Abstract
Extreme climatic events are likely to adversely affect many countries throughout the world, but the degrees among countries may be different. China and Japan are the countries with high incidences of extreme weather/disaster, both facing with the urgent task of addressing climate change. This study seeks to quantitatively compare the impacts of extreme climatic events on socioeconomic systems (defined as vulnerability) of the two countries by simulating the consequences of hypothetical same degree of electricity disruption along with extreme events. To do that, two computable general equilibrium models are constructed, by using which three-stage scenarios are simulated for China and Japan, respectively. The results reveal that China and Japan have unequal socioeconomic vulnerabilities to extreme events. (1) Negative impact of the same degree of power outages is bigger on China’s socioeconomic system than on that of Japan, and this difference is more obvious in the very short-run scenario. (2) The decline of China’s GDP, total output, and employment levels is 2–3 times higher than that of Japan, while the difference of the resident welfare levels is sharper, which of China drops 3–5 times of Japan. (3) Structural factors are the main reason for vulnerability differences between China and Japan, including the differences of expenditure structure, factor input structure for production of life requirement sectors, material and energy dependence for the production of industrial sectors, and usage structure of services outputs. Based on these findings, some policy implications and recommendations for fairness issues on climate change adaptation are proposed.
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Notes
Although we cannot assure the modeling accuracy proposed by Rose and Guha (2004), especially on the stage parameter setting, just as mentioned by its authors it is deemed that this kind of accuracy problem is absolutely not of mortality unless the response function is extremely nonlinear. Moreover, our key point in this study is just to compare the response features of different economies, so it is believed that this modeling scheme is rational enough.
Capital of power sector is reduced by 48.7 and 45.1 % under the base cases in China and Japan, respectively. The simulation results from Rose and Guha (2004) show that the largest capital reduction after earthquake in that region is 44.8 %. Moreover, we also conduct the cases of 15 and 25 % power outage as a sensitivity analysis, and the results not vary largely. Thus, there is reason to believe that the simulated cases are very likely to occur under the pressure of extreme events.
References
Anderson CW, Santos JR, Haimes YY (2007) A risk-based input–output methodology for measuring the effects of the August 2003 northeast blackout. Econ Syst Res 19(2):183–204
Boyd R, Ibarrarán ME (2009) Extreme climate events and adaptation: an exploratory analysis of drought in Mexico. Environ Dev Econ 14(3):371
Department of National Account (2009) Input–output table of China 2007. China Statistics Press, Beijing (in Chinese)
Dwyer L, Forsyth P, Spurr R (2006) Assessing the economic impacts of events: a computable general equilibrium approach. J Travel Res 45(1):59–66
EM-DAT (2013) The international disaster database. http://www.emdat.be/database. Accessed 26 April 2013
Ford JD, Smit B, Wandel J (2006) Vulnerability to climate change in the Arctic: a case study from Arctic Bay, Canada. Glob Environ Chang 16(2):145–160
Füssel H-M (2010) How inequitable is the global distribution of responsibility, capability, and vulnerability to climate change: a comprehensive indicator-based assessment. Glob Environ Chang 20(4):597–611
GOV (2008) http://www.zongyang.gov.cn/DocHtml/1/2011/1/12/20110112941544699.html. Accessed 20 Feb 2013
Hallegatte S (2008) An adaptive regional input–output model and its application to the assessment of the economic cost of Katrina. Risk Anal 28(3):779–799
Heltberg R, Siegel PB, Jorgensen SL (2009) Addressing human vulnerability to climate change: toward a ‘no-regrets’ approach. Glob Environ Chang 19(1):89–99
Horridge M, Madden J, Wittwer G (2005) The impact of the 2002–2003 drought on Australia. J Policy Model 27(3):285–308
Liang Q-M, Wei Y-M (2012) Distributional impacts of taxing carbon in China: results from the CEEPA model. Appl Energy 92:545–551
Löfgren H, Harris RL, Robinson S (2002) A standard computable general equilibrium (CGE) model in GAMS, vol 5. International Food Policy Research Institute, Washington
McCarthy JJ, Canziani OF, Leary NA, Dokken DJ, White KS (2001) Climate change 2001: impacts, adaptation, and vulnerability: contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York
Ministry of Internal Affairs and Communications (2010) Input–output tables for Japan. http://www.stat.go.jp/english/data/io/io05.htm. Accessed 16 Feb 2013
Nordhaus WD (1994) Managing the global commons: the economics of climate change. MIT Press, Cambridge, MA
Okuyama Y (2007) Economic modeling for disaster impact analysis: past, present, and future. Econ Syst Res 19(2):115–124
Paltsev S (2004) Moving from static to dynamic general equilibrium economic models. Notes for a beginner in MPSGE Technical Note (4)
Parry ML (2007) Climate change 2007: impacts, adaptation and vulnerability: Working Group I contribution to the fourth assessment report of the IPCC, vol 4. Cambridge University Press, New York
Rose A, Guha G-S (2004) Computable general equilibrium modeling of electric utility lifeline losses from earthquakes. In: Okuyama Y, Chang SE (eds) Modeling spatial and economic impacts of disasters. pp 119–141
Rose A, Liao SY (2005) Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions. J Reg Sci 45(1):75–112
Rutherford TF (1998) GAMS/MPSGE guide. GAMS Development Corporation, Washington, DC
Rutherford TF (1999) Applied general equilibrium modeling with MPSGE as a GAMS subsystem: an overview of the modeling framework and syntax. Comput Econ 14(1–2):1–46
Rutherford T, Paltsev S (1999) From an input–output table to a general equilibrium model: assessing the excess burden of indirect taxes in Russia. Draft, University of Colorado, Boulder. http://web.mit.edu/paltsev/www/docs/exburden.pdf
Shoven JB, Whalley J (1992) Applying general equilibrium. Cambridge University Press, Cambridge
Steenge AE, Bočkarjova M (2007) Thinking about imbalances in post-catastrophe economies: an input–output based proposition. Econ Syst Res 19(2):205–223
Tatano H, Tsuchiya S (2008) A framework for economic loss estimation due to seismic transportation network disruption: a spatial computable general equilibrium approach. Nat Hazards 44(2):253–265
Tsuchiya S, Tatano H, Okada N (2007) Economic loss assessment due to railroad and highway disruptions. Econ Syst Res 19(2):147–162
Tubi A, Fischhendler I, Feitelson E (2012) The effect of vulnerability on climate change mitigation policies. Glob Environ Chang 22(2):472–482
UNDP (2011) United Nations Development Programme, Human development report 2011: towards the sustainability and equality. United Nations Development Programme Press, Washington (in Chinese)
Unite Nation, World Bank (UN, WB) (2011) Natural hazard and unnatutral hazard: economics of effective prevention. World Bank Press, Washington (in Chinese)
Zhang P, Peeta S (2011) A generalized modeling framework to analyze interdependencies among infrastructure systems. Transp Res Part B Methodol 45(3):553–579
Acknowledgments
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China under Grant Nos. 71020107026, 71001007, and 71203008, the Program for New Century Excellent Talents in University under the Grant No. NCET-12-0039, the National Key Technology R&D Program under the Grant No. 2012BAC20B01, and the National Basic Research Program of China under the Grant No. 2012CB955704. The authors also would like to thank Dr. Hua Liao for his suggestions on an earlier version. We also would like to thank Professor Tad Murty and the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper according to which we improved the content.
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Fan, JL., Liang, QM., Liang, XJ. et al. National vulnerability to extreme climatic events: the cases of electricity disruption in China and Japan. Nat Hazards 71, 1937–1956 (2014). https://doi.org/10.1007/s11069-013-0986-2
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DOI: https://doi.org/10.1007/s11069-013-0986-2