RAQAMLI IQTISODIYOTDA DAVLAT-XUSUSIY SHERIKLIK (DXSH) LOYIHALARINING SAMARADORLIGINI BAHOLASH UCHUN MATEMATIK MODELLASHTIRISH
DOI:
https://doi.org/10.54613/ku.v18i.1641Keywords:
Raqamli iqtisodiyot, davlat–xususiy sheriklik, matematik model, samaradorlikni baholash, risklarni boshqarish, AHP–TOPSIS, Monte–Karlo simulyatsiyasiAbstract
Ushbu tadqiqotda raqamli iqtisodiyot sharoitida davlat–xususiy sheriklik (DXSh) loyihalarining samaradorligini baholash uchun integratsion matematik model ishlab chiqilgan. Modelda samaradorlik funksiyasi E = f(I, R, T, S) shaklida aniqlangan bo‘lib, bunda I – investitsiya parametrlari, R – raqamli risk omillari, T – vaqt dinamikasi va S – ijtimoiy natijalarni ifodalaydi. AHP–TOPSIS integratsiyasi va Monte–Karlo simulyatsiyasi (N = 10 000) qo‘llanilgan. Natijalar shuni ko‘rsatdiki, raqamli risklar loyiha samaradorligini deyarli 35,2% ga kamaytiradi, loyihaning ijobiy samaradorlikka erishish ehtimolligi esa 82,0% ni tashkil etadi.Shuningdek, tadqiqotda ishlab chiqilgan modelning amaliy qo‘llanish imkoniyatlari ham tahlil qilingan. Xususan, model davlat–xususiy sheriklik loyihalarini tanlash, ustuvor yo‘nalishlarni belgilash hamda investitsion qarorlar qabul qilish jarayonida samarali vosita sifatida xizmat qilishi asoslab berilgan. Tadqiqot natijalari raqamli texnologiyalarni joriy etish bilan bog‘liq xavf omillarini oldindan baholash va ularni minimallashtirish mexanizmlarini ishlab chiqishda muhim ahamiyat kasb etadi.
Bundan tashqari, modelning moslashuvchanligi turli iqtisodiy sharoitlar va tarmoqlarga tatbiq etish imkonini beradi. Tadqiqot yakunida raqamli iqtisodiyot sharoitida DXSh loyihalarining barqarorligini oshirish uchun risklarni boshqarish, ma’lumotlar xavfsizligini ta’minlash hamda innovatsion boshqaruv mexanizmlarini joriy etish zarurligi ta’kidlangan. Mazkur integratsion yondashuv iqtisodiy samaradorlikni oshirish bilan birga, ijtimoiy foyda va uzoq muddatli barqaror rivojlanishni ta’minlashga xizmat qilishi qayd etilgan.
Foydalanilgan adabiyotlar:
1. Fabre, A., & Straub, S. (2023). The impact of public–private partnerships (PPPs) in infrastructure, health, and education. Journal of Economic Literature, 61(2), 655–715.
2. Digital Cooperation Organization. (2024). Public–Private Partnerships for the Development of the Digital Economy. DCO Framework Report.
3. Osei–Kyei, R., et al. (2023). A review of the trend of research studies on risk management in public–private partnerships. International Journal of Construction Management, 23(8), 1378–1389.
4. Lysova, E., et al. (2021). Digital transformation of public–private partnership tools. Journal of Risk and Financial Management, 14(3), 121.
5. Liu, L.X., Clegg, S., & Pollack, J. (2024). The effect of public–private partnerships on innovation in infrastructure delivery. Project Management Journal, 55(1), 66–84.
6. Moore, M.A., & Vining, A.R. (2023). PPP performance evaluation: The social welfare goal, principal–agent theory and political economy. Policy Sciences, 56(2), 339–369.
7. Hossin, M.A., et al. (2024). Examining public–private partnership investment in energy towards achieving SDG 7 for ASEAN region. Scientific Reports, 14, 16398.
8. Cheng, Z., Zhu, Y., Wang, H., & Ke, Y. (2024). Developing a suitability assessment model for PPPs: A case in urban China. Humanities and Social Sciences Communications, 11(1), 1–10.
9. Shah, M.A., Kumar, S., Shah, M.A., & Rasool, A. (2024). Examining the relationship among CSFs for delivery of sustainable PPP projects. Engineering, Construction and Architectural Management.
10. Triantaphyllou, E. (2000). Multi–criteria decision making methods. In Multi–criteria decision making methods: A comparative study (pp. 5–21). Springer, Boston, MA.
11. Ning, L., Abbasi, K.R., Hussain, K., Alvarado, R., & Ramzan, M. (2023). Analyzing the role of green innovation and PPPs in achieving SDGs. Environmental Science and Pollution Research, 30, 1–17.
12. Chaube, S., et al. (2024). An overview of multi–criteria decision analysis and the applications of AHP and TOPSIS methods. Int. J. of Mathematical, Engineering and Management Sciences, 9(3), 581–615.
13. Kováčová, M., et al. (2023). New approaches to project risk assessment utilizing the Monte Carlo method. Sustainability, 15(2), 1006.
14. Di Bari, A., Santoro, D., Tarrazon–Rodon, M.A., & Villani, G. (2025). A PPP projects valuation: Real options, competition and anchoring bias. Computational Economics.
15. Ishizaka, A., & Nemery, P. (2013). Multi–criteria decision analysis: methods and software. John Wiley & Sons.
Downloads
Published
Iqtiboslik olish
Issue
Section
License
Copyright (c) 2026 QO‘QON UNIVERSITETI XABARNOMASI
This work is licensed under a Creative Commons Attribution 4.0 International License.