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Improving Energy Poverty Measurement in Southern European Regions through Equivalization of Modeled Energy Costs

Author

Listed:
  • Iñigo Antepara

    (Alokabide, Technical Department, Portal de Gamarra, 1A—2a planta (Edificio el Boulevard), 01013 Vitoria-Gasteiz, Spain)

  • Lefkothea Papada

    (Metsovion Interdisciplinary Research Center, National Technical University of Athens, 9 Heroon Polytechneiou Str., Zographos, 15780 Athens, Greece)

  • João Pedro Gouveia

    (CENSE Center for Environmental and Sustainability Research, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal)

  • Nikolas Katsoulakos

    (Metsovion Interdisciplinary Research Center, National Technical University of Athens, 9 Heroon Polytechneiou Str., Zographos, 15780 Athens, Greece)

  • Dimitris Kaliampakos

    (School of Mining and Metallurgical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Str., Zographos, 15780 Athens, Greece)

Abstract

In many European countries, energy poverty is measured on the basis of real energy bills, as theoretical energy costs are hard to calculate. The UK is an exception—the data inputs for the Low Income-High Cost (LIHC) indicator are based on reasonable energy costs, these data are collected through specially designed surveys, often an intensive and costly procedure. Approaches which calculate energy needs are valid when energy bill data are unreliable or where households restrict consumption. In this analysis, energy poverty levels are evaluated for Greece, the municipality of Évora (Portugal), and the Basque Country (Spain): energy bills are modeled based on building energy performance data and other energy uses, and adjusted according to socio-demographic variables. To this end, equivalization weights are calculated using socio-economic data from the aforementioned southern European countries/regions. Data are analyzed to compare measurements with actual versus modeled bills using the Ten-Percent Rule (TPR) and Hidden Energy Poverty (HEP) against twice the median (2M) indicator, enhancing the identification of households with low energy consumption. In conclusion, theoretical energy needs can be combined with socio-demographic data instead of actual energy bills to measure energy poverty in a simplified way, avoiding the problem of targeting households that under consume.

Suggested Citation

  • Iñigo Antepara & Lefkothea Papada & João Pedro Gouveia & Nikolas Katsoulakos & Dimitris Kaliampakos, 2020. "Improving Energy Poverty Measurement in Southern European Regions through Equivalization of Modeled Energy Costs," Sustainability, MDPI, vol. 12(14), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5721-:d:385262
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    References listed on IDEAS

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    Cited by:

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    3. Roberto Barrella & José Carlos Romero & Lucía Mariño, 2022. "Proposing a Novel Minimum Income Standard Approach to Energy Poverty Assessment: A European Case Study," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    4. Stojilovska, Ana & Guyet, Rachel & Mahoney, Katherine & Gouveia, João Pedro & Castaño-Rosa, Raúl & Živčič, Lidija & Barbosa, Ricardo & Tkalec, Tomislav, 2022. "Energy poverty and emerging debates: Beyond the traditional triangle of energy poverty drivers," Energy Policy, Elsevier, vol. 169(C).
    5. Miguel Macias Sequeira & João Pedro Gouveia, 2022. "A Sequential Multi-Staged Approach for Developing Digital One-Stop Shops to Support Energy Renovations of Residential Buildings," Energies, MDPI, vol. 15(15), pages 1-27, July.
    6. Lilia Karpinska & Sławomir Śmiech & João Pedro Gouveia & Pedro Palma, 2021. "Mapping Regional Vulnerability to Energy Poverty in Poland," Sustainability, MDPI, vol. 13(19), pages 1-17, September.

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