Spatial statistical methods applied to the 2015 brazilian energy distribution benchmarking model: accounting for unobserved determinants of inefficiencies

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dc.creatorGuilherme Dôco Roberti Gil
dc.creatorMarcelo Azevedo Costa
dc.creatorAna Lúcia Miranda Lopes
dc.creatorVinícius Diniz Mayrink
dc.date.accessioned2023-01-11T14:29:56Z
dc.date.accessioned2025-09-08T22:48:43Z
dc.date.available2023-01-11T14:29:56Z
dc.date.issued2017
dc.identifier.doi10.1016/j.eneco.2017.04.009
dc.identifier.issn01409883
dc.identifier.urihttps://hdl.handle.net/1843/48858
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofEnergy Economics
dc.rightsAcesso Restrito
dc.subjectEnergia
dc.subject.otherData Envelopment Analysis
dc.subject.otherSecond stage analysis
dc.subject.otherSpatial statistics
dc.subject.otherBayesian analysis
dc.titleSpatial statistical methods applied to the 2015 brazilian energy distribution benchmarking model: accounting for unobserved determinants of inefficiencies
dc.typeArtigo de periódico
local.citation.epage383
local.citation.spage373
local.citation.volume64
local.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 scores
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA PRODUÇÃO
local.publisher.departmentFCE - DEPARTAMENTO DE CIÊNCIAS ADMINISTRATIVAS
local.publisher.departmentICX - DEPARTAMENTO DE ESTATÍSTICA
local.publisher.initialsUFMG
local.url.externahttp://www.sciencedirect.com/science/article/pii/S0140988317301160doi:10.1016/j.eneco.2017.04.009

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