Bayesian Hierarchical Model for Evaluating Cost-Effectiveness of Off-Grid Communities Systems in Ethiopian Forestry and Renewable Energy Context

Off-grid communities in Ethiopia face challenges in accessing reliable electricity supply, necessitating innovative solutions to enhance energy access and sustainability. A Bayesian hierarchical regression model was employed to estimate the cost-effectiveness metrics of various off-grid system configurations. The model accounts for spatial variability and heterogeneity in resource availability and demand across different regions. The model revealed that solar photovoltaic systems were consistently more cost-effective than hybrid systems (cost savings up to 15%) when considering long-term operational costs and maintenance requirements. This study provides a robust framework for assessing the economic viability of off-grid systems in Ethiopia, particularly in forestry and renewable energy sectors. Policy makers should consider these findings in designing incentives or subsidies for off-grid system adoption to promote sustainable energy access in rural areas. The empirical specification follows Y=β₀+β^ X+ε, and inference is reported with uncertainty-aware statistical criteria.