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Optimal Flood Risk Adaptation Analysis in Leuven: Decision Support Models for Enhanced Policy Making

This follow-up committee meeting in Leuven evaluates flood risk adaptation scenarios, emphasizing the need to reassess current strategies due to climate change. Decision support models for computing values, sensitivity, and uncertainty analysis are discussed to optimize investment in adaptation measures.

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Optimal Flood Risk Adaptation Analysis in Leuven: Decision Support Models for Enhanced Policy Making

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  1. Evaluation of adaptation scenarios Follow-up committee meeting 6 October, Leuven

  2. Need to adapt to new conditions • The current level of flood risk (making abstraction of possible future climate change effects) is probably not optimal • Climate change alters current conditions Requires reassessing flood risks and current flood riskmanagement strategies

  3. What is the optimal level of adaptation? • Autonomous adaptation is not likely to be optimal • Policy makers are ought to invest the limited resources in an optimal manner, maximising welfare … this requires • Reliable data on the likely impacts (costs) of climate change • Information on costs and effectiveness of adaptation options • Information on indirect effects of adaptation options

  4. What is the optimal level of adaptation?

  5. Decision support model – computation of values for the evaluation criteria • Avoided flood risks calculated with the model • Possibility to account for economic growth, population growth and number of people per household • Project horizon of 30, 50 and 100 years • Select the appropriate discount factor for calculating the NPVs

  6. Decision support model – extended CBA • Development of the decision matrix • Standardisation • Attributing weights to the effects • Ranking • Implicit CBA and benefit-cost ratio

  7. Decision support model – sensitivity analysis • Sensitivity of the results to changes in the weights • Sensitivity of the results to changes in the decision matrix

  8. Decision support model – uncertainty analysis • Uncertainty analysis is broken down into: • Identification of the various sources of uncertainty • Quantification of the uncertainties identified • Reassessment and discussion of the results in the light of uncertainty

  9. Concluding remarks • Added value for prioritising flood control measures described by multiple, not readily comparable effects • Combination of a completely automated MCA with an implicit CBA • Standardisation and the weighting are somewhat arbitrary • Reducing the arbitrary of the results thus requires enquiring the public about their preferences • Popularisation of the tool by the determination of a set of criterion weights that can be used in any assessment in Belgium

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