Carbon Emission Reduction Potential in Global Seaborne Metallurgical Coal Trade Through Supply Chain Network Optimisation
海運による冶金用石炭貿易のサプライチェーンネットワーク最適化を通じた炭素排出削減可能性 (AI 翻訳)
Liwei Qu, Lianghui Li, Bochao An, Zeyan Hu
🤖 gxceed AI 要約
日本語
本研究は、強化型蟻コロニー最適化アルゴリズムを用いて、世界の海運冶金用石炭サプライチェーンにおける輸送由来の炭素排出量を最小化する経路を特定した。2022年基準比で炭素強度を25%削減（38.2→28.6 kg CO2eq/t）し、2050年までに累積35～70 Mt CO2eqの追加削減が可能であることを示した。地政学的混乱を考慮したシナリオ分析も実施し、政策介入と排出削減目標のトレードオフを定量化した。
English
This study develops an enhanced Ant Colony Optimization algorithm to identify optimal supply pathways minimizing transportation carbon emissions in global seaborne metallurgical coal trade. It achieves a 25% reduction in carbon intensity against the 2022 baseline and cumulative reductions of 35-70 Mt CO2eq by 2050. Scenario analyses including geopolitical disruptions quantify trade-offs between policy interventions and emission reduction objectives.
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
本論文は、日本の製鉄業や海運業にとっても重要なサプライチェーン最適化による排出削減の可能性を示す。SSBJや有報におけるサプライチェーン排出量開示の実務改善に寄与する知見を提供する。
In the global GX context
This paper demonstrates how mathematical optimization can reduce emissions in hard-to-abate sectors like coal shipping, complementing TCFD/ISSB disclosure frameworks. It offers actionable insights for supply chain decarbonization without capital-intensive technology shifts.
👥 読者別の含意
🔬研究者:Operations research and sustainability researchers will find a novel algorithm linking logistical optimization to emission reduction targets.
🏢実務担当者:Supply chain managers can apply the optimization approach to identify low-carbon shipping routes and assess resilience under geopolitical disruptions.
🏛政策担当者:Policymakers can use the findings to promote supply chain optimization as a near-term climate mitigation strategy.
📄 Abstract（原文）
This study addresses the challenge of designing low-carbon supply chain pathways in the global seaborne metallurgical coal sector by developing an enhanced Ant Colony Optimisation (ACO) algorithm. This quantitative approach bridges operations research and sustainability science by identifying optimal supply pathways to minimise transportation-related carbon emissions. The enhanced framework incorporates coal-specific maritime logistical constraints and maintains Pareto efficiency across a comprehensive global dataset encompassing 201 mines, 11 exporting nations, and 72 destination ports in 26 importing countries. Computational analysis demonstrates that the proposed algorithm achieves a 25% reduction in transportation carbon intensity (from 38.2 to 28.6 kg CO2eq/t) relative to the 2022 baseline. To evaluate supply chain resilience, scenario analyses incorporating geopolitical disruptions, such as the Russian coal sanctions, provide quantitative insights into the trade-offs between policy interventions and emission reduction objectives. Extending projections to 2050 under various demand trajectories yields cumulative emission reductions of 35–70 Mt CO2eq (an average of 53 Mt), representing additional mitigation beyond the 230 Mt of reductions identified in prior research. These findings demonstrate that mathematical optimisation can deliver near-term environmental benefits without requiring capital-intensive technological breakthroughs, thereby supporting global climate mitigation targets.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18073496first seen 2026-05-17 06:56:05
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