INTEGRATING DATA SCIENCE INTO GREEN FINANCING AND ECO-INNOVATIONS: ACHIEVING THE GOALS OF UZBEKISTAN–2030
DOI:
https://doi.org/10.5281/zenodo.17571516Ключевые слова:
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 competitivenessАннотация
This study explores the role of Data Science in accelerating the green transformation of Uzbekistan within the
framework 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 approaches
can enhance transparency in green investment flows, strengthen risk management in sustainable financing, and stimulate
eco-technological innovation ecosystems. Moreover, integrating digital platforms and artificial intelligence into policy
implementation enables more efficient monitoring of environmental indicators, improves forecasting of climate-related
risks, and supports evidence-based decision-making. The findings suggest that Data Science can serve as a critical driver
for aligning Uzbekistan’s green economy transition with global sustainable development goals, ensuring macroeconomic
stability, and fostering competitiveness in international markets. Ultimately, the integration of Data Science into green
financing and eco-innovation practices is not only a technological necessity but also a strategic imperative for achieving
the ambitious objectives of Uzbekistan–2030.
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