THE VALUATION OF ENVIRONMENTAL AMENITIES ACROSS BROAD GEOGRAPHIC REGIONS: AN EMPIRICAL APPLICATION OF THE HEDONIC TRAVEL COST METHODOLOGY TO THE FORESTS OF THE SIERRA NEVADA WILDERNESS Jeffrey Englin Arizona State University Mesa, AZ USA Kevin House University of Nevada, Reno Reno, NV USE

Overview Historical context of the hedonic travel cost model Hedonic travel cost model intuition Theory Implementation Issues Data Results Conclusions and speculations

Historical context of the hedonic travel cost model Proposed by Brown and Mendelsohn in 1984 At the same time RUM models were becoming tractable US Environmental Protection Agency ran a large proposal competition and chose the RUM to develop US Environmental Protection Agency focus was on site remediation Hedonic travel cost models have been applied sporadically (about a dozen times) since 1984

Why refocus now? Hedonic travel cost models produce regional (national) welfare measures Suitable for regional (national) regulations and development objectives Forms a utility theoretic alternative to meta analysis Pendleton and Mendelsohn (2000) show theoretical equivalence with RUM Models Can often be applied where insufficient studies exist for a meta analysis Quantitative possibilities have grown enormously since 1984 – many new tools are available! GIS Econometric tools

Hedonic Travel Cost Model Intuition Follows traditional hedonic theory Consumers choose best bundle of attributes given the prices they face In a this setting the cost is the price they face The precise prices consumers face depends on where they live – or which market they purchase trips in Cost may be out-of-pocket travel cost, time travel cost etc. It is the opportunity cost of the trip. The site chosen is the best bundle of attributes given the travel costs

Theory Model follows classic two-stage hedonic models Stage 1 is to realize that people in different markets probably face different prices In Nancy, Metz, etc look at all the hiking trails residents of each town chose and regress travel costs on the attributes of the trail Stage 2 is to take the different quantities purchased at the individual level prices and regress them on the quantities bought Pool individuals across Nancy, Metz etc and regress the quantities of attributes they purchased on the marginal hedonic prices estimated in stage

Implementation Issues Can be either a survey or visitation count method Surveys need to elicit places visited and cost Visitation counts (common in North America) Develop mean marginal and total willingness to pay measures for site attributes by the population Covers broad geographic areas Values can be aggregated up by total visitation to a place

Data Uses permit data from US Forest Service for Ansel Adams, Golden Trout and John Muir Wilderness Areas Total users total 25,363 There are 57 sites (trails) in the data Data includes 1991, 1992, and 1993 users

Detail of the Study Area

Study area of population

To put population study area in perspective Name of Country/StateArea in km2 Three European Countries France km2 Spain km2 Germany km2 Combined Area km2 Study Area California km2 Arizona km2 Nevada km2 Oregon km2 Washington km2 Study Area Total km2

Site attributes Ecosystem attributes Lodgepole pine Other conifers Riparian meadows Development attributes Trailhead campgrounds Nearby campgrounds Dirt roads

Analysis Two methods have been used to estimate marginal prices, both are applied here Marginal prices and demand are estimated for the attributes of interest Both marginal and total welfare measures are calculated

Econometric and Welfare Results Present Summary of Econometric Price Regression/LP Results Summarize 155 regression marginal price regression estimates uses only sites chosen Summarize 186 LP marginal price estimates uses all possible sites Summarize marginal welfare implications Present Summary Hedonic Demand Systems Seven attributes for each method Summarize total welfare implications (consumer surplus measures)

Summary of the Hedonic Price Estimates Regression ApproachLP Approach TotalSignificantMarginal VariablesunitsNegativePositiveNegativePositive Distance Price Dirt Roadsmiles Riparian Meadowsacres Trailhead Campgrounds0, Nearby Campgrounds0, Peak Elevation 1,000's of feet Lodgepole Pinemiles Other Conifersmiles

On the issue of insignificant and negative marginal prices

Total Marginal Social Value of Site Attributes for an Example Trail in the Ansel Adams Wilderness AttributeQuantityRegressionLinear Programming Dirt Roads Riparian Meadows Trailhead Campgrounds Nearby Campgrounds Peak Elevation Lodgepole Pine Other Conifers Number of Visitors67

Hedonic Price Regression Demand Results Quantities PricesDirtWetTrailheadNearbyPeakLodgepoleOther RoadsMeadowsCampgrounds ElevationPineConifers Dirt Roads ** **0.036 ( )( )( )( )( )( )( ) Wet Meadows ** *10.670**2.617** ( )( )( )( )( )( )( ) Trailhead Campgrounds0.122** ** **0.054** ( )( )( )( )( )( )( ) Nearby Campgrounds * 0.157** ** ( )( )( )( )( )( )( ) Peak Elevation * ** ** ** ( )( )( )( )( )( )( ) Lodgepole Pine0.700**10.670**-0.322** ** 0.215** ( )( )( )( )( )( )( ) Other Conifers **0.054**-0.112**0.276**0.215** ** ( )( )( )( )( )( )( )

Mean Demand System Welfare Results Regression ApproachLP Approach Variable Mean Quantity Mean Marginal Price Consumer Surplus Mean Marginal Price Consumer Surplus Dirt Roads0.49$0.19$28.56$1.09$5.93 Riparian Meadows20.287$0.02$32.69$0.10$ Trailhead Campgrounds0.407-$3.45$39.91$3.49$46.94 Nearby Campgrounds0.297$4.96$53.85$20.95$28.71 Peak Elevation $0.96$210.74$6.58$19, Lodgepole Pine0.872-$0.44$9.63$3.39$14.60 Other Conifers0.187$3.52$3.34$7.88$3.52

Conclusions and speculations Provides a tool to answer large scale questions Can be implemented much more easily with modern tools GIS for attributes, costs Statistical tools easily handle data May work well in European context Can be implemented in countries with few empirical studies No more costly than other surveys (if needed) Less demanding of analyst than RUMs or choice modeling Based solely on observed choices – no stated preference biases