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

KARAASLAN A., Çebi Karaaslan K., Kardeş S.

Energy and Buildings, cilt.349, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 349
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.enbuild.2025.116577
  • Dergi Adı: Energy and Buildings
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Compendex, Environment Index, INSPEC, Pollution Abstracts, Public Affairs Index, Civil Engineering Abstracts
  • Anahtar Kelimeler: Energy policy, Household budget surveys, Household energy expenditure, Income heterogeneity, Variable selection
  • Atatürk Üniversitesi Adresli: Evet

Özet

Household 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 Türkiye 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 socio-economic 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, context-sensitive strategies are needed. By linking household behaviour with institutional realities, the study provides evidence to guide more inclusive and effective energy policy design.