The health impacts of renewable energy consumption in sub-Saharan Africa: A machine learning perspective

Abstract

Recently, countries in sub-Saharan Africa (SSA) have shown increasing interest in transitioning to renewable energy due to climate change. However, the health impacts of renewable energy use are not thoroughly studied in the African context. This study investigates the impacts of renewable energy use on health outcomes, including life expectancy, maternal, and under-five mortality, in 26 SSA countries selected during the period 2000 to 2022. The main contribution of our study is the use of novel machine learning techniques known as Kernel-based Regularized Least Squares (KRLS) to fill gaps in the existing literature. Our study controlled for health expenditure, income, carbon dioxide (CO₂) emissions, and tuberculosis cases. The findings show that (i) renewable energy use significantly improves health outcomes in SSA, including increased life expectancy and reduced maternal and under-five mortality; (ii) carbon dioxide (CO₂) emissions and tuberculosis incidence have negative impacts on health outcomes, leading to decreased life expectancy and increased maternal and under-five mortality rates; (iii) the impact of renewable energy use on life expectancy and maternal mortality is nonlinear at the 25th and 75th percentiles, respectively. The study discusses potential pathways through which renewable energy use impacts health outcomes. The practical policy implication is that African governments and their collaborative partners adopt and implement renewable energy policies and programs, linking them to national health policies, development plans, and budget cycles to improve public health in the region.