Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Srivastava, Shefali; Shardeo, Vipulesh; Dwivedi, Ashish; Paul, Sanjoy Kumar Title A name given to the resource Analyzing enablers of artificial intelligence for decarbonization: implications for circular supply chains Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Annals of Operations Research;Volume;359;Issue;1;pp.953-980 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s10479-025-06843-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10479-025-06843-x</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018773848?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018773848?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Srivastava S., Christ University, Bangalore, India; Shardeo V., FORE School of Management, New Delhi, India; Dwivedi A., Jindal Global Business School, O. P. Jindal Global University, Sonipat, India; Paul S.K., UTS Business School, University of Technology Sydney, Sydney, Australia Description An account of the resource This study comprehensively explores the pivotal position that Artificial Intelligence (AI) enables on the advancement of decarbonization efforts, mainly in the context of Circular Supply Chains (CSCs). Employing a two-stage methodology, this study delves into identifying and analyzing the enablers essential for leveraging AI in the pursuit of decarbonization objectives. In the first stage, a literature review and an exploratory factor analysis are performed to discern the key enablers of AI for decarbonization initiatives. This process resulted in the identification of 15 significant enablers and categorization of enablers into environmental, organizational, institutional, and technological categories. Building upon the findings from the first stage, this study progresses to its second stage, wherein the Grey-Ordinal Priority Approach (G-OPA) is applied to analyze the identified enablers. The results indicate that adopting recyclable materials to enhance the efficiency of supply chains, emphasizing local production for recovery practices through advanced technology, and managing product life-cycle through intelligent and additive manufacturing technologies are the top three enablers. The application of the G-OPA enriches the robustness and comprehensiveness of the analysis, enabling an understanding of the complex interplay among the enablers. By clarifying the key enablers,business planners and designers can migrate from traditional linear supply chains to more sustainable CSCs through the careful implementation of enablers for decarbonization. The Author(s) 2025. Subject The topic of the resource Artificial intelligence; Circular supply chains; Decarbonization; Grey-ordinal priority approach Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 2545330; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online