Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions
This research addresses the equity implications of energy efficiency policies for climate mitigation and fuel poverty, highlighting incremental insights into distributional effects.
The study used causal machine learning to estimate the heterogeneous effects of wall insulation on gas consumption in English households, finding average reductions up to 19% but minimal savings for high energy burden groups due to behavioral reallocation toward thermal comfort.
Reducing domestic energy demand is central to climate mitigation and fuel poverty strategies, yet the impact of energy efficiency interventions is highly heterogeneous. Using a causal machine learning model trained on nationally representative data of the English housing stock, we estimate average and conditional treatment effects of wall insulation on gas consumption, focusing on distributional effects across energy burden subgroups. While interventions reduce gas demand on average (by as much as 19 percent), low energy burden groups achieve substantial savings, whereas those experiencing high energy burdens see little to no reduction. This pattern reflects a behaviourally-driven mechanism: households constrained by high costs-to-income ratios (e.g. more than 0.1) reallocate savings toward improved thermal comfort rather than lowering consumption. Far from wasteful, such responses represent rational adjustments in contexts of prior deprivation, with potential co-benefits for health and well-being. These findings call for a broader evaluation framework that accounts for both climate impacts and the equity implications of domestic energy policy.