Investigating the Environmental Kuznets Curve hypothesis in Kenya: A multivariate analysis

In the quest towards a cleaner environment via the mitigation of climate change and its impact, this study examined the validity of the environmental Kuznets curve (EKC) hypothesis, energy efficiency and energy consumption indicators in Kenya. The study employed an autoregressive distributed lag technique, statistically inspired modification of partial least squares regression and Utest method to analyze four models with data spanning 1971 to 2013. Both the autoregressive distributed lag model and the Utest estimation confirmed an inverted u-shaped curve, thus, validating the environmental Kuznets curve hypothesis in Kenya. The study revealed that an increase in energy consumption exacerbates carbon dioxide emissions in the long-run. The statistically inspired modification of partial least squares regression revealed that electricity from renewable energy sources plays a critical role in carbon dioxide emission reduction. An increase in GDP per capita and household consumption expenditure increases energy consumption. Energy imports had no long-run effect due to the recent oil discovery, coal, prospects of nuclear energy and the potential for more renewable energy sources in Kenya. The study highlights that using sustainable technologies like, inter alia, carbon capture and storage in the exploitation of oil and coal are essential to reducing pollution. Rural-urban migration increases the burden on electric power consumption, thus, reducing energy efficiency if conservation options are not enforced. As a policy implication, engaging the public on energy conservation and management options will help curb energy challenges like load shedding — which appears troubling in Africa.