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A Model to Detect Low Income Urban Areas to Plan Renewable Energy Communities Against Energy Poverty

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14112))

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Abstract

This work is included in a broader research project, aimed at promoting the development of Renewable Energy Communities (RECs) through urban planning in priority areas for intervention. According to a methodology already proposed by the author, priority areas, mapped on an infra-urban scale, are intended to be located where a minimization of the constraints and a maximization of the benefits deriving from the RECs establishment are expected, with particular reference to the reduction of energy poverty. The aim of this work is to detail the already proposed methodology, in order to better assess energy poverty. To this end, a model for the construction of a composite index of urban poverty is proposed, starting from basic indicators, selected following a review of the technical-scientific literature on urban poverty and deprivation or distress. The spatialisation of the obtained index makes it possible to obtain a more detailed map of energy poverty with respect to the previously proposed methodology. The model is applied to the case study of Pagani, in Campania Region (Italy), which is the study area investigated in the article that precedes this work, which allows to compare the results already obtained and appreciate the progress made by the model presented in this paper.

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Notes

  1. 1.

    This study is developed within the research project “Agreement between DiCiv and municipality of Pagani for studies and research to support the formation of the Municipal Urban Plan”. Prof. Roberto Gerundo is the project scientific responsible, while Alessandra Marra is principal investigator and technical coordinator of the research group.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Salerno, 84084, Fisciano, SA, Italy

    Alessandra Marra

Authors
  1. Alessandra Marra
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Correspondence to Alessandra Marra .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Marra, A. (2023). A Model to Detect Low Income Urban Areas to Plan Renewable Energy Communities Against Energy Poverty. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14112. Springer, Cham. https://doi.org/10.1007/978-3-031-37129-5_29

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  • DOI: https://doi.org/10.1007/978-3-031-37129-5_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37128-8

  • Online ISBN: 978-3-031-37129-5

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