持続可能な物流における人工知能：炭素削減と資源最適化の機会
Artificial Intelligence in Sustainable Logistics: Opportunities for Carbon Reduction and Resource Optimization (原題)
Bharathi S., G. Poornima
🤖 gxceed AI 要約
日本語
本論文は、物流分野におけるAI活用が炭素排出削減と資源最適化にどう寄与するかを検討する。ルート・車両最適化、予知保全、需要予測、倉庫自動化、炭素会計などを対象に、二次資料と近年の研究を基に整理。AIは燃料消費・排出・廃棄物の削減に大きな可能性を持つが、その効果はデータ品質、インフラ、投資、組織変革への意欲に左右されると結論づける。
English
This paper reviews how AI applications in logistics—route and fleet optimization, predictive maintenance, demand forecasting, warehouse automation, and carbon accounting—can reduce emissions and optimize resources. Drawing on secondary sources, it finds substantial potential for cutting fuel use, emissions, and waste, but notes benefits depend on data quality, infrastructure, investment, and organizational change. It recommends a strategic, phased, collaborative adoption approach.
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
日本の物流業界は2024年問題による人手不足と脱炭素要請に同時対応が迫られており、AIによる効率化と排出削減の両立は実務的関心が高い。SSBJ開示やScope 3対応を見据え、物流部門の炭素会計・データ整備を進める企業にとって参考になる。
In the global GX context
As global disclosure regimes (ISSB, CSRD, TCFD) push Scope 3 and supply-chain emissions into the spotlight, logistics decarbonization becomes a disclosure-critical issue. This review maps AI's role across the logistics value chain, offering a framework relevant to transition finance and corporate climate target-setting.
👥 読者別の含意
🔬研究者:AI×物流脱炭素の応用領域を俯瞰でき、今後の実証研究の出発点となる。
🏢実務担当者:物流部門でのAI導入による排出削減・コスト最適化のユースケースと導入障壁を把握できる。
🏛政策担当者:物流脱炭素に向けたAI普及のためのインフラ・データ・投資支援策の必要性を示唆する。
📄 Abstract（原文）
Abstract The logistics sector is among the largest contributors to global greenhouse gas emissions on account of its heavy dependence on fossil-fuel-powered transportation, energy-intensive warehousing, and inefficient resource utilisation across supply chains. As governments, consumers, and investors increasingly demand environmentally responsible business practices, organisations are under mounting pressure to decarbonise their logistics operations without compromising service levels or cost competitiveness. Artificial Intelligence (AI) has emerged as a powerful enabler of this transition, offering data-driven tools that can simultaneously improve operational efficiency and environmental performance. This paper examines the role of AI in advancing sustainable logistics, with specific emphasis on opportunities for carbon emission reduction and resource optimisation. Drawing on secondary sources and recent scholarship, the study explores AI applications such as route and fleet optimisation, predictive maintenance, demand forecasting, warehouse automation, smart packaging, electric and autonomous vehicle integration, and carbon accounting systems. The paper further discusses illustrative industry examples, the barriers that hinder widespread AI adoption in logistics, and recommendations for practitioners and policymakers. The findings suggest that while AI offers substantial potential to reduce fuel consumption, emissions, and waste, its benefits are contingent upon data quality, infrastructure readiness, financial investment, and organisational willingness to change. The study concludes that a strategic, phased, and collaborative approach to AI adoption can help logistics firms achieve measurable progress toward carbon neutrality and resource efficiency goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.22720862first seen 2026-10-02 04:38:48 · last seen 2026-10-02 04:39:28
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