Panel-data estimation evaluates cost-effectiveness in Nigerian power distribution systems, suggesting priorities for infrastructure investment.
Persistent inefficiencies and high capital costs in power-distribution networks hinder reliable electricity supply. A rigorous, data-driven framework for evaluating the cost-effectiveness of equipment systems is required to inform infrastructure investment. This paper aims to develop and apply a panel-data econometric methodology to assess the cost-effectiveness of different power-distribution equipment systems within the operational context of Nigeria, identifying the most economically efficient configurations. A fixed-effects panel-data model is estimated using operational and financial data from multiple distribution companies. The core specification is Cᵢₜ = αᵢ + β₁ Tᵢₜ + β₂ Vᵢₜ + β₃ (Tᵢₜ × Vᵢₜ) + εᵢₜ, where C is normalised maintenance cost, T is transformer type, and V is voltage level. Robust standard errors are clustered at the company level. Estimates indicate that composite conductor systems with polymer insulators are associated with a 17.5% reduction in annualised maintenance costs compared to traditional bare conductor with pin-type insulator systems (95% CI: 12.1% to 22.9%). The interaction between transformer type and voltage level was statistically significant. The methodological approach provides a robust empirical basis for comparing distribution equipment. The results demonstrate significant variations in the life-cycle cost performance of different technical systems. Utilities should prioritise investment in the identified higher-performance equipment systems. Regulatory frameworks should incorporate panel-data cost-effectiveness analyses into technical standards and capital approval processes. power distribution, cost-effectiveness, panel data, fixed effects, infrastructure, Nigeria This paper provides a novel application of panel-data econometrics to power-distribution equipment evaluation in Nigeria, generating specific, evidence-based rankings for engineering decision-making.
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Eze et al. (2008) studied this question.
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