Can artificial intelligence and green finance affect economic cycles?

Muhammad Zubair Chishti (), Eyup Dogan and Rima H. Binsaeed

Technological Forecasting and Social Change, 2024, vol. 209, issue C

Abstract: The COVID-19 recession and the Ukraine-Russia War (URW) crisis have added a new layer of complexity to global economic cycles, necessitating the evolution of economic systems and proactive responses to emerging economic challenges. In this context, the recent article introduces artificial intelligence (AI) as a new driver of economic cycles and analyzes its dynamic role alongside the Belt and Road Initiative (BRI), the Paris Agreement (PA), green finance (GB), and economic shocks (ES) in determining global economic cycles. The article employs novel econometric tools, namely the CAViaR-TVP-VAR model, the Quantile Coherence method, panel Quantile on Quantile Kernel-Based Regularized Least Squares (PQQKRLS), and the Quantile-Quantile Granger causality (QQGC) test for robust findings. The outcomes reveal that AI influences economic cycles in the short run while significantly mitigating these cycles in the medium and long run. Furthermore, the BRI exhibits a positive link with economic cycles during the short and medium run; however, it can contribute to economic stability in the long run by impeding economic fluctuations. Similarly, green finance and the PA show mixed influences across various time horizons, except for the long run, which confirms their negative association with economic cycles. Additionally, ES has a direct link with economic cycles across most periods. The robustness check based on the QQGC test and PQQKRLS method supports the main results. Our results identify AI, BRI, and the PA as new drivers of economic cycles with the potential to counter global economic cycles. Therefore, based on these findings, the study proposes several policy implications tailored to different time horizons.

Keywords: Economic cycles; Artificial intelligence; Green finance; Quantile coherence method; Machine learning; Panel QQKRLS method (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:209:y:2024:i:c:s0040162524005389

DOI: 10.1016/j.techfore.2024.123740

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