人工知能による海運の低炭素移行：体系的書誌レビュー
Artificial Intelligence-Enabled Low-Carbon Transition in Shipping: A Systematic Bibliometric Review (原題)
Xiaoyang Liu, Chuanxu Wang, Mingwei Yin, Siyuan Qiu, Yakun Li
🤖 gxceed AI 要約
日本語
2016年以降の海運AI・脱炭素研究479件を書誌分析と主題コーディングで整理。2022年以降に出版が急増し、機械学習・深層学習・船舶エネルギー効率・港湾運用が主要テーマ。データ観測性から炭素検証までの「AI-to-Carbon Value Chain」概念枠組みを提示するが、因果検証ではなく解釈的統合にとどまる。
English
A bibliometric review of 479 papers (2016–2026) on AI-enabled shipping decarbonization. Output surged after 2022, with machine learning, deep learning, ship energy efficiency, and port operations dominating. The authors propose an interpretive 'AI-to-Carbon Value Chain' spanning data observability to carbon verification, though no causal validation is attempted.
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
海運は日本にとって国際競争力とScope 3排出の双方に直結する領域。IMO規制やSSBJ開示を見据え、AI活用による燃費・排出データの検証可能性向上は、海運・物流企業の有報・統合報告書における排出量説明の信頼性強化に寄与しうる。
In the global GX context
Shipping sits at the intersection of IMO decarbonization rules and Scope 3 disclosure under ISSB/CSRD. The proposed AI-to-Carbon Value Chain offers a framing for how AI can support auditable well-to-wake accounting, relevant to transport-sector transition finance and disclosure assurance.
👥 読者別の含意
🔬研究者:AI×海運脱炭素の研究動向と未検証領域（炭素検証・因果モデル）を俯瞰できる。
🏢実務担当者:AI導入が燃費・排出データの検証可能性をどう高めうるか、投資判断の枠組みとして参照可能。
🏛政策担当者:IMO・開示規制下でAI活用の炭素便益をどう検証・標準化すべきかの論点を提供。
📄 Abstract（原文）
Introduction: International shipping faces the dual imperative of decarbonization and the maintenance of safety and service reliability. However, evidence concerning the conditions under which artificial intelligence (AI) generates verifiable carbon benefits remains fragmented. Methods: This review examines 479 English-language articles and reviews retrieved from the Web of Science Core Collection and Scopus and published from 2016 to 23 August 2026 through bibliometric analysis, science mapping, auxiliary document-level thematic coding, and full-text synthesis of six representative reviews and one perspective. A post hoc domain-validation sensitivity analysis used two independently specified deterministic rule sets to test whether broad search terms altered the main conclusions. Results: Publication output accelerated markedly after 2022, with 277 papers (57.83%) published during 2022–2025 and a further 137 records already indexed in the partial year 2026. The two screening rules agreed on 96.87% of records (Cohen’s kappa = 0.753). A conservative sensitivity subset of 437 records, obtained through a strict rule-based title-abstract screen and removal of one retracted and one withdrawn record, reproduced the principal temporal, source-journal, and leading-keyword patterns. Machine learning remained the most frequent keyword, while recent studies increasingly addressed deep learning, ship energy efficiency, port operations, federated learning, and energy management. Discussion: Based on these findings, the review advances an evidence-informed AI-to-Carbon Value Chain (AICV) conceptual synthesis comprising data observability, model credibility, decision executability, system coordination, and carbon verification. This synthesis is interpretive rather than a validated causal framework. Future research should prioritize carbon-ready benchmarks, calibrated physics-informed and causal models, human-in-the-loop field evaluation, network-level coordination, and auditable well-to-wake assessment. Review registration and appraisal: This review was not registered, and no formal protocol was prepared. Because no effect-size synthesis was undertaken, formal study-level risk-of-bias, reporting-bias, and certainty assessments were not applied. Funding: The review received no external funding.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/jmse14181671first seen 2026-09-10 04:42:39
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