65 and over (Pop65) is positively related to the Covid-19 death rate. This is not surprising given the widely observed higher likelihood of pandemic mortality among the elderly population 5 . The estimated coefficient of Density is positive, but it is not statistically significant. Regarding the role of the health-care capacity, the estimated coefficient of Hospital Beds is negative and statistically significant at the 1 percent level, in line with the prediction that the state of the pre-Covid-19 public health infrastructure has played a significant role in controlling mortality rates (Favero, 2020; Sussman, 2020) . This variable captures the extent to which countries were prepared to deal with the Covid-19 cases in providing the support to the infected to recover from the disease. In our second specification in Table 2 , in addition to the pre-Covid-19 characteristics of countries, we incorporate Stringency, combining the extent and the strictness of government policies in response to the pandemic. We also include regional dummies to control for the common regional effects. The results suggest that Stringency is not significantly related to the Covid-19 mortality ratio. This may be due to the fact that Stringency is more likely to affect the number of infections rather than the number of deaths, resulting in an insignificant impact on the case-fatality rate. In Column (3), we incorporate a measure of pre-Covid-19 country-specific Climate Risk, one of our key variables of interest. As can be seen, the estimated relation between the Covid-19 mortality rate and climate risk is positive and significant, indicating that countries that are susceptible to damage from climate change are likely to suffer a higher Covid-19 mortality. As specified above, Climate Risk measure is derived from the observed losses a country has suffered due to weather and climate conditions in the past, signalling its vulnerability to other global threats such as pandemics. Estimation results in Column (3) suggest that countries that suffered losses -both human and economic -from previous emergencies, have also suffered greater mortality in the face of the current coronavirus crisis. In Table 3 , we further examine the relationship between climate risk and Covid-19 mortality by refining the way we measure climate risk. Our earlier measure, Climate Risk, is based on observed losses in the form of the number of actual deaths and economic losses arising from the past climate-related events in individual countries. We now consider an alternative measure of climate risk to capture a range of country-specific characteristics reflecting the ability and capacity of countries to deal with public health emergencies. Clearly, such ability and capacity would also be reflected in the success in reducing the Covid-19 related fatalities. Specifically, in our first specification in Table 3 , we use ND-Gain as a proxy for climate risk. As explained earlier, this measure is derived from two opposing aspects of climate risk, encompassing both the vulnerability and readiness of countries in relation to climate change and the associated risks. A high value of ND-Gain represents the strength of readiness over vulnerability, and hence countries with the highest level of ND-Gain are the ones who have the highest level of readiness and/or the lowest level of vulnerability to climate change. As can be seen from the estimated coefficient in Column (1), a reduction in vulnerability and/or a strengthening of readiness against climate change is associated with a fall in the Covid-19 mortality rate. An additional source of climate risk is related to societal attitudes to climate change, which in turn, is linked to cultural worldviews. It is widely agreed that a society's worldview, defined as 'its common values, shared beliefs and wisdom' provides the basis for individual and societal behaviour, including those towards environmental action (Dunlap & Liere, 1984) . A commonly used measure of cultural attitudes is the degree of individualism versus collectivism in a society. Emphasis on personal achievements and a strong sense of competition are among the key characteristics of individualist cultures. In contrast, in collectivist societies group or societal goals are above the individual needs and preferences. People in collectivist societies are 'integrated into strong, cohesive in-groups, which through people's lifetime continue to protect them in exchange for unquestioning loyalty' (Hofstede, 1991) . We, therefore, incorporate in our analysis a country-specific individualism score, Individualism, reflecting the differences in cultural worldviews prevailing in individual countries (taken from Hofstede database). A higher value of the index represents a more individualistic, and hence less collectivist, society. Including Individualism as an additional regressor in our estimation of the Covid-19 mortality serves two purposes. First, following from the above arguments on the link between individualism and climate change action, Individualism can be viewed as a proxy for climate inaction. In this respect, it can be taken as an alternative to the Climate Risk measure used earlier. Second, although the observation that collectivist countries have done better at fighting the Covid-19 pandemic has featured widely in the debates since the onset of the crisis, there is no formal evidence, as yet, on this relationship. Relatedly, the degree of individualism may impact on the pandemic-mortality rate through linkages other than the climate risk channel. For example, the relative success of Asian countries such as South Korea, Japan and China in controlling the Covid-19 is partly linked to the ability of these countries to impose surveillance on their citizens (Ahn, 2020) . It is also argued that such information exposure requires a culture that tolerates a certain level of surveillance, absent in most western societies which instead chose to restrict movements (Won Sonn, 2020). Further to the role of individualism through the climate action channel, individualism may also impact on mortality through the link between national culture and social cohesiveness. High social cohesiveness, including a high degree of trust among people, and a less individualistic orientation, are, in turn, viewed as an important driver of overall health in a society (Marmot, 2004) . Similarly, collectivism is viewed as an effective mechanism addressing health inequalities (Coburn & Coburn, 2007) . In the context of Covid-19, individualistic societies may be less likely to engage with social distancing, handwashing and wearing masks since they may be less concerned about the favourable impacts of such actions on others. Indeed, Bian et al., (2020) finds that more individualist US counties complied less with social distancing over the period, January-April, 2020. We, therefore, predict that in more individualistic societies the death rate is likely to be higher. Individualism as an additional country-specific regressor in Column (3) of Table 3 . Individualism is estimated with a positive coefficient, significant at the 1 percent level. This finding points to an adverse impact of individualism on the Covid-19 outcomes, suggesting that mortality rates are higher in countries with greater (lower) levels of individualism (collectivism). Similar to our earlier specifications, Column (4) incorporates ND-GAIN in the empirical model together with Individualism. The estimated positive and highly significant relation between the Covid-19 mortality rate, DR1, and individualism prevails in Column (5) which combine both Individualism and Readiness. Overall, the results presented in Tables 2-3 clearly show that the climate indicators and cultural traits exert a significant influence on the Covid-19 mortality rate. We now turn to the economic significance of the variables of interest that emerge as statistically In this section, we conduct several checks to test the robustness of our results. Starting with the measure of Covid-19 related deaths, we incorporate two additional measures in the analysis. First, we acknowledge that the number of reported cases, which is the denominator of our mortality rate, also depends on the ability of countries to carry out tests for the virus. We therefore use an alternative measure of Covid-19 related mortality rates, expressing the number of deaths in relation to population (per 100, 000), rather than as a ratio of the number of observed cases. Second, given that countries are at different stages of the Covid-19 cycle, we carry out our empirical analysis using the cumulative death rates on two earlier dates: 30 th of April and 13 th of May. The results corresponding to these different cases are reported in we define mortality by the ratio of the number of deaths to total cases (the number of deaths per 100,000 population). Variable definitions are presented in Appendix A. Robust standard errors are in parenthesis. ***, **, * denote the significance level at 1%, 5%, and 10%, respectively. As is stated earlier, a good measure of cross-country mortality comparisons is excess death ratio, defined as the number of deaths in excess of historical averages for the corresponding period. Currently this is only available for a very small number of countries, insufficient for reestimating our regression models. We collected data for 16 countries for which excess death numbers are available from a variety of sources (details are provided in Table A1 in the Appendix A). Bivariate relationships between the excess death ratio, defined as the number of excess deaths per 100, 000, and our three measures of interest, namely climate risk, readiness and individualism are displayed in Figure 3 . The relationships established earlier in our formal analysis between mortality, climate risk, readiness and individualism are also visible in these plots. Clearly, given the small size of the sample the displayed relationships are only suggestive rather than definitive at this stage. As an additional robustness check, we have re-estimated our main specifications by utilizing a Tobit regression, as presented in Columns 3 and 6 in Table 4 . The choice of Tobit regression is based on its ability to control for the observed number of zeros for the dependent variable. The presented coefficient estimates in Table 4 are marginal effects that are comparable to the coefficients of the OLS results presented in Columns 1-2 and 4-5. As is clear from Table 4, Tobit regressions produce very similar findings to the OLS results. We have also carried out a number of other robustness checks by controlling for the potential impact of public debt levels; using alternative definitions of health expenditure (as a ratio of GDP and per capita health expenditure); and excluding the US and the UK from our sample. Our results under the alternative specifications are presented in Appendix B. As can be seen, our findings prevail under all three cases. This paper investigates the role of climate risk, preparedness and culture in explaining the cross-country variation in the Covid-19 mortality rates. By using data from 110 countries, we estimate the relation between mortality rate and indicators of climate risk and culture, while controlling for a set of other determinants identified in the existing literature. To the best of our knowledge, this is the first paper formally exploring these interactions. Our results show that the Covid-19 mortality rate is linked to the pre-Covid-19 levels of climate risk, individualism and the readiness of countries to climate change related adversities. The estimated relations are both statistically and economically significant. Specifically, the greater the climate risk and the lower the readiness to climate change, the higher the risk of mortality from the Covid-19. We also find that countries with more individualistic societies fare significantly worse than the collectivist ones in coping with the pandemic, displaying greater mortality rates. Put differently, countries in which individuals look after each other and the environment, key hallmarks of sustainable societies, are better able to cope with climate and public health emergencies. In terms of our control variables, the findings reveal that the Covid-19 mortality rate is closely linked to public health capacity, measured by the share of health expenditure in GDP and the number of hospital beds; the share of those aged 65 and over in population; and economic resilience. We present a series of sensitivity checks and show that our findings are robust to different specifications, alternative definitions of the mortality rate as well as to different estimation methods. Our findings suggest that (1) countries facing higher climate risk were also more exposed to Covid-19 and hence suffered greater pandemic-mortality; (2) countries that were better prepared for the climate emergency were also better placed to fight the coronavirus crisis; (3) collectivist societies did significantly better than individualistic ones in limiting the damage from the pandemic with consistently lower fatality rates. Taken together, our findings point to the crucial need for investment in both climate action and public health infrastructure. A Multi-Risk SIR model with optimally targeted lockdown How South Korea flattened the coronavirus curve with technology. The Conversation Climate change and infectious diseases: from evidence to a predictive framework How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate How deadly is Covid-19? 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