Technical Policy Briefing Notes - 3
Robust Decision Making
Discussion and Applicability
Policy Briefs
Robust Decision Making
Discussion and Applicability
The review and case studies provide a number of
practical lessons on the application of robust
decision making to adaptation. They provide
useful information on the types of adaptation
problem types where RDM might be appropriate,
as well as data needs, resource requirements
and good practice.
RDM is particularly applicable under situations of high uncertainty, where probabilistic information is low or missing.
This is reflected in its use for water resource studies, where the uncertainty is often large (even in terms of the sign of future precipitation changes) from the climate models, combined with other major uncertainties in relation to supply and demand.
The RDM approach can use physical or economic information, that it has broad applicability from detailed economic appraisal through to the consideration of non-market sectors where valuation may be challenging. The potential for stakeholder inputs also allows application where quantitative information is low.
RDM has a particular application in identifying low and no regret options, i.e. in relation to nearterm adaptation strategies that are also likely to enhance long-term resilience (through the analysis of robustness). Indeed, the case studies highlight that these low regret options often emerge from the application. It also has potential to consider how near-term infrastructure investment performs against long-term future (uncertain) scenarios.
Ideally the approach is used to consider multiple sources of uncertainty, not just climate change, but this does increase the level of analysis, and the formal approach (using computer interfaces) is technically complex and data and resource intensive, requiring a high degree of expert knowledge.
The application to climate change alone therefore provides a 'light-touch' and enables the testing of options against climate uncertainty. In such applications, which reduce the approach into quantitative scenario testing, the greater the degree of climate model uncertainty explored, the better (i.e. multi-model and multi-scenario analysis, including issue of downscaling, and including variability as well as trends). Where resource constraints are high, such exercises can prove valuable for helping to identify more robust solutions and moving towards adaptive management under high uncertainty
RDM is particularly applicable under situations of high uncertainty, where probabilistic information is low or missing.
This is reflected in its use for water resource studies, where the uncertainty is often large (even in terms of the sign of future precipitation changes) from the climate models, combined with other major uncertainties in relation to supply and demand.
The RDM approach can use physical or economic information, that it has broad applicability from detailed economic appraisal through to the consideration of non-market sectors where valuation may be challenging. The potential for stakeholder inputs also allows application where quantitative information is low.
RDM has a particular application in identifying low and no regret options, i.e. in relation to nearterm adaptation strategies that are also likely to enhance long-term resilience (through the analysis of robustness). Indeed, the case studies highlight that these low regret options often emerge from the application. It also has potential to consider how near-term infrastructure investment performs against long-term future (uncertain) scenarios.
Ideally the approach is used to consider multiple sources of uncertainty, not just climate change, but this does increase the level of analysis, and the formal approach (using computer interfaces) is technically complex and data and resource intensive, requiring a high degree of expert knowledge.
The application to climate change alone therefore provides a 'light-touch' and enables the testing of options against climate uncertainty. In such applications, which reduce the approach into quantitative scenario testing, the greater the degree of climate model uncertainty explored, the better (i.e. multi-model and multi-scenario analysis, including issue of downscaling, and including variability as well as trends). Where resource constraints are high, such exercises can prove valuable for helping to identify more robust solutions and moving towards adaptive management under high uncertainty
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