Research on the assessment of the multi-regional economic ripple effect caused by disasters—a case study of a flood disaster in Wuhan city on July 6, 2016

doi:10.12006/j.issn.1673-1719.2019.259

Abstract

Abstract:

In order to quantitatively emphasize the importance of economic ripple effect, this paper further integrates the multi-regional module to develop a new assessment model based on the established dynamic indirect economic loss assessment model, and then quantitatively evaluates the economic ripple effect of Hubei province and China caused by a disaster—“2016.07.06 Wuhan Flood”. The results show that: (1) the economic ripple effect suffered by outside disaster area (2430 million CNY) is 28% of direct economic loss (8740 million CNY), which is 4.4 times of indirect economic loss (550 million CNY) suffered by disaster area Wuhan city. The economic ripple loss of Hubei province (except Wuhan city) and China (except Hubei province) is 1557 million CNY and 873 million CNY, respectively; (2) the manufacturing industry in the outside disaster area suffers the most economic ripple effect, including 652 million CNY in Hubei province (except Wuhan city) and 294 million CNY in China (except Hubei province), accounting for 42% and 34% of the total economic ripple losses, respectively. Other sectors with serious damages successively are agriculture, other services and construction, etc. It is recommended that the government policy makers keep focus on the economic ripple loss coming from other regions, formulate targeted loss reduction measures by recognizing the mechanism of loss generation, and carry out more scientific and comprehensive risk prevention and post-disaster recovery and reconstruction.

Key words: Disaster, Loss assessment, Economic ripple effect, Indirect economic loss

ZHANG Zheng-Tao, CUI Peng, LI Ning, LIU Yuan, ZOU Qiang, HUANG Cheng-Fang, WU Sheng-Nan. Research on the assessment of the multi-regional economic ripple effect caused by disasters—a case study of a flood disaster in Wuhan city on July 6, 2016[J]. Climate Change Research, 2020, 16(4): 433-441.

Figures/Tables 7

Fig. 1 The theoretical cascading process of economic ripple effect from disaster area to outside disaster areas

Fig. 2 The theoretical framework of AMIL model and its modules

Table 1 The uncertainty range of indirect economic loss

Table 2 The parameter values and their parameter description in the AMIL model [26]

Table 3 The total economic loss of Wuhan, Hubei (expect Wuhan city) and China (expect Hubei province) caused by Wuhan flood disaster

Table 4 Merged industries

Fig. 3 The economic ripple impact on various sectors in Hubei province and other parts of the country (The thickness of the arrow indicate the size of the ripple impact value with various sectors, unit: 100 million CNY)

References 33

[1]	IPCC. Climate change 2014: impacts, adaptation and vulnerability. Part A: global and sectoral aspects: contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change[M]. New York, USA: Cambridge University Press, 2014
[2]	UNISDR (United Nations International Strategy for Disaster Reduction). Sendai framework for disaster risk reduction 2015 2030 [EB/OL]. 2017 [2019-01-11]. http://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf
[3]	崔鹏, 陈容, 向灵芝, 等. 气候变暖背景下青藏高原山地灾害及其风险分析[J]. 气候变化研究进展, 2014,10(2):103-109. doi: 10.3969/j.issn.1673-1719.2014.02.004 URL
	Cui P, Chen R, Xiang L Z, et al. Risk analysis of mountain hazards in Tibetan Plateau under global warming[J]. Climate Change Research, 2014,10(2):103-109 (in Chinese)
[4]	史培军. 中国综合减灾25年: 回顾与展望[J]. 中国减灾, 2014 (5):32-35.
	Shi P J. 25 years of comprehensive disaster reduction in China: review and prospect[J]. Disaster Reduction in China, 2014 (5):32-35 (in Chinese)
[5]	李宁, 张正涛, 陈曦, 等. 论自然灾害经济损失评估研究的重要性[J]. 地理科学进展, 2017,36(2):256-263. doi: 10.18306/dlkxjz.2017.02.011 URL
	Li N, Zhang Z T, Chen X, et al. Importance of economic loss evaluation in natural hazard and disaster research[J]. Progress in Geography, 2017,36(2):256-263 (in Chinese)
[6]	孔锋, 王一飞, 吕丽莉, 等. 互联互通背景下巨灾对经济影响的全球性和复杂性的进展与展望[J]. 华中师范大学学报: 自然科学版, 2018,52(6):871-882.
	Kong F, Wang Y F, Lv L L, et al. Progress and prospect of global and complex economic impacts of catastrophe under the background of interoperability[J]. Journal of Huazhong Normal University: Natural Sciences, 2018,52(6):871-882 (in Chinese)
[7]	孟永昌, 王铸, 吴吉东, 等. 巨灾影响的全球性: 以东日本大地震的经济影响为例[J]. 自然灾害学报, 2015,24(6):1-8.
	Meng Y C, Wang Z, Wu J D, et al. Global economic impacts of large-scale disasters: case study of the Great East Japan earthquake[J]. Journal of Natural Disasters, 2015,24(6):1-8 (in Chinese)
[8]	Haraguchi M, Lall U. Flood risks and impacts: a case study of Thailand's floods in 2011 and research questions for supply chain decision making[J]. International Journal of Disaster Risk Reduction, 2015,14:256-272 doi: 10.1016/j.ijdrr.2014.09.005 URL
[9]	周蕾, 吴先华, 高歌. 基于MRIO模型的“一带一路”典型国家气象灾害间接经济损失分析: 以2014年中国“威马逊”台风灾害为例[J]. 自然灾害学报, 2018,27(5):3-13.
	Zhou L, Wu X H, Gao G, et al. Analysis of indirect economic loss of meteorological disasters among the Belt and Road typical countries based on the MRIO model: taking the China's typhoon Rammasun in 2014 as an example[J]. Journal of Natural Disasters, 2018,27(5):3-13 (in Chinese)
[10]	吴先华, 周蕾, 高歌, 等. 考虑防灾减灾能力的洪涝灾害灾损率曲线构建: 以里下河地区的李中镇为例[J]. 地理科学进展, 2016,35(2):223-231. doi: 10.18306/dlkxjz.2016.02.009 URL
	Wu X H, Zhou L, Gao G, et al. Flood depth-damage curves for urban properties considering disaster prevention and mitigation capabilities: evidence from Lizhong town, Lixiahe region, China[J]. Progress in Geography, 2016,35(2):223-231 (in Chinese)
[11]	刘希林, 赵源, 苏鹏程. 四川德昌县虎皮弯沟泥石流及灾害损失评估[J]. 灾害学, 2005,20(3):73-77.
	Liu X L, Zhao Y, Su P C, et al. Debris flow in Hupiwan gully of Dechang county, Sichuan and disaster loss assessment[J]. Journal of Catastrophology 2005,20(3):73-77 (in Chinese)
[12]	Dhakal A S, Amada T, Aniya M. Landslide hazard mapping and the application of GIS in the Kulekhani watershed, Nepal[J]. Mountain Research and Development, 1999: 3-16
[13]	李宁, 吴吉东, 崔维佳. 基于ARIO模型的汶川地震灾后恢复重建期模拟[J]. 自然灾害学报, 2012,21(2):68-75.
	Li N, Wu J D, Cui W J. Simulation of post-disaster recovery and reconstruction period of Wenchuan earthquake based on adaptive regional input-output model[J]. Journal of Natural Disasters, 2012,21(2):68-75 (in Chinese)
[14]	吴先华, 徐中兵, 袁迎蕾, 等. 台风灾害的关联经济损失评估: 以江苏省为例[J]. 灾害学, 2014,29(2):77-83.
	Wu X H, Xu Z B, Yuan Y L, et al. Relational economic loss assessment of typhoon disaster: a case study of Jiangsu province[J]. Journal of Catastrophology, 2014,29(2):77-83 (in Chinese)
[15]	刘希林, 赵源. 地貌灾害间接经济损失评估: 以泥石流灾害为例[J]. 地理科学进展, 2008,27(3):7-12. doi: 10.11820/dlkxjz.2008.03.002 URL
	Liu X L, Zhao Y. Estimation on indirect economic losses of geomorphic hazards: taking debris flow as an example[J]. Progress in Geography, 2008,27(3):7-12 (in Chinese)
[16]	唐川, 张军, 万石云 , 等. 基于高分辨率遥感影像的城市泥石流灾害损失评估[J]. 地理科学, 2006,26(3):358-363.
	Tang C, Zhang J, Wan S Y, et al. 2006,26(3):358-363 (in Chinese)
[17]	Chang S. Disasters and fiscal policy: hurricane impact on municipal revenue[J]. Urban Affairs Quarterly, 1983,18(4):511-523 doi: 10.1177/004208168301800407 URL
[18]	Rose A, Oladosu G, Liao S Y. Business interruption impacts of a terrorist attack on the electric power system of Los Angeles: customer resilience to a total blackout[J]. Risk Analysis, 2007,27(3):513-531 doi: 10.1111/j.1539-6924.2007.00912.x URL pmid: 17640205
[19]	Xie W, Li N, Li C, et al. Quantifying cascading effects triggered by disrupted transportation due to the great 2008 Chinese ice storm: implications for disaster risk management[J]. Natural Hazards, 2014,70(1):337-352 doi: 10.1007/s11069-013-0813-9 URL
[20]	Koks E E, Carrera L, Jonkeren O, et al. Regional disaster impact analysis: comparing input-output and computable general equilibrium models[J]. Natural Hazards and Earth System Sciences, 2015,3:7053-7088
[21]	Hallegatte S. Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters[J]. Risk Analysis, 2014,34(1):152-167 doi: 10.1111/risa.12090 URL
[22]	Mendoza-Tinoco D, Guan D, Zeng Z, et al. Flood footprint of the 2007 floods in the UK: the case of the Yorkshire and the Humber region[J]. Journal of Cleaner Production, 2017,168:655-667 doi: 10.1016/j.jclepro.2017.09.016 URL
[23]	Rose A, Liao S Y. Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions[J]. Journal of Regional Science, 2005,45(1):75-112 doi: 10.1111/jors.2005.45.issue-1 URL
[24]	Xia Y, Li Y, Guan D, et al. Assessment of the economic impacts of heat waves: a case study of Nanjing, China[J]. Journal of Cleaner Production, 2018,171:811-819 doi: 10.1016/j.jclepro.2017.10.069 URL
[25]	Koks E E, Thissen M. A multiregional impact assessment model for disaster analysis[J]. Economic Systems Research, 2016,28(4):429-449 doi: 10.1080/09535314.2016.1232701 URL
[26]	Zhang Z T, Li N, Cui P, et al. How to integrate labor disruption into an economic impact evaluation model for postdisaster recovery periods[J]. Risk Analysis, 2019,39(4). DOI: 10.1111/risa.13365 doi: 10.1111/risa.13195 URL pmid: 30239018
[27]	杨卫忠, 张葆蔚, 符日明. 2016年洪涝灾情综述[J]. 中国防汛抗旱, 2017,27(1):26-29.
	Yang W Z, Zhang B W, Fu R M. Overview of floods and floods in 2016[J]. China Flood & Drought Management, 2017,27(1):26-29 (in Chinese)
[28]	张正涛, 李宁, 冯介玲, 等. 从重建资金与效率角度定量评估灾后经济恢复力的变化: 以武汉市“2016.07.06”暴雨洪涝灾害为例[J]. 灾害学, 2018,33(4):211-216.
	Zhang Z T, Li N, Feng J L, et al. Quantitative assessment of changes in post-disaster resilience from the perspective of rescue funds and rescue efficiency: a case study of a flood disaster in Wuhan city on July 6, 2016[J]. Journal of Catastrophology, 2018,33(4):211-216 (in Chinese)
[29]	Hall R E, Jones C I. Why do some countries produce so much more output per worker than others?[J]. The Quarterly Journal of Economics, 1999,114(1):83-116 doi: 10.1162/003355399555954 URL
[30]	Wu J D, Li N, Shi P J. Benchmark wealth capital stock estimations across China's 344 prefectures: 1978 to 2012[J]. China Economic Review, 2014,31:288-302 doi: 10.1016/j.chieco.2014.10.008 URL
[31]	Wang Y. An industrial ecology virtual framework for policy making in China[J]. Economic Systems Research, 2017,29(2):252-274 doi: 10.1080/09535314.2017.1313199 URL
[32]	Lenzen M, Geschke A, Wiedmann T, et al. Compiling and using input-output frameworks through collaborative virtual laboratories[J]. Science of the Total Environment, 2014,485:241-251 doi: 10.1016/j.scitotenv.2014.03.062 URL
[33]	湖北省统计局. 2017年湖北统计年鉴 [M]. 北京: 中国统计出版社, 2018.
	Hubei Provincial Bureau of Statistics. 2017 Hubei statistical yearbook [M]. Beijing: China Statistics Press, 2018 (in Chinese)