Determinants of Household Livelihood Resilience among Climate-Induced Migrants in Urban Slums of Khulna City Corporation, Bangladesh

Climate-induced migrants, especially those resettling in urban slums, are one of the most vulnerable yet under-researched groups when it comes to recognizing the drivers of resilience within the context of socio-urban dynamics in Bangladesh. This study used a survey design with a questionnaire administered to 260 randomly selected HH representatives from slums in Khulna City Corporation (KCC) to determine the resilience status of slum dwellers and the most important factors affecting their resilience. The overall livelihood resilience of slum households is low, as shown by a mean livelihood resilience index (LRI) score of 0.33 across the five livelihood capitals in Khulna. The weakest of the five livelihood capitals are human capital (0.23) and financial capital (0.29). The livelihood resilience level of slum households is mostly moderate and low (91.2%). Resilience is mainly constrained by structural socioeconomic vulnerabilities and shaped primarily by livelihood assets, institutional support, social participation, and adaptive capacities rather than current income alone. The Kruskal–Wallis H test shows that there are significant differences in the livelihood resilience of the households that migrated in response to climate change; female-headed households, smaller households, and multi-earning households, as well as households with higher financial assets, access to institutional support, household living conditions, and environmental engagement are, respectively, more resilient, in both income and all the other components of resilience. Climate migration needs to be part of climate-inclusive, future-oriented, and strong urban development plans that tackle the multi-dimensional vulnerabilities of low-income and marginalized communities. Likewise, future studies should use a longitudinal design with an extensive sample and study region since the current study employed a cross-sectional design with a small sample and study region.