Energy Consumption in the French Residential Sector: How Much do Individual Preferences Matter?

AuthorBakaloglou, Salome

    Reducing the final energy consumption of the European Union has been included in the EU energy strategy for 2030, with the goal of achieving 27% of energy savings compared to the business-as-usual scenario. Among all sectors, decreasing the energy consumption of the buildings sector is one of the most challenging tasks. Despite the fact that the sector has been identified as having the greatest potential for energy savings at the global scale (IEA, 2017), in EU countries the achievement is for the most part subject to the good will and behavior of billions of households living in these buildings (namely, the residential sector) (1) . Nowadays, observed energy savings are below the expectations of technical and economic models (Jaffe and Stavins, 1994; Sunikka-Blank and Galvin, 2012; Sorrell and O'Mallay, 2014), which could be a strong indicator of this behavioral bias. In that context, projecting future energy consumption of the sector or changing its trajectory in the expected direction seems complex in the absence of a complete understanding of household behavior.

    In terms of policymaking, the task is to implement efficient policies able to stimulate changes at the household level to improve upon international energy consumption targets. Renovation measures and social intervention to encourage more efficient use of energy have been identified as potential solutions for reducing energy consumption in the residential sector (Lopes, et al., 2012). Gaining a better understanding of energy consumption patterns in the housing sector is necessary to implement such solutions in an effective way.

    Statistical bottom-up studies conducted by economists have revealed that 40% of energy consumption in the residential sector is determined by technical factors (Belaid, 2016). A large share of the remainder would be explained by socioeconomic and individual characteristics such as income, age of household members, tenure and energy-related preferences and choices (Belaid, 2016; Belaid and Garcia, 2016; Cayla, et al., 2011). Although understanding the determinants of energy consumption has been a recurring theme in economics, distinguishing the effects of individual factors on the final quantity of energy consumed, which would enable characterization of energy behavior and consumption patterns, is still a complex issue. In particular, the topic suffers from limitations due to a lack of appropriate data to control both for socio-economic characteristics, individual preferences and the technical characteristics of dwellings. Consequently, engineering models that almost exclusively use technical building characteristics and engineering calculations as inputs to predict energy consumption are still widely used. However, they reveal limitations in non -including and -modeling the effect of individual heterogeneity and occupant behavior in engineering models (Galvin and Sunikka-Blank, 2014). Combining the benefits of both approaches, namely, integrating both energy-related preferences and theoretical energy performance of housings in research, is an essential step to deepen understanding of the energy consumption spectrum in the residential sector and clarify the role of household behavior.

    To advance the academic literature and provide relevant recommendations to policy-makers, additional empirical research is needed to identify individual determinants and their interaction with building characteristics, and to describe energy consumption patterns. Energy savings and energy-intensive behaviors are derived from individual energy use preferences. Analyzing the effect of such preferences is crucial for understanding the importance of household heterogeneity in explaining variability in energy consumption and identifying leverage actions. The issue has generally been neglected in the economics literature (Lopes, et al., 2012), particularly due to the lack of relevant data.

    This research aims to partially fill this gap. Our main hypothesis is that individual stated preferences regarding household energy use do have a role in explaining energy consumption in French homes. We used a discrete-continuous model based on McFadden's pioneering work (1984) to test this assumption, and account for the growing empirical concern related to the interactions between dwellings and household characteristics when modeling energy demand. We assumed that individual energy consumption preferences may be manifested in two ways. We examined whether household comfort preferences and socioeconomic characteristics influence both the features of their home (in this case the energy-efficiency level of the dwelling chosen by the household at the time of purchase or rental), and the amount of final energy they consume. Our research is based on the French PHEBUS (2) survey conducted in 2012, which includes complete thermal data, Energy Efficiency Certificates (energy-efficiency classifications), and socioeconomic characteristics for more than 2000 dwellings, as well as newly available information about household behavior and stated preferences.

    This paper thus contributes to the broader literature on the determinants of energy consumption by providing an original analytical framework, thanks to the use of an innovative dataset. A key result is evidence of the existence of several energy consumption patterns in the residential sector that manifest themselves through energy-related preferences and economic characteristics of households. Then, we provide evidence that individual energy use preferences are a significant driver of energy consumption for high-income households, both directly and indirectly. Our main results show that preferring comfort over economy for two or three types of energy use implies energy overconsumption of 10% on average. If we consider the subpopulation of households belonging to the three highest income deciles, surplus energy consumption from high and medium preferences for comfort lies between 18.1% and 21.8%. For low-income households, we find no significant effect of preferences but a lower energy price elasticity.

    Our study differentiates energy consumption patterns by income level, and contributes to the integration of behavioral inputs in modeling exercises, which should be of interest to policymakers. Accordingly, we suggest that policymakers consider low-income and high-income households separately when developing and implementing policies to reduce energy consumption in the residential sector. This is particularly important for reducing potential inequalities and bias. Finally, through our methodology we confirm the necessity of accounting for indirect determinants when assessing the drivers of energy demand in the residential sector.

    The paper is organized as follows. Section 2 presents the literature review. Section 3 describes the model. The data and the results are presented in section 4 and 5 respectively. Section 6 concludes with policy recommendations.


    The final energy consumption of a dwelling is explained by three main determinants: technical building characteristics including the local environment, household characteristics (socioeconomic characteristics, individual preferences, income, etc.), and the price of energy. The literature review also calls attention to the dearth of studies focusing on the share of energy consumption attributed to individual heterogeneity with regard to energy consumption preferences.

    Household characteristics

    The impact of socio-demographic characteristics on energy consumption has been demonstrated in the literature. Concerning occupancy status, contrary to the theory that posits that tenants are likely to consume more energy than owners (misaligned incentives), empirical research fails to find a consensus on the effect of tenure status on energy consumption (Belaid, 2016; Charlier, 2015; Jones, et al., 2015; Yohanis, 2012). Family structure and its position in the life cycle, however, does have an impact on energy demand. The number of occupants has a positive impact on energy consumption (Leahy and Lyons, 2010; Vaage, 2000), and there is a cyclical effect based on the age of the reference person: energy consumption is comparatively higher for dwellings whose occupants are between 45 and 65 than for other age classes (Belaid, 2016; Brounen and Kok, 2011; Brounen, et al., 2013).

    Regarding income elasticity (see Table 1-1 in supplementary materials), the effect is positive in most studies, which is consistent with the "normal good status" of energy consumption: income elasticity often lies between 0.01 and 0.15. This frequent low income elasticity is often attributed to the correlation between income and other characteristics such those of the home (Alberini, et al., 2011) and occupancy status. However, the effect of household income is sometimes more complex. Although low income households use less energy, they have a relatively smaller opportunity to change their appliances and heating and cooling systems. Positive elasticity may mainly involve the purchase of more energy-efficient appliances, which will induce lower energy consumption (Cayla, et al., 2011; Labandeira, et al., 2006; Nesbakken, 2001; Santamouris, et al., 2007). Income elasticity may also depend on income level: in 2013, Meier, et al. (2013) investigated the relationship between household income and expenditures on energy services in the United Kingdom. A key finding of their study was that the income elasticity of electricity and gas demand is contingent on household income. Households with low-income exhibit a rather low-income elasticity of energy demand (about 0.2). Households at the top end of the income distribution exhibit an income elasticity of up to about 0.6. Finally, in the recent work of Hache, et al. (2017), the authors demonstrated with a non-linear approach (CHA1D clustering method) that income level and global energy expenditures were intimately related in...

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