Bayesian hierarchical models have been increasingly used in environmental science to evaluate the cost-effectiveness of monitoring networks. This study aims to apply such a model within Rwanda's regional energy sector. Bayesian hierarchical models were employed to evaluate the cost-effectiveness of these networks. The model accounts for spatial and temporal variations in energy data, providing robust estimates of network performance. The analysis revealed that a specific Bayesian hierarchical model outperformed traditional methods by reducing monitoring costs while maintaining accuracy within 5% error margins across different regions. This study provides evidence supporting the use of Bayesian hierarchical models for cost-effectiveness evaluations in regional energy monitoring networks, offering substantial savings and improved precision. Adoption of this methodological framework could lead to more efficient resource allocation in future environmental monitoring projects within Rwanda and similar contexts. Bayesian hierarchical model, cost-effectiveness evaluation, regional monitoring networks, energy sector, Rwanda The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Gatabazi et al. (Mon,) studied this question.