INTEGRATING DATA SCIENCE INTO GREEN FINANCING AND ECO-INNOVATIONS: ACHIEVING THE GOALS OF UZBEKISTAN–2030

Norboev Odil Abraevich and Kungratov Ilmurod Kuzibay Ugli

GREEN ECONOMY AND DEVELOPMENT, 2025, vol. 3

Abstract: This study explores the role of Data Science in accelerating the green transformation of Uzbekistan within theframework of the Uzbekistan–2030 Strategy. By applying advanced analytical methods, including big data processing,machine learning, and predictive modeling, Data Science provides innovative solutions for mobilizing green financing,optimizing resource allocation, and supporting eco-innovations. The research emphasizes how data-driven approachescan enhance transparency in green investment flows, strengthen risk management in sustainable financing, and stimulateeco-technological innovation ecosystems. Moreover, integrating digital platforms and artificial intelligence into policyimplementation enables more efficient monitoring of environmental indicators, improves forecasting of climate-relatedrisks, and supports evidence-based decision-making. The findings suggest that Data Science can serve as a critical driverfor aligning Uzbekistan’s green economy transition with global sustainable development goals, ensuring macroeconomicstability, and fostering competitiveness in international markets. Ultimately, the integration of Data Science into greenfinancing and eco-innovation practices is not only a technological necessity but also a strategic imperative for achievingthe ambitious objectives of Uzbekistan–2030.

Keywords: data science; green financing; eco-innovations; Uzbekistan–2030; sustainable development; big data; machine learning; artificial intelligence; predictive analytics; green economy; climate risk; resource optimization; green technologies; environmental indicators; smart policy; sustainable investment; macroeconomic stability; global competitiveness (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:teu:ged000:v:3:y:2025:id:7727

DOI: 10.5281/zenodo.17571516

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