Climatic Change

, Volume 132, Issue 3, pp 459–473 | Cite as

Challenges in using a Robust Decision Making approach to guide climate change adaptation in South Africa

  • Joseph DaronEmail author


Conventional forecast driven approaches to climate change adaptation create a cascade of uncertainties that can overwhelm decision makers and delay proactive adaptation responses. Robust Decision Making inverts the analytical steps associated with forecast-led methodologies, reframing adaptation in the context of a specific decision maker’s capacities and vulnerabilities. In adopting this bottom-up approach, the aim is to determine adaptation solutions which are insensitive to uncertainty. Yet despite the increased use of the approach in large-scale adaptation projects in developed countries, there is little empirical evidence to test whether or not it can be successfully applied in developing countries. The complex realities of decision making processes, the need to combine quantitative data with qualitative understanding and competing environmental, socio-economic and political factors all pose significant obstacles to adaptation. In developing countries, these considerations are particularly relevant and additional pressures exist which may limit the uptake and utility of the Robust Decision Making approach. In this paper, we investigate the claim that the approach can be deemed valuable in developing countries. Challenges and opportunities associated with Robust Decision Making, as a heuristic decision framework, are discussed with insights from a case study of adapting coastal infrastructure to changing environmental risks in South Africa. Lessons are extracted about the ability of this framework to improve the handling of uncertainty in adaptation decisions in developing countries.


Climate Change Adaptation Adaptation Option Adaptation Decision Robust Decision Make Candidate Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



I would like to acknowledge the helpful cooperation of employees working at the City of Cape Town, particularly Darryl Colenbrander and consultants from Worley Parsons. Wind data for Roman Rock was provided by the South African Institute for Maritime Technology in Simon’s Town. I would also like to acknowledge the valuable input of colleagues at the University of Cape Town, namely Brendan Argent, Mark New, Hannah Beleta, Gina Ziervogel, Peter Johnston and Bruce Hewitson. I am especially grateful to the organizers of the CIRCLE-2 “Workshop on Uncertainty and Climate Change Adaptation” for providing a forum to share these findings. Finally, I would like to thank three anonymous reviewers whose comments and suggestions have helped to improve the manuscript.

Supplementary material

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Climate System Analysis GroupUniversity of Cape TownCape TownSouth Africa

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