Ports handle around 80% of the volume of global trade1. However, many ports are exposed to operational disruptions from extreme weather events, causing costly downtime. The most extreme events can cause extensive physical damage and render ports inoperable for longer periods of time. For instance, operations at the ports of Shanghai and Ningbo are disrupted 5–6 days each year on average because of extreme wind conditions2. In the aftermath of Hurricane Katrina (2005), the port of New Orleans was shut for almost four months3. Such climatic shocks to ports can have systemic impacts, including knock-on effects to other ports, and within supply-chains. For example, Wei et al.4 revealed that every dollar of trade that is disrupted at the port of Los Angeles-Long Beach could have a multiplier effect of 2.9 through domestic supply-chains.
In Verschuur et al. (2023)5, we quantified the annual expected downtime days per year (i.e. downtime risk) associated with operational disruptions (due to weather extremes), as well as the reconstruction time associated with physical damage to ports from climate extremes (cyclone wind, and coastal, fluvial and pluvial flooding), for the 1320 most significant ports globally. Together, climate-related disruptions were found to have a downtime risk of 1.4 days across ports globally, but > 5 days for 5% of ports. Within this Brief Communication, we combine these estimates of port downtime risk with a dataset of (i) ship movements between ports, (ii) maritime transport freight flows, and (iii) dependencies between ports and global supply-chains1. This allows us to quantify the systemic exposure of transport, trade and supply-chains to port disruptions (see Methods). This information is essential to identify cross-border vulnerabilities, as well as preparing ports, firms and countries for port-related shocks, which are not adequately quantified using best practice tools for risk assessment, e.g. in the insurance sector.
We start by extending our previous analysis of port downtime risk5 to quantify the (first-order) knock-on delays at the ports of trading partners. Specifically, we calculate the delays in ship arrivals at a port because of disruptions at a port where the goods are loaded (see Methods). Christodoulou et al. (2019) 6 found that when European ports are subject to coastal flooding, other European ports are most prone to knock-on disruptions, but also ports in North America, northern Africa and the Middle-East. Extended Fig. 1a reproduces our previous results for average climate-related downtime, whilst Extended Fig. 1b shows how these impacts propagate to other ports through port-to-port shipping delays. Both of these versions of disruption are high in cyclone-prone regions, making the two correlated (ρ = 0.50), as ports tend to be connected to ports in their geographic proximity. Moreover, ports having a lower number of trading partners (Figure S1) tend to have higher port-to-port downtime risk because they do not benefit from diversification of partners (see Supplementary Methods). In relative terms, the potential for port-to-port disruptions are particularly high in South Australia, the Middle-East, Western Africa, South America, Western USA, and parts of Northern Europe (Extended Fig. 2). The average disruption at ports in these regions is relatively low, but the potential for knock-on effects from disruptions at the ports is relatively high (> 80%). In fact, these knock-on port-to-port disruptions are found to be larger than direct downtime risk for around two-thirds of ports.
Port disruptions can have wider impacts for international trade and economic activity. For instance, in 2017, the shutdown of Australian coal exporting ports as a result of Cyclone Debbie led to supply shortages in Indian and Chinese steel mills7. To capture such systemic risks, we start by calculating the amount of each country’s maritime imports and exports at-risk due to port downtime, and quantify domestic (i.e. domestic ports used for imports/exports) and cross-border downtime risk (i.e. foreign ports used for transhipments and import/exports at trading partners). In value terms, out of the 207 countries considered, domestic port downtime risk dominates for 30 (26) countries for their imports (exports), while for the remaining countries the cross-border risk dominates. Out of the total maritime trade at-risk, which equals 81 USD billion per year (~ 117 billion tonnes), 63% (58%) of imports (exports) is cross-border risk, although with sectoral differences (see Table S1-2).
Countries with large import cross-border risks include small Pacific islands (Micronesia, Tonga), and countries in Central Asia (Kyrgyzstan, Kazakhstan, Uzbekistan) and Central America (Nicaragua, Guatemala) (Fig. 1a). In terms of exports, a number of landlocked have high cross-border risks, including Chad, Malawi, Andorra and Bhutan (Fig. 1b). Figure 1c shows the top-30 countries in terms of total relative maritime trade at-risk in value terms (Figure S2 shows the same in volume terms). At the top of the list are a number of small island developing states (SIDS), including Northern Mariana Islands (MNP), Grenada (GRN), Dominica (DMA), Saint Vincent and the Grenadines (VCT), and Micronesia (FSM). Many of the SIDS have a high domestic contribution to trade at-risk, while also depending on a relatively small number of hazard-prone partner ports, including transhipment ports, for their trade, contributing to elevated cross-border risk. Still, within the top 30, there are also some of the world’s large economies (China, Japan, Australia, South Africa, Vietnam, Philippines).
The impact of port disruptions may propagate (i.e. first and higher order effects) in unexpected ways because of complex dependencies on specific supply chains that are routed through ports. We therefore quantify how much of each economic sector’s activity (industry output or final consumption) depends directly (firms directly relying on traded goods through ports) or indirectly (firms relying on other firms that traded goods through ports, either first-order or higher order suppliers) on trade flows through each port (11 sectors, 184 countries, 1320 ports, see Methods). Globally, an average of 95.8 USD billion of industry output and 26.3 USD billion of consumption is exposed every year to port disruptions, of which 64% and 60% is cross-border risk, respectively (Table S3 shows sectoral differences).
Figure 2a-b shows the percentage of a country’s industry output and consumption at-risk every year. In terms of industry output, countries in Southern Africa, South-East Asia, and Central America have the largest percentage of industry output at-risk from port disruptions. Countries whose final consumption is most exposed to port disruption are more dispersed. The top10 most at-risk countries include Taiwan, Macau, Hong Kong, and a number of SIDS having more than 26% of their final consumption dependent on imports via critical ports, and on average more than 0.5% of all final consumption expected to be disrupted each year.
Figure 2c shows the most at-risk supply-chains to port downtime per country in terms of consumption and industry output. Some of the most at-risk supply-chains are Wood and Paper manufacturing in Taiwan and South Korea, Mining and Quarrying in France, and Petroleum, Chemical, and Non-Metallic Mineral Products in Macau and Aruba. In terms of industry output, the most at-risk supply-chains of > 1 USD billion output are the Mining and Quarrying industry in Hong Kong, Textiles and Wearing Apparel in Mauritius, Electrical and Machinery in the Philippines and Transport equipment in South Africa and the Dominican Republic.
Our results highlight the scale of global trade and economic activity exposed to port disruptions and pinpoint the systemic vulnerabilities within maritime transport, trade and supply-chains networks. Whilst all our results are presented in terms of annual averages, the losses in any given year may be much greater or less. The results highlight that many SIDS are very susceptible to systemic risks given high direct exposure and cross-border vulnerabilities given limited trade and transport diversification.
Countries and firms that are highly dependent on ports with a significant risk of climate-related disruptions should consider the resilience options at their disposal, which fall into four categories: (1) enhancing ports’ resilience to climate-related disruptions, (2) reducing dependence on maritime trade by enhancing domestic production, (3) trade or transport diversification, and (4) increasing inventories and stocks to make supply chains less vulnerable to port-related disruptions. Our analysis has revealed to which ports each port, country and supply-chain critically dependent upon (see Figures S3-6 for examples), providing crucial information for policy prioritisation. First, countries may wish to take regulatory steps to enhance the resilience of critical domestic ports, whilst using our data to scrutinise the reliability of foreign ports with which they trade. Second, our analysis illustrates the extent to which trade relationships are diversified across ports and allows identifying alternative commodity exporting countries where port disruptions are less frequent. Where this is not feasible, then additional steps, such as increasing back-up inventories or promotion of domestic production may be justified. Finally, our analysis can be further supplemented with projections of future changes in port downtime due to climate change, allowing countries and firms to build resilience against future systemic disruptions.