Multiple pathways to a low-carbon future: Examining evidence from the BRICS+ - Stephen Asare Abankwah; Samuel Osei Afriyie (International Journal of Renewable Energy Development, 2026)
This study examines the structural and distributional drivers of energy transition in BRICS+ economies (Brazil, Russia, India, China and South Africa, Egypt, Ethiopia, Iran, UAE and Indonesia) over the period 1996-2023. We address the critical gap in understanding how economic and environmental factors differentially influence energy transition across varying levels of transition intensity. To account for cross-sectional dependence and endogeneity, we employ a smoothed common correlated effects instrumental variable quantile regression (CCE-IV QR) model, which robustly handles multiple endogenous regressors and unobserved global shocks. Our findings reveal that the drivers of transition are fundamentally stage dependent. The results reveal significant heterogeneity: CO₂ emissions negatively impact energy transition, particularly at lower quantiles, while GDP exhibits a positive association at median and lower quantiles but becomes insignificant at higher levels. Ecological footprint positively influences transition, whereas political stability and technological adoption yield mixed results, with the latter displaying a weakly significant positive impact. Robustness checks, including alternative specifications and subsample analyses, confirm the stability of these findings. These results necessitate a tiered policy framework. Particularly, early-transition economies should break fossil fuel lock-in through fiscal diversification and subsidy reallocation. Mid-transition economies should focus on reforming incumbent energy monopolies and financing decarbonization in hard-to-abate sectors. Advanced-transition economies need to overcome institutional fragmentation by creating cross-sectoral governance to direct investment toward grid modernization and systemic resilience. The methodological innovation of combining CCE with IV quantile regression further enhances the robustness of the analysis, addressing key econometric challenges in energy transition research.