脱炭素化と持続可能な鉱山輸送のためのAI駆動型スマート運搬技術：包括的レビュー
AI-Driven Smart Haulage Technologies for Decarbonization and Sustainable Mine Transportation: A Comprehensive Review (原題)
Justina Senam Lotsu, Nana Yaa Damtewaa Anti, Gilbert Yaw Bimpong, Kwaku Boakye
🤖 gxceed AI 要約
日本語
鉱山の運搬トラックは直接排出の半分以上を占め、脱炭素化の鍵となる。本レビューは2017〜2026年の73件を統合し、経路最適化・電動化・予知保全・炭素削減・ESG性能を整理。強化学習配車で排出30%超減、トロリーアシストで燃料最大91%減などの報告を検証基盤別に格付けし、AI技術とESG成果を結ぶ枠組みを提示する。ただし多くはシミュレーション由来で実証が乏しい。
English
Haul trucks cause over half of direct mining emissions, making haulage decarbonization central to net-zero goals. This review synthesizes 73 sources (2017-2026) on route optimization, electric/hybrid trucks, predictive maintenance, carbon reduction, and ESG performance, grading outcomes by validation basis and linking AI domains to ESG outcomes. RL dispatching cut emissions >30% and trolley assist cut fuel up to 91%, but most results are simulation-based with weak field verification.
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
日本では資源・素材企業のScope 1排出の大半が鉱山・運搬由来であり、SSBJ開示やScope 3対応を進める上で、AI配車や電動化による削減余地と検証ギャップを把握する材料になる。実証データ不足は有報・統合報告書での定量的裏付けの難しさを示す。
In the global GX context
For global disclosure scholarship, this review connects AI-enabled operational levers to ESG outcomes in a hard-to-abate sector, offering a framework relevant to TCFD/ISSB transition-plan credibility. It highlights that most claimed reductions are simulation-based, a caveat for CSRD/ISSB assurance and transition-finance claims.
👥 読者別の含意
🔬研究者:AI×ESG研究の空白領域として、シミュレーション依存と実証不足を埋める研究設計の指針を得られる。
🏢実務担当者:鉱山・素材企業の開示担当は、AI配車・電動化の削減ポテンシャルと検証限界を踏まえたScope 1削減計画に活用できる。
🏛政策担当者:排出実証基盤やサイバーセキュリティ・データガバナンスの整備が、AI脱炭素施策の信頼性確保に必要と示唆される。
📄 Abstract（原文）
Haul trucks generate more than half of direct greenhouse-gas emissions in typical mining operations, making haulage decarbonization central to the industry’s net zero commitments. Artificial intelligence (AI) is increasingly applied to haulage, but existing reviews examine individual technologies rather than linking them to decarbonization and ESG outcomes. This comprehensive review synthesizes 73 peer-reviewed sources from 2017 to 2026 across route optimization, electric and hybrid trucks, predictive maintenance, fleet monitoring, carbon footprint reduction, and ESG performance, grades reported outcomes by validation basis, and develops a framework linking AI-enabled technology domains, through their operational mechanisms, to ESG outcomes. Reinforcement-learning dispatching reduced haulage emissions by more than 30% relative to fixed scheduling in simulation, trolley assist reduced fuel use by up to 91%, and fleet electrification modeling projected GHG reductions of up to 92.6% in a hypothetical case, contingent on grid carbon intensity. Most reported reductions derive from simulation, and AI’s advantage over conventional control of electrified fleets remains undemonstrated. Gaps include scarce field-verified emission baselines, limited modeling of operator behavior, immature cybersecurity and data governance practice, and weak engagement between ESG research and haulage technology. AI-enabled operational measures offer a promising near-term decarbonization pathway that complements, rather than awaits, fleet electrification.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3390/su182010321first seen 2026-10-16 06:17:54 · last seen 2026-10-25 05:56:45
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