Post on 30-Apr-2018
Contextualizing ecosystem services in the present and future urban environment:A Chittenden County, VT case study
Contextualizing ecosystem services in the present and future urban environment:A Chittenden County, VT case study
Ken Bagstad*, Austin Troy, and Brian Voigt*kbagstad@uvm.edu
Ken Bagstad*, Austin Troy, and Brian Voigt*kbagstad@uvm.edu
Land use models for transportationLand use models for transportation
Address some externalities of urban growthModel conditions under alternative scenariosUrban landscape a good place to examine ES:
High value for humansSpatial context usually ignored
Address some externalities of urban growthModel conditions under alternative scenariosUrban landscape a good place to examine ES:
High value for humansSpatial context usually ignored
Study area: Chittenden Co., VTStudy area: Chittenden Co., VT
Vermont’s urban center
24% of state’s population30% of state’s jobs
Subject of multiyear project to integrate land use, transportation, environmental quality
Vermont’s urban center
24% of state’s population30% of state’s jobs
Subject of multiyear project to integrate land use, transportation, environmental quality
Shelburne Road: 1937 2003
Integrated model frameworkIntegrated model framework
Data-intensiveDisaggregatedDynamicDisequilibriumDriven by trends and forecasts
Data-intensiveDisaggregatedDynamicDisequilibriumDriven by trends and forecasts
www.urbansim.org
Modeling in UrbanSimModeling in UrbanSim
Land PriceLand Price
Real Estate DevelopmentReal Estate Development
Residential Land ShareResidential Land Share
AccessibilityAccessibility
Mobility & TransitionMobility & Transition
Location ChoiceLocation Choice
• movers• vacant units• probabilities• site selection
The scenariosThe scenarios
2000-2030Baseline (RPC pop projection)Baseline (RPC+10%)Growth centers/natural areas (RPC pop projection)Growth centers/natural areas (RPC+10%)
2000-2030Baseline (RPC pop projection)Baseline (RPC+10%)Growth centers/natural areas (RPC pop projection)Growth centers/natural areas (RPC+10%)
Accounting for contextAccounting for context
Ecological factors affect provisionSocioeconomic factors affect demandPast value transfer has ignored this -> “static value maps”Spatial analysis the way forward (Boyd and Wainger 2003, Chan et al. 2006, Beier et al. in press)
Ecological factors affect provisionSocioeconomic factors affect demandPast value transfer has ignored this -> “static value maps”Spatial analysis the way forward (Boyd and Wainger 2003, Chan et al. 2006, Beier et al. in press)
Appropriate scale/mapping unitsAppropriate scale/mapping units
Varies by ESGlobal: Carbon/climate stabilityDirectional flow: downstream, downslope,coastline -> inland“Omni-directional”: pollination, aesthetic
Varies by ESGlobal: Carbon/climate stabilityDirectional flow: downstream, downslope,coastline -> inland“Omni-directional”: pollination, aesthetic Fisher et al. in press
Forest carbonForest carbon
Beneficiaries at global scale - aspatial benefits flow from provision to useExtensive past work on Chittenden County carbon budget (Quigley 2008)Regional interest through RGGI
Beneficiaries at global scale - aspatial benefits flow from provision to useExtensive past work on Chittenden County carbon budget (Quigley 2008)Regional interest through RGGI
Watershed servicesWatershed services
Nutrient regulation (P)Impacts recreation, aesthetics, drinking water
Flood preventionImpacts floodplain residents, property owners, farmers
Mapping unit: the watershed (What size? HUC-12? Smaller?)
Nutrient regulation (P)Impacts recreation, aesthetics, drinking water
Flood preventionImpacts floodplain residents, property owners, farmers
Mapping unit: the watershed (What size? HUC-12? Smaller?)
Aesthetic valueAesthetic value
Proximity to open spaceIn vs. outside of city (scarcity)Type (Agriculture an amenity or disamenity? Open space permanently protected?)
Views: lake and mountainView type and quality affectsvalue (Benson et al. 1998)
Mapping unit: the housing unit
Proximity to open spaceIn vs. outside of city (scarcity)Type (Agriculture an amenity or disamenity? Open space permanently protected?)
Views: lake and mountainView type and quality affectsvalue (Benson et al. 1998)
Mapping unit: the housing unit
Results: Forest carbonResults: Forest carbonC sequestrationC storage Scenario
0.81 M T/yr20.4 M T2030 growth centers + 10%
0.82 M T/yr20.9 M T2030 growth centers
0.76 M T/yr19.2 M T2030 baseline + 10%
0.77 M T/yr19.5 M T2030 baseline
1.04 M T/yr20.7 M T1997
Forest loss, 2000-2030
Results: Nutrient regulationResults: Nutrient regulation% increase in P inputs to sensitive areas
Scenario
+1.0%2030 growth centers + 10%
+0.8%2030 growth centers
+0.5%2030 baseline + 10%
+1.0%2030 baseline
Young (1984): eutrophication reducesvalue of property < 200 m from shore 20%
Results: Flood controlResults: Flood control
+ + =
Location of Areas currently Areas of future Locations ofeconomic assets with high forest loss/ high flood
in floodplain impervious cover, impervious surface regulation valuelow forest/wetland cover gain
Results: AestheticsResults: AestheticsUnits /w possible mountain views
Units /w possible lake views
Scenario
56,60216,3602030 growth centers + 10%
49,66814,7112030 growth centers
55,73415,4112030 baseline + 10%
49,51313,6872030 baseline28,2427,8712004
ConclusionsConclusions
Clear differences in ecosystem services provided between scenariosMore compact growth leads to:
Less forest lossLess overall hydrologic impact (though more intense localized impacts)
Clear differences in ecosystem services provided between scenariosMore compact growth leads to:
Less forest lossLess overall hydrologic impact (though more intense localized impacts)
Future directionsFuture directions
Statistical testing of differences between scenariosMore complete analysis of carbon dynamics as land use changesField studies of relationship between urbanization and hydrologic conditionsMore comprehensive approach to economic valuation
Statistical testing of differences between scenariosMore complete analysis of carbon dynamics as land use changesField studies of relationship between urbanization and hydrologic conditionsMore comprehensive approach to economic valuation
Future directionsFuture directions
Integration with NSF-funded ARIES (ARtificial Intelligence for Ecosystem Services) project
Web-based ES DSSEncodes relevant ecological &socioeconomic knowledge tomap ES provision, use, benefitflowsProbabilistic models use Bayesian networks toincorporate uncertaintyAutomated data integrationTransparent, interactiveinterface; capable of scenario planning
Integration with NSF-funded ARIES (ARtificial Intelligence for Ecosystem Services) project
Web-based ES DSSEncodes relevant ecological &socioeconomic knowledge tomap ES provision, use, benefitflowsProbabilistic models use Bayesian networks toincorporate uncertaintyAutomated data integrationTransparent, interactiveinterface; capable of scenario planning More at esd.uvm.edu
AcknowledgementsAcknowledgements
Funding: U.S. Department of Transportation, University of Vermont Transportation CenterBrian Miles (UrbanSim development), Lexie Reiss (scenario development)ARIES team (Ferdinando Villa, Marta Ceroni, Sergey Krivov, Josh Farley, Gary Johnson) -discussions on beneficiaries and service flows
Funding: U.S. Department of Transportation, University of Vermont Transportation CenterBrian Miles (UrbanSim development), Lexie Reiss (scenario development)ARIES team (Ferdinando Villa, Marta Ceroni, Sergey Krivov, Josh Farley, Gary Johnson) -discussions on beneficiaries and service flows