The national energy transition will largely depend on changes in household energy consumption.

In 2015, the residential sector alone was responsible for 30% of final energy consumption and 25% of COemissions in Francea. However, the magnitude of the energy spending is closely correlated with:

  • the type of housing (individual or collective),
  • its energy performance,
  • its heating system,
  • and its geographic location,
  • as well as the conditions for getting bank loans to carry out energy-related renovation projects.

In this context, IFPEN looked at the energy consumption behavior of French households between 1999 and 2013, and studied the trade-off between the quality and cost of the energy services available in the residential sector and overall budget restrictions (share of the budget allocated to energy spending).

Using a methodology borrowed from organizational sciences and strategic marketingb, we built 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(1).


Représentation des ménages français selon un axe revenu/facture énergétique.
Breakdown of French households on the basis of income/energy bill.


According to this typology, energy poverty is a reflection of financial poverty, with the households concerned all belonging to the first two income deciles.

Over and above this observation, the study also shows that households in apartmentsfrequently ignored in the energy efficiency market - are over-represented among energy-poor households, but also among high-income and high-energy-consuming households.

These results will make it possible to target policies to support energy renovation in the residential sector, either public (tax credits, eco-loan, etc.) or private (creation of financial tools) tailored to the different household groups identified(2).


a- Department of observation and statistics (SOeS), French Ministry for the Environment, Energy and the Sea, 2016.
b- G.V. Kass, 1980. Chi-Square Automatic Interaction Detection - CHAID. Journal of Applied Statistics.


(1)  E. Hache, D. Leboullenger, V. Mignon, Beyond average energy consumption in the French residential housing market: A household classification approach - Energy Policy, 2017
>> DOI:10.1016/j.enpol.2017.04.038
(2)  E. Hache, D. Leboullenger, Y a-t-il un banquier pour sauver le climat ? - La Revue de l’Énergie, 2016, 633, 391-398
>> http://www.ophrys.fr/fr/catalogue-detail/2220/revue-de-l-energie-la-n-633-septembre-octobre-2016.html

Scientific contact: Emmanuel Hache