自律航行・スマートポート・低炭素グローバル物流のためのグリーン海事情報学
Green Maritime Intelligence for Autonomous Shipping, Smart Ports and Low-Carbon Global Logistics (原題)
Murali Krishna Pasupuleti
🤖 gxceed AI 要約
日本語
自律航行、スマートポート、気候・海洋センシング、デジタルツイン、低炭素物流を統合した研究アーキテクチャを提示する書籍。航海・速度最適化、港湾混雑とエネルギー調整、予知保全、再エネ統合、回廊規模の物流脱炭素を扱う。安全性・排出・信頼性・透明性・人的監督を多目的に評価し、データ来歴やモデルドリフト、政策制約、説明可能AIを含むライフサイクル運用を論じる。
English
This book proposes an integrated research architecture combining autonomous vessel decision systems, smart-port orchestration, climate/ocean sensing, digital twins and low-carbon logistics. It covers voyage and speed optimization, port congestion and energy coordination, predictive maintenance, renewable integration and corridor-scale decarbonization, evaluating safety, emissions, reliability and transparency as joint multi-objectives. Deployment is framed as a lifecycle problem spanning data provenance, model drift, policy constraints and explainable AI.
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
海運は日本の輸出産業の生命線であり、港湾の脱炭素化・スマート化は国土交通省のグリーン港湾政策やカーボンニュートラルポート構想と直結する。Scope 3のカテゴリ4（上流輸送）・カテゴリ9（下流輸送）算定や、荷主企業の物流脱炭素開示を検討する日本企業にとって、AI活用の運用枠組みとして参照価値がある。
In the global GX context
Maritime shipping sits at the center of global Scope 3 Category 4/9 accounting and the IMO's net-zero framework, yet disclosure scholarship rarely engages operational AI. This book bridges that gap by framing emissions intelligence, digital twins and governance as interoperable infrastructure, offering a reference architecture for green-corridor and port-decarbonization work under CSRD and ISSB-aligned reporting.
👥 読者別の含意
🔬研究者:AI・最適化・因果推論を海運脱炭素に統合する研究設計の枠組みとして参照できる。
🏢実務担当者:荷主・物流担当はScope 3輸送排出の算定・削減に向けたデジタルツインや予知保全の実装指針として活用できる。
🏛政策担当者:港湾・海運の脱炭素政策において、AI導入と説明責任・サイバー安全保障を両立させる制度設計の示唆を得られる。
📄 Abstract（原文）
Abstract Green maritime intelligence integrates autonomous vessel decision systems, smart-port orchestration, climate and ocean sensing, digital twins, data engineering and low-carbon logistics into a unified research architecture. The book develops this architecture from first principles of uncertainty, causality, optimization and governance, then extends it through statistical modelling, machine learning, edge analytics and reproducible digital-twin operations. Particular attention is given to voyage and speed optimization, port congestion and energy coordination, predictive maintenance, cyber-physical assurance, renewable-energy integration and corridor-scale logistics decarbonization. The methodological position is deliberately multi-objective: safety, emissions, reliability, resilience, transparency and human oversight are evaluated together rather than optimized independently. The manuscript also treats deployment as a lifecycle problem in which data provenance, model drift, operating envelopes, policy constraints and institutional accountability are continuously monitored. Global applicability is examined across South Asia, Europe, Africa and the Americas through transferable research designs rather than location-specific numerical claims. The resulting frameworks are intended for researchers, maritime operators, port authorities, logistics planners, technology developers and policymakers seeking scalable pathways from isolated AI pilots to interoperable, trustworthy and low-carbon maritime ecosystems. Keywords green maritime intelligence, autonomous shipping, smart ports, low-carbon logistics, maritime digital twins, vessel autonomy, port optimization, voyage planning, emissions intelligence, edge AI, multimodal sensing, maritime cybersecurity, predictive maintenance, shore power, renewable energy, stochastic optimization, causal inference, MLOps, logistics resilience, green corridors, explainable AI, ocean–climate intelligence, governance, operations research
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.62311/nesx/rb18ag-978-81-68314-82-5first seen 2026-09-14 04:41:26
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