Climate change is already upon us. Millions of people are expected to be displaced due to the severe and slow-onset impacts. These displacements will lead to large-scale movements from high-risk and less resilient areas to safer or more resilient areas, creating a relocation problem: where people should go and when. This complex problem involves factors such as the source and extent of relocation demand, identification and capacity of destinations, movements from origins to destinations, and the well-being and dignity of both the displaced and receiving communities. We intend to solve the resulting relocation problem at different levels, starting with high-level decisions about which destinations to choose and how many people to send there. This will facilitate early preparations, such as infrastructure and service planning, at these destinations, ensuring timely action without delays. But in such a complicated problem, what could be the measure of the success of certain relocation decisions compared to others? We consider it requisite that the level of social integration at the destination locations and the fairness of the flow decisions are pivotal to a successful relocation plan and should be thoroughly analyzed. The moral imperative of fairness in these decisions cannot be overstated. That is why our study focuses on the fairness of movements within the context of a relocation problem: how many people from each origin should go to each climate destination in a way that is fair for both climate migrants and receiving communities. To this end, we formulate an optimization model such that the objectives and constraints reflect the key aspects of the relocation problem to assign the number of people to be relocated from each origin to each destination. The model incorporates multiple fairness metrics into objectives representing the perspectives of different stakeholders. These metrics are then analyzed and compared to evaluate trade-offs in the results.

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