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Longevity and environmental factors -

Better understanding of the determinants of national life expectancy is crucial for economic development, as a healthy nation is a prerequisite for a wealthy nation.

Environmental degradation is one of the critical determinants of life expectancy, which is still under-researched, as the literature suggests.

The empirical investigation is based on the model of Preston Curve, where panel corrected standard errors PCSE and feasible general least square FGLS estimates are employed to explore the long-run effects.

Pairwise Granger causality test is also used to have short-run causality among the variables of interest, taking into account the cross-sectional dependence test and other essential diagnostic tests. The results confirm the existence of the Preston Curve, implying the positive effect of economic growth on life expectancy.

Environmental degradation is found as a threat while health expenditure, clean water and improved sanitation affect the life expectancy positively in the sample countries. The causality test results reveal one-way causality from carbon emissions to life expectancy and bidirectional causalities between drinking water and life expectancy and sanitation and life expectancy.

Our results reveal that environmental degradation is a threat to having improved life expectancy in our sample countries. Based on the results of this study, we recommend that: 1 policy marker of these countries should adopt policies that will reduce carbon emissions and thus will improve public health and productivity; 2 environment-friendly technologies and resources, such as renewable energy, should be used in the production process; 3 healthcare expenditure on a national budget should be increased; and 4 clean drinking water and basic sanitation facilities must be ensured for all people.

Citation: Rahman MM, Rana R, Khanam R Determinants of life expectancy in most polluted countries: Exploring the effect of environmental degradation. PLoS ONE 17 1 : e Received: October 14, ; Accepted: January 5, ; Published: January 21, Copyright: © Rahman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data are publicly and freely available in the World Development Indicators published by World Bank and BP statistical Review of World Energy. Competing interests: The authors have declared that no competing interests exist. Numerous recent studies labelled environmental degradation as the most critical determinant of life expectancy in the world today.

Following Adams and Klobodu [ 1 ] and Mohsin, Abbas [ 2 ], this study has used CO2 emission levels to measure environmental degradation.

According to the World Health Organization [ 3 ], 4. Environmental degradation can adversely impact population health in several ways. Severe outdoor air pollution is responsible for rising chronic diseases e.

Asthma, heart diseases and lung cancer [ 5 , 6 ] and increasing premature mortality [ 7 ]. Others concluded that environmental degradation increases the likelihood of waterborne diseases [ 8 ] such as malaria and dengue fever [ 9 , 10 ].

Previous studies also concluded that environmental degradation increases the variability in the ecosystem, increasing the probability of floods and droughts [ 11 ]. As a result, environmental degradation might cause adverse variations in food production and water quality, which contributes to higher mortality, particularly among infant and elderly populations, as well as vulnerable people from lower socioeconomic background.

Wen and Gu [ 12 ] and Wang et al. Similarly, Majeed and Ozturk [ 14 ] demonstrated that countries with a higher level of environmental degradation experience greater infant mortality and vice-versa. Despite the above empirical evidence, many developing countries continue to disregard decisive actions against environmental degradation.

Chasing higher economic growth, these developing countries exert a lot of pressure on environmental resources e. water, land and forest , and their increasing production fosters higher CO2 emissions and industrial wastes [ 15 — 18 ]. Countries with high levels of environmental degradation fail to realize the long-run positive impact of strong environmental law on economic growth and health [ 19 ].

Their lack of focus on the environment warrants further considerations. This motivates us to pursue this research to fill up the current research gap. This paper used life expectancy as a public health outcome, and the objective of this research is to examine the key determinants of life expectancy in the most polluted countries of the world.

Our main variables of interest are economic growth, proxied by GDP per capita and environmental degradation, proxied by CO 2 emissions per capita. The rationale for selecting these 31 most polluted countries is all of these countries are developing countries where average life expectancy is lower 70 years compared to that of developed countries around 80 years.

Moreover, the variables used in this paper are along the line of past literature. The primary hypothesis of the study is that the positive correlation between economic growth and life expectancy will persist, and environmental degradation will have a significantly higher negative impact on life expectancy than often estimated in empirical studies.

Another hypothesis is that health expenditure per capita [ 21 — 23 ], availability of safe drinking water and sanitation facilitates [ 24 — 26 ] will positively influence longevity.

Following the studies of Majeed and Ozturk [ 14 ], Ebenstein et al. Our findings will be critically important to implement effective public health and environmental policies, in particular with an increasing number of elderly populations in these countries.

In addition, the outcome of this study will also assist in executing focused health interventions for the most at-risk groups of the community, develop an environmental pollution monitoring system and strengthen environmental laws and regulations.

Life expectancy is the average outstanding years of life at a specific age of an individual, which captures the prevailing patterns of mortality for various age groups [ 31 ] concluded that longer life expectancy is desirable for its inherent value as well as for the important life achievements of each individual.

It is considered as one of the most critical parameters of the Human Development Index, and improvement of life expectancy is principal to much medical research.

In addition, good health and longevity are related to higher productivity which is an essential stimulus for sustainable economic growth [ 15 ]. Income level is considered as one of the major drivers of life expectancy, and many researchers have concluded that higher income leads to greater life expectancy in a country [ 21 , 32 , 33 ].

For example, Mackenbach and Looman [ 34 ] found that rising national income reduced the mortality from infectious diseases in European countries over the period of to while they studied the upward shift of the Preston curve the link between life expectancy and per capita real income for the selected European countries.

However, significant disparities in life expectancy are predominant among countries with identical per capita income [ 35 ]. For example, according to the World Bank [ 20 ] data, life expectancy in Bangladesh 72 years and Nepal 70 years are higher than in India 69 years and South Africa 64 years , despite having lower per capita income [ 20 ].

Understanding the determinants of the life expectancy of a nation is a complex issue. Healthcare expenditure is also revealed as a factor with a strong positive impact on life expectancy in the studies of Bein et al.

In terms of developed countries [ 41 — 43 ] found that increasing health expenditure positively impacts life expectancy. In another study on 40 countries of sub-Saharan Africa SSA , Arthur and Oaikhenan [ 44 ] also revealed the improved life expectancy due to increased healthcare expenditure.

However, van del Heuvel and Olaroiu [ 45 ] and Rahman et al. The studies of Filmer [ 46 ] and Barlow and Vissandjee [ 47 ] also support this no impact result. Sanitation is also linked to life expectancy.

Poor sanitation causes the transmission of many diseases such as cholera, diarrhea, hepatitis A, typhoid, etc. According to this report, around , deaths each year occur mainly due to poor sanitation.

Similarly, unclean or contaminated drinking water transmits various diseases that adversely affect life expectancy via infant mortality [ 22 , 49 ].

WHO Report [ 50 ] also notes diarrheal deaths each year, mostly related to unclean drinking water. Islam et al. Along with other known factors, they have found economic freedom, level of corruption, carbon dioxide emission and success in achieving millennium development goals are highly correlated to higher life expectancy.

Past empirical studies have identified other determinants of life expectancy such as lifestyle and occupation [ 52 ], nutrition and food availability [ 53 ], government expenditure on social protection and education level of the population [ 54 ], and availability of healthcare services and professionals [ 55 ] Auster et al.

This seminal work concluded that environmental factors e. education, income, diets, physical activities, and psychological health were more important in reducing mortality in comparison to medical care.

Recently, in a similar study Thornton, J. The current study attempted to incorporate all the available variables determining life expectancy into the empirical model to identify the factors influencing life expectancy in the 31 most polluted countries in the world.

However, some key variables such as education level and lifestyles were not available for all the countries for the period of — Most polluted countries are selected where the average PM2. The countries are Afghanistan, Bahrain, Bangladesh, Bulgaria, Cambodia, Chile, China, Croatia, Czech Republic, Ethiopia, India, Indonesia, Iran, Kazakhstan, Korea Republic, Kuwait, Mexico, Mongolia, Nepal, Nigeria, Pakistan, Peru, Poland, Serbia, Sri Lanka, Thailand, Turkey, Uganda, United Arab Emirates, Uzbekistan and Vietnam.

Also see S2 Appendix. The data were acquired from the World Development Indicator [ 20 ], World Bank open database.

The carbon emissions data for the period from to are not available in the WDI; therefore, these are sourced from the British Petroleum BP Statistical Review of World Energy [ 57 ]. Table 1 shows the summary statistics of the variables that are used in the study.

Average per capita CO 2 emissions are 6 metric tons in the sample countries. Preston [ 33 ] develops a model, known as Preston Curve, to explore the relationship between life expectancy and real GDP per capita and found a positive link between these two variables.

The basic model of the Preston Curve is noted below: 1. Where LIF and GDP represent life expectancy and real GDP per capita a proxy for economic growth , respectively.

The coefficient of GDP is expected to have a positive sign. This study uses the augmented model of Preston Curve by adding some other relevant explanatory variables as stated above.

Therefore, the used model for the study is as follows: 2. CO 2 emissions are believed to impact human life expectancy [ 28 , 59 , 60 ] as a major determinant. It is expected that CO 2 emissions have a negative relationship with life expectancy.

We expect a positive link between LIF and the rest of the explanatory variables. This study uses panel data so that our baseline model will be re-written as follows: 3.

Subscripts i and t indicate country and year, respectively. β1- β5 are the vectors of coefficients for time-varying explanatory variables. ε it is the error terms for country i at year t. All variables are transformed into natural logarithms in order to reduce heteroscedasticity.

This research conducts a panel data approach as this analysis has certain advantages. First, it has both time-series and cross-sectional dimensions. Second, the panel data analysis addresses the individual heterogeneity issue.

Third, this analysis reduces multi-collinearity and increases the degrees of freedom. Lastly, it overcomes the problems associated with time-series analysis [ 61 ].

The test for panel unit root is the first necessary step to verify the stationary properties of the variables. A number of panel unit root tests exist in the literature. In this study, we use four first- and second-generation panel unit root tests for enhancing the robustness of results.

They are Pesaran [ 62 ] test, Im, Pesaran and Shin IPS [ 63 ] test, Fisher [ 64 ] augmented Dickey—Fuller ADF test and Harris and Tzavalis [ 65 ] unit-root test. The null hypothesis for the panel unit root tests is: each data series is non-stationary at the level but stationary at the first difference across countries.

The formulas for the various tests are shown in S3 Appendix. Panel data with autocorrelation, cross-sectional dependence and heteroscedasticity make serious problems for econometric analysis. The existence of cross-sectional dependence in a panel study indicates that there exists a common unnoticed shock among the cross-sectional variable over a time period [ 66 ].

Khan et al. Parks [ 69 ] proposes Feasible Generalized Least Squares FGLS , which is efficient in overcoming group-wise heteroscedasticity, time-invariant cross-sectional dependence and serial correlations.

Beck and Katz [ 70 ] suggest an alternative panel-corrected standard error PCSE estimates to deal with the panel nature of the data. It is believed that FGLS and PCSE effectively deal with heteroscedasticity, serial correlations and cross-sectional dependence. Le and Nguyen [ 71 ] advocate that PCSE and FGLS are two techniques that rectify for autocorrelation and heterogeneity and yield robust standard errors.

Ikpesu et al. Some previous studies use FGLS, which overcomes heteroscedasticity and autocorrelation [ 73 , 74 ]. Alonso et al. This study uses the time-series-cross-sectional Prais-Winsten PW regression with panel-corrected standard errors PCSE as a baseline estimate, which allow for disturbances that are contemporaneously correlated and heteroskedastic across the panel.

The PCSE correction facilitates in avoiding statistical overconfidence, which is often connected with the feasible generalized least-square estimator where the total periods are smaller than total sample countries [ 70 , 76 ]. This study sample consists of 31 countries, and the period of study is for 18 years, — First, this study tests for the existence of heteroscedasticity, cross-sectional dependence and autocorrelation.

Also, to investigate the stationary of the variables, this study adopts the Pesaran [ 62 ] CIPS, the Im-Persaran-Shin unit root test [ 63 ] and the Levin-Lin-Chu unit root test [ 77 ]. Table 2 shows that the cross-sectional dependence exits in all of the variables which can arise because of spatial or spill over effects or due to unobserved common factors [ 78 ].

Due to the presence of cross-section dependence, both the standard homogeneous estimators for panel data Fixed-effect, Random-effect, or First Difference and the heterogeneous Mean Group estimator are inconsistent [ 79 ]. Hence, we addressed this issue to avoid significant size distortion in the regression analysis.

Besides, most of the variables are stationary at the levels, which indicated that the individual observed series are stationary around a deterministic level [ 80 ] and the fixed, random effect and pooled OLS models are fit for this study [ 81 ].

Table 3 demonstrates the results of heteroscedasticity and autocorrelation, indicating that heteroscedasticity and auto-correlation exist in our used panel data.

In this context, this study adopts the Panel-Corrected Standard errors model PCSE to explore the long-run effects of carbon emissions on life expectancy following the panel data estimation, as shown in Eq 4. This method has been adopted following Bailey and Katz [ 82 ], Jönsson [ 83 ], Le et al.

Following the previous studies, this study also uses the FGLS method for checking the robustness of results [ 84 , 86 — 88 ]. Following Asongu et al. Table 4 reports the PCSE long-run estimation results concerning the impact of life expectancy for 31 most polluted countries over the period — Carbon emissions have a significantly negative impact on life expectancy, suggesting that higher the carbon emissions lower the life expectancy.

Therefore, this study finds that carbon emissions is a vital driver of life expectancy. For robustness checks, this study also estimates a model using FGLS. Table 5 reports the determinants of life expectancy. Economic growth appears to have significantly positive effects on life expectancy supporting Preston Curve.

The carbon emissions are shown to have negative effects on life expectancy; health care expenditure, water and sanitation appear to have significant and positive effects on life expectancy. Overall, the results from FGLS demonstrate consistent results with PCSE estimates.

To address the impact of the time trend in the panel data model, this study re-estimated the PCSE and FGLS model using a time trend variable using the assumption of a linear trend in the outcome variables over time. The results demonstrated identical coefficient sings which re-established the soundness of the econometric analysis.

The findings are noted in Table 6 and Table 7 in S1 Appendix. Table 6 shows the short-term causality between life expectancy, carbon emissions, economic growth, healthcare expenditure, drinking water and sanitation.

This study finds that there is a one-way causality running from carbon emissions to life expectancy. In other words, more carbon emissions threaten life expectancy. Additionally, this study reveals that there are bidirectional causal links between life expectancy and drinking water as well as life expectancy and sanitation.

The study, however, found no short-run causality between GDP and life expectancy and between health expenditure and life expectancy.

It is worthy to note that there are some limitations that we faced in terms of data, variable selection, statistical measurements, and estimated results. First, this study had to select a short period of the data set — just because data for all selected variables for all countries were not available beyond this period when the study was conducted.

Since this study is based on balanced panel data set, consideration of extended period was not possible. Second, the estimation used two data sources: World Bank and BP statistics, because CO 2 emissions data were not available in the World Bank data source for the last three years — due to the paucity of data.

These variables may also affect life expectancy. Fourth, cross-sectional dependence, heteroscedasticity and autocorrelation were found in the panel data.

To address this last limitation, this study used appropriate estimation methods, PCSE and FGLS regressions. This paper investigates the determinants of life expectancy in 31 most pullulated countries of the world with a special focus on environmental degradation measured by CO 2 emissions.

These countries are also low-middle income countries. Taking the BP and World Bank annual data for the period of 18 years — , we have used the PCSE model to estimate the long run effects of environmental degradation on life expectancy. Then we have applied FGLS regression to check the consistency of the results found in PCSE regression.

We also check the cross-sectional dependence and perform other essential diagnostic tests for panel data. The Pairwise Granger Causality Tests show one-way causal link from carbon emissions to life expectancy and bidirectional causal links between life expectancy and drinking water, and life expectancy and sanitation.

Thus, our results identify that environmental degradation is a threat for attaining the improved life expectancy in the sample countries. Our findings showed that economic growth has a significant positive association with life expectancy, supporting Preston Curve.

This finding is consistent with theory and the previous research evidence see Luo and Xie [ 91 ] and Wang et al. There are several reasons. Previous studies have shown that increasing national income reduces the adverse impact of infectious diseases in the communities Mackenbach and Looman [ 34 ], increases food availability and consumption [ 53 ], and government expenditure on social protection [ 54 ].

Increasing income is also associated with the higher education level of the population [ 54 ]. Therefore, increased income is one of the major factors determining life expectancy in polluted countries.

The healthcare expenditure has a significant positive impact on life expectancy, implying that higher healthcare expenditure would increase life expectancy.

This result is in line with the results of previous studies indicating that healthcare expenditure is an important factor in life expectancy Bein et al. In addition, higher health expenditure is associated with greater availability of healthcare services and professionals [ 94 ].

Increased availability might have increased the access and use of healthcare in these 31 countries. Hence, we have found a positive impact of health expenditure on life expectancy. This study also found that increasing access to clean water and improved sanitation improves life expectancy.

These results are similar to previous findings of [ 22 , 49 ]. Assess your overall health and lifespan potential with the 5-minute free NOVOS Longevity Assessment. In contrast, rural living provides a quieter, less polluted environment with more access to green spaces and healthy foods.

However, rural areas often lack access to healthcare and social support networks, which can contribute to a lower quality of life and reduced lifespan Singh et al. Rural areas also have higher rates of obesity, smoking, and physical inactivity, which can increase the risk of chronic diseases Singh et al.

Suburban areas offer a good balance between the benefits and drawbacks of urban and rural living. Suburban areas often provide access to healthcare, recreational activities, greenspaces and social opportunities while also providing a quieter, less polluted environment than urban areas Adams et al.

Good air quality is essential for maintaining respiratory health, and exposure to air pollution and allergens can increase the risk of respiratory diseases, such as asthma and chronic obstructive pulmonary disease COPD Kim et al.

Air pollution can also impact cardiovascular health, contributing to heart disease and stroke Brook et al. In fact, a publication produced by the Energy Policy Institute at the University of Chicago EPIC in shared that air pollution has a slightly greater impact on death than smoking, three-times greater impact than alcohol use or unsafe water, and five-times greater than HIV and AIDS.

Specifically, they write:. First-hand cigarette smoke, for instance, reduces global average life expectancy by about 1. Individuals can take steps to maintain good air quality by periodically cleaning air vents and filters and making sure to properly ventilate their homes, especially when cooking or using cleaning products that can release pollutants into the air.

Purifying indoor air with HEPA filters and plants can also contribute to better long-term health. Kim et al. Individuals should discuss their concerns with their employers and seek guidance from relevant experts or authorities in their industry.

Living in a polluted environment can negatively impact multiple hallmarks of aging, including mitochondrial dysfunction , cellular senescence , and inflammation Zhang et al.

Exposure to toxins can also impact genomic instability , stem cell exhaustion , and deregulated nutrient sensing Mannino et al. Air pollution can affect proteostasis , altered cellular communication , and epigenetic alterations , and has been linked to telomere shortening Kim et al.

Additionally, social isolation and lack of social support , which can occur at higher frequencies in rural and urban areas, can impact genomic instability and epigenetic alterations , two hallmarks of aging.

Studies have shown that social isolation can lead to changes in gene expression and DNA methylation patterns, which can have negative health consequences Cacioppo et al.

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