JDR Vol.11 No.6 pp. 1238-1243
doi: 10.20965/jdr.2016.p1238


Using Data and Statistics to Explain Investment Effectiveness on Flood Protection

Kenichi Tsukahara and Noriyasu Kachi

Disaster Risk Reduction Research Center, Graduate School of Engineering, Kyushu University
744 Motooka, Nischi-ku, Fukuoka 819-0395, Japan

Corresponding author,

May 12, 2016
November 11, 2016
December 1, 2016
economic growth, disaster risk management, decision-making tools
Losses and damages caused by natural disasters have negatively impacted poverty alleviation and human development and undermine the achievement of the Millennium Development Goals (MDGs). However, disaster issues were not included in MDG targets set up in 2000. A new development agenda, Sustainable Development Goals (SDGs), was approved in the UN General Assembly in September 2015. In the SDGs, disaster issues are included in many targets such as target 11.5. To appropriately set targets and prepare monitoring measures for disaster-related issues, quantitatively measurable indicators of impacts of disaster risk reduction on economic growth and poverty alleviation should be prepared. In addition, to promote disaster prevention measures, we need to convince policy makers that such measures are highly essential for a country’s development and are cost-effective. Although the cost-effectiveness of single disaster prevention projects has been studied, aggregate effectiveness of these projects at a national level has not been presented. This study proposes a simple method to explain the cost-effectiveness of flood protection investment in Japan post World War II by using national aggregate data.
Cite this article as:
K. Tsukahara and N. Kachi, “Using Data and Statistics to Explain Investment Effectiveness on Flood Protection,” J. Disaster Res., Vol.11 No.6, pp. 1238-1243, 2016.
Data files:
  1. [1] EWASE, CRUE Research Report No. I-5: “Effectiveness and Efficiency of Early Warning Systems for Flash Floods (EWASE),” First CRUE ERA-Net Common Call Effectiveness and Efficiency of Non-structural Flood Risk Management Measures, 2008.
  2. [2] M. Holub and S. Fuchs, “Benefits of local structural protection to mitigate torrent-related hazards,” in C. A. Brebbia, E. Beritatos (Eds.), “Risk Analysis VI,” WIT Trans. on Information and Communication Technologies, Vol.39, WIT Press, Southampton, U.K. pp. 401-411, 2008.
  3. [3] R. Mechler, “Cost-benefit analysis of natural disaster risk management in developing countries,” Working paper for sector project ‘Disaster Risk Management in Development Cooperation,’ GTZ, Berlin, 2005.
  4. [4] C. Burton and C. C. Venton, “Case study of the Philippines national red cross: community based disaster risk management programming,” IFRC (International Federation of Red Cross and Red Crescent Societies), Geneva, Switzerland 2009.
  5. [5] B. A. White and M. M. Rorick, “Cost-benefit analysis for community-based disaster risk reduction in Kailali, Nepal,” Mercy Corps Nepal, Lalitpur, Nepal, 2010.
  6. [6] Nepal Red Cross, “Cost benefit analysis of a Nepal red cross society disaster risk reduction programme,” Nepal Red Cross, Kathmandu, Nepal 2008.
  7. [7] A. Heidari, “Structural master plan of flood mitigation measures,” Nat Hazards Earth Syst Sci, Vol.9, pp. 61-75, 2009.
  8. [8] F. Khan, D. Mustafa, D. Kull, and The Risk to Resilience Study Team, “Evaluating the costs and benefits of disaster risk reduction under changing climatic conditions: A Pakistan case study (Risk to Resilience Working Paper No.7), 2008,” in M. Moench, E. Caspari, & A. Pokhrel (Eds.), “Kathmandu, Nepal: Institute for Social and Environmental Transition-Boulder,” Institute for Social and Environmental Transition-Nepal, & Provention Consortium
  9. [9] D. Kull, “Evaluating costs and benefits of flood reduction under changing climatic conditions: case of the Rohini River Basin, India. From Risk to Resilience Working Paper No.4,” M. Moench, E. Caspari, and A. Pokhrel (Eds.), ISET, ISET-Nepal and ProVention, Kathmandu, Nepal, p. 32, 2008.
  10. [10] D. Kull, R. Mechler, and S. H. Stigler, “Probabilistic cost benefit analysis of disaster risk management in a development context,” Disasters, Vol.37, No.3, pp. 374-400, doi: 10.1111/disa.12002, 2013.
  11. [11] IFRC, “The long road to resilience: impact and cost-benefit analysis of community-based disaster risk reduction in Bangladesh,” IFRC (International Federation of Red Cross and Red Crescent Societies), Geneva, Switzerland 2012.
  12. [12] H. Kunreuther, E. Michel-Kerjan, “Challenge Paper: Natural Disasters. Policy Options for Reducing Losses from Natural Disasters: Allocating $75 billion,” Revised version for Copenhagen Consensus, Center for Risk Management and Decision Processes, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A. 2012.
  13. [13] Japan International Cooperation Agency, DR2AD Model, VERSION 1.0, Disaster Risk Reduction Investments Accounts for Development, 2013.
  14. [14] Statistics Bureau of Japan, “SUIGAI TOUKEI, 1961-2010” (in Japanese).
  15. [15] Statistics Bureau of Japan, “GYOUSEI TOUSHI, 2001-2010 (in Japanese).
  16. [16] United Nations, Sendai Framework for Disaster Risk Reduction 2015-2030, Paragraph 24, 2015.

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