Bayesian Hierarchical Modelling for Cost-Effectiveness Evaluation of Off-Grid Communities in Rwanda

Off-grid communities in Rwanda face challenges in accessing electricity, which impacts agricultural productivity and welfare. A Bayesian hierarchical model was developed to estimate the cost-effectiveness of off-grid community electricity projects in Rwanda, accounting for variability across different agricultural contexts and project sizes. Bayesian inference revealed that a specific solar energy system configuration led to an average annual increase of 12% in crop yields among participating farmers compared to those without access. The Bayesian hierarchical model provided robust uncertainty estimates, which were crucial for decision-making regarding the deployment and maintenance of off-grid renewable energy systems. Further research should focus on long-term economic impacts and sustainability factors to enhance the effectiveness of these projects in Rwandan agricultural settings. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.