Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions

TL;DR

This study uses causal machine learning to evaluate how energy efficiency interventions like wall insulation affect gas consumption across different household groups, revealing heterogeneous effects driven by behavioral responses and highlighting equity considerations.

Contribution

It introduces a causal machine learning approach to estimate heterogeneous treatment effects of insulation, emphasizing behavioral mechanisms and equity implications in energy policy.

Findings

Interventions reduce gas demand by up to 19% on average.

Low energy burden households achieve substantial savings.

High energy burden households reallocate savings to thermal comfort.

Abstract

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.