Climatic Change

, Volume 151, Issue 2, pp 173–187 | Cite as

Systemic vulnerabilities of the global urban-industrial network to hazards

  • Chris ShughrueEmail author
  • Karen C. Seto


Systemic impacts such as global supply chain failures can spread among urban areas through social and economic linkages. Urban vulnerability to hazards has been studied from the perspective of individual cities, but global vulnerability to systemic impacts at the network scale has not been assessed. Here we analyze the structure of global industrial supply chains as a lens to examine how impacts might spread across the global system of cities. We generate a novel urban risk network that describes industrial flows among 1686 urban areas. In contrast to the prevailing view of the global urban system dominated by the largest, wealthiest cities, we show that the functionality of the network is evenly spread across urban areas. These findings suggest that the network is more vulnerable to multiple simultaneous hazards than to singular impacts to urban areas with the highest nodal strength. We also find that clusters of the most strongly connected urban areas transcend administrative boundaries, increasing the possibility for systemic impacts to spread transnationally. These results illuminate the potential for linkages between city-scale vulnerabilities to climate change impacts and systemic vulnerabilities that emerge at the global network scale.

Supplementary material

10584_2018_2293_MOESM1_ESM.docx (29 kb)
ESM 1 (DOCX 28 kb)
10584_2018_2293_MOESM2_ESM.docx (1 mb)
ESM 2 (DOCX 1061 kb)
10584_2018_2293_MOESM3_ESM.docx (33 kb)
ESM 3 (DOCX 32 kb)


  1. Babaei M, Ghassemieh H, Jalili M (2011) Cascading failure tolerance of modular small-world networks. IEEE Trans Circuits Syst II: Express Briefs 58(8):527–531CrossRefGoogle Scholar
  2. Barros VR, Field CB, Dokken DJ, et al. IPCC, (2014): Climate change 2014: impacts, adaptation, and vulnerability. Part B: Regional Aspects Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate ChangeGoogle Scholar
  3. Bergmann L (2013) Bound by chains of carbon: ecological–economic geographies of globalization. Ann Assoc Am Geogr 103(6):1348–1370CrossRefGoogle Scholar
  4. Bierkandt R, Wenz L, Willner SN, Levermann A (2014) Acclimate—a model for economic damage propagation. Part 1: basic formulation of damage transfer within a global supply network and damage conserving dynamics. Environ Syst Decisions 34(4):507–524CrossRefGoogle Scholar
  5. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 2008(10):P10008CrossRefGoogle Scholar
  6. Canis B (2011) Motor vehicle supply chain: effects of the Japanese earthquake and tsunami. Diane Publishing, CollingdaleGoogle Scholar
  7. Center for Hazards and Risk Research - CHRR - Columbia University, Center for International Earth Science Information Network - CIESIN - Columbia University, and International Bank for Reconstruction and Development - The World Bank (2005) Global Flood Proportional Economic Loss Risk Deciles. Palisades, NY: NASA SEDAC. Accessed 05/08/2017
  8. Chongvilaivan A (2012) Thailand’s 2011 flooding: its impact on direct exports and global supply chains (no. 113). ARTNeT working paper seriesGoogle Scholar
  9. Comfort LK (2006) Cities at risk: Hurricane Katrina and the drowning of New Orleans. Urban Aff Rev 41(4):501–516CrossRefGoogle Scholar
  10. Decker, E. H., Elliott, S., Smith, F. A., et al. (2000). Energy and material flow through the urban ecosystem. Annu Rev Energy Environ, 25 CrossRefGoogle Scholar
  11. Derudder B, Witlox F (2005) An appraisal of the use of airline data in assessing the world city network: a research note on data. Urban Stud 42(13):2371–2388CrossRefGoogle Scholar
  12. Dilley M, Chen RS, Deichmann U, A.L. et al (2005) Natural disaster hotspots: a global risk analysis. Disaster risk management series no. 5. The World Bank, Wabshington, D.C. CrossRefGoogle Scholar
  13. Ducruet C, Notteboom T (2012) The worldwide maritime network of container shipping: spatial structure and regional dynamics. Global Netw 12(3):395–423CrossRefGoogle Scholar
  14. Grubler A, et al (2012) Chapter 18 - urban energy systems. Global Energy Assessment - Toward a Sustainable Future (Cambridge University Press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria), pp 1307–1400Google Scholar
  15. Guimera R, Mossa S, Turtschi A, Amaral LN (2005) The worldwide air transportation network: anomalous centrality, community structure, and cities' global roles. Proc Natl Acad Sci 102(22):7794–7799CrossRefGoogle Scholar
  16. Higuchi Y, Inui T, Hosoi T et al (2012) The impact of the Great East Japan Earthquake on the labor market—need to resolve the employment mismatch in the disaster-stricken areas. Japan Labor Rev 9(4):4–21Google Scholar
  17. Hinkel J, Lincke D, Vafeidis AT et al (2014) Coastal flood damage and adaptation costs under 21st century sea-level rise. PNAS 111(9):3292–3297CrossRefGoogle Scholar
  18. Knox PL, Taylor PJ (2005) Toward a geography of the globalization of architecture office networks. J Archit Educ 58(3):23–32CrossRefGoogle Scholar
  19. Levermann A (2014) Make supply chains climate-smart. Nature 506(7486):27CrossRefGoogle Scholar
  20. McCarthy, M. P., Best, M. J., & Betts, R. A. (2010). Climate change in cities due to global warming and urban effects. Geophys Res Lett, 37(9). CrossRefGoogle Scholar
  21. McGranahan G, Balk D, Anderson B (2007) The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. Environ Urban 19(1):17–37CrossRefGoogle Scholar
  22. Otto, C., Willner, S. N., Wenz, L., Frieler, K., & Levermann, A. (2017). Modeling loss-propagation in the global supply network: the dynamic agent-based model acclimate. J Econ Dyn Control 83, 232–269.Google Scholar
  23. Queensland Treasury. (2011). Annual Economic Report 2010–2011Google Scholar
  24. Robinson J (2002) Global and world cities: a view from off the map. Int J Urban Reg Res 26(3):531–554CrossRefGoogle Scholar
  25. Rosado L, Niza S, Ferrão P (2014) A material flow accounting case study of the Lisbon metropolitan area using the urban metabolism analyst model. JIE 18(1):84–101Google Scholar
  26. Sassen S (1994) Global city (Vol. 2). Princeton University Press, New York, London, TokyoGoogle Scholar
  27. Stein J (2011) Massive cuts for Toyota, Nissan. Automotive News.
  28. Taylor PJ (2004) The new geography of global civil society: NGOs in the world city network. Globalizations 1(2):265–277CrossRefGoogle Scholar
  29. Turner BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L et al (2003) A framework for vulnerability analysis in sustainability science. Proc Natl Acad Sci 100(14):8074–8079CrossRefGoogle Scholar
  30. U.S. D.O.T (2016) Research and Innovative Technology Administration, B.T.S., TranStatsGoogle Scholar
  31. UN (2014) World urbanization prospects: the 2014 revision, Highlights. Department of Economic and Social Affairs, Population DivisionUNCrossRefGoogle Scholar
  32. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393(6684):440–442CrossRefGoogle Scholar
  33. Wenz L, Willner SN, Bierkandt R, Levermann A (2014) Acclimate—a model for economic damage propagation. Part II: a dynamic formulation of the backward effects of disaster-induced production failures in the global supply network. Environ Syst Decisions 34(4):525–539CrossRefGoogle Scholar
  34. Wiedmann T, Wilting HC, Lenzen M et al (2011) Quo Vadis MRIO? Methodological, data and institutional requirements for multi-region input–output analysis. Ecol Econ 70(11):1937–1945CrossRefGoogle Scholar
  35. Xia Y, Fan J, Hill D (2010) Cascading failure in Watts–Strogatz small-world networks. Phys A Stat Mech Appl 389(6):1281–1285CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA

Personalised recommendations