Modeling of sustainable development of the Arctic regions of the Russian Federation (on the example of the Murmansk region)

• Main Author: Antipov Sergey
• Format: Article in Journal/Newspaper
• Language: English; Russian
• Published: Peter the Great St. Petersburg Polytechnic University 2022
• Subjects: modeling; sustainable development; adl model; neural network; territorial development; arctic zone; Economics as a science; HB71-74; Arctic; Murmansk
• Online Access: https://doi.org/10.18721/JE.15610; https://doaj.org/article/2cfa6a928cce4aefb6f742967a268202

This article examines the main prerequisites for interregional cooperation between the regions of the Arctic zone of the Russian Federation, as the most important prerequisites for achieving both geopolitical and economic leadership of the country on the world stage. Possible prospects for quantitative modeling of this interaction are considered in view of the importance of numerical assessment of realities and making forecasts that ensure correct decisions. The article examines the features of building a model of sustainable development of the Arctic regions assessing the cumulative impact in three areas: economic, environmental and social. The main aspects of the construction of mathematical models are analyzed both in the field of these spheres and their totality. The requirements for the implementation of such models are put forward and, most importantly, the key parameters necessary for the successful implementation of modeling are determined. The process of forming a model capable of assessing the sustainable development of regions in as much detail as possible is described, as well as requirements are imposed both on the model itself and on the indicators included in it. Possible variants of mathematical models are proposed that are fully capable of satisfying all conditions. The two most preferred models are described: ADL and a model based on a neural network; the areas of their possible application are characterized. An ADL model and a model of neural networks for three spheres in the Murmansk region are being built on the example of statistical data for 30 years from 1988 to 2018. The accuracy of the results obtained is estimated based on a comparison of empirical and actual values, and a numerical error estimate based on the MAPE approach is also given. The comparison of these models is given and the possibilities of their application, as well as the limitations of their use, are analyzed. The possibilities of using the disassembled models with the advantages and limitations are described both for ...