Vol. 54 No. 2 (2026): Published June 30, 2025
DOI https://doi.org/10.18799/26584956/2026/2/2042
Rapid assessment of the economic security level of energy facilities
Ensuring the economic security of industrial facilities is a pressing task in modern conditions. Security implies stable operation despite the impact of external and internal factors. Object. Fuel and energy complex. Aim. To provide an operational assessment of the level of economic security. To monitor an industrial facility in real-time, it is necessary to track the state indicators. Methodology. The paper proposes a methodology for the rapid assessment of the level of economic risk in the fuel and energy complex. The state of the energy sector is assessed by the values of an indicator set characterizing the state of settlements with counterparties, investment activity, various types of profitability, solvency, liquidity, savings in fuel and energy resources, production level, and trade turnover. The main part of the indicators are financial ones representing efficiency and profitability. Each indicator, from the perspective of economic security, can have three states: normal, pre-crisis, and crisis. An artificial neural network is used for their classification. If the value of the indicator is at the pre-crisis or crisis level, then it is necessary to make an appropriate management decision that contributes to the safe operation of the energy facility. The research methods included economic-statistical analysis, indicator monitoring, classification, approximation, ex-pert evaluation, and forecasting. Based on the developed neural network model, the values of the indicators are classified according to safety levels. For each new value of an economic object state indicator, the neural net-work automatically determines the risk class. If an indicator falls into the crisis or pre-crisis zone, managerial decisions must be made to stabilize the situation. Conclusions. The proposed methodology enables the operational determination of the economic security level at any industrial facility.
For citation: Kondrakov O.V., Kondrakov I.V. Rapid assessment of the economic security level of energy facilities. Journal of Wellbeing Technologies, 2026, vol. 54, no. 2, pp. 31–44. https://doi.org/10.18799/26584956/2026/2/2042
Keywords:
fuel and energy complex, economic risk, economic security, threats, state indicators, artificial neural network
References:
СПИСОК ЛИТЕРАТУРЫ
1. Воропай Н.И., Сендеров С.М. Энергетическая безопасность: сущность, основные проблемы, методы и результаты исследований. М.: Институт народнохозяйственного прогнозирования РАН, 2011. 90 с.
2. Энергетическая безопасность России. В.В. Бушуев, Н.И. Воропай, А.М. Мастепанов, Ю.К. Шафраник. Новосибирск: Наука, 1998. 302 с.
3. Jasiūnas J., Lund P.D., Mikkola J. Energy system resilience – a review. Renewable and Sustainable Energy Reviews, 2021, № 150, 111476. https://doi.org/10.1016/j.rser.2021.111476. EDN: ASXLSC.
4. Татаркин А.И. Классификация состояний безопасности региональных экономических и энергетических систем. Екатеринбург, Институт экономики УрО РАН, 2003. 94 с. EDN: QQEGCF.
5. Татаркин А.И., Куклин А.А., Мызин А.Л. Энергетика и экономическая безопасность регионов России. М.: РАН Институт народнохозяйственного прогнозирования, 2001. 38 с.
6. Gong X., Wang Y., Lin B. Assessing dynamic China’s energy security: based on functional data analysis. Energy, 2021, Vol. 217, 119324. DOI: https://doi.org/10.1016/j.energy.2020.119324. EDN: VIDGNK.
7. Huang B., Zhang L., Ma L., Bai W., Ren J. Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index. Energy, 2021, Vol. 228, 120481. DOI: https://doi.org/10.1016/j.energy.2021.120481. EDN: ZHRWIQ.
8. De Pascale A., Arbolino R., Szopik-Depczyńska K., Limosani M., Ioppolo G. A systematic review for measuring circular economy: the 61 indicators. Journal of cleaner production, 2021, Vol. 281, 124942. DOI: https://doi.org/10.1016/j.jclepro.2020.124942. EDN: MOALCE.
9. George P.G., Renjith V.R. Evolution of safety and security risk assessment methodologies towards the use of Bayesian Networks in process industries. Process Safety and Environmental Protection, 2021, Vol. 149, P. 758–775. DOI: https://doi.org/10.1016/j.psep.2021.03.031. EDN: FMCFQV.
10. Li X., Wang J., Yang C. Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy. Neural Computing and Applications, 2023, vol. 35, pp. 2045–2058. DOI: https://doi.org/10.1007/s00521-022-07377-0. EDN: IOQQMH.
11. Protasiewicz J., Szczepaniak P.S. Neural models of demands for electricity-prediction and risk assessment. Przegląd elektrotechniczny (Electrical Review), 2012, Vol. 88, № 6, P. 272–279.
12. Kharazishvili Y., Kwilinski A. Methodology for determining the limit values of national security indicators using artificial intelligence methods. Virtual Economics, 2022, Vol. 5, № 4, P. 7–26. DOI: https://doi.org/10.34021/ve.2022.05.04(1). EDN: XBISCW.
13. Korneev N.V., Korneeva J.V., Yurkevichyus S.P., Bakhturin G.I. An approach to risk assessment and threat prediction for complex object security based on a predicative self-configuring neural system. Symmetry, 2022, Vol. 14, № 1, 102. DOI: https://doi.org/10.3390/sym14010102. EDN: XBEKQR.
14. Moktadir M.A., Dwivedi A., Khan N.S., Paul S.K., Khan S.A., Ahmed S., Sultana R. Analysis of risk factors in sustainable supply chain management in an emerging economy of leather industry. Journal of Cleaner Production, 2021, Vol. 283, 124641. DOI: https://doi.org/10.1016/j.jclepro.2020.124641. EDN: UTOOAK.
15. Антонов И.С., Кривякин К.С., Макаров Н.Н. Оценка и прогнозирование уровня экономической безопасности предприятия. Экономика и предпринимательство, 2022, № 2 (139), С. 985−991. DOI: 10.34925/EIP.2022.139.2.190. EDN: CBUZCP.
16. Анисимова Е.П., Палагина О.А. Современная оценка уровня экономической безопасности предприятия. Ученые заметки ТОГУ, 2022, Т. 13, № 1, С. 125−132. EDN: OTDWAC.
17. Листопад М.Е., Третьякова С.Н. Показатели оценки уровня экономической безопасности и финансовой устойчивости малых предприятий АПК. Естественно-гуманитарные исследования, 2024, № 6 (56), С. 458−462. EDN: BUNDPN.
18. Мальсагов И.Х. Оценка уровня финансово-экономической безопасности предприятия. Экономика и управление: проблемы, решения, 2025, Т. 2, № 1 (154), С. 53−62. DOI: 10.36871/ek.up.p.r.2025.01.02.006. EDN: DXQWUF.
19. Измоденова К.С., Хромцова Л.С. Методические подходы к оценке уровня экономической безопасности предприятий энергетической отрасли с учетом концепции бережливого производства. Экономическая безопасность, 2024, Т. 7, № 5, С. 1155−1176. DOI: 10.18334/ecsec.7.5.121023. EDN: KLQLWP.
20. Воронцов Я.Н. Методика оценки уровня экономической безопасности предприятий золотодобывающей промышленности. Экономика и управление: проблемы, решения, 2024, Т. 14, № 9 (150), С. 42−50. DOI: 10.36871/ek.up.pr2024.09.14.006. EDN: JCZBAQ.
21. Харитонов П.А. Методы оценки уровня экономической безопасности социально-экономических систем: сравнительный анализ. Индустриальная экономика, 2022, Т. 5, № 1, С. 495−499. EDN: TCQJHQ.
22. Helbing D. Systemic risks in society and economics. Helbing D. Social self-organization: agent-based simulations and experiments to study emergent social behavior. Berlin, Heidelberg, Springer, 2012. P. 261–284. DOI: https://doi.org/10.1007/978-3-642-24004-1_14.
23. Регионы России. Социально-экономические показатели. Под ред. А.И. Горобцова. М.: Росстат, 2024. 1081 с.
24. Кондраков О.В. Оценка индикаторов экономической безопасности в энергетической сфере. Известия юго-западного университета. Серия: Экономика. Социология. Менеджмент, 2015, № 2 (15), С. 43−51. EDN: UHUJBR.
REFERENCES
1. Voropai N.I., Senderov S.M. Energy security: essence, main problems, research methods, and results. Moscow, Institute of Economic Forecasting, Russian Academy of Sciences Publ., 2011. 90 p. (In Russ.)
2. Bushuev V.V., Voropai N.I., Mastepanov A.M., Shafranik Yu.K. Energy Security of Russia. Novosibirsk, Nauka Publ., 1998. 302 p. (In Russ.)
3. Jasiūnas J., Lund P.D., Mikkola J. Energy system resilience – a review. Renewable and Sustainable Energy Reviews, 2021, no. 150, 111476. https://doi.org/10.1016/j.rser.2021.111476. EDN: ASXLSC.
4. Tatarkin A.I. Classification of security states of regional economic and energy systems. Ekaterinburg, Institute of Economics, Ural Branch of the Russian Academy of Sciences Publ., 2003. 94 p. (In Russ.) EDN: QQEGCF.
5. Tatarkin A.I., Kuklin A.A., Myzin A.L. Energy and economic security of Russian regions. Moscow, RAS Institute of Economic Forecasting Publ., 2001. 38 p. (In Russ.)
6. Gong X., Wang Y., Lin B. Assessing dynamic China’s energy security: based on functional data analysis. Energy, 2021, vol. 217, 119324. DOI: https://doi.org/10.1016/j.energy.2020.119324. EDN: VIDGNK.
7. Huang B., Zhang L., Ma L., Bai W., Ren J. Multi-criteria decision analysis of China’s energy security from 2008 to 2017 based on Fuzzy BWM-DEA-AR model and Malmquist Productivity Index. Energy. 2021, vol. 228, 120481. DOI: https://doi.org/10.1016/j.energy.2021.120481. EDN: ZHRWIQ.
8. De Pascale A., Arbolino R., Szopik-Depczyńska K., Limosani M., Ioppolo G. A systematic review for measuring circular economy: the 61 indicators. Journal of cleaner production. 2021, vol. 281, 124942. DOI: https://doi.org/10.1016/j.jclepro.2020.124942. EDN: MOALCE.
9. George P.G., Renjith V.R. Evolution of Safety and security risk assessment methodologies towards the use of Bayesian Networks in process industries. Process Safety and Environmental Protection, 2021, vol. 149, pp. 758–775. DOI: https://doi.org/10.1016/j.psep.2021.03.031. EDN: FMCFQV.
10. Li X., Wang J., Yang C. Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy. Neural Computing and Applications, 2023, vol. 35, pp. 2045–2058. DOI: https://doi.org/10.1007/s00521-022-07377-0. EDN: IOQQMH.
11. Protasiewicz J., Szczepaniak P.S. Neural models of demands for electricity-prediction and risk assessment. Przegląd elektrotechniczny (Electrical Review), 2012, vol. 88, no. 6, pp. 272–279.
12. Kharazishvili Y., Kwilinski A. Methodology for determining the limit values of national security indicators using artificial intelligence methods. Virtual Economics, 2022, vol. 5, no. 4, pp. 7–26. DOI: https://doi.org/10.34021/ve.2022.05.04(1). EDN: XBISCW.
13. Korneev N.V., Korneeva J.V., Yurkevichyus S.P., Bakhturin G.I. An approach to risk assessment and threat prediction for complex object security based on a predicative self-configuring neural system. Symmetry, 2022, vol. 14, no. 1, 102. DOI: https://doi.org/10.3390/sym14010102. EDN: XBEKQR.
14. Moktadir M.A., Dwivedi A., Khan N.S., Paul S.K., Khan S.A., Ahmed S., Sultana R. Analysis of risk factors in sustainable supply chain management in an emerging economy of leather industry. Journal of Cleaner Production, 2021, vol. 283, 124641. DOI: https://doi.org/10.1016/j.jclepro.2020.124641. EDN: UTOOAK.
15. Antonov I.S., Krivyakin K.S., Makarov N.N. Assessment and forecasting of the level of economic security of the enterprise. Economics and Entrepreneurship, 2022, no. 2 (139), pp. 985–991. (In Russ.) DOI: 10.34925/EIP.2022.139.2.190. EDN: CBUZCP.
16. Anisimova E.P., Palagina O.A. Modern assessment of the level of economic security of the enterprise. Scientists Notes PNU, 2022, vol. 13, no. 1, pp. 125–132. (In Russ.) EDN: OTDWAC.
17. Listopad M.E., Tretyakova S.N. Indicators for assessing the level of economic security and financial stability of small agricultural enterprises. Natural–Humanitarian Studies, 2024, no. 6 (56), pp. 458–462. (In Russ.) EDN: BUNDPN.
18. Malsagov I.Kh. Assessment of the level of financial and economic security of the enterprise. Economics and Management: Problems, Solutions, 2025, vol. 2, no. 1 (154), pp. 53–62. (In Russ.) DOI: 10.36871/ek.up.p.r.2025.01.02.006. EDN: DXQWUF.
19. Izmodenova K.S., Khromtsova L.S. Methodological approaches to assessing the level of economic security of energy companies, taking into account the concept of lean manufacturing. Economic Security, 2024, vol. 7, no. 5,
pp. 1155–1176. (In Russ.) DOI: 10.18334/ecsec.7.5.121023. EDN: KLQLWP.
20. Vorontsov Ya.N. Methodology for assessing the level of economic security of gold mining enterprises. Economics and Management: Problems, Solutions, 2024, vol. 14, no. 9 (150), pp. 42–50. (In Russ.) DOI: 10.36871/ek.up.pr2024.09.14.006. EDN: JCZBAQ.
21. Kharitonov P.A. Methods for assessing the level of economic security of socioeconomic systems: comparative analysis. Industrial Economics, 2022, vol. 5, no. 1, pp. 495–499. (In Russ.) EDN: TCQJHQ.
22. Helbing D. Systemic risks in society and economics. Helbing D. Social self-organization: Agent-based simulations and experiments to study emergent social behavior. Berlin, Heidelberg, Springer, 2012. pp. 261–284. DOI: https://doi.org/10.1007/978-3-642-24004-1_14.
23. Regions of Russia. Socio-economic indicators. Ed. by A.I. Gorobtsova. Moscow, Rosstat Publ., 2024. 1081 p. (In Russ.)
24. Kondrakov O.V. Evaluation of economic safety indicators in the energy sector. Proceedings of the southwest state university. Series: economics, sociology and management, 2015, no. 2 (15), pp. 43–51. (In Russ.) EDN: UHUJBR.
