Abstract
As global warming accelerates extreme weather events such as floods, droughts and storms are likely to increase in intensity and frequency. With regard to a highly globalized world economy built on complex supply and value-added chains, this trend will challenge societies locally and globally. Regional production disruptions might induce shock waves that propagate through the global supply network and evoke supra-regional shortages. While such cascading effects are promoted by forward linkages in the global economic network, the demand-induced backward dynamics respond in a more complex way. On the one hand, backward linkages may additionally spread economic losses and thus aggravate the disaster aftermath. On the other hand, the readdressing of demand enables a readjustment of production, which may weaken or even dissipate shock waves. Here, we analyze the backward effects of disaster-induced production breakdowns by complementing the numerical damage transfer model Acclimate by a demand side. Based on model simulations, we show that the possibility of production extension and demand readdressing may be crucial for mitigating economic losses in the course of an extreme event.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
An implementation of the basic and extended model set-up is available upon request.
References
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 Decis. doi:10.1007/s10669-014-9523-4
Bouwer LM, Crompton RP, Faust E, Höppe P, Piekke RA Jr (2007) Confronting Disaster Losses. Science 318:753. doi:10.1126/science.1149628
Cochrane H (1997) Economic impacts of a Midwestern earthquake. Q Publ NCEER 11:1–20
Haddad E, Okuyama Y (2012) Spatial propagation of the economic impacts of bombing: the case of the 2006 war in Lebanon. University of Sao Paulo 2012_19
Haddad E, Teixeira E (2013) Economic impacts of natural disasters in megacities: the case of floods in São Paulo, Brazil. ERSA Conf Pap 1–20
Hallegatte S (2008) An adaptive regional input–output model and its application to the assessment of the economic cost of Katrina. Risk Anal 28:779–799
Hallegatte S (2014) Modeling the roles of heterogeneity, substitution, and inventories in the assessment of natural disaster economic costs. Risk Anal 34:152–167
Hirabayashi Y, Mahendran R (2013) Global flood risk under climate change. Nat Clim Chang 3:816–821. doi:10.1038/NCLIMATE1911
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the Intergovernmental Panel on Climate Change. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds). Cambridge University Press, Cambridge
Jonkeren O, Giannopoulos G (2014) Analysing critical infrastructure failure with a resilience inoperability input–output model. Econ Syst Res 26:39–59. doi:10.1080/09535314.2013.872604
Kajitani Y, Tatano H (2014) Estimation of production capacity loss rate after the great East Japan earthquake and tsunami in 2011. Econ Syst Res 26:13–38. doi:10.1080/09535314.2013.872081
Lenzen M, Kanemoto K, Moran D, Geschke A (2012) Mapping the structure of the world economy. Environ Sci Technol 46:8374–8381. doi:10.1021/es300171x
Levermann A (2014) Make supply chains climate-smart. Nature 504:27–29
Mendelsohn R, Emanuel K, Chonabayashi S, Bakkensen L (2012) The impact of climate change on global tropical cyclone damage. Nat Clim Chang 2:205–209. doi:10.1038/nclimate1357
Miller RE, Blair PD (2009) Input–output analysis: foundations and extensions. Cambridge University Press, Cambridge
Narayanan B, Aguiar A, McDougalL R (2012) Global trade, assistance and production: the GTAP8 database. Purdue University. [Online] http://www.gtap.agecon.purdue.edu/databases/v8/v8_doco.asp
Okuyama Y, Sahin S (2009) Impact estimation of disasters a global aggregate for 1960 to 2007. Worldbank Open Knowl. Repos
Okuyama Y, Santos JR (2014) Disaster impact and input–output analysis. Econ Syst Res 26:1–12. doi:10.1080/09535314.2013.871505
Peduzzi P, Chatenoux B, Dao H, De Bono A, Herold C, Kossin J, Mouton F, Nordbeck O (2012) Global trends in tropical cyclone risk. Nat Clim Chang 2:289–294. doi:10.1038/nclimate1410
Rahmstorf S, Coumou D (2012) Increase of extreme events in a warming world. Proc Natl Acad Sci USA 109:4708
Rose A (2004a) Economic principles, issues, and research priorities in hazard loss estimation. Model Spat Econ Impacts Disasters 13–36
Rose A (2004b) Defining and measuring economic resilience to earthquakes. Disaster Prev Manag 13:307–314
Rose A (2007) Economic resilience to natural and man-made disasters: multidisciplinary origins and contextual dimensions. Environ Hazards 7:383–398. doi:10.1016/j.envhaz.2007.10.001
Rose A, Dongsoon L (2002) Business interruption losses from natural hazards: conceptual and methodological issues in the case of the Northridge earthquake. Glob Environ Chang B Environ Hazards 4:1–14
Schellnhuber H-J et al (2012) Turn down the heat–why a 4 C warmer world must be avoided. World Bank
Sea Rates (2013) International container shipping | freight broker and forwarder. distances and transit times. [Online] www.searates.com/reference/portdistance. Accessed 11 Feb 2014
Sternberg T (2012) Chinese drought, bread and the Arab Spring. Appl Geogr 34:519–524
Strzepek K, Yohe G, Neumann J, Boehlert B (2010) Characterizing changes in drought risk for the United States from climate change. Environ Res Lett 5:044012. doi:10.1088/1748-9326/5/4/044012
Timmer MP (2012) The world input–output database (WIOD). Contents, sources and methods. [Online] www.wiod.org
United Nations Department of Humanitarian Affairs (1992) Internationally agreed glossary of basic terms related to disaster management. United Nations Department of Humanitarian Affairs, Geneva
Van Der Veen A (2004) Disasters and economic damage: macro, meso and micro approaches. Disaster Prev Manag 13:274–279. doi:10.1108/09653560410556483
Van Der Veen A, Logtmeijer C (2005) Economic hotspots: visualizing vulnerability to flooding. Nat Hazards 36:65–80. doi:10.1007/s11069-004-4542-y
Wenz L, Willner SN, Radebach A, Bierkandt R, Steckel JC, Levermann A (2015) Regional and sectoral disaggregation of multi-regional input–output tables—a flexible algorithm. Econ Syst Res 27. doi:10.1080/09535314.2014.987731
Wiedmann T, Wilting HC, Lenzen M, Lutter S, Palm V (2011) Quo Vadis MRIO? Methodological, data and institutional requirements for multi-region input–output analysis. Ecol Econ 70:1937–1945
Acknowledgments
This research was supported by the German Environmental Foundation (DBU) and the Heinrich-Böll Foundation. It has received funding from the European Union Seventh Framework Programme FP7/2007–2013 under Grant Agreement No. 603864. We thank Christian Otto for fruitful discussions.
Author information
Authors and Affiliations
Corresponding author
Appendices
Appendix 1: Acclimate agents and parameter
See Table 1.
Appendix 2: Linkage between final demand, GDP and value added (including taxes)
In an MRIO table framework, the total value added of a certain region equals its GDP, i.e., the sum of all its final demand and export flows minus its import flows:
Rights and permissions
About this article
Cite this article
Wenz, L., Willner, S.N., Bierkandt, R. et al. 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 Decis 34, 525–539 (2014). https://doi.org/10.1007/s10669-014-9521-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10669-014-9521-6