Bayesian Hierarchical Model for Measuring Adoption Rates of Power-Distribution Equipment Systems in Uganda,

The adoption of power-distribution equipment systems has been a critical area for improving energy efficiency and reliability in Uganda's rural electrification programmes. A Bayesian hierarchical model was employed to analyse data collected from field surveys conducted in Uganda between and . The model accounts for spatial and temporal variations in adoption rates by incorporating regional-specific covariates into the analysis. A key finding is that the adoption rate of solar-powered systems was significantly higher than those of diesel generators, with a proportion of 78% compared to 22%. This suggests a clear preference for renewable energy sources over traditional fossil fuel-based solutions in rural settings. The Bayesian hierarchical model provided robust estimates of adoption rates and their associated uncertainties, facilitating more informed decision-making for future power distribution projects in Uganda. Given the strong preference for solar-powered systems, further investments should be directed towards expanding solar energy infrastructure to underserved rural areas. Additionally, tailored training programmes on solar system maintenance and operation should be implemented. Bayesian hierarchical model, adoption rates, power distribution equipment, renewable energy, Uganda The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.