Seismic Disruption and Maritime Carbon Emissions for Sustainability in Maritime Transportation: A Natural Experiment from the 2023 Kahramanmaraş 7.6 Mwg Earthquake
海運の持続可能性に対する地震混乱と海上炭素排出：2023年カフラマンマラシュMwg7.6地震からの自然実験 (AI 翻訳)
Vahit Çalışır
🤖 gxceed AI 要約
日本語
2023年トルコ地震によるイスケンデルン湾の港湾活動混乱がCO2排出に与えた影響を、AISデータとグラフニューラルネットワーク（GNN）で分析。地震後、1回の入港あたりCO2排出量が35.9％増加し、約27,574トンの余剰排出を推定。GNNモデルは平時の排出パターンを高精度で予測する一方、混乱期には予測不能となることを示した。
English
This study quantifies CO2 emission changes from the February 2023 Kahramanmaraş earthquakes using AIS port visit data and GNN modeling. It finds a 35.9% increase in per-visit CO2 during the acute disruption phase, with 27,574 tonnes of excess emissions. The GNN model shows high predictability in baseline (R²=0.985) but collapses during disruption (R²=-1.591), highlighting the need for disruption-aware emissions accounting.
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
日本は地震多発国であり、港湾インフラのレジリエンスと排出管理はSSBJ・EU MRV対応の観点から重要。本稿の手法は、災害時の余剰排出を定量化し、サプライチェーン排出の正確な把握に寄与する。
In the global GX context
This paper demonstrates how natural disasters can cause measurable excess emissions, relevant to global disclosure frameworks like EU MRV and ISSB. The GNN-based approach offers a method for distinguishing disruption-driven emissions from normal operations, informing more accurate carbon accounting and resilience planning.
👥 読者別の含意
🔬研究者:The GNN modeling for emission prediction under disruption and the counterfactual analysis framework offer a methodological advance for quantifying non-linear emission drivers.
🏢実務担当者:Port operators and shipping lines can use the findings to incorporate disaster-related emission spikes into their MRV reporting and resilience strategies.
🏛政策担当者:Regulators should consider including disruption scenarios in maritime emission accounting standards to improve transparency and incentivize resilience investments.
📄 Abstract（原文）
Natural disasters disrupt maritime operations; yet, their environmental consequences remain underexplored. This study quantifies CO2 emission changes following the February 2023 İskenderun Bay earthquakes (7.6 Mwg and 7.5 Mwg) using AIS-derived port visit data and graph neural network modeling. Analyzing 25,837 port visits across a 36-month period (January 2022–December 2024), we compared emissions during baseline (pre-earthquake), acute disruption (February–June 2023), and recovery phases. Results revealed a statistically significant 35.9% increase in per-visit CO2 emissions during the acute phase (t = 11.79, p < 0.001, Cohen’s d = 0.27), driven by extended port visit durations (from 77.87 to 105.82 h). Counterfactual analysis estimated 27,574 tonnes of excess CO2 emissions directly attributable to earthquake disruption. Network analysis showed a 23.8% reduction in edge density during the acute phase. The graph neural network (GNN) emission prediction model achieved R2 = 0.985 (baseline) and R2 = 0.997 (recovery) in predicting emission patterns, while the acute phase showed predictability collapse (R2 = −1.591). These findings demonstrate that seismic events generate sustainability-relevant externalities beyond immediate physical damage, and that quantifying disruption-driven excess emissions supports sustainability-oriented port resilience planning and more robust maritime emission accounting (e.g., under the EU MRV framework).
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18042023first seen 2026-06-29 07:50:45
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