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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/48858
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dc.creatorGuilherme Dôco Roberti Gilpt_BR
dc.creatorMarcelo Azevedo Costapt_BR
dc.creatorAna Lúcia Miranda Lopespt_BR
dc.creatorVinícius Diniz Mayrinkpt_BR
dc.date.accessioned2023-01-11T14:29:56Z-
dc.date.available2023-01-11T14:29:56Z-
dc.date.issued2017-
dc.citation.volume64pt_BR
dc.citation.spage373pt_BR
dc.citation.epage383pt_BR
dc.identifier.doi10.1016/j.eneco.2017.04.009pt_BR
dc.identifier.issn01409883pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/48858-
dc.description.resumoIn 2015 the Brazilian regulator presented a DEA benchmarking model to set the regulatory operational cost goals, to be reached in four years for 61 electricity distribution utilities. The DEA model uses: adjusted operational cost as the input variable, seven output variables and weight restrictions. Although non-discretionary variables or en vironmental variables are available in the dataset, the regulator argued that no statistically signicant correlation was found between the DEA efciency scores and the non-discretionary variables. This study evaluates the sta tistical correlation between the DEA efciency scores and the available environmental variables. Spatial statistic methods are used to show that the efciency scores are geographically correlated. Furthermore, due to Brazil's environmental diversity and large territory it is unlikely that only one environmental component is sufcient to adjust inefciencies across the Brazilian territory. Thus, a new combined environmental variable is proposed. Finally, a second stage model using the proposed environmental variable and accounting for a spatial latent struc ture is presented. Results show major differences between original and corrected efciency scores, mainly for utilities located in harsh environments and which originally achieved lower efciency scorespt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA PRODUÇÃOpt_BR
dc.publisher.departmentFCE - DEPARTAMENTO DE CIÊNCIAS ADMINISTRATIVASpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE ESTATÍSTICApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofEnergy Economicspt_BR
dc.rightsAcesso Restritopt_BR
dc.subjectData Envelopment Analysispt_BR
dc.subjectSecond stage analysispt_BR
dc.subjectSpatial statisticspt_BR
dc.subjectBayesian analysispt_BR
dc.subject.otherEnergiapt_BR
dc.titleSpatial statistical methods applied to the 2015 brazilian energy distribution benchmarking model: accounting for unobserved determinants of inefficienciespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttp://www.sciencedirect.com/science/article/pii/S0140988317301160doi:10.1016/j.eneco.2017.04.009pt_BR
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