Files and links (1)
Published VersionCC BY-NC-ND V4.0, Open Access
Journal article
Disaster loss index development and comprehensive assessment: A case study of Shanghai
Ecological Indicators, Vol.166, pp.1-9
2024
Abstract
Accurate measurement of socio-economic loss and the impact of disasters is crucial for effective risk assessment and management. This study introduces a Disaster Severity Index (DSI) and a Disaster Impact Index (DII), along with a composite severity matrix, to evaluate comprehensive disaster losses. By utilizing seven loss indicators across six types of natural disasters and socioeconomic indicators, we thoroughly assessed Shanghai’s historical disaster losses from 1978 to 2020. Our analysis reveals that Shanghai’s most severe annual disaster losses occurred in 1991 and 2005; however, the overall impact on the local socio-economy has been declining. According to the DSI, Chongming (0.88), Songjiang (0.64), and Qingpu (0.51) Districts exhibit the highest severity. In terms of the DII, Songjiang (0.87), Chongming (0.71), and Qingpu (0.64) Districts are most affected. The comprehensive disaster severity grading indicates that Chongming and Songjiang Districts experience the most severe impacts, while Huangpu and Xuhui Districts are less affected. The spatial distribution of disaster losses is concentrated in suburban areas, with less impact in the central city. Our methodology provides a framework for analyzing and comparing historical disaster losses in terms of time (inter-annual), space (administrative divisions), and types of disasters. It can also be used for statistical analysis and regular monitoring of disaster losses. This study not only elucidates the spatiotemporal distribution of historical disaster losses in Shanghai but also validates the efficacy of our comprehensive index models.
Details
- Title
- Disaster loss index development and comprehensive assessment: A case study of Shanghai
- Authors
- Luna Zhao (Author) - Shanghai Normal UniversityJiahong Wen (Author) - Shanghai Normal UniversityChengcheng Wan (Author) - Shanghai Normal UniversityLi Li (Author) - Shanghai Normal UniversityYuxi Chen (Author) - Shanghai Emergency Management Bureau (China)Huan Zhang (Author) - Shanghai Normal UniversityHuan Liu (Author) - Shanghai Normal UniversityJianping Yan (Author) - Shanghai Normal UniversityJianli Liu (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringTongfei Tian (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringYong Shi (Corresponding Author) - Shanghai University of Sport
- Publication details
- Ecological Indicators, Vol.166, pp.1-9
- Publisher
- Elsevier BV
- Date published
- 2024
- DOI
- 10.1016/j.ecolind.2024.112497
- ISSN
- 1872-7034; 1470-160X
- Copyright note
- © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
- Data Availability
- Data will be made available on request.
- Grant note
- This work was supported by the National Natural Science Foundation of China (42171080), and Historical Disaster Investigation and Assessment Project of Shanghai Emergency Management Bureau (11N7295209912021802).
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991061496202621
- Output Type
- Journal article
Metrics
1 File views/ downloads
36 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Biodiversity Conservation
- Environmental Sciences
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites