| Popis: |
This study examines the impact of clean energy technologies on environmental sustainability in 29 sub-Saharan African (SSA) countries while controlling for income, industrialization and trade from 2002 to 2018. We used the generalized quantile regression,which controls variable endogeneity using lagged instruments. In addition, Bayesian panel regression was used for robustness checks. We used the load capacity factor (LCF) as a broad measure of environmental sustainability that captures both nature's supply and man's demand for the environment. The findings show that clean energy technologies (clean fuels and renewable energy), have positive and statistically significant effects on environmental sustainability for nearly all quantiles in SSA. The findings are still the same after verifying the robustness analysis, showing that the coefficients for clean fuels and renewable energy technologies in quantile regression are within the Bayesian probability credible intervals and all have positive impacts on ensuring environmental sustainability in SSA. Furthermore, the results show that economic growth (income) has asymmetric (both negative and positive) effects on environmental sustainability across different quantile, confirming the Load Capacity Curve (LCC) hypothesis in SSA while accounting for clean energy technologies in the model. The findings further indicate that industrialization and trade have heterogeneous impacts on environmental sustainability. Overall, our findings imply that clean energy technologies improve environmental sustainability in SSA. Our main recommendation to policymakers is that sub-Saharan Africa needs to reduce the cost of energy services (i.e., renewable energy and clean fuels for cooking) in order to achieve greater environmental sustainability. |
