On the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithms

Basit Öğe Kaydı
dc.authoridAllahviranloo, Tofigh/0000-0002-6673-3560
dc.authoridAmirteimoori, Alireza/0000-0003-4160-8509
dc.authoridZadmirzaei, Majid/0000-0002-7235-9720
dc.authorwosidAllahviranloo, Tofigh/V-4843-2019
dc.authorwosidzadmirzaei, majid/AGD-9608-2022
dc.authorwosidAmirteimoori, Alireza/I-7703-2019
dc.contributor.authorAmirteimoori, Alireza
dc.contributor.authorAllahviranloo, Tofigh
dc.contributor.authorZadmirzaei, Majid
dc.contributor.authorHasanzadeh, Fahimeh
dc.date.accessioned2024-05-19T14:40:40Z
dc.date.available2024-05-19T14:40:40Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractGreenhouse gases (GHG) remain in the atmosphere for a very long-time causing alarmingly fast warming worldwide (global warming); especially Carbon dioxide (CO2) emissions have become a worldwide concern because of their harmful effects on the climate, and they are considered as an undesirable product of a lot of production systems. Various models dealing with undesirable outputs for measuring environmental efficiency have been employed to control greenhouse gas emissions via forecasting and/or optimizing their emissions. In this regard, this study proposes a novel modified Fuzzy Undesirable Non-discretionary DEA (FUNDEA) model to Measure environmental efficiency, and combine it with some novel artificial intelligence algorithms (Artificial Neural Network (ANN), Gene Expression Programming (GEP) and Artificial Immune System (AIS)) in order to predict optimal values of inefficient Decision-Making Units (DMUs) for being more efficient and mitigating their Co2 emissions in the uncertain environment for the first time herein. The model is applied to a dataset of 24 Iranian forest management units. Although our findings show that 17 DMUs are inefficient with a weak efficiency dispersion, these inefficient DMUs could improve their efficiency border by following the combined approaches (FUNDEA-ANN, FUNDEA-GEP and FUNDEA-AIS). As a consequence, the applied FUNDEA- artificial intelligent approaches are performed very well in predicting the optimal values of CO2 emissions and, hence increasing the total environmental efficiency.en_US
dc.identifier.doi10.1016/j.eswa.2023.119953
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85151805227en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.eswa.2023.119953
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4998
dc.identifier.volume224en_US
dc.identifier.wosWOS:000969955000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectData Envelopment Analysisen_US
dc.subjectEnvironmental Efficiencyen_US
dc.subjectArtificial Neural Networken_US
dc.subjectGene Expression Programmingen_US
dc.subjectArtificial Immune Systemen_US
dc.titleOn the environmental performance analysis: A combined fuzzy data envelopment analysis and artificial intelligence algorithmsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu