njerinjuguna-svg/Benin-electrification

Least-cost electrification analysis for Benin (demand modelling, LCOE comparison, and investment prioritisation for unelectrified settlements) # ⚡ Benin Least-Cost Electrification Analysis > A satellite-enriched, least-cost electrification model for **16,273 unelectrified settlements** > across Benin, West Africa — identifying the optimal technology and financing model for > 3.9 million people currently living without electricity. --- ## 📋 Table of Contents - Overview - Key Findings - Project Structure - Data Sources - Methodology - Demand Estimation - Technology Options & Cost Model - Least-Cost Decision Logic - Investment Prioritisation - Sensitivity Analysis - Assumptions - Outputs - How to Run - Limitations & Future Work --- ## Overview Benin has a rural electrification rate of approximately **13%** — one of the lowest in West Africa. Over **3.9 million people** live without any electricity access, concentrated in remote settlements far from the existing medium-voltage (MV) grid. The state utility SBEE is insolvent, importing 95% of national power from neighbouring countries. This analysis implements a **least-cost electrification model** that: 1. Estimates electricity demand for each unelectrified settlement over a **15-year planning horizon (2025–2040)** 2. Calculates the Levelised Cost of Energy (**LCOE**) for three technology options per settlement 3. Assigns the **least-cost technology** with a productive-use upgrade rule grounded in VIDA's methodology 4. Incorporates **Relative Wealth Index** to map each settlement to a realistic financing model 5. Scores and ranks all 16,273 settlements for **investment prioritisation** 6. Tests **four cost scenarios** to assess recommendation robustness The model is enriched with **seven Google Earth Engine (GEE) satellite layers** — solar radiation, nighttime lights, land cover, terrain slope, vegetation index (NDVI), population density and rainfall — extracted at point level for every unelectrified settlement centroid. This makes the model spatially precise in a way that national-average approaches cannot achieve. --- ## Key Findings | Metr …