Modeling the Ecological-Socio-Economic System of the White Sea and its Watershed

V. V. Menshutkin1, ✉, N. N. Filatov

1 Institute for Regional Economic Studies, Russian Academy of Sciences, Saint Petersburg, Russian Federation

2 Northern Water Problems Institute, Karelian Research Center, Russian Academy of Sciences, Petrozavodsk, Russian Federation

e-mail: nfilatov@rambler.ru

Abstract

Purpose. The work is aimed at developing a cognitive model of the ecological-socio-economic system of the White Sea (Beloe more) and its watershed (called for short Belomor’e). Unlike the previously developed cognitive models for the region, the new model of the system has a hierarchical structure including five sub-models united by a common management system. The model is intended for obtaining prognostic qualitative assessments of the transformations ongoing in a complex system under various scenarios of nature management and climate change. The model makes it possible to determine different targets, which, in their turn, permit to assess the possibilities of improving the population living standards, the environment rational use and protection, and development of the White Sea region social sphere. These factors constitute an important foundation for achieving sustainable development of the region. The results can serve a basis for constructing a system of quantitative models required to develop the management decision support systems.

Methods and Results. The cognitive model of the White Sea is considered to be a tool for synthesizing heterogeneous information about a complex ecological-socio-economic system. The conceptual modeling and the mathematical apparatus of continuous or probabilistic logic are applied. Unlike the traditional cognitive models, the new one implies the variables’ change in time over 100 years. This allows us to describe the relationship between the interaction agents, and to characterize the mechanisms of their mutual adaptation. The time step in the model is preset to be one year. Development of the cognitive models for the White Sea region was supported by the following information: geographic information systems, databases, integrated electronic and paper atlases of the White Sea and its watershed area, original 3D mathematical models of the sea thermohydrodynamics and ecosystem. The patterns of climate change, hydrological conditions and fishing (basic occupation of local population – the Pomors) were studied. At that, the models both for assessing the regional economy state and for forecasting its development are used.

Conclusions. A new cognitive model of the White Sea region ecological-socio-economic system was created based on the hierarchical principle. The developed sub-models relate to various fields of knowledge: economy, demography, oceanography, soil and agrophysics. Dynamics of the model elements over 100 years was demonstrated. Besides, it was shown that with the quasi-cyclic climate fluctuations, the economic parameters change insignificantly, whereas they have a noticeable impact upon the population living standards and the White Sea ecosystem. The demonstrated features resulted from the climate change effects upon the White Sea ecosystem are manifested in the fluctuations of water temperature, phyto- and zooplankton biomass and fishing, but the changes in benthos are hardly noticeable. Dependence of the White Sea region population outflow upon the gross regional product size, availability of production facilities and water quality is shown. Water quality in the region increases, unfortunately, not due to the investments in water treatment, but because of the pollution decrease resulted from the population and production shrinkage.

Keywords

environment, population, economy, cognitive model, forecasting, climate, White Sea, watershed

Acknowledgements

The research was financially provided by the federal budget intended for implementing the state task to the Northern Water Problems Institute, Karelian Research Center, Russian Academy of Sciences and the Institute for Regional Economic Studies, Russian Academy of Sciences (AAAA-A19-119-021390164-1). The authors are grateful to T.R. Minina (IRES RAS) for her valuable assistance in preparing the article, L.E. Nazarova (NWPI, KarRC RAS) for her useful comments, and M.S. Bogdanova (NWPI, KarRC RAS) for her help in preparing the figures.

Original russian text

Original Russian Text © V. V. Menshutkin, N. N. Filatov, 2021, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 37, Iss. 1, pp. 113-131 (2021)

For citation

Menshutkin, V.V. and Filatov, N.N., 2021. Modeling the Ecological-Socio-Economic System of the White Sea and its Watershed. Physical Oceanography, 28(1), pp. 104-121. doi:10.22449/1573-160X-2021-1-104-121

DOI

10.22449/1573-160X-2021-1-104-121

References

  1. Filatov, N.N. and Terzhevik, A.Yu., Eds., 2007. The White [Beloe] Sea and their Watershed under Influenses of Climate and Antropogenic Impact. Petrozavodsk: KRC RAS. 335 p. (in Russian).
  2. Druzhinin, P.V., Filatov, N.N., Moroshkina, M.V., Derusova, O.V., and Potasheva, O.V., 2018. Modeling and Spatial Analysis of Ecological and Economic Condition of the White Sea Reservoir. Proceedings of the International Conference “InterCarto. InterGIS”, 24(1), pp. 130-142. doi:10.24057/2414-9179-2018-1-24-297-309 (in Russian).
  3. Stasenkov, V.A., 2016. Fishing of Navaga Eleginus Nawaga (Koelreuter, 1770). The Bulletin of Fisheries Science, 3(2), pp. 18-26 (in Russian).
  4. Timchenko, I.E., Ivashchenko, I.K. and Igumnova, E.M., 2017. Management of Ecological-Economic Processes of Pollution Accumulation and Assimilation in the Coastal Zone Marine Environment. Physical Oceanography, (1), pp. 68-83. doi:10.22449/1573-160X-2017-1-68-83
  5. Gorelova, G.V. and Ryabtsov, V.N., 2012. Cognitive Approach to the Research of Geopolitical Processes in World Regions and Cognitive Modeling of their Development (on the Example of the Black – Caspian Region). Engineering Journal of Don, 4(2), 90 (in Russian).
  6. Crépin, A.-S., Karcher, M. and Gascard, J.-C., 2017. Arctic Climate Change, Economy and Society (ACCESS): Integrated Perspectives. Ambio, 46(Suppl. 3), pp. 341-354. doi:10.1007/s13280-017-0953-3
  7. Menshutkin, V.V., Filatov, N.N. and Druzhinin, P.V., 2018. A Current State and Forecasting of the Socio-Ecological-Economic System of the White Sea Watershed with Use of Cognitive Simulation. Arctic: Ecology and Economy, (2), pp. 4-17. doi:10.25283/2223-4594-2018-2-4-17 (in Russian).
  8. Menshutkin, V.V. and Filatov, N.N., 2019. Cognitive Modeling of the Fisheries Effect on the Standard of Living in the White Sea Area. Transactions of the Karelian Research Centre of the Russian Academy of Sciences, (9), pp. 145-154. doi:10.17076/lim1120 (in Russian).
  9. Menshutkin, V.V. and Filatov, N.N., 2020. Modeling Optimal Control of the Ecological–Socioeconomic System Water Body–Watershed: Case Study of the White Sea Region. Water Resources, 47(3), pp. 506-515. doi:10.1134/S0097807820030100
  10. Menshutkin, V.V. and Minina, T.R., 2018. Cognitive Modeling as a Research Tool of Ecological and Economic Systems. In: L. P. Sovershaeva, Ed., 2018. Regional'naja Jekonomika i Razvitie Territorij [Regional Economy and Development of Territories]. Saint Рetersburg: SUAI. Issue 1(12), pp. 157-163 (in Russian).
  11. Menshutkin, V.V. and Minina, T.R., 2017. Cognitive Model of the Interaction of Human Society with the Ecological System of the Reservoir. In: L. P. Sovershaeva, Ed., 2017. Regional'naja Jekonomika i Razvitie Territorij [Regional Economy and Development of Territories]. Saint Рetersburg: SUAI. Issue 1(11), pp. 160-166 (in Russian).
  12. Ross, D., 2005. Economic Theory and Cognitive Science: Microexplanation. London: MITPress, 454 p.
  13. Pavlov, S.N., 2011. [Artificial Intelligence Systems]. Tomsk: Electronic Content, 176 p. (in Russian).
  14. Kosko, B., 1993. Fuzzy Thinking. New York: Hyperion, 318 p.
  15. Chernov, I.A., Tolstikov, A.V. and Iakovlev, N.G., 2016. Comprehensive Model of the White Sea: Hydrothermodynamics of Water and Sea Ice. Transactions of the Karelian Research Centre of the Russian Academy of Sciences, (8), pp. 116-128. doi:10.17076/mat397 (in Russian).
  16. Filatov, N.N., Tolstikov, A.V., Bogdanova, M.S. and Menshutkin, V.V., 2014. Development of Information System and Electronic Atlas on the State and Use of Resources of the White Sea and Its Catchment. Arctic: Ecology and Economy, 3(15), pp. 18-29 (in Russian).
  17. Filatov, N.N., Druzhinin, P.V. and Menshutkin V.V., 2019. Information Support of Investigations of Environment and Socio-Economic Conditions of White Sea and Watershed. InterCarto. InterGIS. GI Support of Sustainable Development of Territories: Proceedings of the International Conference, 25(1), pp. 122-137. doi: 10.35595/2414-9179-2019-1-25-122-137
  18. Walliser, B., 2008. Cognitive Economics. Berlin: Springer-Verlag, 185 p. doi:10.1007/978-3-540-71347-0
  19. Filatov, N.N., Nazarova, L.E. and Druzhinin, P.V., 2019. Influence of Climatic and Anthropogenic Factors on the White Sea – Catchment System. Transactions of the Karelian Research Centre of the Russian Academy of Sciences, (9), pp. 30-50. doi.org/10.17076/lim1117 (in Russian).
  20. Freiberger, W. and Grenander, U., 1971. A Course in Computational Probability and Statistics. New York: Springer-Verlag, 156 p. doi:10.1007/978-1-4612-9837-3
  21. Nalimov, V.V., 1974. Probabilistic Model of a Language: Relationship between Natural and Artificial Languages. Moscow: Nauka, 272 p. (in Russian).
  22. Menshutkin, V.V., 2010. The Art of Modeling: Physiology, Ecology, Evolution). Petrozavodsk, 410 p. (in Russian).
  23. Kurzenev, V.A. and Matveenko, V.D., 2018. Economic Growth. Saint Petersburg: Piter, 608 p. (in Russian).
  24. Guzairov, M.B., Ilyasov, B.G., Zakieva, E.Sh. and Gerasimova, I.B., 2013. Cognitive Model of Life Quality Indicator Formation. Vestnik UGATU, 17(2), pp. 215-220 (in Russian).
  25. Kolmakova, I.D., Baikova, E.I. and Kolmakova, E.M., 2017. Economic and Mathematical Methods in Evaluating and Planning the Standard of Living of the Population of the Region. Regional Economics: Theory and Practice, 15(5), pp. 928-936 (in Russian).
  26. Botkin, D.B., 1993. Forest Dynamics: An Ecological Model. New York: Oxford University Press, 309 p. doi:10.5860/choice.31-1511
  27. Kozak, I., Menshutkin, V.V. and Klekowski, R.Z., 2003. Modelowanie Elementów Krajobrazu. Lublin: Towarzystwo Naukowe Katolickiego Uniwersytetu Lubelskiego, 190 p. (in Polish).
  28. Poluektov, R.A., Smolyar, E.I., Terleev, V.V. and Topazh, A.G., 2006. Models of Plant Production Processes. Saint Petersburg: Saint Petersburg University publishing house, 396 p. (in Russian).
  29. Baranov, N.S., 2014. Strategic Role of Northern Territories for Russian Economy. Society and Law, (3), pp. 297-301 (in Russian).

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