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).
Vahit Çalışır (2026) studied this question.