Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Global spread of local cyclone damages through urban trade networks

Abstract

Geophysical hazards stress urban social, economic and political systems, but many studies focus on single locations over short periods. The manner in which a natural disaster propagates across cities globally through urban trade networks remains unexplored. Starting from a novel empirical baseline model for global production and trade, here we develop a dynamical model for the spread of individual cyclone impacts across the world’s cities. We find that cities are vulnerable to economic harm even if they are geographically distant from the location of direct impacts of cyclones. These adverse secondary impacts are responsible for up to three-fourths of the effects of the largest storms and are generated primarily by cyclone exposure in North America and East Asia, in part because of the roles of these regions as principal purchasers and suppliers, respectively, of industrial materials. Vulnerability to adverse secondary impacts of cyclones is highest in cities that are strongly dependent on the global trade network but have relatively few suppliers. Our results suggest that, in addition to improvements in protective infrastructure, urban adaptation to storm damage and climate change might require modifications to trade network linkages.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Secondary impacts exported between world regions across 1,209 historical storm simulations (100 million displaced units of production).
Fig. 2: Secondary impacts from a hypothetical cyclone in Mysore, India, propagate through the network over 48 months.
Fig. 3: Ratio of secondary impacts to direct impacts versus direct impacts for 1,209 simulated storms originating in region indicated with colour.
Fig. 4: Mean fractional secondary impacts per storm of cities versus number of strong supply relationships that connect that city to the network, averaged over 1,209 storm simulations.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Code availability

Simulation code can be accessed at https://github.com/chrisshughrue/GlobalUrbanCycloneImpactSimulation.

References

  1. Summary for Policymakers. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (2019).

  2. Summary for Policymakers. In Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

  3. EM-DAT: The International Disaster Database (CRED, 2009); www.emdat.be

  4. Emanuel, K. A. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century. Proc. Natl Acad. Sci. USA 110, 12219–12224 (2013).

    Article  CAS  Google Scholar 

  5. Mendelsohn, R. et al. The impact of climate change on global tropical cyclone damage. Nat. Clim. Change 2, 205–209 (2012).

    Article  Google Scholar 

  6. Rozenblat, C. & Pumain, D. in Cities in Globalization: Practices, Policies, Theories (eds Taylor, P. et al.) 130–156 (Routledge, 2007).

  7. Levermann, A. Climate economics: make supply chains climate-smart. Nature 506, 27–29 (2014).

    Article  CAS  Google Scholar 

  8. Felbermayr, G. & Gröschl, J. Naturally negative: the growth effects of natural disasters. J. Dev. Econ. 111, 92–106 (2014).

    Article  Google Scholar 

  9. O’Brien, K. L. & Leichenko., R. M. Double exposure: assessing the impacts of climate change within the context of economic globalization. Glob. Environ. Change 10, 221–232 (2000).

    Article  Google Scholar 

  10. Davis, M. Late Victorian Holocausts: El Niño Famines and the Making of the Third World (Verso, 2002).

  11. Vespignani, A. Predicting the behavior of techno-social systems. Science 325, 425–428 (2009).

    Article  CAS  Google Scholar 

  12. Ganesh, A., Massoulié, L. & Towsley, D. The effect of network topology on the spread of epidemics. In Proc. IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies Vol. 2 1455–1466 (IEEE, 2005).

  13. Hussain, M. M. & Howard, P. N. What best explains successful protest cascades? ICTs and the fuzzy causes of the Arab Spring. Int. Stud. Rev. 15, 48–66 (2013).

    Article  Google Scholar 

  14. Helbing, D. Globally networked risks and how to respond. Nature 497, 51–59 (2013).

    Article  CAS  Google Scholar 

  15. Henriet, F., Hallegatte, S. & Tabourier, L. Firm-network characteristics and economic robustness to natural disasters. J. Econ. Dyn. Control 36, 150–167 (2012).

    Article  Google Scholar 

  16. Barrot, J.-N. & Sauvagnat, J. Input specificity and the propagation of idiosyncratic shocks in production networks. Q. J. Econ. 131, 1543–1592 (2016).

    Article  Google Scholar 

  17. Hallegatte, S. & Dumas, P. Can natural disasters have positive consequences? Investigating the role of embodied technical change. Ecol. Econ. 68, 777–786 (2009).

    Article  Google Scholar 

  18. Todo, Y., Nakajima, K. & Matous, P. How do supply chain networks affect the resilience of firms to natural disasters? Evidence from the Great East Japan Earthquake. J. Reg. Sci. 55, 209–229 (2015).

    Article  Google Scholar 

  19. Cavallo, E. et al. Catastrophic natural disasters and economic growth. Rev. Econ. Stat. 95, 1549–1561 (2013).

    Article  Google Scholar 

  20. Inoue, H. & Todo, Y. Propagation of negative shocks across nation-wide firm networks. PloS ONE 14, e0213648 (2019).

    Article  CAS  Google Scholar 

  21. Inoue, H. & Todo, Y. Firm-level propagation of shocks through supply-chain networks. Nat. Sustain. 2, 841–847 (2019).

    Article  Google Scholar 

  22. Boehm, C. E., Flaaen, A. & Pandalai-Nayar, N. Input linkages and the transmission of shocks: firm-level evidence from the 2011 Tōhoku earthquake. Rev. Econ. Stat. 101, 60–75 (2019).

    Article  Google Scholar 

  23. Wenz, L. & Levermann, A. Enhanced economic connectivity to foster heat stress-related losses. Sci. Adv. 2, e1501026 (2016).

    Article  Google Scholar 

  24. Shughrue, C. & Seto, K. C. Systemic vulnerabilities of the global urban-industrial network to hazards. Climatic Change 151, 173–187 (2018).

    Article  Google Scholar 

  25. Shughrue, C. Global Urban Interdependencies and Systemic Risks in a Changing Climate. PhD dissertation, Yale Univ. (2018).

  26. Sassen, S. The Global City: New York, London, Tokyo (Princeton Univ. Press, 1991).

  27. Neal, Z. P. The duality of world cities and firms: comparing networks, hierarchies, and inequalities in the global economy. Glob. Netw. 8, 94–115 (2008).

    Article  Google Scholar 

  28. Beaverstock, J. V., Smith, R. G. & Taylor, P. J. A roster of world cities. Cities 16, 445–458 (1999).

    Article  Google Scholar 

  29. O’Brien, K. et al. Mapping vulnerability to multiple stressors: climate change and globalization in India. Glob. Environ. Change 14, 303–313 (2004).

    Article  Google Scholar 

  30. Hallegatte, S. et al. Future flood losses in major coastal cities. Nat. Clim. Change 3, 802–806 (2013).

    Article  Google Scholar 

  31. Kahn, M. E. et al. Long-term macroeconomic effects of climate change: a cross-country analysis Working Paper No. 26167 (NBER, 2019).

  32. World Urbanization Prospects: The 2011 Revision (DESA, 2014).

  33. Tropical Cyclones Wind Speed Buffers 1969–2009 (UNEP/GRID, 2011); http://sids.grid.unep.ch/layers/geonode:GRID_Cyclones_buffers

  34. Hallegatte, S. An adaptive regional input–output model and its application to the assessment of the economic cost of Katrina. Risk Anal. 28, 779–799 (2008).

    Article  Google Scholar 

  35. Hsiang, S. M. & Jina, A. S. The Causal Effect of Environmental Catastrophe on Long-Run Economic Growth: Evidence from 6,700 Cyclones Working Paper No. 20352 (NBER, 2014).

  36. Lenzen, M. et al. Mapping the structure of the world economy. Environ. Sci. Technol. 46, 8374–8381 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Comments and suggestions from E. Lazarus helped us improve the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

C.S., B.W. and K.C.S. conceived the study and developed model dynamics. C.S. designed the simulation and performed the analyses. B.W. and K.C.S. supervised and provided extensive feedback on the analysis and text. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Chris Shughrue.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Impacts over time for four cities.

Direct, secondary, and total impacts versus time following a single large storm striking Seoul, New York City, Colombo, and Mysore.

Extended Data Fig. 2 Production change over time at city, national, and global scales.

Fractional change in production versus time at the city, country, and global scale by industrial sector for a single large storm striking Mysore, India.

Extended Data Fig. 3 Industrial sector price over time at city, national, and global scales.

Fractional change in unitary industrial input price versus time at the city, country, and global scale by industrial sector for a single large storm striking Mysore, India.

Extended Data Fig. 4 Impacts over time following earthquake scenarios.

Direct (orange), secondary (blue), and total impacts (grey) to the economy of Japan in value added USD versus time for 2011 earthquake (A) and Nankai earthquake scenarios (B).

Extended Data Fig. 5 Comparison of impacts among models for 2011 and Nankai earthquake scenarios.

Fractional total impacts on economy of Japan versus time for 2011 earthquake (blue) and Nankai earthquake (red) scenarios, and measured fractional loss (from ref. 1) following 2011 earthquake (black). Findings from1 reproduced as circles with ±1 s.d. error bars.

Extended Data Fig. 6 Map of historical cyclone tracks.

Cyclone tracks (purple polygons) between 1968 and 20093.

Extended Data Fig. 7 Sensitivity of results to parameters.

Root-mean square deviation of supply output normalized by baseline parameter output. Parameters are varied between 50% and 150% of the baseline value, with 100% representing the baseline parameter. Parameter sensitivity is calculated among randomly selected storms from the dataset.

Supplementary information

Supplementary Information

Supplementary Table 1, Discussion and Figs. 1–7.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shughrue, C., Werner, B. & Seto, K.C. Global spread of local cyclone damages through urban trade networks. Nat Sustain 3, 606–613 (2020). https://doi.org/10.1038/s41893-020-0523-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-020-0523-8

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing