Household electric energy efficiency

dc.contributor.authorMubiinzi Geoffrey
dc.contributor.authorSenyonga Livingstone
dc.contributor.authorKigongo Kaawaase Twaha
dc.contributor.authorWasswa Francis
dc.contributor.authorAdaramola Muyiwa S.
dc.contributor.authorNantongo Maria
dc.date.accessioned2026-11-14T07:34:08Z
dc.date.available2026-11-14T07:34:08Z
dc.date.issued2026-08-27
dc.description.abstractThis study examines household electric energy efficiency in Uganda by assessing how effectively households convert electricity and complementary domestic conditions into welfare-relevant services. The issue is important because electricity access and electricity consumption do not automatically translate into useful household outcomes, especially where reliability, housing quality, appliance ownership, and household resources differ. Using Uganda National Household Survey 2020/2021 data, the study constructs a household electricity-service proxy from electricity-enabled service assets and estimates household efficiency using stochastic frontier analysis, followed by fractional response models, a control-function correction, and predictive validation. The results show substantial heterogeneity in household electricity-service production. The baseline frontier estimates a mean technical efficiency of 0.604, with a median of 0.621 and a 90th percentile of 0.860, indicating that many electricity-using households operate below best-practice service performance. Efficiency is also unequal, with a Gini coefficient of 0.194 and a 90th-to-10th percentile ratio of 3.155. Electricity consumption and daily power availability are positively associated with service output, but the electricity-consumption elasticity is small, at 0.071 under the baseline specification, suggesting less than proportional service gains from additional electricity use. Efficiency is systematically associated with household assets, housing quality, literacy, household composition, electricity cost, and regional location. The evidence provides a service-oriented basis for identifying where household electricity benefits remain constrained. The study concludes that household electricity policy should move beyond connection and consumption metrics toward improving reliability, domestic service conditions, and the household capabilities that shape electricity-service performance.
dc.identifier.citationMubiinzi, G., Senyonga, L., Kaawaase, T. K., Wasswa, F., Adaramola, M. S., & Nantongo, M. (2026). Household electric energy efficiency: A stochastic frontier analysis of Uganda’s survey data. Energy 360, 6, 100085. https://doi.org/10.1016/j.energ.2026.100085
dc.identifier.doihttps://doi.org/10.1016/j.energ.2026.100085
dc.identifier.urihttp://hdl.handle.net/20.500.12280/3369
dc.language.isoen
dc.publisherELSEVIER
dc.relation.ispartofseriesEnergy 360; Volume 6, 100085.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEnergy efficiency
dc.subjectElectricity services
dc.subjectStochastic frontier
dc.subjectTechnical efficiency
dc.subjectHeteroskedastic inefficiency
dc.subjectFractional logit
dc.subjectPredictive validation
dc.titleHousehold electric energy efficiency
dc.title.alternativeA stochastic frontier analysis of Uganda’s survey data
dc.typeArticle

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