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The role of individual preferences to explain the energy performance gap

Author

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  • Salomé Bakaloglou

    (Université de Montpellier, CSTB, Chaire Economie du Climat)

  • Dorothée Charlier

    (IAE Savoie Mont Blanc)

Abstract

The aim of this research is to understand the role of socio-economic characteristics and individual preferences to explain the energy performance gap in the residential sector. The gap reflects the difference between theoretical energy consumption of home assessed by engineering models and real energy consumption. Using the ratio of the two consumptions as a measure of the gap, we perform a quantile regression to tease out the effects of preferences on the entire distribution of the energy performance gap spectrum instead of focusing on the conditional average. As a result, this research provides an original contribution: depending on the sense of the gap, our findings suggest that some significant drivers are individual preferences for comfort over economy, explaining until 12% of the gap variability, and poverty. In such a context, some warnings to public authorities are provided regarding the issues of rebound effect and household welfare.

Suggested Citation

  • Salomé Bakaloglou & Dorothée Charlier, 2018. "The role of individual preferences to explain the energy performance gap," Policy Papers 2018.08, FAERE - French Association of Environmental and Resource Economists.
    • Handle: RePEc:fae:ppaper:2018.08
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    References listed on IDEAS

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    More about this item

    Keywords

    Residential energy consumption; Quantile treatment effect; Quantile regression; Energy Performance Gap; Households Ôpreferences;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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