Identifying at-risk populations is essential for designing effective energy poverty interventions. Using data from the HILDA Survey, a longitudinal dataset representative of the Australian population, and a multidimensional index of energy poverty, we develop a machine learning model combined...

Evidence on how energy poverty persistence and vulnerability to key factors are distributed across different population groups remains scarce. This paper seeks to bridge this gap by analyzing the dynamics and determinants of energy poverty within population clusters. The significance of the...

Energy poverty and health appear to be closely related, yet robust evidence on whether and how they mutually influence each other over time is still limited. We employ a dynamic latent class model on rich longitudinal data from the Household, Income, and Labor Dynamics in Australia Survey to...

Identifying populations at risk of deprivation is crucial for effective policy design. Yet, much existing research focuses on single aspects, such as income or material deprivation, and often abstracts from deprivation dynamics. This study addresses this gap by analyzing the dynamics and...