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 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 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).
Vahit Çalışır (Mon,) studied this question.