Machine-Learning-Enhanced Measuring of Multidimensional Energy Poverty: Insights from a Pilot Survey in Portugal and Denmark

Energy poverty, a multidimensional socio-economic challenge, significantly affects the welfare of many people across Europe. This paper aims to alleviate energy poverty by exploring sustainable energy practices and policy interventions, using pilot household survey data collected within an EU project in Portugal and Denmark. A novel multidimensional energy poverty index (MEPI) is developed to assess energy poverty through different dimensions—such as heating and cooling comfort, financial strain, access to energy-efficient appliances, and overall health and well-being. In a next step, for selecting features, machine learning techniques, including recursive feature elimination and random forest analysis, are employed. These methods help to reduce the number of irrelevant and mutually correlated predictors. Subsequently, a logistic regression model is used to predict energy-poor households based on selected socio-economic and policy-related factors. The logistic regression model results indicate that sustainable energy-saving behaviors and supportive government policies can indeed effectively mitigate energy poverty. Furthermore, to analyze the impact of the determined features, the shapley additive explanations (SHAP) method is being utilized. Finally, the main findings are further evaluated via scenario simulation analysis.