Page 14 - GHES-3-1

Global Health Economics and
Sustainability
Energy consumption and life expectancy in West Africa

to exhibit the same pattern of behavior with life expectancy 3. Methodology
as per capita energy use is a good substitute for income.
This conclusion is in line with that of Ostwald (1909), who 3.1. Theoretical framework
observed that energy consumption correlates with life The energy consumption–life expectancy nexus has been
expectancy. Therefore, Sargentis et al. (2021a) concluded investigated through the framework of Grossman’s theory
that energy production and consumption are strong and of healthcare demand. In this model, health is viewed as
positive indicators of life expectancy. a form of capital in which individuals can invest. Health
capital is accumulated through investments in factors,
2.2.3. Country-specific studies such as medical care, preventive measures, and a healthy
Using ARDL, Jebbin & Adebisi (2023) examined the energy lifestyle. The theoretical foundation of the present study
consumption–life expectancy relationship in Nigeria; is Grossman’s theory of healthcare demand. The theory
they found that per capita electricity consumption and is relevant in optimizing health outcomes through the
income per capita improve life expectancy, whereas fossil allocation of resources. The model is stated as follows:
fuel, alternative, and nuclear energy negatively affect life H = F(Xt) (3.1)
expectancy. Similarly, Akintunde et al. (2021) investigated
the energy consumption–life expectancy nexus in Nigeria where H represents health outcome, and X is a
from 1980 to 2017; they found that life expectancy vector of individual inputs in the health production
decreases further when energy consumption interacts with function (i.e., energy consumption, GDP per capita,
poverty. This finding reinforced the notion that an increase corruption perception index, health expenditure, and
in income is essential for increasing energy consumption food production). The functional relationship among the
and explained the continued use of fossil fuels by poor variables is stated as follows:
households. This result clearly shows that as the majority LEXP = f(REC, NRE, GDPpc, HEXP, CPI, FP) (3.2)
of people in Africa live below the poverty line, they would
be adjudged energy-poor and consequently have low life Equation 3.2 implies that life expectancy (LEXP) is
expectancy. Using ARDL, Ezeh et al. (2020) examined a function of renewable energy consumption (REC),
the nexus between household electricity consumption non-renewable energy consumption (NREC), GDP per
and life expectancy; they found that household electricity capita (GDPpc), health expenditure (HEXP), corruption
consumption significantly and positively affects life perception index (CPI), and food production (FP). The
expectancy. linearized form of Equation 3.2 is as follows:
Using ARDL, Omolua et al. (2023) investigated the LEXP = β + β REC + β NREC + β GDPpc + β HEXP+
0
nexus between corruption and life expectancy in Nigeria; β CPI + β FP+μ 1 1 2 3 4
5
6
they found that corruption has a significant and positive where LEXP denotes life expectancy at birth. REC
effect on life expectancy in Nigeria and recommended denotes renewable energy consumption, NREC denotes
stringent measures to tackle corruption. This finding non-renewable energy consumption, GDPpc denotes GDP
seemingly suggests that corruption is good for health
outcomes, but their recommendations contradict the per capita, HEXP denotes government health expenditure,
findings. Akokuwebe & Adekanbi (2017) examined how CPI denotes the corruption perception index, and FP
corruption impedes service delivery in relation to the denotes food production. μ is the disturbance/error term.
distribution of drugs and how it affects mortality in Nigeria β is a constant, and β , β β , and β are parameters to be
2, 3
4
1
0
in 2016. estimated. GDP , CPI, FP, and HEXP were added to the
PC
model as control variables for life expectancy.
A critical assessment of the abovementioned empirical
studies, especially cross-country studies, shows that these 3.2. Estimation technique
studies lumped all countries together irrespective of their CS-ARDL was used in this study because this method
income level. Qiang et al. (2023) showed that the renewable overcomes the issue of endogeneity and cross-sectional
energy–life expectancy relationship varies with different dependence in our dataset.
income levels, therefore, the energy consumption–life
expectancy nexus cannot be meaningfully determined 3.3. A priori expectation
while overlooking the level of development or income of LEXP/REC > 0, LEXP/NREC < 0, LEXP/GDPpc > 0, LEXP/
the countries being studied. Therefore, this study used HEXP > 0, LEXP/CPI < 0, LEXP/FP > 0.
the World Bank country grouping based on income: low,
lower-middle, upper-middle, and high-income countries Except CPI, which is expected to have a negative
to fill this gap in the literature. relationship with LEXP, all of the other variables are expected

Volume 3 Issue 1 (2025) 6 https://doi.org/10.36922/ghes.3518