Disaster assessment for the “Belt and Road” region based on SDG landmarks

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Big Earth Data Pub Date : 2021-08-20 DOI:10.1080/20964471.2021.1901359
Li Wang, Yuanhuizi He, Yuelin Zhang, Lei Wang, Huicong Jia, Quan Zhou, Bo Yu, Mei-mei Zhang, Zhengyang Lin, Fang Chen
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引用次数: 4

Abstract

ABSTRACT In this study, based on the EM-DAT (The Emergency Events Database) database, disaster assessment for the “Belt and Road” region was carried out in relation to the indicator of the Sustainable Development Goals (SDGs) agenda launched in 2015. A new method for diagnosing trends in the indicators based on the Theil-Sen median method is proposed. In addition, using the data available in the EM-DAT, an overview of disaster records is used to quantify disasters for a total of 73 countries. The disaster trends for the period 2015‒2019 were found to demonstrate the following. (1) As a result of geological and climate conditions, Asia and Africa are high-risk disaster areas and disasters have caused considerable economic losses and affected the populations in developing and underdeveloped countries in these regions. (2) The clear positive value of found for China reflects the country’s encouraging achievements in disaster prevention and mitigation. (3) The value of was observed to be increasing in South Asia, northwest Africa and South Africa, with the increase in India and Mauritania being the most serious. The new method proposed in this paper allows the real trend in the indicator in various countries to be derived and provides critical intelligence support for international disaster risk reduction plans and sustainable development goals.
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基于可持续发展目标里程碑的“一带一路”地区灾害评估
本研究基于EM-DAT(突发事件数据库)数据库,根据2015年启动的可持续发展目标(SDGs)议程指标,对“一带一路”地区进行灾害评估。提出了一种基于Theil-Sen中值法的指标趋势诊断新方法。此外,利用EM-DAT中提供的数据,灾害记录概述用于量化总共73个国家的灾害。2015-2019年期间的灾害趋势表明:(1)受地质和气候条件的影响,亚洲和非洲是灾害高风险地区,灾害给这些地区的发展中国家和欠发达国家造成了相当大的经济损失和人口影响。(2)发现对中国具有明显的正面价值,反映了中国在防灾减灾方面取得的令人鼓舞的成就。(3)南亚、西北非洲和南非的值都在增加,其中印度和毛里塔尼亚的增加最为严重。本文提出的新方法可以得出各国指标的真实趋势,为国际减灾计划和可持续发展目标提供关键的情报支持。
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来源期刊
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
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