Does Education Mediate the Relationship Between Renewable Energy and Ecological Footprint in Africa? Evidence From New Artificial Intelligence Methods

Energy transition is now an important agenda for policymakers across the world. Some sub-Saharan Africa (SSA) countries have made sizeable efforts to curtail the threats of climate change, being the worst-hit region of the world, by improving renewable energy consumption (REN), creating awareness on environmental issues, and taking climate action. However, the extent to which education and REN have contributed to alleviating ecological deterioration needs to be assessed using a robust technique that integrates a spatiotemporal weighting framework with neural network architectures. Therefore, the current study investigates the effects of REN on the ecological footprint (EF) and also examines the mediating role of education in the REN-EF nexus in SSA countries using the Geographically Neural Network Weighted Regression (GNNWR) model introduced by Yin et al. (2024). The GNNWR results suggest that REN and globalization enhance environmental quality by alleviating the EF. Conversely, economic growth worsens environmental quality, while education moderates the effect of REN on EF in SSA countries. The robustness of the GNNWR results is confirmed by the Temporally Neural Network Weighted Regression (GTNNWR) technique, while adding the stable behavior over time between the independent variables and EF. However, some spatial variation exists that the GNNWR model explicitly counters. In line with the findings, the region needs a regulatory framework to coordinate the content and type of education available at all levels of education. Fostering community engagement and building capacity for sustainability will help Africa overcome its environmental challenges and also ease the drive towards carbon neutrality by 2063.