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On the power of indicators: how the choice of fuel poverty indicator affects the identification of the target population

Author

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  • Florian Fizaine

    (IREGE - Institut de Recherche en Gestion et en Economie - USMB [Université de Savoie] [Université de Chambéry] - Université Savoie Mont Blanc)

  • Sondès Kahouli

    (AMURE - Aménagement des Usages des Ressources et des Espaces marins et littoraux - Centre de droit et d'économie de la mer - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer - UBO - Université de Brest - IUEM - Institut Universitaire Européen de la Mer - IRD - Institut de Recherche pour le Développement - INSU - CNRS - Institut national des sciences de l'Univers - UBO - Université de Brest - CNRS - Centre National de la Recherche Scientifique - CNRS - Centre National de la Recherche Scientifique)

Abstract

In light of the creation of the EU Energy Poverty Observatory (EPOV) in January 2018 and the increase in debates on how fuel poverty is measured, we propose a critical analysis of fuel poverty indicators and demonstrate that choosing a given fuel poverty indicator and, in particular, its threshold level, is central to the identification of the fuel-poor population. First, we conducted an inter-indicator analysis to show how profiles of fuel-poor households vary depending on the indicator selected. More specifically, after identifying groups of affected households using a set of objective and subjective indicators, we designed a multidimensional approach based on a combination of a multiple correspondence analysis and a hierarchical and partitioning clustering analysis to study their characteristics. Using this framework, we highlight the difficulty of identifying a "typical profile" of fuel-poor households due to the significant variability in their characteristics, and we show that the composition of the population depends on the choice of the indicator. Second, we applied an intra-indicator analysis using two objective expenditure-based indicators with thresholds. In particular, we conducted a sensitivity analysis based on a logit model including variables describing household and dwelling characteristics. We show that the profiles of fuel-poor households as well as the drivers of fuel poverty vary considerably with the chosen threshold level. Given these findings, we stress the need to review how we currently rely on conventional fuel poverty indicators to identify target groups and give some recommendations.

Suggested Citation

  • Florian Fizaine & Sondès Kahouli, 2018. "On the power of indicators: how the choice of fuel poverty indicator affects the identification of the target population," Post-Print halshs-01957436, HAL.
  • Handle: RePEc:hal:journl:halshs-01957436
    DOI: 10.1080/00036846.2018.1524975
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01957436
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    Cited by:

    1. Keran Sarah Boyd & Christian Calvillo & Tanja Mueller & Xiaoyi Mu & Tong Zhu, 2023. "The Intersection of Fuel and Transport Policy in Scotland: A Review of Policy, Definitions and Metrics," Energies, MDPI, vol. 16(13), pages 1-14, June.
    2. Marlena Piekut, 2020. "Patterns of Energy Consumption in Polish One-Person Households," Energies, MDPI, vol. 13(21), pages 1-31, October.
    3. Kelly, J. Andrew & Clinch, J. Peter & Kelleher, L. & Shahab, S., 2020. "Enabling a just transition: A composite indicator for assessing home-heating energy-poverty risk and the impact of environmental policy measures," Energy Policy, Elsevier, vol. 146(C).
    4. Dorothée Charlier & Sondès Kahouli, 2018. "Fuel poverty and residential energy demand: how fuel-poor households react to energy price fluctuations," Post-Print halshs-01957771, HAL.
    5. Huang, Luling & Nock, Destenie & Cong, Shuchen & Qiu, Yueming (Lucy), 2023. "Inequalities across cooling and heating in households: Energy equity gaps," Energy Policy, Elsevier, vol. 182(C).
    6. Rodriguez-Alvarez, Ana & Orea, Luis & Jamasb, Tooraj, 2019. "Fuel poverty and Well-Being:A consumer theory and stochastic frontier approach," Energy Policy, Elsevier, vol. 131(C), pages 22-32.
    7. Agbim, Chinelo & Araya, Felipe & Faust, Kasey M. & Harmon, Dana, 2020. "Subjective versus objective energy burden: A look at drivers of different metrics and regional variation of energy poor populations," Energy Policy, Elsevier, vol. 144(C).
    8. Cheng, Zhiming & Tani, Massimiliano & Wang, Haining, 2021. "Energy poverty and entrepreneurship," Energy Economics, Elsevier, vol. 102(C).
    9. Yun, Na, 2023. "Nexus among carbon intensity and natural resources utilization on economic development: Econometric analysis from China," Resources Policy, Elsevier, vol. 83(C).
    10. Kahouli, Sondès, 2020. "An economic approach to the study of the relationship between housing hazards and health: The case of residential fuel poverty in France," Energy Economics, Elsevier, vol. 85(C).
    11. Llorca, Manuel & Rodriguez-Alvarez, Ana & Jamasb, Tooraj, 2020. "Objective vs. subjective fuel poverty and self-assessed health," Energy Economics, Elsevier, vol. 87(C).
    12. Chesser, Michael & Hanly, Jim & Cassells, Damien & Berrill, Jenny, 2022. "Fuel poverty measurements in residential America. Who are the most vulnerable?," Finance Research Letters, Elsevier, vol. 50(C).
    13. Dorothee Charlier and Sondes Kahouli, 2019. "From Residential Energy Demand to Fuel Poverty: Income-induced Non-linearities in the Reactions of Households to Energy Price Fluctuations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    14. Siksnelyte-Butkiene, Indre & Streimikiene, Dalia & Balezentis, Tomas, 2022. "Addressing sustainability issues in transition to carbon-neutral sustainable society with multi-criteria analysis," Energy, Elsevier, vol. 254(PA).
    15. Okushima, Shinichiro, 2021. "Energy poor need more energy, but do they need more carbon? Evaluation of people's basic carbon needs," Ecological Economics, Elsevier, vol. 187(C).
    16. Dorothée Charlier, 2021. "Explaining the energy performance gap in buildings with a latent profile analysis," Post-Print hal-03894155, HAL.
    17. Charlier, Dorothée, 2021. "Explaining the energy performance gap in buildings with a latent profile analysis," Energy Policy, Elsevier, vol. 156(C).
    18. Moore, David & Webb, Amanda L., 2022. "Evaluating energy burden at the urban scale: A spatial regression approach in Cincinnati, Ohio," Energy Policy, Elsevier, vol. 160(C).
    19. Liang, Shuai & Wang, Peng & Jia, Cunlu & Zhu, Jialan, 2023. "Studying green financing, factor allocation efficiency, and regional productivity growth in renewable energy industries," Renewable Energy, Elsevier, vol. 214(C), pages 130-139.
    20. Kahouli, Sondès & Okushima, Shinichiro, 2021. "Regional energy poverty reevaluated: A direct measurement approach applied to France and Japan," Energy Economics, Elsevier, vol. 102(C).
    21. Indre Siksnelyte-Butkiene, 2021. "A Systematic Literature Review of Indices for Energy Poverty Assessment: A Household Perspective," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    22. Charlier, Dorothée & Legendre, Bérangère & Ricci, Olivia, 2021. "Measuring fuel poverty in tropical territories: A latent class model," World Development, Elsevier, vol. 140(C).
    23. Pedro Macedo & Mara Madaleno & Victor Moutinho, 2022. "A New Composite Indicator for Assessing Energy Poverty Using Normalized Entropy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(3), pages 1139-1163, October.
    24. Karpinska, Lilia & Śmiech, Sławomir, 2021. "Breaking the cycle of energy poverty. Will Poland make it?," Energy Economics, Elsevier, vol. 94(C).
    25. Deller, David & Turner, Glen & Waddams Price, Catherine, 2021. "Energy poverty indicators: Inconsistencies, implications and where next?," Energy Economics, Elsevier, vol. 103(C).

    More about this item

    Keywords

    Fuel poverty; Group identification; Indicator dismantling; Multidimensional analysis; Sensitiv-ity analysis;
    All these keywords.

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