qainarQainar Journal of Social ScienceQainar Journal of Social Science2958-72122958-7220Q University10.58732/2958-7212-2026-2-65-82qainar-578Research ArticleСтатьиАнализ социальных рисков интеллектуального видеомониторинга в КазахстанеAnalysis of Social Risks of Intelligent Video Monitoring in Kazakhstanhttps://orcid.org/0009-0006-0548-4982СамойловА. А.SamoilovA. A.

PhD докторант 

Алматы 

Andrey A. Samoilov – PhD candidate 

Almaty 

a.samoilov@kbtu.kzСмагулЖ. Т.SmagulZh. T.

мастер 

Алматы 

Zhanibek T. Smagul – Master 

Almaty 

zhanibek.smagul@mail.ruМамырбаевА. Н.MamyrbayevA. N.

PhD 

Алматы 

Almas N. Mamyrbayev – PhD 

Almaty 

a.mamyrbayev@kbtu.kzКазахстанско-Британский технический университетКазахстанKazakh-British Technical UniversityKazakhstan202601072026526582Copyright © Самойлов А.А., Смагул Ж.Т., Мамырбаев А.Н., 20262026Самойлов А.А., Смагул Ж.Т., Мамырбаев А.Н.Samoilov A.A., Smagul Z.T., Mamyrbayev A.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.journal-kainar.kz/jour/article/view/578

Рост цифровизации и развитие концепции «умный город» способствуют активному внедрению проектов государственночастного партнерства (далее – ГЧП) с использованием технологий искусственного интеллекта в сфере городской инфраструктуры и обеспечения безопасности дорожного движения. Целью данного исследования является оценка социальных рисков проекта интеллектуального видеомониторинга «Сергек» в Казахстане, реализуемого в рамках концепции «умный город», с использованием методов системной динамики и анализа чувствительности показателя дисконтированного денежного потока. Методология исследования основана на изучении данных проекта с последующим построением модели проекта с помощью системной динамики (далее – СД), которая позволяет продемонстрировать различные сценарии влияния факторов риска. В исследовании использованы данные открытых источников, включая сведения PPP Portal Республики Казахстан, данные акимата г. Алматы и Национального банка РК. Результаты моделирования показали, что наиболее высокий показатель дисконтированного денежного потока (далее – DCF) наблюдается в базовом сценарии без учета рисков и достигает 7 668 340 000 тенге в 2024 г., снижаясь до 6 781 390 000 тенге в 2025 г. Наибольшее негативное влияние на финансовую устойчивость проекта оказывает социальный риск, связанный с недобором поступлений от штрафов, при котором значение DCF снижается до 6 287 440 000 тенге. Полученные результаты свидетельствуют о том, что социальный риск является наиболее значимым фактором финансовой устойчивости проекта интеллектуального видеомониторинга.

The growth of digitalization and the development of the smart city concept contribute to the active implementation of public-private partnership (hereinafter – PPP) projects using artificial intelligence technologies in the field of urban infrastructure and road safety. The purpose of this study is to assess the social risks of the Sergek intelligent video monitoring project in Kazakhstan, implemented within the framework of the smart city concept, using methods of system dynamics and sensitivity analysis of the discounted cash flow indicator. The research methodology is based on the study of project data, followed by the construction of a project model using system dynamics (hereinafter – SD), which allows us to demonstrate various scenarios of the impact of risk factors. The study uses data from open sources, including information from the PPP Portal of the Republic of Kazakhstan, data from the Akimat of Almaty and the National Bank of the Republic of Kazakhstan. The simulation results showed that the highest discounted cash flow (hereinafter – DCF) is observed in the baseline scenario without taking into account risks and reaches 7,668,340,000 tenge in 2024, decreasing to 6,781,390,000 tenge in 2025. The greatest negative impact on the financial stability of the project is the social risk associated with a shortage of revenue from fines, at which the DCF value is reduced to KZT 6,287,440,000. The results obtained indicate that social risk is the most significant factor in the financial stability of an intelligent video monitoring project.

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The authors declare that there are no conflicts of interest present.