IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp17669.html
   My bibliography  Save this paper

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

Author

Listed:
  • Budría, Santiago

    (Universidad Nebrija)

  • Fermé, Eduardo

    (University of Madeira)

  • Freitas, Diogo Nuno

    (University of Madeira)

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.

Suggested Citation

  • Budría, Santiago & Fermé, Eduardo & Freitas, Diogo Nuno, 2025. "Toward Proactive Policy Design: Identifying 'To-Be' Energy-Poor Households Using Shap for Early Intervention," IZA Discussion Papers 17669, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17669
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp17669.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prakash, Kushneel & Awaworyi Churchill, Sefa & Smyth, Russell, 2022. "Are you puffing your Children's future away? Energy poverty and childhood exposure to passive smoking," Economic Modelling, Elsevier, vol. 114(C).
    2. Koomson, Isaac & Awaworyi Churchill, Sefa, 2022. "Employment precarity and energy poverty in post-apartheid South Africa: Exploring the racial and ethnic dimensions," Energy Economics, Elsevier, vol. 110(C).
    3. Shuchen Cong & Destenie Nock & Yueming Lucy Qiu & Bo Xing, 2022. "Unveiling hidden energy poverty using the energy equity gap," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Kocak, Emrah & Ulug, Eyup Emre & Oralhan, Burcu, 2023. "The impact of electricity from renewable and non-renewable sources on energy poverty and greenhouse gas emissions (GHGs): Empirical evidence and policy implications," Energy, Elsevier, vol. 272(C).
    5. Zhang, Ziyu & Shu, Hongting & Yi, Hong & Wang, Xiaohua, 2021. "Household multidimensional energy poverty and its impacts on physical and mental health," Energy Policy, Elsevier, vol. 156(C).
    6. Koen Decancq & María Ana Lugo, 2013. "Weights in Multidimensional Indices of Wellbeing: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 7-34, January.
    7. Sullivan, Joe H. & Warkentin, Merrill & Wallace, Linda, 2021. "So many ways for assessing outliers: What really works and does it matter?," Journal of Business Research, Elsevier, vol. 132(C), pages 530-543.
    8. Abbas, Khizar & Li, Shixiang & Xu, Deyi & Baz, Khan & Rakhmetova, Aigerim, 2020. "Do socioeconomic factors determine household multidimensional energy poverty? Empirical evidence from South Asia," Energy Policy, Elsevier, vol. 146(C).
    9. Alem, Yonas & Demeke, Eyoual, 2020. "The persistence of energy poverty: A dynamic probit analysis," Energy Economics, Elsevier, vol. 90(C).
    10. Phoumin, Han & Kimura, Fukunari, 2019. "Cambodia's energy poverty and its effects on social wellbeing: Empirical evidence and policy implications," Energy Policy, Elsevier, vol. 132(C), pages 283-289.
    11. Fry, Jane M. & Farrell, Lisa & Temple, Jeromey B., 2022. "Energy poverty and retirement income sources in Australia," Energy Economics, Elsevier, vol. 106(C).
    12. Alem, Yonas & Demeke, Eyoual, 2020. "The Persistence of Energy Poverty: A Dynamic Probit Analysis," EfD Discussion Paper 20-16, Environment for Development, University of Gothenburg.
    13. Dalla Longa, Francesco & Sweerts, Bart & van der Zwaan, Bob, 2021. "Exploring the complex origins of energy poverty in The Netherlands with machine learning," Energy Policy, Elsevier, vol. 156(C).
    14. Crentsil, Aba Obrumah & Asuman, Derek & Fenny, Ama Pokuaa, 2019. "Assessing the determinants and drivers of multidimensional energy poverty in Ghana," Energy Policy, Elsevier, vol. 133(C).
    15. Shatakshee Dhongde & Prasanta K. Pattanaik & Yongsheng Xu, 2019. "Well‐Being, Deprivation, and the Great Recession in the U.S.: A Study in A Multidimensional Framework," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(S1), pages 281-306, November.
    16. Drescher, Katharina & Janzen, Benedikt, 2021. "Determinants, persistence, and dynamics of energy poverty: An empirical assessment using German household survey data," Energy Economics, Elsevier, vol. 102(C).
    17. Zhang, Quanda & Appau, Samuelson & Kodom, Peter Lord, 2021. "Energy poverty, children's wellbeing and the mediating role of academic performance: Evidence from China," Energy Economics, Elsevier, vol. 97(C).
    18. Simshauser, Paul & Miller, Wendy, 2023. "On the impact of targeted and universal electricity concessions policy on fuel poverty in the NEM's Queensland region," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1848-1857.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ackermann, Klaus & Awaworyi Churchill, Sefa & Smyth, Russell, 2023. "High-speed internet access and energy poverty," Energy Economics, Elsevier, vol. 127(PB).
    2. Budría, Santiago & Li Donni, Paolo & Zucchelli, Eugenio, 2025. "Sick and Cold? Evidence on the Dynamic Interplay between Energy Poverty and Health," IZA Discussion Papers 17678, Institute of Labor Economics (IZA).
    3. Lan, Jing & Khan, Sufyan Ullah & Sadiq, Muhammad & Chien, Fengsheng & Baloch, Zulfiqar Ali, 2022. "Evaluating energy poverty and its effects using multi-dimensional based DEA-like mathematical composite indicator approach: Findings from Asia," Energy Policy, Elsevier, vol. 165(C).
    4. Keyu Chen & Chao Feng, 2022. "Linking Housing Conditions and Energy Poverty: From a Perspective of Household Energy Self-Restriction," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    5. Burguillo, Mercedes & del Río, Pablo & Juez-Martel, Pedro, 2025. "How have the COVID pandemic and the war in Ukraine affected energy poverty?," Applied Energy, Elsevier, vol. 377(PD).
    6. Huang, Yatao & Jiao, Wenxian & Wang, Kang & Li, Erling & Yan, Yutong & Chen, Jingyang & Guo, Xuanxuan, 2022. "Examining the multidimensional energy poverty trap and its determinants: An empirical analysis at household and community levels in six provinces of China," Energy Policy, Elsevier, vol. 169(C).
    7. Yang, Yaofeng & Jiang, Mingchun & Li, Xiuqing & Chen, Yajuan, 2024. "Energy poverty and household development resilience: Household level evidence from China," Energy, Elsevier, vol. 309(C).
    8. Muhammad Shafiullah & Zhilun Jiao & Muhammad Shahbaz & Kangyin Dong, 2023. "Examining energy poverty in Chinese households: An Engel curve approach," Australian Economic Papers, Wiley Blackwell, vol. 62(1), pages 149-184, March.
    9. Fu Wang & Hong Geng & Donglan Zha & Chaoqun Zhang, 2023. "Multidimensional Energy Poverty in China: Measurement and Spatio-Temporal Disparities Characteristics," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 45-78, August.
    10. Yuxiang Xie & E. Xie, 2023. "Measuring and Analyzing the Welfare Effects of Energy Poverty in Rural China Based on a Multi-Dimensional Energy Poverty Index," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    11. Abbas, Khizar & Butt, Khalid Manzoor & Xu, Deyi & Ali, Muhammad & Baz, Khan & Kharl, Sanwal Hussain & Ahmed, Mansoor, 2022. "Measurements and determinants of extreme multidimensional energy poverty using machine learning," Energy, Elsevier, vol. 251(C).
    12. Soto, Gonzalo H & Martinez-Cobas, Xavier, 2024. "Green energy policies and energy poverty in Europe: Assessing low carbon dependency and energy productivity," Energy Economics, Elsevier, vol. 136(C).
    13. Liu, Zhong & Zhou, Zuanjiu & Liu, Chang, 2023. "Estimating the impact of rural centralized residence policy interventions on energy poverty in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    14. Ren, Zhiyuan & Zhu, Yuhan & Jin, Canyang & Xu, Aiting, 2023. "Social capital and energy poverty: Empirical evidence from China," Energy, Elsevier, vol. 267(C).
    15. Ang'u, Cohen & Muthama, Nzioka John & Mutuku, Mwanthi Alexander & M’IKiugu, Mutembei Henry, 2023. "Analysis of energy poverty in Kenya and its implications for human health," Energy Policy, Elsevier, vol. 176(C).
    16. Elizabeth Nsenkyire & Jacob Nunoo & Joshua Sebu & Omowumi Iledare, 2023. "Household Multidimensional Energy Poverty: Impact on Health, Education, and Cognitive Skills of Children in Ghana," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(1), pages 293-315, February.
    17. Omar, Md Abdullah & Hasanujzaman, Muhammad, 2021. "Multidimensional energy poverty in Bangladesh and its effect on health and education: A multilevel analysis based on household survey data," Energy Policy, Elsevier, vol. 158(C).
    18. Jun Zhang & Yuang He & Jing Zhang, 2022. "Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach," IJERPH, MDPI, vol. 19(2), pages 1-21, January.
    19. Sen, Kanchan Kumar & Karmaker, Shamal Chandra & Hosan, Shahadat & Chapman, Andrew J. & Uddin, Md Kamal & Saha, Bidyut Baran, 2023. "Energy poverty alleviation through financial inclusion: Role of gender in Bangladesh," Energy, Elsevier, vol. 282(C).
    20. Tiwari, Sunil & Si Mohammed, Kamel & Guesmi, Khaled, 2023. "A way forward to end energy poverty in China: Role of carbon-cutting targets and net-zero commitments," Energy Policy, Elsevier, vol. 180(C).

    More about this item

    Keywords

    Energy poverty; panel data; explainable AI; time-series analysis; public policy; temporal dynamics; feature importance;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp17669. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.