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dc.contributor.authorMarcillo Delgado, Juan Carlos
dc.contributor.authorOrtego Martínez, María Isabel
dc.contributor.authorPérez Foguet, Agustí
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Sostenibilitat
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.date.accessioned2020-05-13T11:55:03Z
dc.date.available2021-03-18T01:28:20Z
dc.date.issued2019-06
dc.identifier.citationMarcillo, J.; Ortego, M.I.; Pérez-Foguet, A. A compositional approach for modelling SDG7 indicators: case study applied to electricity access. "Renewable and sustainable energy reviews", Juny 2019, vol. 107, p. 388-398.
dc.identifier.issn1364-0321
dc.identifier.otherhttps://www.researchgate.net/publication/331864872_A_compositional_approach_for_modelling_SDG7_indicators_Case_study_applied_to_electricity_access
dc.identifier.urihttp://hdl.handle.net/2117/187397
dc.description.abstractMonitoring energy indicators has acquired a renewed interest with the 2030 Agenda for Sustainable Development, and specifically with goal 7 (SDG7), which seeks to guarantee universal access to energy. The predominant criteria to monitor SDG7 are given in a set of individual indicators. Along this line, the UN indicators proposed in the 47th session of the UN Statistical commission are a practical starting point. A relevant characteristic of these indicators is that they can be expressed as proportions from a whole, i.e., they are compositions. Notably, directly implementing traditional multivariate models onto indicators that are proportions without an intermediate process can lead to spurious analysis. Here, we aim to assess the application of compositional data analysis(CoDa) to follow up on the temporal trend indicators of the energy sector in the context of SDG7, with a case study for the most affected areas addressing the problem of electricity access. Following CoDa methodology, we first use a log-ratio transformation to bring compositions to real space and then apply three multivariate methods: linear regression, generalized additive models and support vector machine. We also address other characteristic problems of the electricity access indicators, such as data quality, which was treated by considering models with interactions. In sum, CoDa facilitates a controlled management of the parts that make up population based indicators, suggesting that modelling evolution of compositions as individual components – even the standard splitting of country data into rural and urban “access to” indicator –should be avoided.
dc.description.sponsorshipThis research has been partially funded by the Ministerio de Economía y Competitividad del Gobierno de España (MINECO/FEDER, Ref: MTM2015-65016-C2-2-R); and by the Agència de Gestió d′Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (Ref. 2017 SGR 656 and 2017 SGR 1496).
dc.format.extent11 p.
dc.language.isoeng
dc.rights© 2019. Elsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Desenvolupament sostenible::Indicadors de sostenibilitat
dc.subject.lcshSustainable Development Goals--Evaluation
dc.subject.otherSustainable Development Goals
dc.subject.otherSDG
dc.subject.otherCompositional data analysis
dc.subject.otherTrend analysis
dc.subject.otherEpsilon support vector machine
dc.subject.otherGeneralized additive model
dc.titleA compositional approach for modelling SDG7 indicators: case study applied to electricity access
dc.typeArticle
dc.subject.lemacDesenvolupament sostenible -- Avaluació
dc.contributor.groupUniversitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis
dc.contributor.groupUniversitat Politècnica de Catalunya. EScGD - Engineering Sciences and Global Development
dc.identifier.doi10.1016/j.rser.2019.03.028
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1364032119301674
dc.rights.accessOpen Access
local.identifier.drac24008601
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/MTM2015-65016-C2-2-R
local.citation.authorMarcillo, J.; Ortego, M.I.; Pérez-Foguet, A.
local.citation.publicationNameRenewable and sustainable energy reviews
local.citation.volume107
local.citation.startingPage388
local.citation.endingPage398


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