Technological Pathways to Low-Carbon Supply Chains: Evaluating the Decarbonization Impact of AI and Robotics
低炭素サプライチェーンへの技術的経路：AIとロボティクスの脱炭素効果の評価 (AI 翻訳)
Mariem Mrad, M. Frikha, Y. Boujelbène, M. Rahmouni
🤖 gxceed AI 要約
日本語
本論文は、AIとロボティクスがサプライチェーンの排出削減に与える影響を文献レビューで分析した。AIは輸送ルート最適化などで排出削減に寄与する一方、ロボットは倉庫業務の効率化を通じて間接的に貢献する。導入障壁として、AIの高エネルギー消費やデータ非互換性が指摘され、デジタル基盤のエネルギー効率改善とデータガバナンス強化が重要と結論づけている。
English
This study reviews 83 articles to analyze how AI and robotics can decarbonize supply chains. AI reduces transport emissions via routing optimization and load consolidation; robotics improves energy efficiency in warehousing. Barriers include high AI energy consumption and data interoperability issues. The authors highlight the need for energy-efficient digital infrastructure and cross-organizational data governance to realize net benefits.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
SSBJ開示基準ではサプライチェーン排出（Scope 3）の把握が求められており、AI・ロボットによる排出削減は実務上の関心事。本論文は日本企業が低炭素サプライチェーンを構築する上での技術的選択肢と課題を整理しており、統合報告書や投資家対応にも示唆を与える。
In the global GX context
This paper provides a structured framework for leveraging AI and robotics in supply chain decarbonization, directly relevant to global disclosure frameworks (TCFD/ISSB) that require Scope 3 emissions management and transition planning. It offers actionable insights for companies seeking digital solutions to meet climate targets and regulatory demands like CSRD or SEC climate rules.
👥 読者別の含意
🔬研究者:Provides a comprehensive taxonomy of AI and robotics applications in supply chain decarbonization and identifies underexplored barriers like energy trade-offs.
🏢実務担当者:Offers a clear framework for selecting and implementing AI/robotics solutions to reduce supply chain emissions, with attention to data governance and scalability.
🏛政策担当者:Empty
📄 Abstract（原文）
Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/logistics10020031first seen 2026-05-15 17:56:09
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