Understanding climate-induced migration through computational modeling: A critical overview with guidance for future efforts. (October 2018)

• Record Type: Journal Article
• Authors: Till, Charlotte; Haverkamp, Jamie; White, Devin; Bhaduri, Budhendra
• Is Part Of: Journal of defense modeling and simulation. Volume 15:Number 4(2018:Oct.)
• Page Start: 415
• Page End: 435
• Publication Date: 2018-10
• Subjects: Research process -- migration -- model selection -- climate induced migration -- model -- simulation -- future planning; Military art and science -- Computer simulation -- Periodicals; 355.0011305
• DOI: 10.1177/1548512916679038
• Languages: English
• ISSNs: 1548-5129

Abstract

Climate change has the potential to displace large populations in many parts of the developed and developing world. Understanding why, how, and when environmental migrants decide to move is critical to successful strategic planning within organizations tasked with helping the affected groups, and mitigating their systemic impacts. One way to support planning is through the employment of computational modeling techniques. Models can provide a window into possible futures, allowing planners and decision makers to test different scenarios in order to understand what might happen. While modeling is a powerful tool, it presents both opportunities and challenges. This paper builds a foundation for the broader community of model consumers and developers by: providing an overview of pertinent climate-induced migration research, describing some different types of models and how to select the most relevant one(s), highlighting three perspectives on obtaining data to use in said model(s), and the consequences associated with each. It concludes with two case studies based on recent research that illustrate what can happen when ambitious modeling efforts are undertaken without sufficient planning, oversight, and interdisciplinary collaboration. We hope that the broader community can learn from our experiences and apply this knowledge to their own modeling research efforts.