Carbon Shadow Pricing and Efficiency in Multimodal Freight Transport Networks: A Z-number DEA Analysis
マルチモーダル貨物輸送ネットワークにおける炭素シャドープライシングと効率性：Z-number DEA分析 (AI 翻訳)
Hossein Zangooei Dovom, Mir Saman Pishvaee, Hadi Sahebi
🤖 gxceed AI 要約
日本語
CO₂シャドープライス50ドル/トンをZ-number DEAに組み込み、イランの59の物流センターを評価。鉄道依存の拠点は効率性が向上する一方、道路依存の拠点は低下。鉄道シェアとランク改善に強い負の相関（r=-0.94）。不確実性下での気候対応型貨物分析の再現可能なツールを提供。
English
Integrates a $50/ton CO₂ shadow price into Z-number DEA to evaluate carbon cost internalization across 59 Iranian logistics centers. Rail-intensive sites show efficiency increases up to +9.4% and rank improvements, while road-dependent hubs show decreases down to -15.8% and rank declines. A strong negative correlation (r=-0.94) links rail share to rank improvement. Provides a replicable tool for climate-responsive freight analysis under uncertainty.
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
日本の物流分野でも、炭素価格内部化が輸送モード選択に与える影響を評価する本手法は有用。SSBJ開示要請に伴うScope 3排出量削減策として、鉄道シフトの定量的効果を検証可能。
In the global GX context
The paper demonstrates how carbon shadow pricing can reshape freight infrastructure priorities, offering a replicable DEA-based tool for climate scenario analysis under data uncertainty—directly relevant for TCFD/ISSB-aligned transport decarbonization strategies.
👥 読者別の含意
🔬研究者:Demonstrates a novel integration of Z-number DEA with carbon pricing for multimodal freight efficiency analysis under uncertainty.
🏢実務担当者:Provides a method to quantify the impact of carbon cost internalization on logistics network performance, useful for planning modal shifts.
🏛政策担当者:Highlights the potential of carbon pricing to incentivize rail freight investments, informing infrastructure prioritization.
📄 Abstract（原文）
We integrate a $50/ton CO₂ shadow price into a Z-number Data Envelopment Analysis (DEA) framework to evaluate carbon cost internalization across 59 logistics centers in Iran’s freight transport network. Reflecting the transport sector’s key role in emissions and energy use across multimodal freight corridors, rail-intensive sites show score increases up to +9.4% and rank improvements, while road-dependent hubs show score decreases down to −15.8% and rank declines. A strong negative correlation (r = −0.94) links rail share to rank improvement. Internalizing carbon externalities reshapes infrastructure priorities toward low-carbon modal configurations, offering a replicable tool for climate-responsive freight analysis under uncertainty
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.32866/001c.163951first seen 2026-07-29 04:49:56
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