Destination Selection in Environmental Migration with TOPSIS

The effects of climate change will lead to the forced displacement of millions and will cause dramatic changes to human settlement and migration patterns. The most vulnerable populations will travel as environmental migrants through a complicated quasi-governmental resettlement system of aid camps in the hope of finding long-term placements. These people deserve safe housing and the location they permanently settle in has critical socio-political impacts. Prior research has generally focused on post-conflict or post-disaster relief location selection for a facility at a single point in time or single-period refugee resettlement, with even less work dedicated to environmental migration. Furthermore, the scale of this work is typically limited to a city or country with the geographic area available for relocation remaining static, while in a climate change scenario the habitable land changes over time. We extend the problem of single-period resettlement to multi-period resettlement using the technique for order preference by similarity to ideal solution (TOPSIS), a straightforward multi-criteria decision-making method. We propose a method to iterate resettlement across multiple planning periods and incorporate geospatial, cultural, environmental, and capacity criteria. The set of alternatives, or destinations countries, will change with each planning period to represent the changing habitable environment. Ratios of weights between iterations remain constant. TOPSIS will produce a ranked list of destination sites. The methodology will be illustrated with a generated data set using a set of vulnerable source locations and a set of destination sites, both of which will change in each planning period. We found more variation in the rankings between periods than with standard TOPSIS, as well as greater sensitivity to weights. This work can be applied to any sort of long-term multi-criteria location selection problems (e.g., store openings and closings under changing consumer demand).