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A Stochastic Model for energy poverty analysis

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  • Papada, Lefkothea
  • Kaliampakos, Dimitris

Abstract

So far, the lack of a common, effective way of measuring energy poverty has been detected as a major weakness in handling the energy poverty problem. One of the main causes has been the complexity of modeling the “required energy consumption” of households, as demanded by the official 10% indicator, and its replacement in calculations by the “actual energy consumption”, which, as is well known, underestimates the real needs of households. This weakness is addressed in this paper, through the development of the “Stochastic Model of Energy Poverty” (SMEP). The development of the model includes, firstly, the modeling of energy consumption at household level and, subsequently, the transition from household level to country level through stochastic analysis (Monte-Carlo simulation). Through Sensitivity Analysis, the impact of various parameters on energy poverty is quantified for the first time, by determining their weighting factors. Applied to the case of Greece, it is found that energy poverty reaches 70.4%, with income being the decisive factor affecting energy poverty at 63%, while other variables (Htot, etc.) follow at significantly lower percentages. The findings can be used in order to assess in advance the effectiveness of energy poverty measures, making the model a valuable policy tool.

Suggested Citation

  • Papada, Lefkothea & Kaliampakos, Dimitris, 2018. "A Stochastic Model for energy poverty analysis," Energy Policy, Elsevier, vol. 116(C), pages 153-164.
  • Handle: RePEc:eee:enepol:v:116:y:2018:i:c:p:153-164
    DOI: 10.1016/j.enpol.2018.02.004
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    References listed on IDEAS

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    Cited by:

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    2. Ntaintasis, E. & Mirasgedis, S. & Tourkolias, C., 2019. "Comparing different methodological approaches for measuring energy poverty: Evidence from a survey in the region of Attika, Greece," Energy Policy, Elsevier, vol. 125(C), pages 160-169.
    3. Llorca, Manuel & Rodriguez-Alvarez, Ana & Jamasb, Tooraj, 2020. "Objective vs. subjective fuel poverty and self-assessed health," Energy Economics, Elsevier, vol. 87(C).
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    5. Yuxiang Ye & Steven F. Koch & Jiangfeng Zhang, 2020. "Modelling Required Energy Consumption with Equivalence Scales," Working Papers 202014, University of Pretoria, Department of Economics.
    6. Milena N Rajić & Miroslav B Milovanović & Dragan S Antić & Rado M Maksimović & Pedja M Milosavljević & Dragan Lj Pavlović, 2020. "Analyzing energy poverty using intelligent approach," Energy & Environment, , vol. 31(8), pages 1448-1472, December.
    7. Woźniak, Justyna & Krysa, Zbigniew & Dudek, Michał, 2020. "Concept of government-subsidized energy prices for a group of individual consumers in Poland as a means to reduce smog," Energy Policy, Elsevier, vol. 144(C).
    8. Andrea Boeri & Valentina Gianfrate & Saveria Olga Murielle Boulanger & Martina Massari, 2020. "Future Design Approaches for Energy Poverty: Users Profiling and Services for No-Vulnerable Condition," Energies, MDPI, Open Access Journal, vol. 13(8), pages 1-18, April.
    9. Johannes Thema & Florin Vondung, 2020. "Expenditure-Based Indicators of Energy Poverty—An Analysis of Income and Expenditure Elasticities," Energies, MDPI, Open Access Journal, vol. 14(1), pages 1-18, December.

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