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dc.creatorVlontzos G., Pardalos P.M.en
dc.date.accessioned2023-01-31T11:37:06Z
dc.date.available2023-01-31T11:37:06Z
dc.date.issued2017
dc.identifier10.1016/j.rser.2017.03.054
dc.identifier.issn13640321
dc.identifier.urihttp://hdl.handle.net/11615/80713
dc.description.abstractOne of the most important policy reforms for the European Union (EU) agriculture was the implementation of the Agenda 2000, which establishes a new framework for subsidies management, decoupled from both crop and animal production for the vast majority of products. One of the main goals of this new policy framework is the improvement of its environmental impact. Additionally, there is a need for the implementation of new efficiency assessment and prognostication tools for the evaluation of EU farming, because the influence of market forces has been increased substantially. Having in mind the efficacy of Data Envelopment Analysis (DEA) methodology, it is used to calculate and quantify the environmental efficiency of EU countries’ primary sectors. In this paper, the DEA Window methodology is used to assess GHG emissions efficiency and identify efficiency change of EU countries’ primary sectors, under the strong influence of Common Agricultural Policy (CAP), quantifying by this way its positive or negative impact on a national basis, providing at the same time hints for counteractive actions. The main results provide the significant differences among EU countries, with the less developed ones to perform low environmental efficiency rates. Moreover, countries which their output depends to a large extend on arable crops achieve low efficiency rates too. Finally, Artificial Neural Networks (ANNs) are being used as a tool to estimate future performance of EU countries primary sectors on the topic of Greenhouse Gas (GHG) emissions as an undesirable output of agricultural production process. The validation performance characteristics, as well as the linear fit to this output-target relationship, closely intersect the bottom-left and top-right corners of the plot. The combination of these methodologies provides a new methodological approach for CAP evaluation and prognostication, appropriately adjusted to the new market oriented framework for EU agricultural production. © 2017 Elsevier Ltden
dc.language.isoenen
dc.sourceRenewable and Sustainable Energy Reviewsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85015638542&doi=10.1016%2fj.rser.2017.03.054&partnerID=40&md5=a50f8821c6da81ecdb12ae22b4d82cde
dc.subjectAgricultureen
dc.subjectCommerceen
dc.subjectCropsen
dc.subjectData envelopment analysisen
dc.subjectDeep neural networksen
dc.subjectEfficiencyen
dc.subjectEnvironmental impacten
dc.subjectGreenhouse gasesen
dc.subjectNeural networksen
dc.subjectAgricultural productionsen
dc.subjectCommon agricultural policyen
dc.subjectEfficiency assessmenten
dc.subjectEnvironmental efficiencyen
dc.subjectFuture performanceen
dc.subjectMethodological approachen
dc.subjectPerformance characteristicsen
dc.subjectWindow analysisen
dc.subjectGas emissionsen
dc.subjectElsevier Ltden
dc.titleAssess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networksen
dc.typeotheren


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