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Beyond average energy consumption in the French residential housing market: A household classification approach

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  • Emmanuel Hache
  • Déborah Leboullenger
  • Valérie Mignon

Abstract

The need to reduce Green House Gases emissions has jointly lead to increasing concerns regarding the efficiency of national mitigation agendas and the potential exposure of certain households to energy poverty. Hence, the comprehension of the key determinants that influence the energy demand appears to be crucial for the effectiveness and fairness of energy policies. We particularly consider that targeting specific households groups rather than looking for a unique national target level of energy consumption would be more effective. This article explores the scope of having a disaggregated energy consumption market to design policies aimed at curbing residential energy consumption or lowering its carbon intensity. Using a clustering method based on CHAID (Chi Square Automatic Interaction Detection) methodology, we find that the different levels of energy consumption in the French residential sector are related to socio-economic, dwelling and regional characteristics. Then, we build a typology of energy-consuming households where targeted groups (fuel poor, high income and high consuming households) are clearly and separately identified through a simple and transparent set of characteristics. This classification represents an efficient tool for energy efficiency programs and energy poverty policies but also for potential investors, which could provide specific and tailor-made financial tools for the different groups of consumers. Furthermore, our approach is helpful to design an energy efficiency score that could reduce the rebound effect uncertainty for each identified household group.

Suggested Citation

  • Emmanuel Hache & Déborah Leboullenger & Valérie Mignon, 2016. "Beyond average energy consumption in the French residential housing market: A household classification approach," EconomiX Working Papers 2016-6, University of Paris Nanterre, EconomiX.
  • Handle: RePEc:drm:wpaper:2016-6
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    Cited by:

    1. Damette, Olivier & Delacote, Philippe & Lo, Gaye Del, 2018. "Households energy consumption and transition toward cleaner energy sources," Energy Policy, Elsevier, vol. 113(C), pages 751-764.
    2. Dorothée Charlier & Sondès Kahouli, 2018. "Fuel poverty and residential energy demand: how fuel-poor households react to energy price fluctuations," Post-Print halshs-01957771, HAL.
    3. Salomé Bakaloglou and Dorothée Charlier, 2019. "Energy Consumption in the French Residential Sector: How Much do Individual Preferences Matter?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    4. Scarpellini, Sabina & Alexia Sanz Hernández, M. & Moneva, José M. & Portillo-Tarragona, Pilar & Rodríguez, María Esther López, 2019. "Measurement of spatial socioeconomic impact of energy poverty," Energy Policy, Elsevier, vol. 124(C), pages 320-331.
    5. Dorothee Charlier and Sondes Kahouli, 2019. "From Residential Energy Demand to Fuel Poverty: Income-induced Non-linearities in the Reactions of Households to Energy Price Fluctuations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    6. Florian Fizaine & Sondès Kahouli, 2018. "On the power of indicators: how the choice of the fuel poverty measure affects the identification of the target population," Policy Papers 2018.01, FAERE - French Association of Environmental and Resource Economists.

    More about this item

    Keywords

    Energy consumption; residential sector; clustering method; France.;

    JEL classification:

    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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