Mortality rate, carbon emissions, renewable energy and per capita income nexus in Sub-Saharan Africa

Abstract

This study exclusively contributes to the health-environment discourse by using mortality rates, carbon emissions (proxy for environmental degradation), renewable energy and real per capita income to investigate these intrinsic relationships. This study uses an unbalanced sample of 47 Sub-Saharan African countries from 2005–2019 to reveal that: (1) both carbon emissions and renewable energy are associated with higher mortality rates; (2) real per capita income is associated with reducing mortality rates; (3) per capita income attenuates the effect of renewable energy on mortality rates, (4) persistency in mortalities exist; and (5) the health-environment-energy-income dynamics differ across income groups. Additionally, this study submits that the interaction of renewable energy and real per capita income dampens the positive effect of renewable energy on mortality rates and supports the argument that income levels lessen the extent of mortalities. Besides, these results vividly show that real per capita income reduces the devastating effect of renewable energy on infant and under-5 mortality rates from 0.942% to 0.09%, 2.42% to 0.55%, 1.04% to 0.09% and 2.8% to 0.64% for high and middle-income countries, respectively. This is a novel and significant contribution to the health-environment literature. Hence, real per capita income is a crucial determinant of mortality rate. Policy recommendations are discussed.

Impact of carbon emissions sources on life expectancy in Asia and the Pacific region

“…To address the first study objective, the study adapts the econometric construct of prior studies (Adeleye et al, 2022;Adeleye, Tiwari, et al, 2023;Azam et al, 2023;Barua et al, 2022;Das & Debanth, 2023;Radmehr & Adebayo, 2022) to express life expectancy as a function of each emissions source and a set of control variables. For empirical analysis, Equation ( 2) is specified explicitly as:…”

“…Real per capita income is associated with improving health and life expectancy (Luo & Xie, 2020; Mackenbach & Looman, 2013; Martín Cervantes et al, 2020; Wang et al, 2020). Renewable energy is also associated with improving human health (Adeleye et al, 2022; Adeleye, Tiwari, et al, 2023). Health expenditures can influence health and life expectancy (Arthur & Oaikhenan, 2017; Cremieux et al, 1999; Hitiris & Posnett, 1992; Martin Cervantes et al, 2019; Nixon & Ulmann, 2006; Singh & McCullough, 2022) and basic sanitation is correlated with improved wellbeing (Adeleye et al, 2022; Barua et al, 2022; Islam et al, 2018).…”

“…Renewable energy is also associated with improving human health (Adeleye et al, 2022; Adeleye, Tiwari, et al, 2023). Health expenditures can influence health and life expectancy (Arthur & Oaikhenan, 2017; Cremieux et al, 1999; Hitiris & Posnett, 1992; Martin Cervantes et al, 2019; Nixon & Ulmann, 2006; Singh & McCullough, 2022) and basic sanitation is correlated with improved wellbeing (Adeleye et al, 2022; Barua et al, 2022; Islam et al, 2018). Following Rahman et al (2022), we test the hypothesis that life expectancy is influenced by carbon emissions while controlling for other socioeconomic variables.…”

“…To address the first study objective, the study adapts the econometric construct of prior studies (Adeleye et al, 2022; Adeleye, Tiwari, et al, 2023; Azam et al, 2023; Barua et al, 2022; Das & Debanth, 2023; Radmehr & Adebayo, 2022) to express life expectancy as a function of each emissions source and a set of control variables. For empirical analysis, Equation (2) is specified explicitly as: and where and are intercepts; and are the main parameters under investigation; and are parameters for the four control variables; $$…”

“…The IV‐GMM is used for sensitivity and robustness checks to ascertain the consistency of the results, while the SGMM is engaged to specifically address the third objective. Health‐related studies (Adeleye et al, 2022; Adeleye, Tiwari, et al, 2023; Shobande, 2020) have used these estimation approaches to model health outcomes. For instance, bootstrapped estimations are known to offer better precisions of the estimates.…”