Understanding the interactions of tropical cyclones in global supply chains

The increasing frequency and intensity of tropical cyclones, driven by climate change, pose significant risks to global supply chains, amplifying economic vulnerabilities. This study explores how interactions between multiple extreme events influence the propagation of indirect economic costs, focusing on the compounding effects that arise within interconnected systems. Leveraging a combination of the CLIMADA risk modeling platform and the ARIO  indirect impact economic model, we generate synthetic ensembles of tropical cyclones. These simulations allow us to analyze direct and indirect economic impacts at global and regional scales.

Our results reveal that compounding events can, in some cases, mitigate indirect losses. This effect arises from the accumulation of reconstruction demand, which stimulates production across sectors, particularly those heavily involved in rebuilding, such as construction and manufacturing. The interplay between reconstruction demand and overproduction mechanisms creates a virtuous cycle, accelerating recovery and offsetting consequent losses.

However, the observed mitigation is highly dependent on the underlying modeling assumptions and sectoral resolution of the modeled economy. Indeed, some adverse indirect economic consequences only emerge when employing economic data with a higher granularity of sectors. When such higher granularity of sectors is combined with less optimistic assumptions on adaptation capacity, not only does the mitigation effect disappear, but observed outcomes show significantly aggravated indirect losses.

This study underscores the complexity of modeling compounding risks and highlights the importance of carefully chosen parameters and granularity of economic data, as qualitatively different results can emerge. In this context, ARIO serves as an effective tool for exploring the drivers of indirect economic impacts from extreme events, providing valuable insights to guide and enhance more advanced modeling approaches.