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

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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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APA

Marra, A. (2023). A Model to Detect Low Income Urban Areas to Plan Renewable Energy Communities Against Energy Poverty. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14112 LNCS, pp. 353–363). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37129-5_29

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