Economic Signals and Artificial Intelligence for Sustainable Transport and Low-Carbon Logistics Systems
持続可能な交通と低炭素物流システムのための経済シグナルと人工知能 (AI 翻訳)
Turdiev Abdullo Sagdullaevich, S. Aarthi, R. N. Ravikumar, Shoyimkulov Asror, Jabbarov Umarbek
🤖 gxceed AI 要約
日本語
サプライチェーンの脱炭素化において、経済指標とAIを組み合わせたフレームワークを提案。都市交通や貨物物流の事例に基づき、AI最適化と排出量ベースの価格設定やインセンティブが有効であることを示す。協調的なシグナル計画が持続可能で効率的なモビリティと物流ネットワークに必要と結論。
English
This chapter proposes a framework combining economic indicators and AI to decarbonize transport and logistics. Based on case studies, it shows that AI optimization paired with emissions-based pricing and incentives can enhance supply chain efficiency and reduce logistics-related emissions. Collaborative signal planning is essential for sustainable mobility and logistics networks.
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
日本の物流業界では、AIを活用した効率化と排出削減が進んでおり、本論文のフレームワークは実務や政策に示唆を与える。特に、経済シグナル（排出量価格など）とAIの統合は、日本のGX政策とも整合する。
In the global GX context
This paper adds to the global conversation on green logistics by showing how AI and economic incentives can drive decarbonization in supply chains. It is relevant to companies pursuing SBTi-aligned targets and to policymakers designing emissions pricing mechanisms.
👥 読者別の含意
🔬研究者:Provides a conceptual foundation for empirical studies on AI-driven decarbonization in logistics.
🏢実務担当者:Logistics companies can explore AI-based optimization combined with carbon pricing to reduce emissions and costs.
🏛政策担当者:Policymakers can use the framework to design incentive schemes that integrate AI for low-carbon transport.
📄 Abstract（原文）
Supply chains involve transport and logistics systems that are significant sources of global carbon emission and thus their decarbonization is a key sustainability issue. This chapter discusses how economic indicators and Artificial Intelligence (AI) may be combined to promote low-carbon transport and logistic systems. It takes a conceptual and analytical approach based on recent case studies on urban transport and freight logistics. The paper shows that the optimization through AI, when accompanied by the emissions-based pricing and incentives, could be an effective way of enhancing the efficiency of the supply chain and minimizing logistics-related emissions. The chapter makes a contribution because it suggests a single framework that interconnects economic indicators, AI-based decision-making, and regulatory policies. The results indicate that efficient transport decarbonization involves collaborative signal planning to provide sustainable, efficient and scalable mobility and logistics networks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4018/979-8-3373-9003-1.ch007first seen 2026-05-17 05:40:58
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