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Two-stage DEA model to evaluate technical efficiency on deployment of battery electric vehicles in the EU countries

dc.contributor.authorNeves, Sónia Almeida
dc.contributor.authorMarques, António Cardoso
dc.contributor.authorMoutinho, Vitor
dc.date.accessioned2020-09-15T14:21:53Z
dc.date.available2020-09-15T14:21:53Z
dc.date.issued2020
dc.description.abstractThe transportation sector represents an important barrier to decarbonising economies. The in- troduction of electric vehicles seems to be a promising solution; however, the intensive use of such vehicles remains a challenge for economies. By using the two-stage Data Envelopment Analysis (DEA) method, this paper aims to provide useful insights to enlarge Battery Electric Vehicles (BEV) market share. In the first stage, it calculates the efficiency scores for 20 European countries for both BEV adoption and policies supporting electric mobility, considering an output- oriented DEA method with constant returns to scale, and using annual data from 2010 to 2018. It is a non-parametric method, which makes it possible to determine the technical efficiency of the countries under study, i.e., the ability of these countries to transform their inputs into outputs. It calculates the efficiency frontier and determines if the countries are (or not) on this frontier. In the second stage, it examines the role of some determinants of electric mobility using the effi- ciency scores previously calculated by applying a fractional regression model. The main findings show that few countries are performing on the efficiency frontier. Additionally, renewable electricity generation increases a countries’ DEA score and contributes to bringing the inefficient countries closer to the efficiency frontier. Contrary, the existence of peak periods of electricity consumption decreases the DEA score and moves the inefficient countries further away from the frontier. This paper highlights the need to design transport and electricity policies jointly in order to ensure that the intensive use of BEV contributes towards renewables accommodation.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.trd.2020.102489pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/10444
dc.language.isoengpt_PT
dc.relationResearch Unit in Business Sciences
dc.subjectBattery electric vehiclespt_PT
dc.subjectDemand Side Managementpt_PT
dc.subjectEfficiencypt_PT
dc.subjectData envelopment analysispt_PT
dc.subjectFractional Regression modelpt_PT
dc.titleTwo-stage DEA model to evaluate technical efficiency on deployment of battery electric vehicles in the EU countriespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Unit in Business Sciences
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04630%2F2020/PT
oaire.citation.startPage102489pt_PT
oaire.citation.titleTransportation Research Part D: Transport and Environmentpt_PT
oaire.citation.volume86pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameNeves
person.familyNameMarques
person.givenNameSónia Almeida
person.givenNameAntónio Cardoso
person.identifierR-00H-KXQ
person.identifier1880954
person.identifier.ciencia-idD618-C02E-8E45
person.identifier.ciencia-idBE19-EAEF-30CF
person.identifier.orcid0000-0002-0079-9620
person.identifier.orcid0000-0002-9906-3874
person.identifier.ridT-8303-2017
person.identifier.ridD-2235-2011
person.identifier.scopus-author-id57195604008
person.identifier.scopus-author-id36169680100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.embargofctCopyrights cedidos à revistapt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication0dfb8b49-b2ad-4a47-8478-a3ac0a8c639f
relation.isAuthorOfPublication.latestForDiscovery0dfb8b49-b2ad-4a47-8478-a3ac0a8c639f
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