Household energy burdens in Europe at times of geopolitical energy price volatility
Heterogeneous energy price elasticities across EU households, and who carried the 2022 price shock
European gas prices reached an unprecedented peak of 346 EUR per MWh in August 2022, exacerbating a trend that had begun in 2021 following Russia's full-scale invasion of Ukraine, and the Joint Research Centre has estimated that inflation over this period may have increased energy poverty specifically by around 5 percentage points. Understanding how such volatility distributes across households is essential for designing effective and equitable energy policy. We estimate heterogeneous household energy price elasticities across distinct income levels and EU countries using Causal Forests, a robust non-parametric method, applied to microdata from the European Household Budget Surveys, estimating on the 2010 and 2015 waves and testing model fit on the 2020 wave.
We find strong heterogeneity in the impacts of energy price increases for gas and electricity, with lower-income households facing higher energy burdens throughout. Gas price elasticities decrease continuously as income rises, confirming the regressive nature of gas price shocks, whereas electricity elasticities fall rapidly up to around 20,000 EUR of income before stabilising. Hindcasting to 2022 price conditions indicates that the poorest households are the ones likely to experience notable increases in energy burdens: in the 2020 sample, households in the lowest income quintile carried an average burden of 9.0% against 2.4% in the highest, and the projected increases concentrate in that first quintile. The magnitude varies considerably with a country's dependence on Russian fossil fuel imports, with Hungary and Romania driving much of the difference between groups.
We also integrate objective expenditure-based energy poverty indicators from the Household Budget Surveys with subjective measures from the European Union Statistics on Income and Living Conditions, such as households' perceived ability to keep their homes adequately warm and to make ends meet, by systematically applying three data fusion methods: a hybrid kNN-lightGBM model, a Gaussian Copula Synthesizer, and Wasserstein Generative Adversarial Networks. The fused datasets suggest limited sensitivity of subjective energy deprivation measures to price changes, although we note this may reflect fusion uncertainty rather than a genuine absence of response, and it highlights both the potential and the limitations of combining multiple EU microdata sources. We argue that alleviating energy poverty requires targeted short-term financial assistance for vulnerable households alongside sustained long-term investment in building efficiency, the electrification of heating, and energy system resilience.
@article{poblete-cazenave_household_2026,
title = {Household energy burdens in {Europe} at times of geopolitical energy price volatility},
volume = {65},
copyright = {All rights reserved},
url = {https://doi.org/10.1016/j.esr.2026.102163},
doi = {10.1016/j.esr.2026.102163},
journal = {Energy Strategy Reviews},
author = {Poblete-Cazenave, Miguel and Pelz, Setu and Pachauri, Shonali},
year = {2026},
pages = {102163},
}