Contextualizing ecosystem services in the present and future urban environment: A Chittenden County, VT case study

Transcription

1 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

2 Land use models for transportation Address some externalities of urban growth Model conditions under alternative scenarios Urban landscape a good place to examine ES: High value for humans Spatial context usually ignored

3 Study area: Chittenden Co., VT Vermont s urban center 24% of state s population 30% of state s jobs Subject of multiyear project to integrate land use, transportation, environmental quality Shelburne Road:

4 Integrated model framework Data-intensive Disaggregated Dynamic Disequilibrium Driven by trends and forecasts

5 Modeling in UrbanSim Accessibility Land Price Mobility & Transition Location Choice Real Estate Development Residential Land Share movers vacant units probabilities site selection

6 The scenarios Baseline (RPC pop projection) Baseline (RPC+10%) Growth centers/natural areas (RPC pop projection) Growth centers/natural areas (RPC+10%)

7 Accounting for context Ecological factors affect provision Socioeconomic factors affect demand Past 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)

8 Appropriate scale/mapping units Varies by ES Global: Carbon/climate stability Directional flow: downstream, downslope, coastline -> inland Omni-directional : pollination, aesthetic Fisher et al. in press

9 Forest carbon Beneficiaries at global scale - aspatial benefits flow from provision to use Extensive past work on Chittenden County carbon budget (Quigley 2008) Regional interest through RGGI

10 Watershed services Nutrient regulation (P) Impacts recreation, aesthetics, drinking water Flood prevention Impacts floodplain residents, property owners, farmers Mapping unit: the watershed (What size? HUC-12? Smaller?)

11 Aesthetic value Proximity to open space In vs. outside of city (scarcity) Type (Agriculture an amenity or disamenity? Open space permanently protected?) Views: lake and mountain View type and quality affects value (Benson et al. 1998) Mapping unit: the housing unit

12 Results: Forest carbon Forest loss, Scenario baseline 2030 baseline + 10% 2030 growth centers 2030 growth centers + 10% C storage 20.7 M T 19.5 M T 19.2 M T 20.9 M T 20.4 M T C sequestration 1.04 M T/yr 0.77 M T/yr 0.76 M T/yr 0.82 M T/yr 0.81 M T/yr

13 Results: Nutrient regulation Scenario 2030 baseline 2030 baseline + 10% 2030 growth centers 2030 growth centers + 10% % increase in P inputs to sensitive areas +1.0% +0.5% +0.8% +1.0% Young (1984): eutrophication reduces value of property < 200 m from shore 20%

14 Results: Flood control + + = Location of Areas currently Areas of future Locations of economic assets with high forest loss/ high flood in floodplain impervious cover, impervious surface regulation value low forest/wetland cover gain

15 Results: Aesthetics Scenario baseline Units /w possible lake views 7,871 13,687 Units /w possible mountain views 28,242 49, baseline + 10% 2030 growth centers 2030 growth centers + 10% 15,411 14,711 16,360 55,734 49,668 56,602

16 Conclusions Clear differences in ecosystem services provided between scenarios More compact growth leads to: Less forest loss Less overall hydrologic impact (though more intense localized impacts)

17 Future directions Statistical testing of differences between scenarios More complete analysis of carbon dynamics as land use changes Field studies of relationship between urbanization and hydrologic conditions More comprehensive approach to economic valuation

18 Future directions Integration with NSF-funded ARIES (ARtificial Intelligence for Ecosystem Services) project Web-based ES DSS Encodes relevant ecological & socioeconomic knowledge to map ES provision, use, benefit flows Probabilistic models use Bayesian networks to incorporate uncertainty Automated data integration Transparent, interactive interface; capable of scenario planning More at esd.uvm.edu

19 Acknowledgements Funding: U.S. Department of Transportation, University of Vermont Transportation Center Brian 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