BAXMAL TUMANINING TABIIY-GEOGRAFIK, IQTISODIY VA IJTIMOIY SALOHIYATI

Authors

  • Xabibxon Iskandar o‘g‘li Misirxonov Toshkent davlat iqtisodiyot universiteti tadqiqotchisi

DOI:

https://doi.org/10.5281/zenodo.23279716

Abstract

Baxmal tumanining tabiiy-geografik, iqtisodiy va ijtimoiy salohiyatini kompleks baholash hududning barqaror rivojlanish yo‘nalishlarini aniqlash va ilmiy asoslangan boshqaruv qarorlarini ishlab chiqishda muhim ahamiyatga ega. Tadqiqotning maqsadi Baxmal tumanidagi tabiiy-geografik sharoitlar, iqtisodiy omillar va ijtimoiy ko‘rsatkichlarning hududning umumiy rivojlanish salohiyatiga ta’sirini baholashdan iborat. Tadqiqotda AHP–regressiya–korrelatsiya modeli asosida ko‘p mezonli baholash yondashuvi qo‘llanilib, tabiiy, iqtisodiy va ijtimoiy ko‘rsatkichlar o‘rtasidagi o‘zaro bog‘liqliklar tahlil qilindi. Baxmal tumanining turli zonalari bo‘yicha AHP og‘irliklarining yakuniy reyting natijalariga ta’siri baholandi hamda korrelatsion va regressiya tahlillari amalga oshirildi. 2000–2023-yillar davomida yer resurslaridan foydalanishdagi o‘zgarishlar, aholi sonining o‘sishi, investitsiyalar, infratuzilmaning rivojlanishi va o‘rtacha daromad darajasining oshishi hududiy rivojlanish reytinglarining o‘zgarishiga ta’sir ko‘rsatgani aniqlandi. Korrelatsiya va regressiya tahlillari empirik ma’lumotlar bilan model natijalari o‘rtasida muvofiqlik mavjudligini ko‘rsatdi. Olingan natijalar Baxmal tumanining barqaror rivojlanishini ta’minlash, hududiy salohiyatdan samarali foydalanish va tuman darajasida rejalashtirish hamda boshqaruv qarorlarini takomillashtirish bo‘yicha amaliy tavsiyalar ishlab chiqishga xizmat qiladi.

Keywords

hududiy rivojlanishni baholash, AHP–regressiya–korrelatsiya modeli, zonaviy tahlil, tog‘li hududlarni rejalashtirish, kompleks reyting, tabiiy-geografik salohiyat, ijtimoiy-iqtisodiy salohiyat, ko‘p mezonli baholash, qaror qabul qilish

References

  1. 1.Abhulimen, A. O., Okolie, S. I., Ojo, J. O. Utilizing AI-driven predictive analytics to reduce credit risk and enhance financial inclusion. (2024). International Journal of Frontline Research in Multidisciplinary Studies, 3(1), 45–58.
  2. 2.Agosto, A., Cortesi, A., Masi, L. Bayesian learning models to measure the relative impact of ESG factors on credit ratings. (2023). International Journal of Data Science and Analysis, 9(2), 51–62.
  3. 3.Baldi, F., Pandey, R. The role of ESG scoring and greenwashing risk in explaining the yields of green bonds: A conceptual framework and an econometric analysis. (2022). Global Finance Journal.
  4. 4.– Art. 100761.
  5. 5.Çetin, D. T. İklim finansmanında yeşil tahviller ve yapay zekâ ile kurumsal yeşil tahvil tahmini. (2022). Journal of Research in Business, 7(1), 15–28.
  6. 6.Citterio, A., Teodori, C. The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress. (2022). Finance Research Letters, 46.
  7. 7.Dan, A., Gherghina, Ş. The determinants of green bond issuance in the European Union. (2021). Journal of Risk and Financial Management, 14(4).
  8. 8.Ferrara, M., Perito, M., Musella, M. Machine learning predictive modeling for assessing climate risk in finance. (2024). WSEAS Transactions on Environment and Development, 20, 52–60.
  9. 9.Hu, B., Li, C. Unlocking causal relationships in commercial banking risk management: An examination of explainable AI integration with multi-factor risk models. (2023). Journal of Financial Risk Management, 12(3), 120–132.
  10. 10.Judijanto, L., Hartono, R. Penerapan Artificial Intelligence untuk Analisis Risiko Proyek Green Bonds di Indonesia. (2025). Sanskara Akuntansi dan Keuangan.
  11. 11.Karamadi, S., Ramesh, P., Singh, A. Predictive modelling of investor sentiment and preferences in green bonds using machine learning. (2025). 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE).
  12. 12.Lombardi Netto, A., de Souza, C. A., Oliveira, R. M. Multi-criteria analysis of green bonds: Hybrid multimethod applications. (2021). Sustainability, 13(21).
  13. 13.Ogunruku, O. O., Olatunji, O., Ajiboye, M. The evolution of green fintech: Leveraging AI and IoT for sustainable financial services and smart contract implementation. (2024). World Journal of Advanced Research and Reviews, 13(1), 45–58.
  14. 14.Peters, G. W., Hassani, H., Kostakis, A. The role and significance of green bonds in funding transition to a low carbon economy: A case study forecasting portfolios of green bond instrument returns. (2022). Social Science Research Network.
  15. 15.Reece, S., Zhang, M., Patel, V. Assessing the potential of AI for spatially sensitive nature-related financial risks. (2024). arXiv.
  16. 16.Rehman, M., Shahbaz, M., Ali, S. Do oil shocks affect the green bond market?. (2023). Energy Economics, 117.
  17. 17.Taghizadeh-Hesary, F., Yoshino, N. Analyzing the characteristics of green bond markets to facilitate green finance in the post-COVID-19 world. (2021). Sustainability, 13(6).
  18. 18.Tülgen Çetin, D. İklim finansmanında yeşil tahviller ve yapay zekâ ile kurumsal yeşil tahvil tahmini. (2022). Journal of Research in Business, 7(1), 15–28.

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Published

2026-10-10

How to Cite

Misirxonov, X. I. o‘g‘li. (2026). BAXMAL TUMANINING TABIIY-GEOGRAFIK, IQTISODIY VA IJTIMOIY SALOHIYATI. GREEN ECONOMY AND DEVELOPMENT, 4(10), 302–309. https://doi.org/10.5281/zenodo.23279716

Issue

Vol. 4 No. 10 (2026): «Yashil iqtisodiyot va taraqqiyot» jurnali 10-son

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Articles

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Copyright (c) 2026 GREEN ECONOMY AND DEVELOPMENT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

https://doi.org/10.5281/zenodo.23279716

Pages

302-309

Citation

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Vol. 4 No. 10 (2026): «Yashil iqtisodiyot va taraqqiyot» jurnali 10-son