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Extreme climate events have been on the rise in both their frequency and intensity, displacing millions of people in vulnerable countries worldwide in recent years. This calls for prioritizing resettlement plans in adaptation frameworks and strategies in these countries. Toward this end, this article provides a methodological and empirical contribution in resettlement capacity assessment for climate change adaptation. It examines the effect of using weights while constructing composite resettlement capacity indices and empirically assesses the resettlement capacity of locations in Bangladesh using one hundred indicators from thirty-one data sources. We categorize the indicators into two main dimensions: assets, being inputs available for a viable livelihood; and conditions, or factors that constrain or promote the use of these assets. These are further divided into five asset and six condition subdimensions. We create both weighted and unweighted overall-, dimension-, and subdimension-specific resettlement capacity indices using an additive hierarchical index construction approach, whereby the weights are derived from expert assessment of the relevance of the dimensions and subdimensions. We then employ latent cluster analysis to identify clusters with similar capacity profiles. We find that although the distribution and mean values of the weighted and the unweighted resettlement capacity indices differ, they tend to highly correlate and have similar distributional patterns, leading to comparable conclusions. We identify four unique resettlement capacity clusters that are distinct in asset, condition, and subdimension resettlement capacity scores. These clusters exhibit a clear spatial pattern throughout Bangladesh, with the northern, western, and central (southern and eastern) areas characterized by higher (lower) resettlement capacity clusters. These findings provide important policy implications with respect to climate change-related displacement.

Päivi Lujala

Affiliated Senior Researcher