AI, Maritime Decarbonization, and Ocean Conservation
AI、海事脱炭素、そして海洋保全 (AI 翻訳)
M. Spalding
🤖 gxceed AI 要約
日本語
本論文は、国際海運の脱炭素化と海洋保全におけるAIの役割を包括的に検討する。航海最適化、風力推進補助、自動運航、港湾調整、予知保全、船体設計最適化、船体メンテナンスロボティクスなどへの応用を分析し、同時にAIデータセンターのエネルギー消費という環境フットプリントにも言及する。IMOの2023年GHG戦略（2050年ネットゼロ）を背景に、気候目標と海洋生態系保護を両立する研究方向性を提案する。
English
This paper comprehensively examines AI's role in maritime decarbonization and ocean conservation. It analyzes applications in voyage optimization, wind-assisted propulsion, vessel automation, port coordination, predictive maintenance, ship design optimization, and hull cleaning robotics, while also addressing AI's own environmental footprint from data centers. Against the IMO 2023 GHG Strategy (net-zero by 2050), it proposes research directions that advance both climate goals and marine ecosystem protection.
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
日本は世界有数の海運国であり、IMO規制対応と海運DXが急務。本論文はAI活用によるCO2削減と海洋生態系保護の両立を提示し、日本企業の技術開発・投資判断に示唆を与える。
In the global GX context
With IMO's 2023 strategy targeting 20-30% reduction by 2030, this paper provides a timely review of AI applications for global shipping decarbonization. It bridges climate mitigation and ocean conservation, relevant to ISSB/TCFD-aligned disclosure for maritime firms.
👥 読者別の含意
🔬研究者:A structured overview of AI's maritime decarbonization applications with ecological co-benefits, useful for mapping cross-domain research gaps.
🏢実務担当者:Identifies AI tools (voyage optimization, predictive maintenance) that shipping operators can pilot to meet IMO targets.
🏛政策担当者:Highlights need for transparent AI emissions accounting in maritime regulation and potential for dual climate-ocean benefits.
📄 Abstract（原文）
International shipping contributes approximately 3% of global carbon dioxide emissions while serving as the circulatory system of global commerce. The International Maritime Organization’s 2023 GHG Strategy mandates net-zero emissions by or around 2050, with indicative targets requiring a 20–30% reduction by 2030 and a 70–80% reduction by 2040. From a coastal and ocean conservation perspective, these targets represent more than climate mitigation—they offer an opportunity to reduce the maritime sector’s broader ecological footprint, including underwater noise pollution, chemical contamination from antifouling coatings, and the transfer of invasive species through biofouling. This article examines the role of artificial intelligence in supporting maritime decarbonization across multiple domains: voyage optimization, wind-assisted propulsion management, vessel automation, port coordination, predictive maintenance, ship design optimization, and hull maintenance robotics. Critically, the analysis also addresses AI’s own environmental footprint—the substantial energy demands of data centers that power these technologies—and emphasizes the importance of transparent accounting of AI-related emissions. The article proposes research directions that advance both climate objectives and marine ecosystem protection.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18052337first seen 2026-06-29 06:09:43
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