Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/70678

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dc.contributor.authorRibeiro, Fernandopor
dc.contributor.authorFerreira, Paula Varandaspor
dc.contributor.authorAraújo, Maria Madalena Teixeira depor
dc.contributor.authorBraga, A. C.por
dc.date.accessioned2021-03-10T17:05:39Z-
dc.date.available2021-03-10T17:05:39Z-
dc.date.issued2018-07-
dc.identifier.issn0960-1481-
dc.identifier.urihttps://hdl.handle.net/1822/70678-
dc.description.abstractWhile renewable energy technologies (RET) increase their share in power generation systems worldwide, some questions remain open, namely those concerning the opinion of the populations on new projects of these technologies. Given the long period of planning and large capital sums required by RET and, in some cases, the fact of being subsidized, it is desirable for decision-makers to acknowledge the public opinion and at least perceive if the opinions are rooted on biased perceptions. In this paper we propose a methodology for public perception and awareness assessment, involving an initial phase of data collection by means of a survey, followed by a phase of regression models construction resulting in predictive models of expected perceptions and attitudes towards RET. The models were translated in a free and easy to use computational Excel application and its usefulness was demonstrated for the case of four electricity RET in Portugal: hydro, wind, biomass and solar. (C) 2018 Elsevier Ltd. All rights reserved.por
dc.language.isoengpor
dc.publisherElsevier 1por
dc.rightsopenAccesspor
dc.subjectRenewable energy technologiespor
dc.subjectPublic opinionpor
dc.subjectOrdered logistic regressionpor
dc.subjectBinary logistic regressionpor
dc.subjectExcel simulation toolpor
dc.titleModelling perception and attitudes towards renewable energy technologiespor
dc.typearticlepor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0960148118301149por
oaire.citationStartPage688por
oaire.citationEndPage697por
oaire.citationVolume122por
dc.date.updated2021-03-10T16:09:44Z-
dc.identifier.eissn1879-0682-
dc.identifier.doi10.1016/j.renene.2018.01.104por
dc.subject.wosScience & Technology-
sdum.export.identifier4357-
sdum.journalRenewable Energypor
Aparece nas coleções:CAlg - Artigos em revistas internacionais / Papers in international journals

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