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This research paper proposes an optimization-based method for selecting a small number of relevant scenarios from large sets generated by computer simulations, combining criteria for both policy vulnerability and future diversity. The method was tested on climate resilient infrastructure cases for three African river basins.

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  • The paper introduces a new optimization-based method for selecting a small number of relevant scenarios from large simulation outputs by combining two previously separate approaches: one focused on illuminating policy vulnerabilities and another focused on spanning a diverse range of futures.
  • The proposed method was applied to a real-world case study concerning climate resilient infrastructure for the Volta, Orange, and Zambezi river basins in Africa.
  • The results indicate that the integration of vulnerability- and diversity-based criteria offers a transparent and systematic approach to scenario selection, and that introducing these criteria stepwise allows for an examination of how different criteria affect the final selection of scenarios.

Cite the original document

APA
Stockholm Environment Institute (2016). Scenarios, Vulnerability and Diversity. https://www.sei.org/publications/scenarios-vulnerability-and-diversity/
Chicago
Stockholm Environment Institute. Scenarios, Vulnerability and Diversity. 2016. https://www.sei.org/publications/scenarios-vulnerability-and-diversity/.
Wikipedia
{{cite report |author=Stockholm Environment Institute |title=Scenarios, Vulnerability and Diversity |date=9 July 2016 |url=https://www.sei.org/publications/scenarios-vulnerability-and-diversity/ |access-date=17 August 2026 |via=Climate Insights Directory}}
BibTeX
@techreport{stockholmenvironmentinstitute2016scenarios, author = {{Stockholm Environment Institute}}, title = {{Scenarios, Vulnerability and Diversity}}, institution = {Stockholm Environment Institute}, year = {2016}, month = jul, url = {https://www.sei.org/publications/scenarios-vulnerability-and-diversity/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }

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