Affiliation:
1. School of Statistics and Planning, Makerere University, Kampala, Uganda
2. Department of Statistical Sciences, University of Padua-Italy, Padua, Italy
Abstract
This study aims to identify classes and patterns of household energy utilization and the predictive factors that determine class membership. Energy is an essential part of a household's socio-economic status. By examining the household's energy utilization patterns, we can better understand how to formulate and implement efficient strategies for adopting clean energy. This study aims at identifying homogenous classes with respect to their energy patterns in Uganda and examining predictive factors of household class membership. The study uses data on 2,138 households from the 2019/2020 Uganda National Household Survey. Using latent class analysis models, a data-driven method, the study identified four latent household classes; ‘Solar-firewood’ (41%), ‘Electricity-charcoal’ (33%), ‘Moderate energy-user’ (19%) and ‘Low energy-user’ (7%). Results from the study show that the main drivers of household energy choice for cooking and lighting were age, education level, housing conditions and wealth status of the household head. This study contributes to understanding the classes and patterns of household energy utilization patterns in Uganda. These findings may help policymakers predict which latent class a household falls into in order to guarantee efficient targeting of household energy utilization policies and strategies seeking transition to cleaner energy sources.
Funder
Department of Statistical Sciences, University of Padua – Italy