Bibliographic Details
| Title: |
Natural Disasters and Rehabilitation: Post‐Disaster Aid, Corruption, Misallocation, and Mistargeting. |
| Authors: |
Ahmad, Muhammad Irshad1,2 (AUTHOR), Shen, Qiong2 (AUTHOR), Boota, Muhammad Waseem3 (AUTHOR), Liu, Ruifeng4 (AUTHOR), Ma, Hengyun4 (AUTHOR) h.y.ma@163.com |
| Source: |
Sustainable Development. Jan2026 Supplement 1, Vol. 34, p893-914. 22p. |
| Subject Terms: |
*Natural disasters, *Sustainable development, Corruption, Disaster relief, Racial inequality, Resource allocation |
| Abstract: |
The increasing frequency of natural disasters, such as floods, droughts, and tsunamis, has made vulnerable communities less resilient, pushing them toward long‐term poverty and food insecurity. Effective post‐disaster rehabilitation is critical to restoring livelihoods, infrastructure, and food security. However, challenges such as corruption, misallocation, and mistargeting undermine post‐disaster aid programs. This study systematically reviews 86 peer‐reviewed articles (1990–2023) using the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) protocol to investigate aid inefficiencies in disaster recovery. The findings reveal that aid often fails to reach the most affected communities, being diverted to unaffected areas due to political influence and local elites, exacerbating inequalities. Corruption further hampers institutional performance and long‐term disaster resilience efforts. The study calls for transparent, accountable, and inclusive strategies for aid distribution, aligning with SDG 10 (reduced inequalities) and SDG 11 (sustainable cities and communities). Future research should focus on gender‐sensitive strategies, local governance, and technological innovations to enhance aid transparency and effectiveness. [ABSTRACT FROM AUTHOR] |
|
Copyright of Sustainable Development 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.) |
| Database: |
GreenFILE |