Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/37813
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dc.contributor.authorMustafa, Fairouzen_UK
dc.contributor.authorSmolarski, Janen_UK
dc.contributor.authorElamer, Ahmed Aen_UK
dc.date.accessioned2026-01-27T01:10:24Z-
dc.date.available2026-01-27T01:10:24Z-
dc.date.issued2025-12en_UK
dc.identifier.urihttp://hdl.handle.net/1893/37813-
dc.description.abstractThis research examines the potential of artificial intelligence (AI) to improve sustainability reporting, particularly in relation to environmental, social and governance (ESG) issues. Despite growing interest in the field, the integration of AI in sustainability remains underexplored, especially in terms of its impact on data accuracy, transparency and sustainability reporting effectiveness. This study conducts a systematic literature review (SLR) of 135 peer-reviewed articles to identify significant research gaps and presents a comprehensive framework that integrates AI technologies, such as machine learning, Industry 4.0 innovations and decision support systems (DSS), with sustainability reporting practices. The findings support the need for stronger theoretical and practical frameworks to effectively leverage AI's capabilities in sustainability reporting. The originality of this study is found in its innovative approach to connecting AI technologies with sustainability reporting, a field characterised by fragmentation and underdevelopment in research. This study introduces a broad framework and takes a critical look at the unintended externalities of AI, such as increased inequality and environmental costs. It does this by challenging existing sustainability frameworks, like the GRI and SASB, to change with the times and keep up with new technologies. The emphasis on both the advantages and possible drawbacks of AI in sustainability reporting substantiates the study's publication, providing fresh insights into AI's role in enhancing ethical, transparent and effective ESG disclosures. The study offers recommendations for managers and policymakers aimed at improving the accuracy, transparency and credibility of ESG disclosures via AI-driven solutions, thereby promoting more effective sustainability practices. This paper provides a framework for future research and practical application of AI in sustainability reporting, with the goal of enhancing academic knowledge and real-world practices in the pursuit of sustainable development.en_UK
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.relationMustafa F, Smolarski J & Elamer A (2025) The Convergence of Artificial Intelligence and Sustainability Reporting: A Systematic Review of Applications, Challenges and Future Directions. <i>Business Strategy and the Environment</i>, 34 (8), pp. 9761-9784. https://doi.org/10.1002/bse.70090en_UK
dc.rights© 2025 The Author(s). Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectartificial intelligenceen_UK
dc.subjectdecision support systemsen_UK
dc.subjectenivronmental impacten_UK
dc.subjectinnovationen_UK
dc.subjectmachine learningen_UK
dc.subjectsustainability reportingen_UK
dc.titleThe Convergence of Artificial Intelligence and Sustainability Reporting: A Systematic Review of Applications, Challenges and Future Directionsen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1002/bse.70090en_UK
dc.citation.jtitleBusiness Strategy and the Environmenten_UK
dc.citation.issn1099-0836en_UK
dc.citation.issn0964-4733en_UK
dc.citation.volume34en_UK
dc.citation.issue8en_UK
dc.citation.spage9761en_UK
dc.citation.epage9784en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderUniversity of Stirlingen_UK
dc.author.emailfairouz.mustafa@stir.ac.uken_UK
dc.citation.date24/07/2025en_UK
dc.contributor.affiliationAccounting & Financeen_UK
dc.contributor.affiliationAlfaisal Universityen_UK
dc.contributor.affiliationAlfaisal Universityen_UK
dc.identifier.isiWOS:001534593400001en_UK
dc.identifier.scopusid105011982930en_UK
dc.identifier.wtid2217360en_UK
dc.contributor.orcid0000-0003-1185-7627en_UK
dc.contributor.orcid0000-0002-9241-9081en_UK
dc.date.accepted2025-07-07en_UK
dcterms.dateAccepted2025-07-07en_UK
dc.date.filedepositdate2026-01-26en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorMustafa, Fairouz|0000-0003-1185-7627en_UK
local.rioxx.authorSmolarski, Jan|en_UK
local.rioxx.authorElamer, Ahmed A|0000-0002-9241-9081en_UK
local.rioxx.projectProject ID unknown|University of Stirling|en_UK
local.rioxx.freetoreaddate2026-01-26en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2026-01-26|en_UK
local.rioxx.filenameBus Strat Env - 2025 - Mustafa - The Convergence of Artificial Intelligence and Sustainability Reporting A Systematic.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1099-0836en_UK
Appears in Collections:Accounting and Finance Journal Articles

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