Arnbjerg-Nielsen, Karsten

Drews, Martin

Garrè, Luca

Gregg, Jay Sterling

Halsnæs, Kirsten

Kaspersen, Per Skougaard

Zhou, Qianqian

Åström, Helena Lisa Alexandra

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 19, 2017Economic assessment of climate change adaptation options incorporating Bayesiannetworks: An integrated frameworkGregg, Jay Sterling; Zhou, Qianqian; Åström, Helena Lisa Alexandra; Kaspersen, Per Skougaard; Drews,Martin; Halsnæs, Kirsten; Garrè, Luca; Arnbjerg-Nielsen, KarstenPublication date:2013Link back to DTU OrbitCitation (APA):Gregg, J. S., Zhou, Q., Åström, H. L. A., Kaspersen, P. S., Drews, M., Halsnæs, K., ... Arnbjerg-Nielsen, K.(2013). Economic assessment of climate change adaptation options incorporating Bayesian networks: Anintegrated framework [Sound/Visual production (digital)]. Risk Change Annual Meeting, Denmark, 18/11/2013Economic assessment of climate change adaptation options incorporating Bayesian networks: An integrated framework  Jay S. Gregg Qianqian Zhou Helena Lisa Alexandra Åström Per Skougaard Kaspersen Martin Drews Kirsten Halsnæs Luca Garre Karsten Ambjerg-Nielsen  Purpose • A framework for climate change adaptation decision making • Handles complex inter-relationships between impacts and adaptation options  • Flexible structure for applied climate change adaptation decision making. Outline • 1.Introduction – 1.1 Adaptation in the context of responses to climate change – 1.2 Analytical structure • 2. Scenario construction – 2.1. Socioeconomic scenarios – 2.2 Climate modeling and climate scenarios – 2.3 Future System Scenarios • 3. Impacts – 3.1. Impact Assessment – 3.2 Costs – 3.3 Risk • 4.  Adaptation – 4.1 Identification of Adaptation Options – 4.2 Bayesian Network Approach – 4.3 Adaptation Costs and Benefits • 5. Discussion and Conclusions   Introduction Analytical Structure  Regional Climate ModelSocio- economic scenarioDownscaled  ProjectionsProjected state of human controlled system Adaptation BenefitsImpact Costs RisksDecision MakingAdaptation CostsImpact AssessmentAdaptation OptionsScenario Construction Impact AnalysisAdaptationScenarios • Socioeconomic – Question of scale (Global vs. Local) – Uncertainty bracketed with scenario analysis • Climatic – Regional climate models – Downscaling • Coupled scenarios Theoretical scenario development: understanding the risks from climate change and the benefits of adaptation measures.  TimeImpactsHistorical Data FutureReference Scenario: no modeledclimate changeAlternative Scenario:Future impacts, with modeled climate changeAdaptation options, with modeledclimate changeimpacts relative to fixed referenceResidualImpactsBenefits of AdaptationAdapted from Metroeconomica (2004) and the Danish Government (2008) Impacts • Assessment – characterize change in the climate variable or the extreme weather event (intensity, frequency and duration) – obtain spatially explicit information on asset exposure and vulnerability  – develop asset specific thresholds and damage functions – quantify of both the physical and economic impacts • Costs – Cost-Benefit-Analysis; Net Present Value with discounting – Multi-Criteria Decision Analysis • Risk – probability of an extreme event multiplied by the consequence of an event Adaptation Options • Identification – Non-linearity – Mapping • Bayesian Network Approach – used to quantify risk in the system – determine the probability distribution for each specific impact – Static  Dynamic • Costs and Benefits – Priority setting – Uncertainty analysis  Linkages of impacts due to climate change for an urban area with adaptation options MitigationWorkplace flexibilityRetrofitted buildingsImproved sewer systemDykes, dams, etc.Emergency ResponseEvacuation planningImproved infiltrationManaged wetland and riparian zonesClimate ChangeGlobal sea level rise Increased probability of storm surgesIncreased probability of extreme precipitation eventsIncreased probability of flooding Sewer Da ageBasement floodingBuilding floodingPower line damageIncreased fire riskLoss of productivityTraffic delaysRoad damageLoss of recreational areasLoss of visual amenityHealth and moralityEnvironmental damageProperty lossResettlementA static ID for urban flood risk assessment  Risk MapCosts and DecisionsSystem ConfigurationAdaptation DecisionTriggersMitigantControlConsequenceHazardAdaptation CostProtection CostEADProtectionDecisionClimate ChangeExtreme PrecipitationUrbanizationFloodingAsset ProtectionImpact on AssetDynamic ID for urban flood risk assessment under non-stationary conditions  System ConfigurationAsset ProtectionClimate ChangeExtreme PrecipitationPresentFloodingAdaptation DecisionAdaptation CostImpact on AssetEADProtectionDecisionProtection CostSystem ConfigurationAsset ProtectionExtreme Precipitation50 yearsFloodingAdaptation DecisionAdaptation CostImpact on AssetEADProtectionDecisionProtection CostSystem ConfigurationAsset ProtectionExtreme Precipitation100 yearsFloodingAdaptation DecisionAdaptation CostImpact on AssetEADProtectionDecisionProtection CostUrbanizationDiscussion and Conclusions + The main strength of the BN approach is the ability to represent complex dynamic systems and the inter-linkages between various nodes in the system.  - complex set of input data required for the analysis & can only deal with continuous values in a limited manner, and these types of variables are common in environmental assessment.   Status • Submitted May 31, 2013 • One review completed • Editor requested other reviewer names Upcoming • Transportation Analysis of Copenhagen 

Economic assessment of climate change adaptation options incorporating Bayesian networks: An integrated framework

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Economic assessment of climate change adaptation options incorporating Bayesian networks: An integrated framework

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