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A Two-Stage DEA Model to Evaluate the Technical Eco-Efficiency Indicator in the EU Countries

2021-03Journal article Open access
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dc.contributor.authorMoutinho, Victor
dc.contributor.authorMadaleno, Mara
dc.date.accessioned2021-10-28T11:14:05Z
dc.date.available2021-10-28T11:14:05Z
dc.date.issued2021-03
dc.description.abstractThis paper evaluates the evolution of eco-efficiency for the 27 European Union (EU) countries over the period 2008–2018, provided the traditional high concerns of the EU concerning the economic growth-environmental performance relationship. The EU has triggered several initiatives and regulations regarding environmental protection over the years, but as well the Sustainable Development Goals demand it. Under this setting, we conduct a two-stage analysis, which computes eco-efficiency scores in the first stage for each of the pairs EU 27-year, through the nonparametric method data envelopment analysis (DEA), considering the ratio GDP per capita and greenhouse gas emissions (GHG). In the second stage, scores are used as a dependent variable in the proposed fractional regression model (FRM), whose determinants considered were eight pollutants (three greenhouse gases and five atmospheric pollutants). CO2/area and N2O/area effects are negative and significant, improving the eco-efficiency of the EU 27 countries. When the efficient European countries are excluded from the estimations, the results evidence that CO2/area and CH4/area decrease the DEA score. The country with the lowest GHG emissions and pollutant gases was Ireland, being the country within the considered period that mostly reduced emissions, particularly SOx and PM10, increasing its score.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMoutinho, V., & Madaleno, M. (2021a). A two-stage dea model to evaluate the technical eco-efficiency indicator in the eu countries. International Journal of Environmental Research and Public Health, 18(6), 3038. https://doi.org/10.3390/ijerph18063038pt_PT
dc.identifier.doi10.3390/ijerph18063038pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/11238
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationResearch Unit in Business Sciences
dc.relationResearch Unit on Governance, Competitiveness and Public Policy
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectAir pollutantspt_PT
dc.subjectData envelopment analysispt_PT
dc.subjectEco-efficiencypt_PT
dc.subjectFractional regression modelspt_PT
dc.subjectGHG emissionspt_PT
dc.titleA Two-Stage DEA Model to Evaluate the Technical Eco-Efficiency Indicator in the EU Countriespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Unit in Business Sciences
oaire.awardTitleResearch Unit on Governance, Competitiveness and Public Policy
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04630%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04058%2F2020/PT
oaire.citation.titleInternational Journal of Environmental Research and Public Healthpt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMoutinho
person.familyNameSilva Madaleno
person.givenNameVictor
person.givenNameMara Teresa da
person.identifierA-6356-2018
person.identifier.ciencia-id211A-65B8-8EA8
person.identifier.ciencia-idC614-127A-AE55
person.identifier.orcid0000-0003-0811-9033
person.identifier.orcid0000-0002-4905-2771
person.identifier.scopus-author-id57010555300
person.identifier.scopus-author-id36548856100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication5d1abfe3-e724-48b6-a64a-d03ec90d0f62
relation.isAuthorOfPublication58852036-2210-4721-9637-cb00342724b6
relation.isAuthorOfPublication.latestForDiscovery58852036-2210-4721-9637-cb00342724b6
relation.isProjectOfPublicationa9940477-25e6-4969-9d12-c913c900c23a
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