Typhoon Threats to the Global Shipping Network: Contrasting Systemic Risks from Climate-Driven Natural Attacks and Degree-Based Deliberate Attacks

The global shipping network, which handles over 80% of international trade volume, is increasingly exposed to disruptions from typhoons and other extreme weather events under climate change. However, conventional network vulnerability assessments often overlook the geographically heterogeneous nature of such natural hazards. Here, we introduce a typhoon-related systemic vulnerability model (GMSN-TV) that integrates three core components: typhoon exposure, port network sensitivity, and national adaptive capacity, to quantify the Typhoon Vulnerability Index (TVI) of 1075 major ports across 2017 and 2021. Our analysis reveals four key findings. First, the global shipping network became structurally sparser between 2017 and 2021, with edges declining by 17.84% and network efficiency decreasing by 4.22%, rendering it more susceptible to climate-related disruptions. Second, simulated TVI-based natural attacks and conventional degree-based deliberate attacks induce fundamentally different risk patterns: removing the top 10% high-TVI ports in 2021 caused a 6.3% decline in network efficiency, whereas removing the top 10% hub ports resulted in a 20.1% decline, a difference of 13.8 percentage points; however, natural attacks proved more effective at isolating peripheral ports, generating an isolated node ratio of 1.16% compared to 0.00% under deliberate attacks. Third, when removing the top 50% high TVI ports, the contribution of typhoon vulnerability to network degradation increased from 13.77% in 2017 to 15.87% in 2021. Fourth, high-vulnerability ports exhibit significant spatial clustering, with the Northwest Pacific region (50.8%) and the North Atlantic region (29.5%) collectively accounting for over 80% of global high-vulnerability ports in 2021. Compared to conventional topology-based assessments, the GMSN-TV analytical framework proposed in this study integrates typhoon hazard data with network topology, providing a novel scientific tool with enhanced identification efficacy and accuracy. It successfully captures local network disintegration effects entirely missed by traditional deliberate attacks, revealing an isolated node ratio of 12.5% after removing 70% of high-TVI ports. This demonstrates the tool’s precision in identifying hidden high-risk peripheral nodes, enabling decision-makers to prioritize climate adaptation investments for critical maritime infrastructure more accurately.