The nexus among artificial intelligence, supply chain and energy sustainability: A time-varying analysis

Yufei Zhong, Xuesheng Chen, Zhixian Wang and Regina Fang-Ying Lin

Energy Economics, 2024, vol. 132, issue C

Abstract: Studying the interrelationship among artificial intelligence, supply chain and energy market is crucial to achieving sustainable development. The research uses the TVP-SV-VAR methodology to recognise the ever-changing correlation among the artificial intelligence index (AII), global supply chain pressure indicator (GSCPI) and global energy-related uncertainty index (GEUI). In light of quantitative discussions, it is presented that AII exerts negative influences on GSCPI and GEUI, highlighting that the development of artificial intelligence would facilitate global supply chain stability and energy sustainability; in turn, GSCPI and GEUI have positive and adverse influences on AII. Through comparing, the correlation between AII and GEUI is only reflected in the short term, whereas the interrelationship between AII and GSCPI could be observed in the short-, medium- and long-run situations. In addition, GSCPI exerts positive and negative influences on GEUI in the short, medium and long runs, whereas the positive effects of GEUI on GSCPI means the uncertainty in the energy market might destroy the supply chain across the globe. In the context of a new round of scientific and technological revolution and industrial transformation, this study will provide significant recommendations to maintain global supply chain stability and energy sustainability by applying artificial intelligence technology.

Keywords: Artificial intelligence; Global supply chain; Energy sustainability; TVP-SV-VAR (search for similar items in EconPapers)
JEL-codes: C32 E32 O33 Q43 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:132:y:2024:i:c:s0140988324001877

DOI: 10.1016/j.eneco.2024.107479

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