Identifying predictors for energy poverty in Europe using machine learning.

• We use a machine learning (ML) approach to study energy poverty (EP) risk in Europe. • We produce an EP risk classifier with accuracies ranging from 60% to 75%. • Through ML we identify three EP predictors: income, household size, and floor area. • We suggest the presence of universal predictors complemented by contextual ones. • Increasing data collection efforts in Europe can improve EP policy research insights. In this paper we identify drivers for energy poverty in Europe using machine learning. The establishment of predictors for energy poverty valid across countries is a call made by many experts, since it could provide a basis to effectively target energy-poor households with adequate policy measures. We apply a "low income, high expenditure" framework that classifies households as being at risk of energy poverty to a dataset from a survey conducted at the household-level in 11 European countries with vastly different economies, cultures, and climates. A gradient boosting classifier is successfully trained on a set of socio-economic features hypothesized as predictors for energy poverty in this diverse set of countries. The classifier's internal model is analyzed, providing novel insights into the intricacies that underlie energy poverty. We find that besides the main driver - income - floor area and household size can be confirmed as predictors. Our results suggest the presence of universal predictors that are valid across Europe, and contextual ones that are governed by local characteristics. To facilitate advanced research into energy poverty in Europe, we recommend to increase and streamline household data collection efforts, both at the country- and EU-level. [ABSTRACT FROM AUTHOR]