Development of Knowledge-based Intelligence for Sustainability Assessment of Russian Regions
https://doi.org/10.15514/ISPRAS-2025-37(4)-27
Abstract
The paper presents the development of a Knowledge-based Intelligence for Sustainability Assessment (KISA) system for the comprehensive assessment of the sustainability of Russian regions, which uses a large language model (LLM) with retrieval-augmented generation (RAG) technology and Rosstat data. KISA automatically selects relevant indicators based on users’ textual queries, determines their weights, and calculates regional ratings, overcoming the limitations of traditional methods associated with high resource costs, subjectivity, and low adaptability. The system reduces the time required for rating formation to 10 minutes – 140 times faster than existing approaches; financial costs are reduced by a factor of 16 due to the minimization of expert participation. The agreement with expert evaluations is 68%, confirming the validity of the method. KISA provides a web interface with map visualization, enhancing flexibility in analysis; the possibility of improvement through the addition of new sources ensures the continuous incorporating experts’ experience. The results of the study contribute to the improvement of regional sustainability assessment and can be used in management decision-making.
Keywords
sustainability assessment,
Russian regions,
artificial intelligence,
large language model LLM,
retrieval-augmented generation RAG.
Supporting agencies: This work was supported by the Ministry of Science and Higher Education of the Russian Federation (project No. FSNM-2024-0005).
About the Authors
Danil Sergeevich FEDOSEEV
HSE University
Russian Federation
Russian Federation
Graduate of the Bachelor’s program in Software Engineering at the National Research University – Higher School of Economics. Research interests: natural language processing, large language models, retrieval-augmented generation.
Alexey Dmitrievich NEROSLOV
Perm National Research Polytechnic University
Russian Federation
Russian Federation
First-year master’s student at Perm National Research Polytechnic University in the State and Municipal Administration program. Research interests: economics, regional economics of Russia, socio-economic indicators of the performance of the constituent entities of the Russian Federation.
Viacheslav Vladimirovich LANIN
HSE University
Russian Federation
Russian Federation
Senior Lecturer of the Department of Information Technologies in Business of the National Research University – Higher School of Economics. Research interests: modeling languages, domain specific modeling, language toolkits, CASE tools, simulation systems.
References
1. Перфилов В. А. Сущность и типы устойчивости развития региональных социально-экономических систем // ПСЭ. №2., 2012 г., стр 264-266. / Perfilov V. The essence and types of sustainability of regional socio-economic systems. Problems of the modern economy, 2012, vol. 2, pp. 264-266 (In Russian).
2. Jovovic R., Draskovic M., Delibasic M., Jovovic M. The concept of sustainable regional development–institutional aspects, policies and prospects. Journal of International Studies, 2017, vol. 10, No. 1,
3. pp. 255-266.
4. Inside the World Bank’s new inequality indicator: The number of countries with high inequality. [Online]. Available at: https://blogs.worldbank.org/en/opendata/inside-the-world-bank-s-new-inequality-indicator--the-number-of-, accessed: 22 May 2025.
5. О национальных целях развития Российской Федерации на период до 2030 года и на перспективу до 2036 года: Указ Президента РФ от 7 мая 2024 г. № 309 / Decree of the President of the Russian Federation. No. 309. “On the national development goals of the Russian Federation for the period up to 2030 and for the future up to 2036” Available at: https://mvd.consultant.ru/documents/1058493, accessed: 22 May 2025 (In Russian).
6. Mareeva S. Socio-economic inequalities in modern Russia and their perception by the population. The Journal of Chinese Sociology, 2020, vol. 7, No.1, 10 p.
7. Shatalova O., Kasatkina, E. Socio-economic inequality of regions in the Russian Federation: Measurement issues and long-term evaluation. Economic and Social Changes: Facts, Trends, Forecast, 2022, vol. 15, No. 4, pp. 74-87.
8. Ramos T. Sustainability Assessment: Exploring the Frontiers and Paradigms of Indicator Approaches. Sustainability, 2019, vol. 11, No. 3, 824 p.
9. Zhong R., Pei F., Yang K., Xia Y., Wang H., & Yan G. Coordinating socio-economic and environmental dimensions to evaluate regional sustainability—towards an integrative framework. Ecological Indicators, 2021, vol. 130, 108085 p.
10. D’Adamo I., Falcone P., Imbert E., Morone P. Exploring regional transitions to the bioeconomy using a socio-economic indicator: The case of Italy. Economia Politica, 2022, vol. 39, No. 3, pp. 989-1021.
11. Ziegler D., Wolff S., Agu A.-B., Cortiana G., Umair M., Durfort F., Neumann E., Walther G., Kristiansen J., Lienkamp M. How to Measure Sustainability? An Open-Data Approach. Sustainability, 2023, vol. 15, No. 4, 3203 p.
12. Швец И. Ю., Швец Ю. Ю., Чиж-Гвязда Э. Оценка устойчивого инновационного развития региона // Вестник Ассоциации вузов туризма и сервиса. том 9. № 1. 2015 г., стр. 14-21. / Shvec I., Shvec Ju., Chizh-Gvjazda Je. Assessment of the sustainable innovative development of the region. Bulletin of the Association of Universities of Tourism and Service, 2015, vol. 9, pp. 14-21 (In Russian).
13. Яшина Н. И., Яшин С. Н., Вилейшикова А. А. Методический инструментарий оценки социально-экономической безопасности регионов на основе формирования системы целевых показателей производственной и непроизводственной сфер развития регионов. Вестник Нижегородского университета им. Н.И. Лобачевского. Серия: Социальные науки. Вып. 2, № 62, 2021 г., стр. 45-54. DOI: 10.52452/18115942_2021_2_45. / Jashina N. I., Jashin S. N., Vilejshikova A. A. Methodological tools for assessing the socio-economic security of regions based on the formation of a system of target indicators for industrial and non-industrial areas of regional development. Bulletin of the Nizhny Novgorod Lobachevsky University, 2021, vol. 2, No. 62, pp. 45-54 (In Russian). DOI: 10.52452/18115942_2021_2_45.
14. Бородин С. Н. Модель оценки устойчивого развития региона на основе индексного метода // Экономика региона. том 19, № 1, 2023 г., стр. 45-59. DOI 10.17059/ekon.reg.2023-1-4. / Borodin S. A model for assessing the sustainable development of a region based on the index method. Economy of the region. 2023, vol. 19, No. 1, pp. 45-59 (In Russian). DOI 10.17059/ekon.reg.2023-1-4.
15. Lindfors A. Assessing sustainability with multi-criteria methods: A methodologically focused literature review. Environmental and Sustainability Indicators, 2021, vol. 12, 100149 p.
16. Сбер Аналитика: Мониторинг экономики региона / SberAnalytics “Monitoring of regional economies”, Available at: https://sberanalytics.ru/products/gossector/mr, accessed: 22 May 2025 (In Russian).
17. Форсайт. Аналитическая платформа / Foresight Analytics Platform, Available at: https://www.fsight.ru/platform/, accessed: 22 May 2025 (In Russian).
18. Olawumi T., Chan D. Cloud-based sustainability assessment (CSA) system for automating the sustainability decision-making process of built assets. Expert Systems with Applications, 2022, vol. 188, 116020 p.
19. Федосеев Д. С., Нерослов А. Д., Ланин В. В. Разработка информационной системы для оценки устойчивости регионов России. Сборник материалов III студенческой научно-практической конференции им. Л.Л. Любимова. 2025 г., стр. 170-175. / Fedoseev D., Neroslov A., Lanin V. Development of an Information System for Assessing the Sustainability of Russian Regions. Collection of materials of the III student scientific and practical conference named after L.L. Lyubimov at HSE, 2025, pp. 170-175, Available at: https://perm.hse.ru/editorial_publishing/Lyubimov_Conference3, accessed: 22 May 2025 (In Russian).
20. Oliveira V., Teixeira D., Rocchi L., Boggia, A. Geographic Information System Applied to Sustainability Assessments: Conceptual Structure and Research Trends. ISPRS International Journal of Geo-Information, 2022, vol. 11, No. 11, 569 p.
21. Arfanuzzaman M. Harnessing artificial intelligence and big data for SDGs and prosperous urban future in South Asia. Environmental and sustainability indicators, 2021, vol. 11, 100127 p.
22. Greif L., Röckel F., Kimmig A., Ovtcharova J. A systematic review of current AI techniques used in the context of the SDGs. International Journal of Environmental Research, vol. 19, No. 1, 2025.
23. Tikhomirov M., Chernyshev, D. Improving Large Language Model Russian adaptation with preliminary vocabulary optimization. Lobachevskii Journal of Mathematics, 2024, vol. 45, No. 7, pp. 3211-3219.
24. Kojima T., Gu S., Reid M., Matsuo Y., Iwasawa Y. Large language models are zero-shot reasoners. Advances in neural information processing systems, 2022, vol. 35, pp. 2219-22213.
25. Bronzini M., Nicolini C., Lepri B., Passerini A., Staiano J. Glitter or gold? Deriving structured insights from sustainability reports via large language models. EPJ Data Science, 2024, vol. 13, No. 41.
26. Arslan M., Mahdjoubi L., Munawar, S. Driving sustainable energy transitions with a multi-source RAG-LLM system. Energy and Buildings, 2024, vol. 324, 114827 p.
27. Ren S., Tomlinson B., Black R., Torrance A. Reconciling the contrasting narratives on the environmental impact of large language models. Scientific Reports, 2024, vol. 14, No. 1, 26310 p.
28. Vázquez-Ingelmo A., García-Holgado A., García-Peñalvo, F. C4 model in a software engineering subject to ease the comprehension of uml and the software. In 2020 IEEE Global Engineering Education Conference, 2020, pp. 919-924.
29. Nwokeji J., Matovu R. A systematic literature review on big data extraction, transformation and loading (etl). In Intelligent Computing: Proceedings of the 2021 Computing Conference, 2021, vol. 2,
30. pp. 308-324.
31. Narayanan P. Engineering Machine Learning and Data REST APIs using FastAPI. In Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms, 2024,
32. pp. 323-359.
33. Douze M., Guzhva A., Deng C., Johnson J., Szilvasy G., Mazaré P.-E., Lomeli M., Hosseini L., & Jégou H. The faiss library. arXiv preprint. arXiv:2401.08281, unpublished.
34. Паздникова Н. П., Нерослов А. Д., Федосеев Д. С. Комплексная оценка территориальной устойчивости и потенциалов (КОТУП): свидетельство о государственной регистрации базы данных № 2024621915 Российская Федерация; № 2024620239; 2024 г./ Pazdnikova N. P., Neroslov A. D., Fedoseev D. S. Certificate of State registration of the database No. 2024621915 Russian Federation. Comprehensive assessment of territorial stability and potentials (CATSP), 2024.
35. Нерослов А. Д., Федосеев Д. С., Паздникова Н. П. Комплексная оценка территориальной устойчивости и потенциалов (КОТУП): свидетельство о государственной регистрации программы для ЭВМ № 2025617985 Российская Федерация. 2025 г. / Neroslov A. D., Fedoseev D. S., Pazdnikova N. P. Certificate of state registration of the computer program No. 2025617985 Russian Federation. Comprehensive Assessment of Territorial Sustainability and Potentials (CATSP), 2025.
Review
For citations:
FEDOSEEV D.S., NEROSLOV A.D., LANIN V.V. Development of Knowledge-based Intelligence for Sustainability Assessment of Russian Regions. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2025;37(4):207-218. https://doi.org/10.15514/ISPRAS-2025-37(4)-27
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