Economics and management of national economy

Fire risk assessment in light of socio-economic factors

Economic Annals-XXI

Economic Annals-ХХI: Volume 166, Issue 7-8, Pages: 37-40

Citation information:
Kyrylaha, N., & Chernenko, V. (2017). Fire risk assessment in light of socio-economic factors. Economic Annals-XXI, 166(7-8), 37-40. doi: https://doi.org/10.21003/ea.V166-07

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Nataliya Kyrylaha
PhD (Physics and Mathematics),
Associate Professor,
Kremenchuk Mykhailo Ostrohradskyi National University
20 Pershotravneva Str., Kremenchuk, 39600, Ukraine
natalykiril582@gmail.com
ORCID ID: http://orcid.org/0000-0002-2629-8867

Varvara Chernenko
PhD (Physics and Mathematics),
Associate Professor,
Kremenchuk Mykhailo Ostrohradskyi National University
20 Pershotravneva Str., Kremenchuk, 39600, Ukraine
cher.var.petr@gmail.com
ORCID ID: http://orcid.org/0000-0002-2728-6876

Fire risk assessment in light of socio-economic factors

Absract. Introduction. The analysis of fire statistics is one of the most important tasks in making managerial decisions by the State Fire Service. Ensuring fire safety in the state is a vital factor in adequate prediction of the number of fires. Therefore, the problem of qualitative mathematical modelling of fire statistics arises.

The purpose of this article is the construction of adequate econometric models to forecast the number of fires in the different countries of the world.

Results. Based on the latest statistics provided by the Centre for Fire Statistics of the International Association of Fire and Rescue Services (CTIF) for 2003-2015, the authors of the article conducted a multivariate analysis of variance analysis and examined the key factors affecting the number of fires worldwide, such as population size, the country’s territory and gross domestic product (GDP) per capita. The use of cluster analysis has made it possible to distinguish five groups of countries that have similar risk factors for fire. With regard to the examined factors, the authors propose time series models with a distributed lag for two countries, Ukraine and France, and forecasted future values for the number of fires.

Conclusions. The factors affecting the number of fires in the country can be divided into two groups: objective (territory, natural and climatic conditions) and socio-economic (to reveal their impact, it is necessary to analyse such indicators as density of fires, population density and GDP per capita).

Analysing the worked out models which include socio-economic and objective factors, we can conclude that for Ukraine and France the influence of social and economic factors differs. For Ukraine, the low population density factor is positive, i.e. it contributes to lowering the density of fires. For France, the situation is the opposite. The density of GDP per capita, by contrast, greatly contributes to reducing the density of fire cases in France, however in Ukraine its effect is manifested in a negative way.

Keywords: World Fire Statistics; ANOVA; Fire Risk; Fire Case; GDP; Cluster Analysis; Model Distributed Lag; Forecast; France; Ukraine

JEL Classification: С32; C38; С51

DOI: https://doi.org/10.21003/ea.V166-07

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Received 1.06.2017

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