1987—2016年全球陆地气候气象及水文类灾害直接经济损失时空特征及贡献率分析

doi:10.13249/j.cnki.sgs.2020.07.014

地理科学 ›› 2020, Vol. 40 ›› Issue (7): 1171-1180.doi: 10.13249/j.cnki.sgs.2020.07.014

1987—2016年全球陆地气候气象及水文类灾害直接经济损失时空特征及贡献率分析

马恒¹^,²^,³^,⁴^,⁵(), 刘甜¹^,²^,³^,⁴, 牟翠翠⁵, 史培军¹^,²^,³^,⁶^,*()

1. 北京师范大学地表过程与资源生态国家重点实验室，北京 100875
2. 北京师范大学环境演变与自然灾害教育部重点实验室，北京 100875
3. 北京师范大学应急管理部?教育部减灾与应急管理研究院，北京 100875
4. 北京师范大学地理科学学部，北京 100875
5. 兰州大学资源环境学院，甘肃兰州 730000
6. 青海师范大学高原科学与可持续发展研究院，青海西宁 810008

收稿日期:2019-10-19 出版日期:2020-07-10 发布日期:2020-12-07
通讯作者: 史培军 E-mail:maheng18@foxmail.com;spj@bnu.edu.cn
作者简介:马恒（1996-），男，山东菏泽人，博士研究生，主要从事气候变化与自然灾害研究。E-mail： maheng18@foxmail.com
基金资助:
国家重点研发计划项目（2016YFA0602404）、国家自然科学基金创新研究群体项目（41621061）资助

Spatial-temporal Patterns and Influencing Factor Contributions of Direct Economic Loss from Climatological-Meteorological-Hydrological Hazards in the World in 1987-2016

Ma Heng¹^,²^,³^,⁴^,⁵(), Liu Tian¹^,²^,³^,⁴, Mu Cuicui⁵, Shi Peijun¹^,²^,³^,⁶^,*()

1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2. Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
3. Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Beijing Normal University, Beijing 100875, China
4. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
5. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, Gansu, China
6. Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810008, Qinghai, China

Received:2019-10-19 Online:2020-07-10 Published:2020-12-07
Contact: Shi Peijun E-mail:maheng18@foxmail.com;spj@bnu.edu.cn
Supported by:
National Key Research Program of China (2016YFA0602404), Creative Research Groups of National Natural Science Foundation of China （41621061）

摘要/Abstract

摘要：

基于EM-DAT数据库1987—2016年全球陆地气候气象及水文类（气候相关类）灾害灾情数据及居民消费者价格指数（CPI）、GDP等社会资料数据，对直接经济损失、损失占比（直接经济损失/GDP）等在不同尺度上计算分析，并量化了频次、GDP、频次与GDP联合影响对损失占比变化的贡献率。结果表明：① 全球直接经济损失随时间变化呈显著上升趋势，损失占比先上升后下降，世界防灾减灾能力不断加强，但应对巨灾能力仍然较弱；② 收入水平越高区域遭受直接经济损失越严重，但中低和中高收入区域经济损失占比明显高于低收入和高收入区域；③ 直接经济损失严重地区有欧洲、东亚、南亚、东南亚及北美洲中南部地区，而损失占比较高地区主要有东亚、东南亚和加勒比海地区；④ 初期灾次对气候气象及水文类灾害损失占比变化起主导作用但影响力逐渐减弱，GDP和频次与GDP联合影响对损失占比变化作用逐渐增强，不同收入区域存在一定差异。

关键词: 气候气象及水文类灾害, 直接经济损失, 损失占比, 区域灾害系统论

Abstract:

With the development of society and the growth of population, the impact of global climate change on human society is increasing rapidly and some countries and regions may suffer more climate-related hazards, which include climatological hazards (drought, glacial lake outburst and wildfire), meteorological hazards (extreme temperature, fog and storm) and hydrological hazards (flood, landslide and wave action). These hazards happened more frequently as time went by, and the direct economic losses had also increased, posing many challenges to the development of security and human. Direct economic loss is the most direct and concentrated manifestation of hazards in terms of impact, whose absolute and relative values are the key indicators for characterizing the hazards and disasters. Systematic study of the spatial-temporal patterns and influencing factor contributions of direct economic loss from climatological-meteorological-hydrological hazards in the world can help us master the development rules of hazards, the ability to respond disasters in different income regions and the contributions of different factors to disaster loss to provide a certain reference for disaster prevention and mitigation, and they’re also important for planning and sustainable development in different countries and regions. Based on the global climatological-meteorological-hydrological hazards and disaster data in EM-DAT from 1987 to 2016 and social data such as Consumer Price Index (CPI) and Gross Domestic Product (GDP), the direct economic loss and loss ratio (direct economic loss/GDP) of the hazards were calculated and analyzed on different spatial-temporal scales respectively, the contribution rate of frequency of hazards and disasters, GDP and their coupling effects to disaster loss ratio were analyzed as well. The results showed that: 1) The total direct economic loss of the disasters showed a significant upward trend over time, while the loss ratio of the disasters went up and then went down. The ability of the whole world to prevent and mitigate disasters had been strengthened, but its ability to cope with catastrophe was still weak. 2) The higher income area suffered more serious direct economic loss of disasters, while the economic disaster loss ratio in lower middle and upper middle income areas were significantly higher than the loss ratio in low income and high income areas. 3) The areas with severe losses of the hazards included Europe, South Asia, Southeast Asia, East Asia and the south-central region of North America, while the areas with high loss ratio of the disaster included Southeast Asia, East Asia and the Caribbean. 4) Frequency of the hazards and disasters played a leading role in the loss ratio of climate-related disasters in the preliminary stage, and its contribution had a downward trend; while the GDP and the coupling effects dominated in the later with an increasing impact in the changing of loss ratio, and there are some differences in different income regions.

Key words: climatological-meteorological-hydrological hazards, direct economic loss of disaster, disaster loss ratio, regional disaster system theory

中图分类号:

马恒, 刘甜, 牟翠翠, 史培军. 1987—2016年全球陆地气候气象及水文类灾害直接经济损失时空特征及贡献率分析[J]. 地理科学, 2020, 40(7): 1171-1180.

Ma Heng, Liu Tian, Mu Cuicui, Shi Peijun. Spatial-temporal Patterns and Influencing Factor Contributions of Direct Economic Loss from Climatological-Meteorological-Hydrological Hazards in the World in 1987-2016[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(7): 1171-1180.

图/表 8

图 1

图 2

图 3

表 1

图 4

图 5

图 6

表 2

参考文献 31

[1]	杨旭. 综合自然灾害风险评价权重研究[D]. 北京: 北京师范大学, 2016.
	Yang Xu. Research on integrated risk assessment weight of multiple natural hazards at global scale. Beijing: Beijing Normal University, 2016.
[2]	徐怀礼. 灾害经济学研究[D]. 长春: 吉林大学, 2007.
	Xu Huaili. Economics research on disaster. Changchun: Jilin University, 2007.
[3]	Hirabayashi Y, Mahendran R, Koirala S et al Global flood risk under climate change[J]. Nature Climate Change, 2013. 3 (9): 816- 821. doi: 10.1038/nclimate1911
[4]	李宏. 基于国民财富损失控制的自然灾害防灾减灾研究[D]. 大连: 东北财经大学, 2011.
	Li Hong. An inquiry into natural disaster prevention and mitigation based on controlling losses of natural wealth. Dalian: Dongbei University of Finance & Economics, 2011.
[5]	孟永昌. 重大自然灾害的全球性经济影响研究[D]. 北京: 北京师范大学, 2017.
	Meng Yongchang. Study on the impact of large-sacle natural disasters on global economy. Beijing: Beijing Normal University, 2017.
[6]	Guha-Sapir D, Vos F, Below R et al. Annual disaster statistical review 2011: the numbers and trends[R]. Centre for Research on the Epidemiology of Disasters (CRED), 2012.
[7]	Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change[M]. Cambridge: Cambridge University Press, 2012.
[8]	Burke M, Hsiang S M, Miguel E Global non-linear effect of temperature on economic production[J]. Nature, 2015. 527 (7577): 235- 9. doi: 10.1038/nature15725
[9]	Hallegatte S, Green C, Nicholls R J et al Future flood losses in major coastal cities[J]. Nature Climate Change, 2013. 3 (9): 802- 806. doi: 10.1038/nclimate1979
[10]	Ward P S, Shively G E Disaster risk, social vulnerability, and economic development[J]. Disasters, 2017. 41 (2): 324- 351. doi: 10.1111/disa.12199
[11]	Tanoue M, Hirabayashi Y, Ikeuchi H Global-scale river flood vulnerability in the last 50 years[J]. Scientific Reports, 2016. 6, 36021. doi: 10.1038/srep36021
[12]	Wu J, Han G, Zhou H et al Economic development and declining vulnerability to climate-related disasters in China[J]. Environmental Research Letters, 2018. 13 (3): 034013. doi: 10.1088/1748-9326/aaabd7
[13]	吴吉东, 傅宇, 张洁, 等 1949－2013年中国气象灾害灾情变化趋势分析[J]. 自然资源学报, 2014. 29 (9): 1520- 1530. doi: 10.11849/zrzyxb.2014.09.007
	Wu Jidong, Fu Yu, Zhang Jie et al Meteorological dsaster trend analysis in China: 1949-2013[J]. Journal of Natural Resources, 2014. 29 (9): 1520- 1530. doi: 10.11849/zrzyxb.2014.09.007
[14]	唐兵, 安瓦尔 1949－1990年塔里木盆地自然灾害时空分布特征研究[J]. 干旱区资源与环境, 2012. 26 (12): 124- 129.
	Tang Bing, An Wa’er Research on temporal and spatial distribution characteristics of natural disasters in Tarim Basin during 1949-1990[J]. Journal of Arid Land Resources and Environment, 2012. 26 (12): 124- 129.
[15]	石勇, 许世远, 石纯, 等自然灾害脆弱性研究进展[J]. 自然灾害学报, 2011. 20 (2): 131- 137.
	Shi Yong, Xu Shiyuan, Shi Chun et al Progress in research on vulnerability of natural disasters[J]. Journal of Natural Disasters, 2011. 20 (2): 131- 137.
[16]	刘彤, 闫天池气象灾害损失与区域差异的实证分析[J]. 自然灾害学报, 2011. 20 (1): 84- 91.
	Liu Tong, Yan Tianchi Positive analysis of meteorological disaster loss and regional difference[J]. Journal of Natural Disasters, 2011. 20 (1): 84- 91.
[17]	司瑞洁, 温家洪, 尹占娥, 等 EM-DAT灾难数据库概述及其应用研究[J]. 科技导报, 2007. 25 (6): 60- 67. doi: 10.3321/j.issn:1000-7857.2007.06.012
	Si Ruijie, Wen Jiahong, Yin Zhan’e et al Emergency events database (EM-DAT) and its applications[J]. Science & Technology Review, 2007. 25 (6): 60- 67. doi: 10.3321/j.issn:1000-7857.2007.06.012
[18]	UNISDR (United Nations International Strategy for Disaster Reduction). Sendai framework for disaster risk reduction 2015-2030[R]. Geneva: UNISDR, 2016.
[19]	史培军. 灾害风险科学[M]. 北京: 北京师范大学出版社, 2016.
	Shi Peijun. Disaster risk science. Beijing: Beijing Normal University Publishing Group, 2016.
[20]	史培军四论灾害系统研究的理论与实践[J]. 自然灾害学报, 2005. 14 (6): 1- 9. doi: 10.3969/j.issn.1004-4574.2005.06.001
	Shi Peijun Theory and practice on disaster system research in a fourth time[J]. Journal of Natural Disasters, 2005. 14 (6): 1- 9. doi: 10.3969/j.issn.1004-4574.2005.06.001
[21]	韩钦梅, 吕建军, 史培军湖北省暴雨人口暴露时空特征与贡献率研究[J]. 灾害学, 2018. 33 (4): 194- 199.
	Han Qinmei, Lv Jianjun, Shi Peijun Spatial temporal characteristics and contribution rate of rainstorm population exposure in Hubei[J]. Journal of Catastrophology, 2018. 33 (4): 194- 199.
[22]	何研, 方建, 史培军湖北省暴雨经济暴露时空变化及贡献率研究[J]. 自然灾害学报, 2018. 27 (3): 112- 120.
	He Yan, Fang Jian, Shi Peijun Spatial-temporal patterns of economic exposure to rainstorm and influencing factors contribution in Hubei province[J]. Journal of Natural Disasters, 2018. 27 (3): 112- 120.
[23]	秦莲霞, 张庆阳, 郭家康美国气象灾害防治理念[J]. 中国减灾, 2012. 21 (192): 51- 53.
	Qin Lianxia, Zhang Qingyang, Guo Jiakang The concept of American meteorological disaster prevention[J]. Disaster Reduction in China, 2012. 21 (192): 51- 53.
[24]	周石硚美国灾害地理[J]. 地理科学进展, 1992. 11 (2): 24- 30. doi: 10.11820/dlkxjz.1992.02.007
	Zhou Shiqiao The geography of American disaster[J]. Progress in Geography, 1992. 11 (2): 24- 30. doi: 10.11820/dlkxjz.1992.02.007
[25]	Moriyama K, Sasaki D, Ono Y et al Comparison of global databases for disaster loss and damage data[J]. Journal of Disaster Research, 2018. 13 (6): 1007- 1014. doi: 10.20965/jdr.2018.p1007
[26]	余芳东世界银行关于国家收入分类方法及问题探讨[J]. 中国统计, 2016. (6): 32- 35.
	Yu Fangdong The classification in national income methods and issues of World Bank[J]. China Statistics, 2016. (6): 32- 35.
[27]	JRC (European Commission Joint Research Centre). Index for risk management-INFORM[M]. Luxembourg: The European Union, 2014.
[28]	李玉文, 徐中民, 王勇环境库兹涅茨曲线研究进展[J]. 中国人口·资源与环境, 2005. 15 (5): 7- 14. doi: 10.3969/j.issn.1002-2104.2005.05.002
	Li Yuwen, Xu Zhongmin, Wang Yong Study on the Environmental Kuznets Curve[J]. China Population Resources and Environment, 2005. 15 (5): 7- 14. doi: 10.3969/j.issn.1002-2104.2005.05.002
[29]	伍光和. 自然地理学[M]. 北京: 高等教育出版社, 2008.
	Wu Guanghe. The physical geography. Beijing: Higher Education Press, 2008.
[30]	梁宜欧洲洪水: 既是天灾, 也是人祸[J]. 水利电力科技, 2002. (3): 1- 3.
	Liang Yi European floods: Both natural and man-made disasters[J]. Water Resources and Electric Power, 2002. (3): 1- 3.
[31]	刘甜, 方建, 马恒, 等全球陆地气候气象及水文灾害死亡人口时空格局及影响因素分析(1965−2016年)[J]. 自然灾害学报, 2019. 28 (3): 8- 16.
	Liu Tian, Fang Jian, Ma Heng et al Spatiotemporal patterns and influencing factors of mortality of climatological-meteorological-hydrological disaster in the world (1965-2016)[J]. Journal of Natural Disasters, 2019. 28 (3): 8- 16.

收入水平	1987—1996年	1997—2006年	2007—2016年	平均值
低	0.23	0.16	0.16	0.18
中低	0.37	0.37	0.24	0.32
中高	0.37	0.30	0.18	0.29
高	0.09	0.15	0.11	0.12

收入水平	1997—2006年			2007—2016年
收入水平	GDP影响	频次影响	联合影响	GDP影响	频次影响	联合影响
低	16.17	65.06	18.77	31.94	11.03	57.02
中低	9.34	55.93	34.73	43.14	2.37	54.49
中高	12.56	59.06	28.37	52.57	0.74	46.68
高	11.85	58.28	29.87	47.36	31.15	21.49

1987—2016年全球陆地气候气象及水文类灾害直接经济损失时空特征及贡献率分析

Spatial-temporal Patterns and Influencing Factor Contributions of Direct Economic Loss from Climatological-Meteorological-Hydrological Hazards in the World in 1987-2016

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 31

相关文章 1

Metrics

本文评价

推荐阅读 0