Search arXivSearch

arXiv · 2603.26548

Impact of Residential Retrofits on Gas and Electricity Consumption in France

Abstract

This study examines the impact of residential energy retrofits on household energy consumption in France using smart meter data from nearly 2,500 Hello Watt users, using a two-period difference-in-differences design. The dataset combines daily electricity and gas consumption collected through smart meters, hourly temperatures from Météo France, and user-declared home and retrofit information. As a control, we use a group composed of homes of Hello Watt users that are similar to the treated homes, but did not undergo any renovations. The average treatment effect on the treated is estimated with the estimator of Sant'Anna & Zhao (2020). Estimates are reported by energy source (electricity vs. gas) and by retrofit type. The retrofit measures considered are limited to single interventions: wall insulation, attic insulation, floor insulation, installation of an air-to-air heat pump, or installation of an air-to-water heat pump. A comprehensive retrofit is defined separately as the simultaneous implementation of at least two of these measures. Our results show that insulation works cause a significant decrease in both electricity and gas consumption (3% to 13% and 5% to 16% respectively, depending on the retrofit type). We also estimate the reduction on the heating consumption only (7% to 27% for electrical heating and 7% to 19% for gas heating). We also study retrofits that consist in replacing a gas boiler with an air-to-water heat pump, resulting in a cut of 85% in carbon emissions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Charly Andral, Laetitia Leduc, Guillaume Matheron, Yukihide Nakada. 2026-03-27. Impact of Residential Retrofits on Gas and Electricity Consumption in France. https://arxiv.org/abs/2603.26548

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Relegation, promotion and the components of scoring in water polo

Relegation, not the championship, decides the outcome of many European water polo leagues: one club has won every recent national title in Italy, Spain and Hungary. We make the relegation decision our object of inference. Serie A1 relegates one club for finishing last and a second through a three-match play-out, and we ask at each stage how much can be known and when. Promoted clubs arrive with no top-flight record, so we also compare four ways of setting their prior. These analyses rest on the first Bayesian hierarchical model of water polo, which splits scoring into even-strength, man-up and penalty components, models opportunities and conversions separately, and lets abilities evolve between seasons. Treating the components as independent proves untenable, since they compete for a common budget of possessions. Fitted to a new dataset of 940 matches, the model identifies the directly relegated club from the ninth round, before the league table does, predicts the play-out field better than the table, and shows the play-out itself to be close to a coin toss. Promoted clubs start below the league on every component except drawing exclusions: knowing a club was promoted improves forecasts; knowing how does not.

stat.AP

Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices

Area-based social risk indices summarize residents' socioeconomic conditions but incompletely capture physical features of place that may affect health. We evaluated whether numerical representations of physical place produced by four geospatial foundation model families from 2022 satellite data explained residual variance in tract-level associations between the Area Deprivation Index, Social Deprivation Index, and Social Vulnerability Index with health outcomes. We used LightGBM to predict variables from the American Community Survey and 40 chronic disease and health-behavior outcomes from CDC PLACES across 82,646 census tracts in the contiguous United States, evaluating performance across 10 held-out states. Among survey variables, models were moderately predictive of some variables including housing type (R-squared up to 0.54) but weak for disability, unemployment, and income disparity. For health outcomes, models explained up to 54% of variance left unexplained by social risk indices, with the largest gains for annual checkups, arthritis, and high blood pressure. Mean total variance explained by geospatial foundation models across the 40 health-related outcomes increased from 0.31 in the smallest tract-size decile to 0.39 in the largest. Geospatial foundation models capture health-relevant features of place not represented by conventional social risk indices and may usefully augment them in epidemiological analyses.

stat.AP

Transporting summary measures of relative effects from randomised trials to the treated patient population: an application to breast cancer endocrine therapy

Randomised trials often report relative treatment effects, such as risk ratios and hazard ratios, for trial populations. Clinical decision-making, however, often benefits from estimates of absolute treatment effects in the population eligible for treatment. Trial participants may not represent this target population well, and restrictions on access to individual participant trial data can further complicate absolute effect estimation. Routine care data are often representative of the target population but may be subject to uncontrolled confounding. We consider estimation of the average treatment effect on the treated (ATT), an absolute measure, by combining a representative sample of treated routine care patients with summary measures (i.e., estimated risk or hazard ratios) from either a randomised trial or a meta-analysis of trials. Under marginal or conditional transportability assumptions, the ATT is shown to be identifiable. The implications of collapsibility of the effect measure on transportability are discussed, and plug-in estimators of the ATT are presented. Simulation studies are used to assess finite sample performance of the estimators in a range of settings. The proposed methods are applied to estimate the ATT of endocrine therapy on 15-year breast cancer mortality using results from a meta-analysis of randomised trials and England's National Disease Registration Service.

stat.AP