Development and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodology
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The 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.
Кључне речи:
Ecological footprint / Monte Carlo / numerical modelИзвор:
FIKUSZ 2018 International Conference, Budapest, Hungary, 2018Издавач:
- Obuda University, Keleti Faculty of Business and Management, Budapest, Hungary
Финансирање / пројекти:
- Развој нових информационо-комуникационих технологија, коришћењем напредних математичких метода, са применама у медицини, телекомуникацијама, енергетици, заштитити националне баштине и образовању (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-44006)
Институција/група
Mašinski fakultetTY - CONF AU - Janković, Radmila AU - Mihajlović, Ivan PY - 2018 UR - https://machinery.mas.bg.ac.rs/handle/123456789/5592 AB - The 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. PB - Obuda University, Keleti Faculty of Business and Management, Budapest, Hungary C3 - FIKUSZ 2018 International Conference, Budapest, Hungary T1 - Development and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodology UR - https://hdl.handle.net/21.15107/rcub_machinery_5592 ER -
@conference{ author = "Janković, Radmila and Mihajlović, Ivan", year = "2018", abstract = "The 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.", publisher = "Obuda University, Keleti Faculty of Business and Management, Budapest, Hungary", journal = "FIKUSZ 2018 International Conference, Budapest, Hungary", title = "Development and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodology", url = "https://hdl.handle.net/21.15107/rcub_machinery_5592" }
Janković, R.,& Mihajlović, I.. (2018). Development and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodology. in FIKUSZ 2018 International Conference, Budapest, Hungary Obuda University, Keleti Faculty of Business and Management, Budapest, Hungary.. https://hdl.handle.net/21.15107/rcub_machinery_5592
Janković R, Mihajlović I. Development and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodology. in FIKUSZ 2018 International Conference, Budapest, Hungary. 2018;. https://hdl.handle.net/21.15107/rcub_machinery_5592 .
Janković, Radmila, Mihajlović, Ivan, "Development and implementation of the numerical model for predicting the values of ecological footprint, based on the Monte Carlo methodology" in FIKUSZ 2018 International Conference, Budapest, Hungary (2018), https://hdl.handle.net/21.15107/rcub_machinery_5592 .