Evidence for residential building retrofitting practices using explainable AI and socio-demographic data

Abstract

Extensive retrofits and effective policy measures are needed to meet the ambitious climate goals, particularly in the UK, with the EU’s oldest residential building stock. Researchers must investigate the factors influencing retrofits to enable effective and targeted policy measures. To date, however, there is a lack of holistically large-scale quantitative studies accounting for such factors. At the same time, great potential is seen in data-driven solutions and the use of explainable artificial intelligence (XAI). We address this research gap by combining supervised machine learning with XAI employing a three-stage approach: First, we consolidate datasets of Energy Performance Certificates from England and Wales from which we extract conducted retrofits, house prices, and socio-demographic information. Second, we apply an eXtreme Gradient Boosting (XGBoost) model that predicts whether a building has been retrofitted or not. Lastly, we use SHapley Additive exPlanations values (SHAP) as an XAI technique to identify the key factors and relationships that influence the implementation of retrofits. We succeed in substantiating results previously obtained in qualitative or small-scale studies and also find that retrofit-related policies already implemented in regional cases, such as the ”Better Homes for Yorkshire” initiative, can successfully achieve large-scale success through replication in other regions. Further, our results suggest the implementation of income-based CO2 taxes as a reasonable and easy-to-implement policy measure.