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Impact of Population Income on the Number of Forest Fires: a Case Study

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In the regions of Siberia and the Far East of Russia, anthropogenic causes of forest fires reach 80-93%. The article examines the change in the number of fires in an environment of deteriorating financial conditions of the population. Statistical analysis of panel data is used as a research approach. The following models have been considered and evaluated: linear regression, Fixed Effects model (FE-model), and random effects model (RE-model). Almost all the models have showed good results related to the influence of socio-economic factors on the number of forest fires. The dynamics of real monthly wages has turned out to be statistically significant in influencing the dynamics of the number of forest fires. The Poisson RE-model is the most adequate, since visits to the forest that affect the occurrence of forest fires are often accidental.
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Forest Fires; Panel Data Analysis; Wage; Temperature; Rainfall

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F.R. Steward, A Mechanistic Fire Spread Model, Combust. Sci. Technol., Vol. 4(Issue 1):177-186, 1971.

F.T.P. Torres, G.S. Lima, B.F. Alvares, Fire behavior variables and hazard indices of forest fires, Rev. Arvore, Vol. 42(Issue 3), 2018.

S.M. Vonskii, V.A. Zhdanko Principles for the Development of Meteorological Indicators of fire Hazard in the Forest. Guidelines (LenNIILKh, 1976).

V.G. Nesterov, Issues of Modern Forestry (Selkhozgiz, 1961).

V.E. Khodakov, M.V. Zharikova, Forest Fires: Research Methods (Grin DC, 2011).

M.Y. Zdereva, M.V. Vinogradova, Medium-range forecasting of the extent of forest fire hazard from meteorological conditions, Russ. Meteorol. Hydrol. Vol. 34(Issue 1):8-15, 2009.

C. Ferrara, M. Marchi, M. Carlucci, A. Mavrakis, P. Corona, L. Salvati, The 2007 crisis and Greek wildfires: a multivariate analysis of suppression times, Environ. Monit. Assess. Environmental Monitoring and Assessment, Vol. 190(Issue 12), 2018.

D. F. Efremov, A. S. Zakharenkov, M. А. Kopeikin, E. P. Kuzmichev, M. I. Smetanina, V. V. Soldatov, Forest Fire Prevention and Control in the Russian Forest Management System (World Bank, 2012).

Y.A. Andreev, Influence of anthropogenic and natural factors on the occurrence of fires in forests and settlements, Doctor of technical science dissertation, Research Institute of Fire Defense of the Ministry of the Russian Federation for Civil Defense, Emergencies and Elimination of Consequences of Natural Disasters, Moscow, 2003.

A.M. Grishin, Mathematical Modeling of Forest Fires and New Ways to Deal with them (Nauka, 1992).

S. Archibald, A.C. Staver, S.A. Levin, Evolution of human-driven fire regimes in Africa, Proc. Natl. Acad. Sci. U. S. A, Vol. 109(Issue 3):847-852, 2012.

I. Bistinas, D. Oom, A.C.L. Sá, S.P. Harrison, I.C. Prentice, J.M.C. Pereira, Relationships between human population density and burned area at continental and global scales, PLoS One, Vol. 8(Issue 12):1-12, 2013.

S.C.P. Coogan, F.N. Robinne, P. Jain, M.D. Flannigan, Scientists' warning on wildfire - a canadian perspective, Can. J. For. Res., Vol. 49(Issue 9):1015-1023, 2019.

H. Clarke, R. Gibson, B. Cirulis, R.A. Bradstock, T.D. Penman, Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia, J. Environ. Manage, Vol. 235:34-41, 2019.

N.V. Baranovsky, The combined effect of anthropogenic load and thunderstorm activity on the probability of forest fires, Fire Explos. Saf., Vol. 18(Issue 3):52-56, 2009.

S.Y. Krechetova, Development of algorithms and software for solving the problems of lightning fire hazard in the forests of the Altai Mountains, PhD dissertation, Altai State University, Barnaul, 2007.

M.E. Castillo Soto, The identification and assessment of areas at risk of forest fire using fuzzy methodology, Appl. Geogr. Elsevier Ltd, Vol. 35(Issue 1-2):199-207, 2012.

N.J. Gralewicz, T.A. Nelson, M.A. Wulder, Spatial and temporal patterns of wildfire ignitions in Canada from 1980 to 2006, Int. J. Wildl. Fire, Vol. 21(Issue 3):230-242, 2012.

A. Badia, P. Serra, S. Modugno, Identifying dynamics of fire ignition probabilities in two representative Mediterranean wildland-urban interface areas, Appl. Geogr. Elsevier Ltd, Vol. 31(Issue 3):930-940, 2011.

V.A. Glagolev, Assessment and forecast of vegetation fires in the territory of the Jewish Autonomous Region. Institute of Water and Ecology Problems FEB RAS, Khabarovsk, 2015.

L. Salvati, F. Ranalli, "Land of Fires": Urban Growth, Economic Crisis, and Forest Fires in Attica, Greece, Geogr. Res, Vol. 53(Issue 1):68-80, 2015.

E. Chuvieco, I. Aguado, M. Yebra, H. Nieto, J. Salas, M. P. Martín, L. Vilar, J. Martínez, S. Martín, P. Ibarra, J. de la Riva, J. Baeza, F. Rodríguez, J.R. Molina, M.A. Herrera, R. Zamora, Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecol. Modell., Vol. 221(Issue 1):46-58, 2010.

J. Martínez-Fernández, E. Chuvieco, N. Koutsias, Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression, Nat. Hazards Earth Syst. Sci., Vol. 13(Issue 2):311-327, 2013.

J.A. Cardille, S.J. Ventura, M.G. Turner, Environmental and social factors influencing wildfires in the Upper Midwest, United States, Ecol. Appl., Vol. 11(Issue 1):111-127, 2001.[0111:EASFIW]2.0.CO;2

M. Marchi, F. Chianucci, C. Ferrara, G. Pontuale, E. Pontuale, A. Mavrakis, N. Morrow, F. Rossi, L. Salvati, Sustainable land-use, wildfires, and evolving local contexts in a Mediterranean Country, 2000-2015, Sustain., Vol. 10(Issue 11), 2018.

E. Chuvieco, B. Allgöwer, J. Salas, Integration of Physical and Human Factors in Fire Danger Assessment (World Scientific Publishing Company, 2003, pp. 197-218).

A.K. Tulokhonov, S.D. Puntsukova, Forest fires in the Republic of Buryatia in the context of climate change, Soc. Polit. Econ. Law, (Issue 3):72-78, 2016.

E.I. Umnyakova, A.A. Provotorova, Economical-statistical analysis of food cost level at the economic crisis of 2014-2015, Sci. World, Vol. 3(Issue 12):86-88, 2015.

K. Grala, R.K. Grala, A. Hussain, W.H. Cooke, J.M. Varner, Impact of human factors on wildfire occurrence in Mississippi, United States, For. Policy Econ. Elsevier, Vol. 81:38-47, 2017.

G. Baselli, F. Contreras, M. Lillo, M. Marín, R.A. Carrasco, Optimal decisions for salvage logging after wildfires, Omega, Vol. 96:102076, 2020.

S. Anatolyev, Econometry for Trained. Lecture course (Russian Economic School, 2003).

T.A. Ratnikova, Panel Data Analysis in "Stata". Guidelines (High School of Economics, 2004)

The main indicators of the socio-economic situation of municipalities. (Accessed 22 February 2022). Available:

Methodology for calculating the indicator "Level of real average monthly wages" for the reporting period. Appendix No. 4 to the Decree of the Government of the Russian Federation dated July 17, 2019 No. 915: 915. 2019. 5 p.

Foreign Currency Market. (Accessed 22 February 2022). Available:

Specialized datasets. (Accessed 22 February 2022). Available:

I.M. Gubenko, K.G. Rubinshtein, Comparative analysis of fire hazard index calculation methods, Proc. Hydrometeorol. Res. Cent. Russ. Fed., (Issue 347):207-222, 2012.

B.G. Sherstyukov, A.B. Sherstyukov, Forest fires in Russia under climate warming in XXI century, Environ. Monit. Ecosyst. Model. Challenges, Vol. 25:300-313, 2013.

Remote Monitoring Information System of the Federal Forestry Agency. Fire hazard monitoring unit. (Accessed 22 February 2022). Available:

Y. Kim, P.M. Steiner, Causal graphical views of fixed effects and random effects models, Br. J. Math. Stat. Psychol., Vol. 74(Issue 2):165-183, 2021.

A.H. Schempf, J.S. Kaufman, Accounting for context in studies of health inequalities: A review and comparison of analytic approaches, Ann. Epidemiol., Vol. 22(Issue 10):683-690, 2012.

P. Clarke, C. Crawford, F. Steele, A. Vignoles, Revisiting fixed- and random-effects models: some considerations for policy-relevant education research, Educ. Econ., Vol. 23(Issue 3): 259-277, 2015.

L.M. Braun, D.A. Rodriguez, Y. Song, K.A. Meyer, C.E. Lewis, J.P. Reis, P. Gordon-Larsen, Changes in walking, body mass index, and cardiometabolic risk factors following residential relocation: Longitudinal results from the CARDIA study, J. Transp. Heal., Vol. 3(Issue 4):426-439, 2016.

W. Lu, X. Wang, X. Zhan, A. Gazdar, Meta-analysis approaches to combine multiple gene set enrichment studies, Stat. Med., Vol. 37(Issue 4):659-672, 2018.

M.H. Giang, T.D. Xuan, B.H. Trung, M.T. Que, Total factor productivity of agricultural firms in Vietnam and its relevant determinants, Economies, Vol. 7(Issue 1):1-12, 2019.

F.L. Schmidt, I.S. Oh, T.L. Hayes, Fixed- versus random-effects models in meta-analysis: Model properties and an empirical comparison of differences in results, Br. J. Math. Stat. Psychol., Vol. 62(Issue 1):97-128, 2009.

E. Kontopantelis, D. Reeves, Performance of statistical methods for meta-analysis when true study effects are non-normally distributed: A simulation study, Stat. Methods Med. Res., Vol. 21(Issue 4):409-426, 2012.

M. Verbeek, Models based on Panel Data (Jonh Wiley & Sons, Ltd, 2004, pp. 341-388).

J.A. Hausman, Specification tests in econometrics, Econometrica, Vol. 46(Issue 6):1251-1271, 1978.

M. Boubeta, M.J. Lombardía, M. Marey-Pérez, D. Morales, Poisson mixed models for predicting number of fires, Int. J. Wildl. Fire, Vol. 28(Issue 3):237-253, 2019.

D. Mandallaz, R. Ye, Prediction of forest fires with Poisson models, Can. J. For. Res., Vol. 27(Issue 10):1685-1694, 1997.

J. Marchal, S.G. Cumming, E.J.B. McIntire, Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada, Ecography, Vol. 40(Issue 1):200-209, 2017.

T.A. Ratnikova, Introduction to econometric analysis of panel data, Econ. J. High. Sch. Econ., (Issue 2):267-316, 2006.

Shitikov V.K., Mastitsky S.E. Classification, Regression and Other Data Mining Algorithms Using R (Institute of Ecology of the Volga river basin of RAS, 2017).

E.V. Egorova, M.V. Radionova, Application of the Poisson model for analysis of panel data, Int. J. Appl. Sci. Technol. "Integral", (Issue 3):350-631, 2019.

M.A. Vinokurov, The economic crisis in Russia in 2014 and possible ways to overcome it, Izv. Irkutsk State Econ. Acad., Vol. 25(Issue 2): 261-267, 2015.

S.K. Dubinin, Financial Crisis 2014-2015, J. New Econ. Assoc., Vol. 26(Issue 2):219-225, 2015.

I.N. Trofimova, The dynamics of the socio-economic situation of Russians in the crisis of 2014-2015: settlement factor, Polit. Soc., Vol. 126(Issue 6):702-708, 2015.

S.E. Kostyaev, The economic crisis 2014-2016 in Russia and its financial aspects, Kaluga Econ. Bull., (Issue 1):73-75, 2016.

F. Schneider, G. Kirchgässner, Financial and world economic crisis: What did economists contribute? Public Choice, Vol. 140(Issue 3-4):319-327, 2009.

A.M. Spence, The financial and economic crisis and the developing world, J. Policy Model., Vol. 31(Issue 4):502-508, 2009.

A. Bondarenko, Summer 2010: heat in Russia and floods in Pakistan, Sci. Russ., (Issue 2):28-31, 2013.

V.V. Popova, Summertime warming in the European part of Russia and extreme heat in 2010 as manifestation of large-scale atmospheric circulation trends in the late 20th-early 21st centuries, Russ. Meteorol. Hydrol., Vol. 39(Issue 3):159-167, 2014.

Chinese imports of Russian forest. (Accessed 22 February 2022). Available:


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