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Assessing the economic impact of tsunami and nuclear power plant disasters in Shizuoka, Japan: a dynamic inter-regional input–output (IRIO) approach


Natural disasters cause damage to regional economies. This study investigated how production activities in 35 municipalities of Shizuoka Prefecture recover when such activities stop due to a nuclear power plant disaster and tsunami that may be caused by a Nankai Megathrust Earthquake (NME). We developed a dynamic inter-regional input–output model (IRIO) featuring a production bottleneck due to a shortage of inputs and other goods. Using a simulation model, the economic damage caused by single and combined disasters was evaluated based on several recovery scenarios. The effect of depopulation in Japan on post-disaster recovery processes was also examined. This analysis provides useful disaster preparedness information for local governments and companies.

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  1. 1.

    In this situation, there are two bottlenecks: the production technology bottleneck caused by the inability to substitute input goods and the traffic bottleneck caused by the interruption of the transportation network. This study focuses on the former bottleneck in production technology.

  2. 2.

    In general, labor and capital tend to have different recovery dynamics. This model makes no distinction between labor and capital and we assume a common recovery rate for simplicity. This assumption should be relax considering labor mobility after a disaster for further study.

  3. 3.

    Inventory is not considered in this model. Inventory is likely to occur during a disaster and can be useful during recovery. The role of inventory is discussed by Baker and Santos (2010) and Hallegate (2014).

  4. 4.

    The overlapping hazard maps use the tsunami inundation area for each prefecture. The hazard map of Shizuoka Prefecture uses the tsunami inundation area presented in “Estimation of ground motion and tsunami inundation of earthquake occurring along the Sagami Trough” (revised in March 2016). The inundation area of the six cases of the Nankai Megathrust Earthquake and the three cases of the largest earthquake occurring along the Sagami Trough were superimposed. In this study, only the inundation area of the tsunami was targeted, so the damage caused by earthquake shaking and liquefaction was not considered.

  5. 5.

    In the Nuclear Emergency Countermeasures Guide, for nuclear disaster countermeasures, an area is designated for preparing preventive protective measures called a Precautionary Action Zone (PAZ), an area with a radius of three to five km from a nuclear power plant. An area is also designated for preparing emergency protective measures, an Urgent Protective Action Planning Zone (UPZ), which is area with a radius of approximately 30 km from a nuclear power plant (Nuclear Regulatory Commission). In the past, the range of priority areas for conducting disaster prevention measures in preparation for a nuclear disaster was assumed to be an area with a radius of eight to 10 km from a nuclear power plant, but since the Fukushima nuclear accident, regional disaster prevention plans and evacuation plans related to nuclear disaster countermeasures have been formulated with a range of 30 km.

  6. 6.

    The scale of economic damage in the production sector is approximated by the number of employees. The damage rate at the municipal level is estimated using the number of employees at the small zone level. Employment data for about 3,600 small zones are aggregated into 35 municipalities. We assume a situation in which all production activities in the disaster area are immediately stopped after the nuclear accident and resulting tsunami. The damage rate might vary depending on the tsunami’s inundation depth and the level of radiation risk, but these issues are not considered in this analysis.

  7. 7.

    The case of simulation is set by combining several data sets. Each coefficient of the dynamic model in normal time (0th period) is obtained from the 2011 interregional input–output table. The direct damage rate in the 1st period is estimated from the 2014 economic census (small zone data) and tsunami hazard map. For the future population decline rate, the data for the period from 2015 to 2045 is used. Note that due to data constraints, they are not adjusted for the same year in the future.

  8. 8.

    The process of recovery of the added value is given exogenously. This enables our analysis to consider a variety of scenarios. The linear recovery function has the advantage that the recovery period can be simply calculated as 1 / α if there are no indirect effects. It is also possible to assume a different recovery function. If you want to consider the situation in which the recovery rate differs according to production sector, see, e.g., Li and Haimes (2006).


  1. Akhtar R, Santos JR (2013) Risk-based input-output analysis of hurricane impacts on interdependent regional workforce systems. Nat Hazards 65(1):391–405

    Article  Google Scholar 

  2. Ashiya T (2005) Changes in the economic structure due to the Great Hanshin-Awaji earthquake viewed from the Hyogo prefecture input-output Table. Input-Output 13(1):45–56

    Article  Google Scholar 

  3. Barker K, Santos J (2010) Measuring the efficacy of inventory with a dynamic input-output model. Int J Prod Econ 126:130–143

    Article  Google Scholar 

  4. Cabinet Office Central Disaster Prevention Council (2012) Estimated damage from the Nankai Megathrust Earthquake (first report) breakdown of the number of cases where the number of deaths is highest in each prefecture (in Japanese)

  5. Cabinet Office of Japan (2011) Regional economy 2011-reconstruction from the Earthquake, Regional Revitalization, (in Japanese). Accessed December 28, 2020

  6. Cabinet Office Central Disaster Prevention Council. (2013) Damage estimation of the Nankai Megathrust Earthquake (Second Report). Economic Damage, pp. 14 (in Japanese)

  7. Dietzenbacher E, van der Linden JA, Steenge AE (1993) The regional extraction method: applications to the European Community. Econ Syst Res 5:185–206

    Article  Google Scholar 

  8. Galbusera L, Giannopoulos G (2018) On input-output models in disaster impact assessment. International Journal of Disaster Risk Reduction 30:186–198

    Article  Google Scholar 

  9. Ghosh A (1985) Input-output approach in an allocation system. Economica xxv(97):58–64

    Article  Google Scholar 

  10. Haimes YY, Jiang P (2001) Leontief-based model of risk in complex Interconnected Infrastructures. J Infrastruct Syst 7(1):1–12

    Article  Google Scholar 

  11. Hallegatte S (2014) Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters. Risk Anal 34(1):152–167

    Article  Google Scholar 

  12. Japan Meteorological Agency (2020) About Nankai Trough earthquake, eqev/data/nteq/nteq.html. Accessed December 28, 2020 (in Japanese)

  13. Japan Society of Civil Engineers (2018) Technical study report on countermeasures against huge disaster causing “national difficulties” (in Japanese)

  14. Kabuta F (2014) Analysis of the risk of food supply restriction by input-output analysis: forward linkage effect measurement using a Ghosh-type model incorporating the bottleneck effect. Agric Forestry Fisheries Policy Res 23:1–21 (in Japanese)

    Google Scholar 

  15. Koks E, Thissen M (2016) A multiregional impact assessment model for disaster analysis. Econ Syst Res 29(4):429–449

    Article  Google Scholar 

  16. Kunreuther, H. and Rose, A. (2004) The Economics of Natural Hazards. Edward Elgar Publishing

  17. Leontief W (1936) Quantitative input and output relations in the economic system of the United States. Rev Econ Stat 18:105–125

    Article  Google Scholar 

  18. Leung M, Haimes YY, Santos JR (2007) Supply- and output-side extensions to the inoperability input-output model for Interdependent infrastructures. J Infrastruct Syst 13(4):299–310

    Article  Google Scholar 

  19. Lian C, Haimes YY (2006) Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input-output model. System Eng 9(3):241–258

    Article  Google Scholar 

  20. Ministry of Economy, Trade and Industry (2020) Statistical tables by prefecture and 21 major cities,, (in Japanese). Accessed December 28, 2020.12.28

  21. Ministry of Land, Infrastructure, Transport and Tourism. (2019). Hazard map portal site, Accessed December 1, 2019 (in Japanese)

  22. Manaki, S. (2013). Economics of Disasters, CHUOKEIZAI-SHA (in Japanese)

  23. Miller RE, Blair PD (2009) Input-output analysis foundations and extensions, 2nd edn. Cambridge University Press, Cambridge

    Book  Google Scholar 

  24. National Institute of Population and Social Security Research (2017) Estimation of future population in Japan. (in Japanese)

  25. National Police Agency of Japan (2020) Police activities and damage situation of the Great East Japan earthquake in 2011. (in Japanese). Accessed December 18, 2020

  26. Nozaki H (2016) Use of earthquake insurance amount paid in the Great East Japan earthquake and its economic ripple effect. Insur J 633:30–60 (in Japanese)

    Google Scholar 

  27. Okiyama, M., Tokunaga, S., and Akune, Y. (2012) Multiplier analysis on the negative supply shock to the affected areas of the Great East Japan earthquake and the economic ripple effect of reconstruction-SAM between two regions, RIETI Discussion Paper Series, 12-P-024 (in Japanese)

  28. Okuyama, Y., Hewings, G., Kim, T., Boyce, D., Ham, H., and Sohn, J. (1999) Economic impacts of an earthquake in the New Madrid seismic zone: a multiregional analysis. In Elliot and McDonough (Eds.), Optimizing Post-Earthquake Lifeline System Reliability: Proceedings of the 5th U.S. Conference on Lifeline Earthquake Engineering August 12–14, 1999. Technical Council on Lifeline Earthquake Engineering

  29. Oosterhaven J, Bouwmeester M (2016) A new approach to modelling the impacts of disruptive events. J Reg Sci 56(4):583–595

    Article  Google Scholar 

  30. Orsi MJ, Santos JR (2010) Estimating workforce-related economic impact of a pandemic on the Commonwealth of Virginia. IEEE Trans Syst Man Cybern A 40(2):301–305

    Article  Google Scholar 

  31. Shibusawa H, Hanaoka R (2018) The economic damages caused by tsunami and the resilience of regional economies: using an inter-regional input-output model in Aichi prefecture of Japan. Studies in Regional Science 48(2):221–234 (in Japanese)

    Article  Google Scholar 

  32. Shibusawa H, Hanaoka R (2020) Recovery process of municipal economies after a tsunami in Aichi prefecture, Japan: a dynamic input-output approach. In: John M, Shibusawa H, Higano Y (eds) Environmental Economics and Computable General Equilibrium. Springer, Singapore

    Google Scholar 

  33. Shibusawa H, Matsushima D (2020) Evaluating the economic damage caused by tsunami and recovery process of production sectors in Shizuoka Prefecture. J Human Environ Symbiosis 36(1):21–31 (in Japanese)

    Google Scholar 

  34. Shibusawa H, Miyata Y (2017) Evaluating production effects of economic activity in zones surrounding the nuclear power station in Shizuoka prefecture, Japan. Asia-Pacific J Regional Sci 1(2):291–306

    Article  Google Scholar 

  35. Shimoda M, Fujikawa K (2012) Input-output analysis model and supply restrictions due to the Great East Japan earthquake. Input-Output 20(2):133–146 (in Japanese)

    Article  Google Scholar 

  36. Shishido S (2010) Input-Output Analysis Handbook, Toyo Keizai (in Japanese)

  37. Statistics Bureau, Ministry of Internal Affairs and Communications (2015) 2014 Economic Census-Basic Survey- aggregates by Town and Oaza (in Japanese)

  38. Tatano H, Tsuchiya S (2008) A framework for economic loss estimation due to seismic transportation network disruption: a spatial computable general equilibrium approach. Nat Hazard 44:253–265

    Article  Google Scholar 

  39. Tokui J, Arai N, Kawasaki K, Miyakawa T, Fukao K, Arai S, Edamura K, Kodama N, Noguchi N (2012) Economic impact of the Great East Japan earthquake—comparison with past disasters, supply chain breaking effect, effect of electric power supply restriction, RIETI Policy Discussion Paper Series, 12-P-004 (in Japanese)

  40. Tokunaga S, Okiyama M (2014) Model analysis of reconstruction and regional regeneration from the earthquake, BUNSHINDO (in Japanese)

  41. Tokunaga S, Resosudarmo BP (2017) Spatial Economic Modelling of Megathrust Earthquake in Japan: Impact, Reconstruction, and Regional Revitalization. Springer

    Book  Google Scholar 

  42. Toyoda T, Kawachi A (1997) Estimation of industrial damage caused by the Great Hanshin-Awaji earthquake. Natl Econ Magazine 176(2):1–15 (in Japanese)

    Google Scholar 

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This research was partially supported by JSPS Grants-in-Aid for Scientific Research, JP17H02521 and JP20K01664.

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Correspondence to Hiroyuki Shibusawa.

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Shibusawa, H., Matsushima, D. Assessing the economic impact of tsunami and nuclear power plant disasters in Shizuoka, Japan: a dynamic inter-regional input–output (IRIO) approach. Asia-Pac J Reg Sci (2021).

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  • Tsunami
  • Nuclear power plant
  • Economic damage
  • Resilience
  • Inter-regional input–output analysis (IRIO)

JEL Classification

  • Q54
  • R15