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.
| Original language | English |
|---|---|
| Pages (from-to) | 415-435 |
| Number of pages | 21 |
| Journal | The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology |
| Volume | 15 |
| Issue number | 4 |
| DOIs | |
| State | Published - Oct 1 2018 |
Funding
This research was funded by the Intelligence Community Postdoctoral Research Fellowship Program and an ORNL Laboratory Directed Research and Development grant. Charlotte was able to complete this paper with support from a Fulbright New Zealand Science & Innovation Graduate Student Award. Guidance and support from the Geographic Information Science and Technology Group at ORNL was vital to the completion of this paper; specifically the summer mentorship provided by Devin White, and the project guidance provided by Budhendra Bhaduri. We would also like to thank ORAU, ORISE, the ORNL HERE program, and all those who provided references and input, as well as those who gave vital feedback, during the numerous drafts and iterations of this paper. Special thanks to the manuscript reviewers for their time and recommendations. Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the US Department of Energy under contract no. DEAC05-00OR22725. Guidance and support from the Geographic Information Science and Technology Group at ORNL was vital to the completion of this paper; specifically the summer mentorship provided by Devin White, and the project guidance provided by Budhendra Bhaduri. We would also like to thank ORAU, ORISE, the ORNL HERE program, and all those who provided references and input, as well as those who gave vital feedback, during the numerous drafts and iterations of this paper. Special thanks to the manuscript reviewers for their time and recommendations. Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the US Department of Energy under contract no. DEAC05-00OR22725. This research was funded by the Intelligence Community Postdoctoral Research Fellowship Program and an ORNL Laboratory Directed Research and Development grant. Charlotte was able to complete this paper with support from a Fulbright New Zealand Science & Innovation Graduate Student Award.
| Funders | Funder number |
|---|---|
| US Department of Energy | |
| U.S. Department of Energy | DEAC05-00OR22725 |
| Laboratory Directed Research and Development |
Keywords
- Research process
- climate induced migration
- future planning
- migration
- model
- model selection
- simulation
