An AIS–MRV Consistency-Enhanced Dynamic Network Framework for Shipping Traffic Resilience Assessment and Disruption Recovery Characterization
海運交通の回復力評価と混乱回復特性のためのAIS-MRV整合性強化動的ネットワークフレームワーク (AI 翻訳)
Ruolan Zhang, Wei Shen, Dejian Wei, Chuankao Yang, Mingyang Pan
🤖 gxceed AI 要約
日本語
本論文は、AISとMRVデータを統合した動的ネットワークフレームワークを提案し、海運交通の回復力とグリーン運航を評価する。実際のAISデータとDGX年間データを用いて、機能性曲線とレジリエンス・グリーン指標を算出し、ネットワーク摂動下での頑健性を確認した。MRV整合性テストでは、AISプロキシとCO2排出量の正の相関を示した。
English
This paper proposes a dynamic network framework integrating AIS and MRV data to assess shipping traffic resilience and green operations. Using real AIS and full-year DGX data, it computes functionality curves and Resilience-Green Index values, demonstrating robustness under perturbations. The MRV consistency test shows positive correlations between AIS proxies and CO2 emissions.
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、港湾管理やサプライチェーン強靭化に資する。AIS-MRV統合は、日本の排出量報告制度や港湾のグリーン化政策に示唆を与える。
In the global GX context
This framework contributes to global maritime sustainability by linking operational data (AIS) with regulatory reporting (MRV), supporting ISSB-aligned disclosure and port resilience planning.
👥 読者別の含意
🔬研究者:Provides a reproducible method for quantifying maritime network resilience and green performance using AIS-MRV integration.
🏢実務担当者:Port authorities and shipping companies can use the framework to identify vulnerable segments and optimize green operations.
🏛政策担当者:Informs maritime regulators on using AIS data to complement MRV reporting for better emissions oversight.
📄 Abstract（原文）
Coastal shipping systems experience complex functional degradation and recovery under port congestion, extreme weather, channel restrictions, and environmental constraints. To support sustainable maritime governance and port management, this paper proposes an AIS–MRV consistency-enhanced dynamic network assessment framework. The framework converts vessel trajectories into a dynamic maritime traffic network composed of ports, anchorages, fairways, and traffic corridors, and it constructs normal-state baselines by region, time window, and vessel type. It jointly measures system functionality, resilience loss, recovery time, network efficiency, anchorage congestion, route deviation, and an AIS-derived green operational penalty. It also compares AIS-derived green activity proxies with MRV annual CO2 reports to assess external consistency. The short-term real-AIS experiment identifies 70 traffic nodes and produces comparable functionality curves and Resilience–Green Index values for five representative port regions. The DGX full-year AIS baseline experiment processes 365 daily AIS files and generates 22.76 million vessel-hour records. Under network-parameter perturbations, the Spearman correlations of the Q* time series range from 0.900 to 1.000, and the Spearman correlation of the five-region RGI ranking remains 1.000. The Los Angeles/Long Beach event window shows a standardized functionality difference of −0.076 relative to spatial controls, with a bootstrap 95% confidence interval of [−0.090, −0.023]. The five regions show an index range of 0.3168–0.8391, and Puget Sound remains the top-ranked region in 86.27% of 10,000 random weight perturbations. The MRV consistency test indicates a moderate positive correlation between the AIS vessel-size proxy and annual CO2 emissions, while the size-weighted AIS activity proxy is also positively correlated with reported emissions. The framework provides a reproducible basis for identifying vulnerable shipping segments, assessing traffic recovery, and supporting port management, congestion governance, and green shipping decisions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18157917first seen 2026-08-09 05:49:08
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