{ "background": "The modernisation of power-distribution infrastructure is critical for economic development. In Ghana, the adoption of advanced equipment, such as amorphous core transformers and automated reclosers, has been pursued with varying intensity, yet a systematic, longitudinal analysis of adoption drivers and rates is lacking.", "purpose and objectives": "This study aims to methodologically evaluate and compare the adoption trajectories of different power-distribution equipment systems. Its objective is to quantify adoption rates and identify the key technical and economic factors influencing them using a panel-data framework.", "methodology": "A comparative panel-data analysis was conducted using a uniquely compiled dataset from utility records. The core specification is a fixed-effects model: $AdoptionRate{it} = \\alphai + \\beta1 TechSpec{it} + \\beta2 Cost{it} + \\beta3 GridAge{it} + \\epsilon_{it}$, where $i$ denotes district and $t$ denotes year. Estimation uses robust standard errors clustered at the regional level.", "findings": "The analysis reveals a statistically significant divergence in adoption rates, with equipment offering higher operational efficiency being adopted approximately 40% faster than conventional alternatives. The coefficient for cost was negative and significant (p < 0.01, 95% CI [-0.18, -0.07]), indicating price sensitivity is a major constraint.", "conclusion": "Adoption patterns are not uniform and are strongly influenced by a trade-off between technical performance characteristics and capital expenditure. The panel-data approach provides a robust framework for tracking infrastructure modernisation.", "recommendations": "Policymakers and utilities should consider targeted subsidies for high-efficiency equipment to accelerate adoption. Future planning models should integrate the identified panel estimators for more accurate forecasting.", "key words": "panel data, infrastructure adoption, power distribution, fixed-effects model, Ghana", "contribution statement": "This paper provides the first application of a comparative panel-data model to longitudinally track and explain the adoption of diverse