E. V. Butko, A. N. Ilchenko. The Economic and Mathematical Model of Identifying Regional Financial Resources Requirements for Handling Natural Disasters Taking Into Account the Weather Risk Factor
UDC 332.1:336:614.8
E. V. Butko, A. N. Ilchenko. The Economic and Mathematical Model of Identifying Regional Financial Resources Requirements for Handling Natural Disasters Taking Into Account the Weather Risk Factor
The article is based on the materials of research supported by grant No. 15-46-03180 from the Russian Foundation for Basic Research.
BUTKO Elena Valerievna, Postgraduate at the Department of Administration and Economic and Mathematical Modeling, Ivanovo State University of Chemistry and Technology (Ivanovo, Russian Federation) (e-mail: lena210292@mail.ru). ORCID: http://orcid.org/0000-0002-3068-2781
ILCHENKO Angelina Nikolaevna, Doctor of Economic Sciences, Director of the Center for Innovation and Antirecessionary Technologies, Ivanovo State University of Chemistry and Technology (Ivanovo, Russian Federation) (e-mail: ciat@isuct.ru).
Key words: region, economic and mathematical model, regional financial reserve, weather and economic risk, emergency
Abstract. The paper presents the developed economic and mathematical model of identifying a region’s needs for financial resources for the next financial period (year) to handle natural disasters. A model experiment was carried out on the basis of the Ivanovo Region’s indicators related to the wildfires, a type of emergency situations. An analysis of possibilities of application of the developed model for regions with similar weather conditions and those with different weather conditions was performed.
Synopsis. Introduction: currently, because of the limited financial resources of the regions, there is a need to develop and assess the role and possibilities of application of mathematical methods and models in making managerial decisions in the sphere of financing activities of the regional structures of the Russian Emergency Ministry. There are no specific methods of calculations related to identification of the amount of reserve funds for handling natural disasters. It is assumed that mathematical modeling may fill this gap.
Materials and Methods: the authors used the methods of mathematical statistics and carried out the time series analysis applied to the statistical data on the past natural disasters on the territory of the Ivanovo Region in 2000—2015. The existing approaches to the construction of economic and mathematical models taking into account the factor of the weather and economic risk were adopted.
Results: an economic and mathematical model to identify regional needs for financial resources for handling natural disasters most characteristic for the Ivanovo Region was developed. A simulation experiment and testing of the developed model was carried out on the basis of the statistical data of the region for a group of natural disasters (forest fires).
Discussion and Conclusions: on the basis of a model experiment, it may be concluded that it is possible to use mathematical models to optimize administrative decisions in the sphere of funding the territorial structures of the Russian Emergency Ministry. The main provisions of the article may be used when constructing economic and mathematical models of administrative decision-making concerning the amounts of the reserved funds in the budget for other regions.
REFERENCES
1. Kardash V.A. Jekonomika optimal’nogo pogodnogo riska v APK [Optimum weather risk economy in agriculture]. Moscow: API; 1989. 167 p. Available from: http://elibrary.ru/item.asp?id=21652626 (accessed 15.06.2016). (In Russ.)
Contribution of the authors:
BUTKO Elena Valerievna — preparation of the initial draft text of the article, its revision.
ILCHENKO Angelina Nikolaevna — academic advising, specification of the concept and methodology of the article.
For citation: Butko E.V., Ilchenko A.N. The Economic and Mathematical Model of Identifying Regional Financial Resources Requirements for Handling Natural Disasters Taking Into Account the Weather Risk Factor. REGIONOLOGIYA = REGIONOLOGY. 2017; 1(98):56—66.
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