Carbon Footprint and Energy Trade-offs in Global Logistics Network
グローバルロジスティクスネットワークにおけるカーボンフットプリントとエネルギー消費のトレードオフ分析 (AI 翻訳)
Chinedu Michael Chiejine, Endurance Ikechi Eke
🤖 gxceed AI 要約
日本語
本研究では、国際輸送200件のシミュレーションデータを用いて、各輸送モード（航空、道路、鉄道、海運）の二酸化炭素排出原単位を算出し、輸送コストと配送時間が排出量に与える影響を回帰分析で検証した。その結果、輸送コストの増加が排出量を有意に押し上げることが示され、持続可能な物流にはモーダルシフトや炭素価格付け、再生可能エネルギー活用などが重要と結論づけている。
English
This study uses simulated data from 200 international shipments to compute carbon intensities of air, road, rail, and sea transport. Regression analysis shows that transport costs (coefficient 0.0004, p<0.001) and delivery time (0.018, p=0.003) are positively associated with emissions, explaining 97.5% of variance. The paper recommends modal shift to rail and sea, carbon pricing, renewable energy, and AI-based logistics optimization for sustainable logistics.
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CO2排出削減が急務であり、本論文はモーダルシフトや炭素価格付けなど具体的な施策の効果を定量的に示しており、日本のGX政策（物流分野での排出削減目標設定など）に示唆を与える。
In the global GX context
This paper provides empirically derived carbon intensities for global freight modes, strengthening the evidence base for Scope 3 supply chain emission reduction strategies. Its regression findings on cost and time trade-offs are directly applicable to corporate decarbonization planning and modal shift incentives under TCFD/ISSB frameworks.
👥 読者別の含意
🔬研究者:The regression model (R2=0.975) offers a replicable approach for estimating logistics emissions and the trade-offs between cost, speed, and carbon.
🏢実務担当者:Logistics managers can use the modal carbon intensities (air:500, road:100, rail:30, sea:20 gCO2/t-km) to prioritize low-carbon modes and optimize supply chain routing.
🏛政策担当者:The study supports modal shift policies, carbon pricing, and infrastructure investment in rail and sea to reduce logistics emissions.
📄 Abstract（原文）
The carbon footprint and energy trade-offs of global logistics networks are examined in this study. Emissions were computed based on carbon intensity, distance, and cargo weight using a simulated quantitative dataset of 200 international shipments via air, road, rail, and ocean transportation. According to the average modal data, air freight had the highest carbon intensity at roughly 500 gCO₂/ton-km, followed by rail at 30 gCO₂/ton-km, road transport at 100 gCO₂/ton-km, and sea transport at 20 gCO₂/ton-km. These averages support the idea that slower modes are more energy-efficient while faster modes are often more carbon-intensive. The link between delivery time, transportation cost, and carbon emissions was investigated using a multiple regression model. Transport costs had a significant positive impact on emissions, according to the regression results (coefficient = 0.0004, p < 0.001), whereas delivery time had a lesser but still beneficial impact (coefficient = 0.018, p = 0.003). The model described 97.5% of the variation in carbon emissions, with an intercept of 1.39 tCO₂ (R2 = 0.975). The study comes to the conclusion that cost, speed, and environmental responsibility must all be balanced for sustainable logistics. It suggests switching to rail and sea as a mode of transportation, pricing carbon, adopting renewable energy, electrification, AI-based routing, and more cooperation between logistics stakeholders.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.37745/ijeats.13/vol14n2110first seen 2026-05-28 05:12:50 · last seen 2026-06-03 05:15:18
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