Climate change has had detrimental effects on North African nations, making them among the most vulnerable regions. This paper adds to the literature by assessing the short- and long-run impacts of key socioeconomic factors, i. e. , income, population, and energy intensity, on the Load Capacity Factor (LCF) in North African nations from 1990 to 2022. It also extends the traditional Environmental Kuznets Curve framework by investigating the validity of the Load Capacity Curve (LCC) hypothesis. To account for slope heterogeneity and cross-sectional dependence, the study employs the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model. The Method of Moments Quantile Regression (MM-QR) is further applied to account for distributional heterogeneity and check the robustness of the CS-ARDL findings. Finally, the JKS panel non-causality test is used to investigate causal relationships among the variables. The CS-ARDL model suggests the validity of the LCC hypothesis, with a turning point at 9, 054 in GDP per capita. The MM-QR results also confirm the LCC hypothesis and further show that the turning point declines as LCF levels rise. Additionally, the study confirms the detrimental impacts of population on the environment, while energy intensity does not exhibit a significant impact at lower quantiles. Based on these results, the study suggests the need for stringent environmental policies to enhance awareness and accelerate the energy transition in North Africa.
Ayad et al. (Fri,) studied this question.