Prevalence and determinants of vulnerability among Sundarbans mangrove forest resource-dependent communities in cyclone-prone southwestern coastal districts of Bangladesh

IF 2.9 Q1 FORESTRY Trees, Forests and People Pub Date : 2025-03-01 Epub Date: 2024-11-23 DOI:10.1016/j.tfp.2024.100739
Md. Tanvir Hossain , Tunvir Ahamed Shohel , Md. Nasif Ahsan , Md. Nazrul Islam
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Abstract

Vulnerability assessment is crucial for reducing the impact of natural disasters on marginalized coastal communities. In Bangladesh, studies have addressed the vulnerability of coastal populations, but they often overlook the Sundarbans mangrove forest resource-dependent communities (SMFRDCs). These marginalized groups, reliant on the forest, frequently face natural disasters such as cyclones, increasing their vulnerability. This study seeks to assess the extent of vulnerability among SMFRDCs and uncover the critical risk factors contributing to it. This cross-sectional survey was conducted in three southwestern coastal districts of Bangladesh: Satkhira, Khulna, and Bagerhat. Data were collected from 782 SMFRDCs in three Upazila (sub-district) of selected coastal districts using a structured interview schedule (SIS) and following a multistage stratified random sampling approach. The data were analyzed using IBM SPSS Statistics v27 for Windows. A one-sample binomial test was performed to assess the prevalence of vulnerability. Additionally, bivariate analyses (Pearson’s Chi-square [χ2], Yates’s Correction for Continuity [χ2Yates], Phi [φ], and Cramer’s V [φc]), and multivariable binary logistic regression (MBLR) were conducted to identify the associated risk factors. The findings of the one-sample binomial indicate that among the coastal Upazila, the vulnerability prevalence was highest in Mongla (53.4 %; 95 % CI: 47.4 % - 59.3 %). Additionally, honey collectors were found to be more vulnerable to natural disasters, with a prevalence of 60.8 % (95 % CI: 52.7 % - 68.6 %) among the SMFRDCs. The MBLR findings indicate that SMFRDCs with over 31 years of experience and involvement in multiple occupations were less vulnerable to natural disasters. Likewise, SMFRDCs with better household materials, transport, and livestock assets were less vulnerable to natural disasters. In contrast, those with medium-to-high domestic assets were more at risk. Additionally, having land and access to loans reduced the likelihood of vulnerability. SMFRDCs with better natural, physical, and political capital were also less vulnerable. The findings show that vulnerability among SMFRDCs varied by geospatial location and occupation. Socio-demographics, household resources, and livelihood capitals were key predictors of the household vulnerability of SMFRDCs. To reduce this vulnerability, it is recommended that organizations – governmental and non-governmental – work together to create comprehensive plans involving SMFRDCs in both planning and implementing disaster risk reduction strategies by addressing individual and community-level factors.
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孟加拉国西南沿海易受气旋影响地区孙德尔本斯红树林资源依赖型社区脆弱性的流行程度及其决定因素
脆弱性评估对于减少自然灾害对边缘沿海社区的影响至关重要。在孟加拉国,研究解决了沿海人口的脆弱性,但它们往往忽视了孙德尔本斯红树林资源依赖型社区(smfrdc)。这些依赖森林的边缘群体经常面临飓风等自然灾害,增加了他们的脆弱性。本研究旨在评估smfrdc的脆弱性程度,并揭示导致脆弱性的关键风险因素。这项横断面调查是在孟加拉国的三个西南沿海地区进行的:satkira、Khulna和Bagerhat。采用结构化访谈计划(SIS)和多阶段分层随机抽样方法,从选定的沿海地区三个Upazila(街道)的782个smfrdc收集数据。使用IBM SPSS Statistics v27 for Windows对数据进行分析。采用单样本二项检验评估脆弱性的普遍程度。此外,采用双变量分析(Pearson 's Chi-square [χ2]、Yates 's Correction for Continuity [χ2Yates]、Phi [φ]和Cramer 's V [φc])和多变量二元逻辑回归(MBLR)来确定相关危险因素。单样本二项分析结果表明,在沿海乌巴齐拉地区,脆弱性患病率最高的是勐拉(53.4%);95% ci: 47.4% - 59.3%)。此外,采蜜者被发现更容易受到自然灾害的影响,在smfrdc中患病率为60.8% (95% CI: 52.7% - 68.6%)。MBLR的研究结果表明,拥有超过31年经验和从事多种职业的smfrdc不太容易受到自然灾害的影响。同样,拥有更好的家庭材料、运输和牲畜资产的小户型农村也不太容易受到自然灾害的影响。相比之下,那些拥有中高国内资产的人面临的风险更大。此外,拥有土地和获得贷款的机会减少了脆弱性的可能性。拥有更好的自然资本、物质资本和政治资本的小户型frdc也不那么脆弱。研究结果表明,小城镇边远地区的脆弱性因地理空间位置和职业而异。社会人口统计学、家庭资源和生计资本是小户型农村受灾家庭脆弱性的关键预测因素。为了减少这种脆弱性,建议政府和非政府组织共同努力,通过解决个人和社区层面的因素,在规划和实施减少灾害风险战略方面,制定涉及中小受灾地区的综合计划。
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来源期刊
Trees, Forests and People
Trees, Forests and People Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
4.30
自引率
7.40%
发文量
172
审稿时长
56 days
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