Smart Seas: The Impact of Artificial Intelligence on International Shipping and Trade Dynamics

This thesis examines the impact of Artificial Intelligence (AI) adoption on operational efficiency and trade dynamics in global container shipping. Focusing on 59 major container ports worldwide over the period 2020–2023, the study investigates whether the implementation of AI-enabled systems such as predictive maintenance, berth optimization, intelligent traffic management, and automated terminal operations is associated with measurable improvements in port performance. A novel port-year panel dataset is constructed by combining UNCTAD port call statistics, the Liner Shipping Connectivity Index (LSCI), World Bank container throughput data (TEU), the Container Port Performance Index (CPPI), and an original indicator of AI adoption at the port level. The empirical analysis employs two-way fixed-effects regression models to estimate the relationship between AI adoption and median vessel time in port, controlling for trade volume and maritime connectivity. Descriptive evidence suggests that AI-enabled ports exhibit consistently lower median time in port compared to non-AI ports. Regression results indicate that AI adoption is associated with a modest reduction in vessel time in port (approximately 2–3%), although the effect is not statistically significant at conventional levels. The findings suggest that AI contributes to operational improvements primarily through broader performance enhancements captured by CPPI scores, rather than generating large standalone effects. Overall, the study provides an integrated quantitative and qualitative assessment of how digital transformation and AI technologies are reshaping container port efficiency and influencing international shipping and trade dynamics. While AI alone does not produce dramatic short-term efficiency gains, it appears to form part of a wider structural shift toward smarter, more resilient, and sustainability-oriented maritime systems.