Prioritizing Resource and Policy Barriers to Post-Disaster Recovery in Pakistan: A Multi-Expert Fermatean Fuzzy AHP Analysis

Natural disasters have a widespread impact globally, often posing significant challenges to sustainable development in affected regions. Effective disaster recovery requires coordinated efforts from various stakeholders; however, this process is frequently hindered by inadequate resources and policies. Consequently, a thorough exploration of resource- and policy-related barriers is essential for sustainable recovery. This study aims to identify and prioritize resource and policy barriers (RPBs) that hamper the ongoing post-flood recovery process in Pakistan. Twenty-eight barriers were selected through an extensive literature review and further classified into five categories: human, financial, infrastructure, technological, and policy and governance. The fermatean fuzzy analytical hierarchical process (FF-AHP) was used to determine the weights of the selected barriers and prioritize them according to their local and global weights. The results reveal that financial resource barriers are the most important category, while low income of households, lack of appropriate disaster management policies, and unemployment are the top three barriers that significantly affect the post-disaster recovery process. Additionally, the study provides a comparative analysis to highlight the effectiveness of the proposed model over the spherical fuzzy analytical hierarchy process (SF-AHP). The study’s findings can provide valuable insights for decision-makers, assisting them in identifying critical challenges and implementing effective recovery strategies for sustainable development.

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Publication Details

Journal
Sustainability
Published
2024-11-28
DOI
https://doi.org/10.3390/su162310414
Citations
7
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
3.43

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article

Prioritizing Resource and Policy Barriers to Post-Disaster Recovery in Pakistan: A Multi-Expert Fermatean Fuzzy AHP Analysis

Abid Khan, Bader Alhafi Alotaibi, Inam Danish Khan, Ashfaq Ahmad Shah
7 citations
Sustainability
Multi-Criteria Decision Making
3.43
article

Prioritizing Resource and Policy Barriers to Post-Disaster Recovery in Pakistan: A Multi-Expert Fermatean Fuzzy AHP Analysis

Abid Khan, Bader Alhafi Alotaibi, Inam Danish Khan, Ashfaq Ahmad Shah
article en
7 citations

Abstract

Natural disasters have a widespread impact globally, often posing significant challenges to sustainable development in affected regions. Effective disaster recovery requires coordinated efforts from various stakeholders; however, this process is frequently hindered by inadequate resources and policies. Consequently, a thorough exploration of resource- and policy-related barriers is essential for sustainable recovery. This study aims to identify and prioritize resource and policy barriers (RPBs) that hamper the ongoing post-flood recovery process in Pakistan. Twenty-eight barriers were selected through an extensive literature review and further classified into five categories: human, financial, infrastructure, technological, and policy and governance. The fermatean fuzzy analytical hierarchical process (FF-AHP) was used to determine the weights of the selected barriers and prioritize them according to their local and global weights. The results reveal that financial resource barriers are the most important category, while low income of households, lack of appropriate disaster management policies, and unemployment are the top three barriers that significantly affect the post-disaster recovery process. Additionally, the study provides a comparative analysis to highlight the effectiveness of the proposed model over the spherical fuzzy analytical hierarchy process (SF-AHP). The study’s findings can provide valuable insights for decision-makers, assisting them in identifying critical challenges and implementing effective recovery strategies for sustainable development.

SustainabilityVol. 16(23)
Shanghai University of Political Science and Law (CN), King Saud University (SA), China Agricultural University (CN), Taizhou University (CN)
King Saud University
Openalex Percentile: Top 7%
Multi-Criteria Decision Making
3.43
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