Valuing energy efficiency and clean heating

There is no pathway to Net Zero without the near-complete decarbonisation of heat in housing. In this project, we seek to provide high quality evidence on progress in relation to this goal to support public policy and wider debate.

Aims and objectives

This project aims to track progress with the transition to higher energy efficiency and clean heating for domestic properties, and to understand public attitudes to this, through their willingness to pay more for ‘greener’ housing.

·       To track progress with energy efficiency and the adoption of clean heat overall

·       To examine the distribution of public support for the transition and to monitor the emergence of any new inequalities arising in this process

·       To understand people’s willingness to pay for energy efficiency and clean heat as a crucial factor shaping decisions to invest in the transition, and how this may vary across the housing stock

·       To assess how willingness to pay may vary over time, particularly in relation to energy price fluctuations

Existing work and next steps

We have focussed initially on the role of energy efficiency in house buying and renting decisions, using the evidence provided by Energy Performance Certificates (EPCs). We examined overall progress in achieving greater energy efficiency in this study for England, exploring variations between neighbourhoods and local authorities.

In work led by Dr Yunbei Ou, we reviewed evidence from across Europe on people’s willingness to pay more for more energy efficient properties. That was followed by a study of the impacts of the 2022 energy price crisis on house buying behaviour in Greater Manchester, UK. Our work shows that higher energy efficiency is valued, particularly in house sales, and that the energy price crisis increased this‘ green premium’.

The next stages in our work will include an exploration of how the ‘green premium’ varies across the housing stock. Again focussing on Greater Manchester, we will use causal machine learning models to provide stronger insights into the impact which energy efficiency has on house values.