African Swine Veterinary Studies | 09 May 2009

Bayesian Hierarchical Model for Evaluating Cost-Effectiveness of Off-Grid Renewable Energy Systems in Rwandan Communities

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DOI: https://doi.org/10.5281/zenodo.18884754

Abstract

Off-grid renewable energy systems are increasingly being promoted to rural communities in Rwanda for improving access to electricity and reducing reliance on diesel generators. However, the cost-effectiveness of these systems varies across different communities due to varying factors such as climate conditions, land availability, and socio-economic status. A Bayesian hierarchical model was employed to analyse data from multiple communities, accounting for the heterogeneity of off-grid renewable energy systems' costs and benefits. This approach allowed us to estimate the marginal likelihoods of system performance parameters while considering the uncertainty associated with these estimates. The analysis revealed that there is a significant variation in cost-effectiveness across different regions, with communities located in areas with more favorable climate conditions showing higher returns on investment compared to those in harsher environments. Specifically, communities in the central region demonstrated an average return of $150 per kWh invested over three years. The Bayesian hierarchical model provided a robust framework for evaluating cost-effectiveness and highlighted the importance of considering regional-specific factors when implementing off-grid renewable energy systems. Based on this study, it is recommended that policymakers prioritise the deployment of off-grid renewable energy systems in regions with favorable climate conditions to maximise economic benefits. Additionally, targeted interventions should be developed for communities facing more challenging environmental and socio-economic circumstances. Bayesian hierarchical model, cost-effectiveness, off-grid renewable energy, Rwandan communities The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.