The main aim of this current study is to empirically scrutinize the determinants of energy consumption for 24 African countries sub-grouped into three panels based on income levels: low-, lower-middle-, and upper-middle-income countries, from 1990 to 2015. Due to the presence of heterogeneity and cross-sectional reliance among country groups, recently developed econometric approaches, which include cross-sectional Im, Pesaran, and Shin together with cross-sectional Augmented Dickey-Fuller stationarity tests, Pedroni and Westerlund-Edgerton cointegration assessment, dynamic common correlated effect estimation approach and Dumitrescu-Hurlin Granger causality test are employed. Empirically, our findings depict analyzed variables are stationary and characterized by long-term stability affiliations for all panels. Economic growth, urbanization, population growth, and oil price with labor and capital stock as intermittent variables had palpable significant positive sway on energy consumption for all panels though their respective weight of contribution differed from one country group to another. The granger test of causation unveiled that (i) among all panels, urbanization and energy consumption are connected bidirectionally, whereas population growth causes energy consumption; (ii) a one-way causal link from economic growth to energy use is evidenced in low-income African countries, whereas a two-sided connection is confirmed in both lower-middle- and upper-middle-income economies; (iii) a bilateral causal association in low-income African nations is observed amid oil price and energy use, while a uni-lateral relationship extends from oil price to energy consumption in both lower-middle- and upper-middle-income nations in Africa. Such new methodologies and findings reveal that the long-term estimated effects as well as causal affiliations amid variables are skewed by different income levels of African countries in an attempt to conserve energy. Policy recommendations are further propose.
Keywords: Africa; Dynamic common correlated effect; Dynamic heterogeneous panel data model; Energy consumption; Income level.