Toward Proactive Policy Design: Identifying 'To-Be' Energy-Poor Households Using Shap for Early Intervention

Santiago Budría (), Eduardo Fermé () and Diogo Nuno Freitas ()
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Santiago Budría: Universidad Nebrija
Eduardo Fermé: University of Madeira
Diogo Nuno Freitas: University of Madeira

No 17669, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: 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.

Keywords: Energy poverty; panel data; explainable AI; time-series analysis; public policy; temporal dynamics; feature importance (search for similar items in EconPapers)
JEL-codes: C53 D12 I32 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2025-02
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