{ "background": "Power-distribution infrastructure in many developing nations faces significant reliability and safety challenges due to ageing assets and operational stresses. A systematic, data-driven methodology for evaluating equipment risk and forecasting the impact of interventions is required to optimise capital expenditure and enhance grid resilience.", "purpose and objectives": "This case study aims to develop and apply a novel methodological framework for the evaluation of power-distribution equipment. Its core objective is to estimate the causal effect of systematic equipment replacement and maintenance programmes on technical risk reduction within a national utility.", "methodology": "A panel-data econometric model is constructed using utility operational and asset data. The model, $Risk{it} = \\beta0 + \\beta1 Intervention{it} + \\beta2 X{it} + \\alphai + \\epsilon{it}$, estimates risk reduction, where $\\alpha_i$ denotes transformer-unit fixed effects and robust standard errors are clustered at the feeder level. The analysis integrates technical failure data, environmental variables, and maintenance records.", "findings": "The panel-data estimation indicates a statistically significant reduction in aggregate technical risk following targeted interventions. A one-standard-deviation increase in the intervention index is associated with a 22% decrease in the predicted probability of a major fault (95% CI: 18% to 26%). The analysis identifies transformer overloading and environmental corrosion as the dominant residual risk factors.", "conclusion": "The methodological framework provides a robust, evidence-based tool for prioritising infrastructure investment. The results confirm that structured intervention programmes can substantially mitigate distribution network risk, though persistent environmental and load-related challenges require ongoing management.", "recommendations": "Utilities should adopt panel-data estimation for long-term investment planning and performance tracking. It is recommended to increase sensor deployment for critical environmental and load data to refine the model. Future programmes should target regions with high corrosion exposure as a priority.", "key words": "power distribution, risk assessment, panel data, infrastructure management, econometric modelling, grid resilience", "cont