Multi-Criteria Decision Analysis of Energy Transition Pathways in Senegal toward 2035 Using the Analytic Hierarchy Process

Abstract

Senegal’s energy system is undergoing a transition shaped by rising electricity demand, energy-security concerns, environmental objectives, and the growing role of domestic natural gas and renewable resources. This study applies the Analytic Hierarchy Process (AHP) to compare four energy-transition pathways towards 2035: an ambitious renewable transition, a balanced gas-and-renewables mix, domestic gas dominance, and a slow-transition pathway. Six criteria were considered: total discounted cost, power-system resilience and stability, environmental impact, job creation and local content, energy independence, and resource availability. Pairwise comparisons were undertaken by 11 students specialising in energy together with energy-sector experts, and consistency ratios were reported for the comparison matrices. The analysis demonstrates that the four pathways involve different trade-offs across economic, technical, environmental, social, and resource-related criteria. The criteria weights indicated that cost and resilience were the most influential factors (0.284 each), followed by environmental impact (0.158), employment (0.122), energy independence (0.089), and resource availability (0.063). Based on the final weighted scores, the balanced gas and renewables mix obtained the highest score (0.296), followed by domestic gas dominance (0.250), the ambitious renewable transition (0.242), and the slow transition pathway (0.212). These results indicate that no pathway performs uniformly best across all criteria. Within the study’s stated decision framework, the balanced pathway is presented as a compromise among affordability, system stability, environmental performance, employment considerations, energy independence, and resource availability. The findings illustrate the value of a structured multi-criteria framework for comparing alternative national energy-transition pathways while making the underlying decision criteria explicit.