JOURNAL ARTICLE
Assessment of Dar es Salaam's resilience to climate change disasters using the Climate Disaster Resilience Index (CDRI).
Published In: Singapore Journal of Tropical Geography, 2024, v. 45, n. 3. P. 475 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Mkasimongwa, Simon William; Fakour, Hoda; Hassani, Hussein Juma; Sultan, Basma Abdulla; Lai, Hsin‐Chih 3 of 3
Abstract
Climate change is becoming an increasingly significant issue in Africa, and the need for climate resilience assessment has intensified. Dar es Salam is one of Africa's emerging megacities. With a population of over seven million, which continues to grow, there is an urgent need to understand the city's ability to deal with natural disasters. The Climate Disaster Resilience Index (CDRI) was used in this study to assess the city's ability to withstand and cope with climatic hazards. The Index was quantified using sets of dimensions (social, physical, economic, natural, and institutional), with various parameters indicating the city's abilities, strengths, and vulnerabilities to potential climate‐related disasters. Despite being moderately resilient to climate change disasters, the results of our study indicate that the city's economic and institutional features obtained the lowest scores and the least resilience level. The study's findings provide a perspective on aspects of the city management sectors in terms of resilience and which should be given greater consideration in order to strengthen the city's current and future resilience. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Singapore Journal of Tropical Geography. 2024/09, Vol. 45, Issue 3, p475
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:0129-7619
- DOI:10.1111/sjtg.12546
- Accession Number:179877825
- Copyright Statement:Copyright of Singapore Journal of Tropical Geography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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