Network effects govern the evolution of maritime trade
Significance Over 70% of the total value of international trade is carried by sea, accounting for 80% of all cargo volumes. Maritime trade flows impact both the economic development of the concerned regions and their ecosystems. Shipping routes are constantly evolving and likely to be affected by the consequences of climate change, while, at the same time, ships are a considerable source of pollution. This work performs a rigorous and comprehensive analysis of maritime trade flows on a global scale over a long period, taking into account aspects of evolution, reactions to shocks, and discovering predictive models. We use machine learning methods to uncover the single dominant dynamics that govern the evolution of the structure and the intensity of maritime trade. Maritime transport accounts for over 80% of the world trade volume and is the backbone of the global economy. Global supply chains create a complex network of trade flows. The structure of this network impacts not only the socioeconomic development of the concerned regions but also their ecosystems. The movements of ships are a considerable source of CO2 emissions and contribute to climate change. In the wake of the announced development of Arctic shipping, the need to understand the behavior of the maritime trade network and to predict future trade flows becomes pressing. We use a unique database of daily movements of the world fleet over the period 1977 2008 and apply machine learning techniques on network data to develop models for predicting the opening of new shipping lines and for forecasting trade volume on links. We find that the evolution of this system is governed by a simple rule from network science, relying on the number of common neighbors between pairs of ports. This finding is consistent over all three decades of temporal data. We further confirm it with a natural experiment, involving traffic redirection from the port of Kobe after the 1995 earthquake. Our forecasting method enables researchers and industry to easily model effects of potential future scenarios at the level of ports, regions, and the world. Our results also indicate that maritime trade flows follow a form of random walk on the underlying network structure of sea connections, highlighting its pivotal role in the development of maritime trade.
Proceedings of the National Academy of Sciences