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Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Author: search.filters.author.Chishti, Muhammad ZubairSubject: search.filters.subject.Artificial Intelligence

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  • Article
    Citation - WoS: 26
    Citation - Scopus: 35
    Can Artificial Intelligence and Green Finance Affect Economic Cycles?
    (Elsevier Science inc, 2024-12) Chishti, Muhammad Zubair; Dogan, Eyup; Binsaeed, Rima H.
    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.
  • Article
    Citation - WoS: 70
    Citation - Scopus: 74
    Understanding the Effects of Artificial Intelligence on Energy Transition: The Moderating Role of Paris Agreement
    (Elsevier, 2024) Chishti, Muhammad Zubair; Xia, Xiqiang; Dogan, Eyup
    This study contributes to the existing literature by investigating and confirming a range of diverse outcomes related to the interplay of factors shaping the global energy transition (ET). Employing advanced methodologies, including the extension of the QVAR technique to short-term (SR), medium-term (MR), and long-term (LR) connectedness analysis, as well as the application of the CQ method to explore relationships within varying market conditions and timeframes, the study examines the interconnectedness of critical variables: artificial intelligence (AI), the Belt and Road Initiative (BRI), the Paris Agreement (PA), green technologies (GT), geopolitical risk (GPR), and ET. The findings highlight several crucial insights. Firstly, all selected variables demonstrate substantial interconnectedness across different time horizons, except for MR, which exhibits comparatively weaker connectedness than SR and LR. Secondly, independent series reveal diverse impacts on ET across various market conditions and periods. For example, in SR, most series produce mixed effects on ET, with BRI having primarily adverse consequences and GPR predominantly yielding positive impacts. In MR, the influence of AI, PA, and GT on ET varies, while BRI enhances ET, and GPR essentially hampers it. Notably, in LR, AI, BRI, PA, and GT significantly promote ET, while GPR disrupts its progress. Additionally, the study underscores the dynamic and time-varying nature of the relationships between AI, BRI, PA, GT, GPR, and ET across different market conditions, thus holding essential implications for shaping global policies to foster sustainable energy transitions.