Приказ основних података о документу

dc.creatorJanković, Radmila
dc.creatorMihajlović, Ivan
dc.date.accessioned2023-03-09T20:09:11Z
dc.date.available2023-03-09T20:09:11Z
dc.date.issued2018
dc.identifier.isbn978-963-449-114-9
dc.identifier.urihttps://machinery.mas.bg.ac.rs/handle/123456789/5592
dc.description.abstractThe impact of human activities on the environment can be observed through the ecological footprint the biologically productive area of land and water that is needed to satisfy human demands. In order to achie ve and maintain sustainability, Earth’s natural capital needs to be preserved. Thus, it is of high importance for scientific and general community to analyze and predict the ecological footprint in order to successfully manage natural resources and protect the environment. The aim of this paper is to develop and implement a numerical model based on Monte Carlo methodology, for predicting the values of ecological footprint (EF). The model is based on systematic analysis of six input variables: (1) Rural popu lation, (2) Urban population, (3) GDP per capita, (4) Energy use, (5) Electric power consumption, (6) Electricity production, and one output variable which is the total ecological footprint of consumption. The dataset included data from European, North Ame rican, South American, Asian and African countries, as well from Australia. Predicted values from the model were then compared with the measured ones, in order to verify the accuracy of the model.sr
dc.language.isoensr
dc.publisherObuda University, Keleti Faculty of Business and Management, Budapest, Hungarysr
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/44006/RS//sr
dc.rightsopenAccesssr
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceFIKUSZ 2018 International Conference, Budapest, Hungarysr
dc.subjectEcological footprintsr
dc.subjectMonte Carlosr
dc.subjectnumerical modelsr
dc.titleDevelopment and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodologysr
dc.typeconferenceObjectsr
dc.rights.licenseBYsr
dc.rights.holderObuda University, Keleti Faculty of Business and Management, Budapest, Hungarysr
dc.identifier.fulltexthttp://machinery.mas.bg.ac.rs/bitstream/id/13734/FIKUSZ_2018_Proceedings.pdf
dc.identifier.rcubhttps://hdl.handle.net/21.15107/rcub_machinery_5592
dc.type.versionpublishedVersionsr


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