Environmental Hazards and Life Expectancy in Africa: Evidence From GARCH Model

Abstract

This study investigates the extent to which environmental hazards affect the life expectancy in Africa using Nigeria time series data spanning from 1960 to 2017. The study adopted generalized autoregressive conditional heteroscedasticity (GARCH) model in estimating the total number of 58 (years) observations to ensure robustness in the estimation results. The estimation results show that environmental hazards in terms of carbon dioxide (CO 2) emission from solid fuel consumption reduce life expectancy (LEX) by 1 month and 3 weeks with a statistically significant result. Also, income, as proxied by GDP, extends LEX by 1 year 6 months with statistically insignificant result, while population growth (POPG) equally extends LEX by 5 years 5 months due to increase in human resource/manpower which enhances agricultural productivity in Africa. Based on the empirical findings, there is a need for the African Union (AU) to adopt a policy regulating the excessive CO 2 emission from solid fuel consumption to ameliorate the negative consequences it exerts on the lifespan of the African population. Also among other policy recommendations, the economies in Africa should increase budgetary allocations to science and technology sector to drift the economies from solid fuel consumption to more robust electricity/digital driven technology and hybrid-energy efficient mechanisms.

Empirical Evidence for the Impact of Environmental Quality on Life Expectancy in African Countries

“…54 This suggests that if Africa utilized its hydropower potential effectively it could be expected to simultaneously reduce environmental pollution and improve life expectancy. Furthermore, the results of this study agree with those of other studies, 21 – 23 , 25 , 2 , 27 – 30 even though they used different indicators to measure environmental quality. Hence, the present study confirms that environmental quality is crucial to increasing life expectancy in African nations.…”

“… 21 – 23 , 25 , 2 , 27 – 30 For example, the results are similar to those of similar to a study by Mariani et al 21 which used life expectancy at birth (in years) and EPI to measure the target variables which may have produced similar results. Similarly, to measure health status, this study and others 22 , 23 , 25 , 2 , 27 – 30 used a better and more easily available proxy (either life expectancy or mortality rate), which may explain the similarities in the findings. The data for the dependent variable of this study and most other studies 22 , 23 , 2 , 28 , 30 were sourced from the World Bank.…”

“…Similarly, to measure health status, this study and others 22 , 23 , 25 , 2 , 27 – 30 used a better and more easily available proxy (either life expectancy or mortality rate), which may explain the similarities in the findings. The data for the dependent variable of this study and most other studies 22 , 23 , 2 , 28 , 30 were sourced from the World Bank. Furthermore, a few other studies 23 , 28 employed the latest methodology (ARDL).…”

“…Matthew et al 28 and Nkalu and Edeme 30 have examined the impact of environmental pollution on life expectancy in Nigeria. However, findings concerning a single country (especially a country with an oil-based economy) cannot represent the majority of African countries and its policy recommendations may not, therefore, work for most African countries.…”

Environmental Quality and Health Status: Dynamic Ordinary Least Square Estimation for Developing Asian Countries

“…The impact of per capita income on health is positive and significant at the 1%, indicating that improvement in income provides better healthcare opportunities to individuals. Nkalu and Edeme (2019) and Zaidi and Saidi (2018) have identical results about the effect of income on health status. Urban population is negatively associated with the health variable and is significant at the 5% level of significance.…”