Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/314566 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 17669
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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 with SHAP (SHapley Additive exPlanations) values to document the short- and long-term effects of individual and contextual factors—such as income, energy prices, and regional conditions—on future energy poverty outcomes. The findings emphasize the importance of policies focused on income stability and may be used to shift the policy focus from reactive measures, which address existing poverty, to preventive strategies that target households showing early signs of vulnerability.
Schlagwörter: 
Energy poverty
panel data
explainable AI
time-series analysis
public policy
temporal dynamics
feature importance
JEL: 
I32
D12
C53
Dokumentart: 
Working Paper

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