Trade data

Trade introduces a crucial additional layer to shipping by linking vessel movements to the broader economic forces driving global commerce. Understanding trade flows, both in terms of volume and direction, allows for more accurate projections of shipping activity, as these flows dictate where and when shipping routes might grow. By analyzing historic trade patterns alongside shipping data, we can identify trends in demand, shifts in trade routes, and the impact of economic cycles on maritime transport. This integration is used in our work to develop scenarios that are aligned with the different climate change mitigation and adaptation trajectories.

Trade by ship type and commodity

We use primarily BACI data to integrate trade flows into our shipping analysis, as it provides a harmonized and reconciled view of global trade. BACI data, built on the United Nations Comtrade dataset, addresses discrepancies between import and export reports by estimating and removing CIF costs from imports and weighting data reliability based on reporting accuracy. This reconciliation ensures a more consistent and unbiased trade flow dataset, allowing us to better link cargo movements to shipping activity.

Each Harmonized System (HS) 6-digit code represents a specific product category, which can be linked to the type of vessel typically used for its transportation. For instance, bulk carriers handle commodities like iron ore, coal, and grain, while container ships transport manufactured goods and electronics. Tankers are used for liquid cargo such as crude oil and chemicals, and specialized vessels move vehicles, refrigerated goods, or oversized equipment. By mapping trade data at the HS 6-digit level to ship types, we can more accurately model cargo distribution across different segments of the shipping industry. In the plot below, we present a tree map for the year 2019, visually illustrating the relationship between traded goods and the corresponding vessel types used for their transport, the area each rectangle representing the quantity (in metric tonnes) transported.

Correlation between GDP and shipping

We leverage the well-established correlation between GDP and trade to project future trade pathways based on GDP forecasts. Since economic growth drives both production and consumption, higher GDP levels generally lead to increased trade volumes, while economic downturns tend to reduce trade activity. By analyzing historical trends from 1997 to 2020, we observe how changes in GDP influence trade patterns across countries. Using projected GDP scenarios from the SSP Explorer, for example, we can estimate future trade flows and assess their impact on shipping demand, fleet composition, and global trade networks. This approach helps refine long-term shipping forecasts by incorporating macroeconomic dynamics into trade and transport modeling. We can empirically observe in the plot below that there is a near-liner relation between the logarithms of GDP and trade for individual countries across the years, regardless of the continents (different colors, see legend) they belong too.

This relation can also be illustrated by looking at macro indicators at a global level. For instance, the plot below illustrates the strong correlation between global GDP and overall trade, highlighting how economic fluctuations directly impact trade volumes. Major drops in GDP, such as during the 2008 financial crisis, result in sharp declines in global trade as demand contracts, production slows, and supply chains face disruptions. The aftermath of the crisis saw a significant downturn in trade, followed by a gradual recovery as economies stabilized. Similar patterns can be observed in other economic shocks.

If we zoom in to country-level exports, the relation between GDP and trade still holds, allowing for a more detailed analysis of how economic activity influences maritime trade patterns in a finer scaler toward a country-specific examination, helping to identify trends, outliers, and potential drivers of shipping activity in relation to economic performance. Select a country below to see how GDP and trade has changed over time.

Bilateral trade flows

In our scenarios, we are primarily interested in bilateral trade between country pairs and how it may change in the future due to shifts in each country’s macroeconomic indicators, such as GDP and population. This type of analysis forms the foundation of the Gravity Model of Trade, that is introduced in the next page. By assessing prospective shipping scenarios through the lens of bilateral trade, we can account for structural changes in global trade networks, such as the emergence of new economic hubs or the decline of traditional trade routes. Historical data on bilateral trade (both in monetary terms and as percentage shares) among a group of 25 countries is shown in the plot below.

Author: Diogo Kramel
Model: Gravity model of trade Repository: GitHub
Data Version: v1.0.0 | 2025-02-13
Latest Update: March 24, 2025
Contact: diogo.kramel@ntnu.no