Disentangling the determinants of household energy expenditure: A quantile regression approach with machine learning

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dc.contributor.authorKaraaslan, Abdulkerim
dc.contributor.authorKaraaslan, Kubranur Cebi
dc.contributor.authorKardes, Serkan
dc.date.accessioned2026-02-08T15:15:11Z
dc.date.available2026-02-08T15:15:11Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractHousehold energy consumption is a key driver of national energy demand and emissions. Understanding household behaviour is therefore essential for designing effective and socially sensitive energy policies. This study investigates the determinants of household energy expenditure in T & uuml;rkiye using microdata from the 2023 Household Budget Survey. Variable selection was conducted with machine learning algorithms, and quantile regression was applied to capture heterogeneity across different expenditure levels. The results show that socioeconomic and housing characteristics shape energy spending in diverse ways. In lower quantiles, household type, vehicle fuel choice, and heating systems are more influential, while in upper quantiles, income, residence type, and the number of automobiles dominate. Across all quantiles, appliance efficiency and fuel preferences remain important levers. These findings highlight that uniform policies are unlikely to succeed and that tailored, contextsensitive strategies are needed. By linking household behaviour with institutional realities, the study provides evidence to guide more inclusive and effective energy policy design.
dc.identifier.doi10.1016/j.enbuild.2025.116577
dc.identifier.issn0378-7788
dc.identifier.issn1872-6178
dc.identifier.scopus2-s2.0-105018905730
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.enbuild.2025.116577
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5659
dc.identifier.volume349
dc.identifier.wosWOS:001599338000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Sa
dc.relation.ispartofEnergy and Buildings
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectHousehold energy expenditure
dc.subjectEnergy policy
dc.subjectVariable selection
dc.subjectIncome heterogeneity
dc.subjectHousehold budget surveys
dc.titleDisentangling the determinants of household energy expenditure: A quantile regression approach with machine learning
dc.typeArticle

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