Abstract
{ "background": "The reliability of power-distribution infrastructure is a critical determinant of economic development and social welfare. In Ethiopia, ageing assets and operational challenges have historically led to significant service interruptions and safety risks, necessitating a robust analytical framework for policy intervention.", "purpose and objectives": "This policy analysis aims to methodologically evaluate the state of the nation's power-distribution equipment systems and to develop a forecasting model for quantifying the risk reduction achievable through targeted infrastructure investment and maintenance policies.", "methodology": "A time-series forecasting model was developed using historical failure-rate data and asset-condition indices. The core model is a generalised linear model with a logit link: $\\log(\\frac{p}{1-p}) = \\beta0 + \\beta1 X1 + \\beta2 X_2 + \\epsilon$, where $p$ is the probability of a major fault. Model parameters were estimated using maximum likelihood, with robust standard errors to account for heteroscedasticity.", "findings": "The analysis forecasts that a sustained annual investment increase of 15% in targeted asset replacement would reduce the system-wide probability of catastrophic failure by an estimated 42% (95% CI: 38% to 46%) over the forecast period. The model indicates diminishing returns beyond this investment threshold.", "conclusion": "Strategic, data-driven investment in distribution infrastructure can substantially mitigate systemic risk. The forecasting model provides a quantifiable basis for prioritising policy decisions and capital expenditure.", "recommendations": "Policymakers should adopt the presented forecasting framework for long-term infrastructure planning. Investment should be prioritised towards geographically identified high-risk clusters and the replacement of specific, ageing equipment classes identified by the model.", "key words": "infrastructure risk, forecasting model, power distribution, policy analysis, asset management", "contribution statement": "This paper provides a novel, empirically grounded forecasting methodology that directly links infrastructure investment levels to quantifiable reductions in system-wide failure risk, offering a new tool for