Presentation on theme: "Patrick Walsh Charles Griffiths Dennis Guignet Heather Klemick David Simpson US EPA: National Center for Environmental Economics."— Presentation transcript:
Patrick Walsh Charles Griffiths Dennis Guignet Heather Klemick David Simpson US EPA: National Center for Environmental Economics
Introduction Property Values in the Chesapeake Bay – Two main phases First phase: county level analysis in MD along the Bay – Spatial Dependence – Multiple indicators of water quality – Two stage hedonic analysis Second phase: broader parts of MD, VA, DC, DE. – May eventually expend, depending on data availability.
Background Literature Past hedonic analyses of WQ – Brashares (1985) – Steinnes (1992) – Michael et al. (1996) – Boyle et al. (1999) – Michael et al. (2000) – Boyle and Taylor (2001) – Poor et al. (2001) – Gibbs et al. (2002) – Krysel et al. (2003) – Walsh et al. (2011) Hedonic Analyses of WQ in Chesapeake Bay – Leggett and Bockstael (2000): Fecal Coliform – Poor et al. (2007): “Ambient” water quality : Total Inorganic Nitrogen, TSS
Data Phase I: MD PropertyView – Full set of MD parcels – 1996-2011 property sales – GIS maps, Land Use Data Phase II: DE, DC, VA
Water Quality Data Interpolated WQ data from Chesapeake Bay Program Office – 1 km x 1 km cells Multiple Depths – TN, TP, TSS, Chl a, DO, Clarity Monitoring Stations – ~200 Stations throughout watershed Watershed Model – Reach-level: larger segments of rivers/streams Local Data – Anne Arundel County Fecal Coliform, beach closures – Montgomery County Tree Canopy Link homes to nearest waterbodies via GIS – Control for density of water nearby
Representing Water Quality What endpoints do people care about? – Policy levers versus perceptions? Objective versus subjective measures Temporal range of indicator – Annual value most common – Also, trends in WQ Similar to Michael et al. (2000). Later link to survey data. – Ask about WQ perceptions
Methods First stage – estimate implicit prices, marginal willingness to pay Second Stage – use implicit prices to estimate demand function – Non-marginal benefits Spatial Econometrics to control for spatial dependence Double Counting
Benefits Projections – Future forecasts for interpolated cells – Watershed model forecasts (at a less granular level) Use estimated demand function to calculate benefits
Extensions University of Vermont detailed canopy analysis in Montgomery County – Increased tree planting as part of TMDL Pfisteria outbreaks – Several highly publicized cases Algae blooms Fish kills Sickness from exposure
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