| Issue |
EPJ Web Conf.
Volume 369, 2026
4th International Conference on Artificial Intelligence and Applied Mathematics (JIAMA’26)
|
|
|---|---|---|
| Article Number | 02014 | |
| Number of page(s) | 12 | |
| Section | XAI and Data-Driven Optimization in Energy, Environment, and Economic Systems | |
| DOI | https://doi.org/10.1051/epjconf/202636902014 | |
| Published online | 13 May 2026 | |
https://doi.org/10.1051/epjconf/202636902014
Can Artificial Intelligence–Driven Green Banking Accelerate the Circular Bioeconomy in Sustainable Agriculture?: A econometric model approach
1 Samarkand Institute of Economics and Service, Samarqand, Uzbekistan
2 Department of Business and Management, Oriental University, Shota Rustaveli Street, 100120, Tashkent, Uzbekistan.
3 Banking and Finance Academy of Republic of Uzbekistan, Tashkent, Uzbekistan
4 Tashkent state university of economics, Tashkent, Uzbekistan
* Corresponding author’s: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 13 May 2026
Abstract
Recent empirical evidence highlights the importance of artificial intelligence–driven green banking to mitigate against the high environmental degradation to agricultural productivity in developing economies. This paper analyses the relationship between artificial intelligence–driven banking services and sustainable agricultural performance with green finance’s institutional framework using propensity score matching. This paper devotes the empirical attempt to understanding the interaction between different dimensions of the banking sector and rapidly growing circular bioeconomy activities at different stages of development. The level of heterogeneity between the two types of agricultural producers is determined using analytic hierarchy process and estimations show that digitally supported farms have lower operational risks than traditional farms. Using panel econometric techniques, we have gained valuable insights into the continuous evolution of interactions between financial institutions, agri-tech firms and circular bioeconomy initiatives at different development stages for reaping the benefits of sustainable finance systems in particular and keeping environmental resilience at acceptable levels. After controlling all the structural and macroeconomic variables, the results show that green credit allocation and digital banking penetration are positively related for the overall agricultural output and negatively related for carbon intensity which confirms that inherent difference between technological capabilities among these two farming system types. Overall, in the context of sustainable agriculture, the results provide strong evidence in favor of policy coordination where higher financial inclusion with fierce competition from fintech institutions and its innovation networks reduce environmental externalities.
Key words: Artificial intelligence–driven banking / green finance / sustainable agriculture / circular bioeconomy / propensity score matching
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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