Russian Federation
Russian Federation
Russian Federation
UDC 332.132
This article presents a method for assessing the ecological and economic condition of the Penza Region districts using the agglomerative clusterization method. The study revealed that the districts within the region can be divided into four main clusters (A, B, C and D) depending on their general, environmental and economic indicators. As a result of the study, areas with stable, stagnant and negative development were identified by groups of factors. This allowed us to identify which areas need additional support and development measures. In addition, in the plane of the 10-year period under review (from 2012 to 2022), areas that moved from one cluster to another were identified. The reasons for such transitions were identified, which allowed us to make assumptions about the further development of these areas. Recommendations for improvement have been proposed for areas with an unfavorable development scenario. These recommendations are aimed at rational land use and improving the performance of relatively successful areas. They may include measures to develop infrastructure, support small and medium-sized businesses, improve the environmental situation, and other measures aimed at improving the quality of life of the population and developing the economy of the districts. Thus, the conducted research allowed to obtain a more complete picture of the ecological and economic condition of the Penza region districts. It can serve as a basis for the development of strategies and development programs aimed at improving the quality of life of the population and increasing the competitiveness of the regional economy. This study is also of practical importance, allowing us to identify the most problematic areas and develop specific support measures for them. The results of the study can be used by the authorities in making decisions on the allocation of resources and the development of territorial development programs.
sustainable development, rational environmental management, economics, ecology, indicators, agglomerative clustering, assessment
1. Antropov D.V. Analiz effektivnosti upravleniya zemel'nymi resursami regiona na osnove primeneniya metodiki kompleksnogo (klasternogo) zonirovaniya territoriy (na primere zemel' sel'skohozyaystvennogo naznacheniya) // Mezhdunarodnyy sel'skohozyaystvennyy zhurnal. 2018. № 5. S. 16-19.
2. Dubrovskiy A.V. Metodicheskie podhody k modelirovaniyu i prognozirovaniyu racional'nogo ispol'zovaniya zemel'nyh resursov s primeneniem geotehnologiy // Vestnik SGUGiT (Sibirskogo gosudarstvennogo universiteta geosistem i tehnologiy). 2022. T. 27, № 3. S. 145-156.
3. Ishamyatova I.H., Antropov D.V. Kompleksnoe (klasternoe) zonirovanie gorodov Penzenskoy oblasti na osnove ekologo-ekonomicheskih pokazateley territorii // Kreativnaya ekonomika. 2023. № 11. S. 4263-4290.
4. Ishamyatova I.H., Geras'kin M.M., Glazunova V.M. Regional'nye informacionnye sistemy kak instrument prostranstvennogo razvitiya territoriy // Zemleustroystvo, kadastr i monitoring zemel'. 2024. № 10. S.601-612.
5. Minakir P. A. Rossiyskoe ekonomicheskoe prostranstvo. Strategicheskie tupiki // Ekonomika regiona. 2019. №15(4). S. 967-980.
6. Murasheva A.A., Lepehin P.P. Informacionno-modeliruyuschaya sistema dlya resheniya regional'nyh ekologicheskih problem // Nauki o Zemle. 2015. № 1. S. 24-32.
7. Ovsyannikov A.O. Analiz vnutrennih zatrat na nauchnye issledovaniya i razrabotki po sub'ektam Rossiyskoy Federacii pri pomoschi klasternogo analiza RapidMiner // Nauchno-prakticheskiy elektronnyy zhurnal Alleya Nauki. 2018. № 6 (22).
8. Ognivcev S. B. Cifrovizaciya ekonomiki i ekonomika cifrovizacii APK // Mezhdunarodnyy agropromyshlennyy zhurnal. 2019. №2. S. 77-80.
9. Papaskiri T.V. Metody formirovaniya sistem avtomatizirovannogo zemleustroitel'nogo proektirovaniya // Vestnik Rossiyskogo universiteta druzhby narodov. Seriya: Agronomiya i zhivotnovodstvo. 2015. № 2. S. 38-44.
10. Ponomarev V.P., Beloglazova I.Yu. Primenenie faktornogo i klasternogo statisticheskogo analiza v medicine // Perspektivnye informacionnye tehnologii: mezhdunarodnaya nauchno-tehnicheskaya konferenciya. 2016. S. 589-592.
11. Tret'yak V. P. Mnogovariantnost' ispol'zovaniya klasternoy tehnologii // Nauka. Innovacii. Obrazovanie. 2008. №3(4). S. 87–98.
12. Hlystun V.N., Murasheva A.A., Stolyarov V.M. Konceptual'nye podhody k razrabotke i realizacii programmy vovlecheniya v oborot neispol'zuemyh sel'skohozyaystvennyh zemel' // Ekonomika sel'skohozyaystvennyh i pererabatyvayuschih predpriyatiy. 2020. № 5. S 2-11.
13. Charykova O. G., Markova, E. S. Regional'naya klasterizaciya v cifrovoy ekonomike // Ekonomika regiona. 2019. № 15(2). S. 409-419.
14. Shestakov R. B., Lovchikova E. I. Klasterizaciya regionov na osnove bazovyh agrarno-ekonomicheskih kriteriev // Ekonomika regiona. 2023. № 19(1). S. 178-191.
15. Bhatnagar A., Vrat, P. & Shankar, R. (2019). Multi-criteria clustering analytics for agro-based perishables in cold-chain. Journal of Advances in Management Research. № 16(4). S. 563-593.
16. Vandana B. & Kumar S. (2019). Hybrid K-Mean Clustering Algorithm for Crop Production Analysis in Agriculture International. Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 9 no. 2, pp. 9-13.
17. Novikova N. V., Strogonova E. V. (2020). Regional aspects of studying the digital economy in the system of economic growth drivers. Journal of New Economy, vol . 21, no. 2, pp. 76-93.
18. Zhanyun Wang, Wei Deng, Shaoyao Zhang, Hao Zhang, (2024). Improved trade-off model of land use functions: Differentiated objective setting by territory spatial planning. Ecological Indicator, vol. 160, no. 4, http://doi.org/10.1016/j.ecolind.2024.111881
