Can Artificial Intelligence–Driven Green Banking Accelerate the Circular Bioeconomy in Sustainable Agriculture?: A econometric model approach

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.

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