A Comprehensive Survey of Artificial Intelligence and Robotics for Reducing Carbon Emissions in Supply Chain Management

Mariem Mrad, Mohamed Amine Frikha and Younes Boujelbene
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Mariem Mrad: Faculty of Economics and Management of SFAX, Tunisia.
Younes Boujelbene: Faculty of Economics and Management of SFAX, Tunisia.

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Abstract: Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM.

Date: 2025-08-04
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Published in Logistics, 2025, 9 (3), pp.104. ⟨10.3390/logistics9030104⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05570735

DOI: 10.3390/logistics9030104

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