Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation
Abstract This paper outlines the concept of sustainability in regard to regional development, examines basic premises of modeling regional relations in the Arctic zone of the Russian Federation, describes the principles and investigates the specifics of building models for sustainable development in...
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Online Access: | http://dx.doi.org/10.1088/1755-1315/539/1/012102 https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012102/pdf https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012102 |
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crioppubl:10.1088/1755-1315/539/1/012102 2024-09-09T19:19:58+00:00 Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation Antipov, S K Didenko, N I Lobatyuk, V V Sosnina, M N 2020 http://dx.doi.org/10.1088/1755-1315/539/1/012102 https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012102/pdf https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012102 unknown IOP Publishing http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining IOP Conference Series: Earth and Environmental Science volume 539, issue 1, page 012102 ISSN 1755-1307 1755-1315 journal-article 2020 crioppubl https://doi.org/10.1088/1755-1315/539/1/012102 2024-06-24T04:17:55Z Abstract This paper outlines the concept of sustainability in regard to regional development, examines basic premises of modeling regional relations in the Arctic zone of the Russian Federation, describes the principles and investigates the specifics of building models for sustainable development in regions of the Arctic. It presents methods to build a model that can fully characterize sustainable regional development in the Russian Arctic and lays out requirements for such a model and its corresponding indicators. The paper also considers possibilities for using and adapting already existing mathematical models exemplified by an autoregressive distributed lag (ADL) model and a neural network model. The authors specify under what conditions these models can fully meet all the requirements, what statistical indicators are of key importance to a numerical representation of sustainable development, what factors can be prioritized to be incorporated in the models, what limitations they have and why these very models show the most promise in terms of both analyzing the current state of affairs and making forecasts. The paper presents the results of testing the models on the basis of real statistical data gathered across the Murmansk region and compares the derived outcomes to draw conclusions from the obtained data. It also summarizes the findings from the built models and assesses the prospects for their further application in forecasting sustainable regional development and utilizing projected outcomes in making management decisions to implement the strategy for the development of the Russian Arctic regions. Article in Journal/Newspaper Arctic IOP Publishing Arctic Murmansk IOP Conference Series: Earth and Environmental Science 539 012102 |
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Abstract This paper outlines the concept of sustainability in regard to regional development, examines basic premises of modeling regional relations in the Arctic zone of the Russian Federation, describes the principles and investigates the specifics of building models for sustainable development in regions of the Arctic. It presents methods to build a model that can fully characterize sustainable regional development in the Russian Arctic and lays out requirements for such a model and its corresponding indicators. The paper also considers possibilities for using and adapting already existing mathematical models exemplified by an autoregressive distributed lag (ADL) model and a neural network model. The authors specify under what conditions these models can fully meet all the requirements, what statistical indicators are of key importance to a numerical representation of sustainable development, what factors can be prioritized to be incorporated in the models, what limitations they have and why these very models show the most promise in terms of both analyzing the current state of affairs and making forecasts. The paper presents the results of testing the models on the basis of real statistical data gathered across the Murmansk region and compares the derived outcomes to draw conclusions from the obtained data. It also summarizes the findings from the built models and assesses the prospects for their further application in forecasting sustainable regional development and utilizing projected outcomes in making management decisions to implement the strategy for the development of the Russian Arctic regions. |
format |
Article in Journal/Newspaper |
author |
Antipov, S K Didenko, N I Lobatyuk, V V Sosnina, M N |
spellingShingle |
Antipov, S K Didenko, N I Lobatyuk, V V Sosnina, M N Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation |
author_facet |
Antipov, S K Didenko, N I Lobatyuk, V V Sosnina, M N |
author_sort |
Antipov, S K |
title |
Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation |
title_short |
Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation |
title_full |
Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation |
title_fullStr |
Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation |
title_full_unstemmed |
Comparing Models of Sustainable Development for the Arctic Region of the Russian Federation |
title_sort |
comparing models of sustainable development for the arctic region of the russian federation |
publisher |
IOP Publishing |
publishDate |
2020 |
url |
http://dx.doi.org/10.1088/1755-1315/539/1/012102 https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012102/pdf https://iopscience.iop.org/article/10.1088/1755-1315/539/1/012102 |
geographic |
Arctic Murmansk |
geographic_facet |
Arctic Murmansk |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
IOP Conference Series: Earth and Environmental Science volume 539, issue 1, page 012102 ISSN 1755-1307 1755-1315 |
op_rights |
http://creativecommons.org/licenses/by/3.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1755-1315/539/1/012102 |
container_title |
IOP Conference Series: Earth and Environmental Science |
container_volume |
539 |
container_start_page |
012102 |
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1809760065362591744 |