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