Decarbonizing Coastal Shipping: Voyage-Level CO2 Intensity, Fuel Switching and Carbon Pricing in a Distribution-Free Causal Framework
沿岸海運の脱炭素化：航海レベルのCO2原単位、燃料転換、および分布自由な因果フレームワークにおける炭素価格 (AI 翻訳)
Murat Yıldız, Abdurrahim Akgundogdu, Güldem Elmas
🤖 gxceed AI 要約
日本語
本研究は、IMOのCIIとEU ETSに対応するため、航海レベルのCO2原単位を予測する分布自由な因果フレームワークを開発した。ナイジェリア沿岸4航路の1440航海データを用い、物理知識に基づくLightGBMとLOROプロトコルで未観測航路を高精度予測（MAE 40.7 kg CO2/nm）。Causal Forestsにより燃料転換効果の不均一性を発見し、炭素価格が約100 USD/tCO2を超えると経済的燃料転換が可能になることを示した。
English
This study develops a distribution-free causal forecasting framework for voyage-level CO2 intensity, using 1440 real-world voyages on four Nigerian coastal routes. A physics-informed LightGBM model with leave-one-route-out protocol predicts unseen corridors (MAE 40.7 kg CO2/nm). Causal Forests reveal heterogeneous fuel-switching effects (−74 to +29 g CO2/nm), and targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO2.
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
日本は海運大国であり、IMO CIIやEU ETSへの対応が急務。本フレームワークは、航路別の燃料戦略や炭素価格感応度分析を提供し、日本船社の脱炭素計画や政策策定に示唆を与える。
In the global GX context
This paper directly addresses IMO CII and EU ETS requirements for maritime decarbonization. It provides a causal, uncertainty-aware tool for route-specific fuel strategies and identifies carbon price thresholds, offering actionable insights for global shipping operators and regulators.
👥 読者別の含意
🔬研究者:The distribution-free causal framework combining physics-informed ML, conformal prediction, and Causal Forests advances methodology for emission forecasting and treatment effect estimation.
🏢実務担当者:Shipping operators can use the route-specific fuel strategy insights to optimize fuel switching under carbon pricing, moving beyond uniform fleet-wide policies.
🏛政策担当者:The finding that targeted fuel switching requires carbon prices ~100 USD/tCO2 provides a benchmark for designing effective carbon pricing mechanisms under IMO and EU ETS.
📄 Abstract（原文）
Coastal shipping plays a critical role in meeting maritime decarbonization targets under the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII) and the European Union Emissions Trading System (EU ETS); however, operators currently lack robust tools to forecast route-specific carbon intensity and evaluate the causal benefits of fuel switching. This study developed a distribution-free causal forecasting framework for voyage-level Carbon Dioxide (CO2) intensity using an enriched panel of 1440 real-world voyages across four Nigerian coastal routes (2022–2024). We employed a physics-informed monotonic Light Gradient Boosting Machine (LightGBM) model trained under a strict leave-one-route-out (LORO) protocol, integrated with split-conformal prediction for uncertainty quantification and Causal Forests for estimating heterogeneous treatment effects. The model predicted emission intensity on completely unseen corridors with a Mean Absolute Error (MAE) of 40.7 kg CO2/nm, while 90% conformal prediction intervals achieved 100% empirical coverage. While the global average effect of switching from heavy fuel oil to diesel was negligible (≈−0.07 kg CO2/nm), Causal Forests revealed significant heterogeneity, with effects ranging from −74 g to +29 g CO2/nm depending on route conditions. Economically, targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO2. These findings demonstrate that effective coastal decarbonization requires moving beyond static baselines to uncertainty-aware planning and targeted, route-specific fuel strategies rather than uniform fleet-wide policies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18020723first seen 2026-05-05 22:44:21
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。