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RATIONING ACCESS TO PROTECTED NATURAL AREAS: AN ECONOMIC ANALYSIS CHRISTOPHER M. FLEMING: 

RATIONING ACCESS TO PROTECTED NATURAL AREAS: AN ECONOMIC ANALYSIS CHRISTOPHER M. FLEMING

BACKGROUND: 

BACKGROUND

BACKGROUND CONT’D…: 

BACKGROUND CONT’D… This research aims to build a recreation demand model for a natural area in Queensland, in order to evaluate the effects of alternative management policies. In particular, the model will attempt to address the issue of overcrowding and estimate the welfare implications of using alternative methods to ration access to natural areas. The research will also look more closely at what is meant by ‘overcrowding’ or ‘congestion’ and how it affects visitor satisfaction. But first, the literature…

FOUNDATIONS: 

FOUNDATIONS UTILITARIANISM Originating in the works of David Hume (1711-1776) and Jeremy Bentham (1748-1832), and more completely expressed in the works of John Stuart Mills (1806-1873), in particular Utilitarianism (1863). NEOCLASSICAL TRADITION Theories of consumer preferences, utility, demand and marginal analysis formalised by William Jevons (1835-1883) and Carl Menger (1840-1921) WELFARE ECONOMICS In particular Alfred Marshall (1842-1924) and his Principles of Economics (1890) and Arthur Cecil (A..C.) Pigou (1877-1959) in his The Economics of Welfare (1920).

ENVIRONMENTAL VALUATION: 

ENVIRONMENTAL VALUATION CONSUMERS’ SURPLUS Used by Marshall for welfare analysis. John Hicks (1941) developed a set of money measures of utility changes – ‘compensating’ and ‘equivalent’ variation. . NON-MARKET VALUATION A number of methods; the two most common are the Travel Cost Method (RP) and Contingent Valuation (SP)

ENVIRONMENTAL VALUATION CONT’D…: 

ENVIRONMENTAL VALUATION CONT’D…

RECREATION DEMAND MODELS: 

RECREATION DEMAND MODELS PARTICIPATION MODELS Use quantity of visits to fit a standard neoclassical (Marshallian) demand function. (The Clawson-Knetsch approach) SITE SELECTION MODELS Use discrete choice models to model site selection based on Random Utility Maximisation (RUM) (McFadden, 1974; Hanemann, 1978) Revealed preference techniques are preferred by some because they are based on actual behaviour. Restricting our attention to this branch of the literature. Hotelling’s letter and Clawson and Knetsch’s subsequent papers have lead to a rich literature on modelling demand for recreation sites. They can be split into two types.

Slide8: 

Cameron (1992), using a participation model, was the first to combine stated and revealed preference data. Adamowicz et al. (1994) use the idea in a RUM model. Several authors (Kling, 1997; McConnell et al., 1999) have followed their lead. Hanley et al. (2002) use a RUM model to consider alternative means of rationing access to outdoor recreation areas. In a similar vein, Grijalva et al. (2002) use a national level RUM model to estimate welfare losses resulting from access restrictions to rock climbing sites in the U.S. Bateman et al. (2003) consider the use of Geographical Information Systems (GIS) in travel cost analysis. RECENT INNOVATIONS

Slide9: 

Fisher and Krutilla (1972) seek to maximise recreation benefits in an area subject to congestion. Cicchetti and Smith (1973; 1976) use a stated preference survey to estimate the effect of congestion on willingness to pay for wilderness experiences. Freeman and Haveman (1977) introduce heterogeneous tastes for congestion. McConnell (1977) models beach congestion. Cesario (1980) considers the value of establishing new sites in relieving the congestion of existing sites. More recently, Jakus and Shaw (1997) incorporate a congestion measure into a model using revealed preference data. Boxall et al. (2003) demonstrate that the welfare effects of congestion vary across areas, stages of a trip, and individuals. MODELLING CONGESTION

Slide10: 

LEISURE LITERATURE Wagar (1964) introduced the concept of carrying capacity for recreational sites. Limits of Acceptable Change were put forward by Stankey et al. (1985) as an alternative to carrying capacity. This has become the preferred approach. Numerous studies attempt to link number of encounters with visitor experience quality (Stankey, 1973; Shelby, 1980). Overall the findings have been mixed. Stewart and Cole (2001), using a diary method, find that the relationship is negative but weak, implying use restrictions are hard to justify on the basis of enhanced visitor experience alone. NOTE: The transport literature (unsurprisingly) also addresses congestion.

Slide11: 

The intended approach is to use a combination of revealed and stated preference data to estimate a recreation demand model of a case study site (yet to be determined) in Queensland. The choice of site (for example whether it is a single site with few substitutes or one of a series of sites) will in part determine the choice of model. This will involve an on-site survey of visitors, including questions designed to elicit a measure of visitors’ perceptions of congestion. Once a model has been estimated, the effect of alternative management policies will be simulated. It is hoped to be able to provide useful information to resource managers, to enable them to better understand the issues surrounding natural areas experiencing high levels of visitation. METHODOLOGY

Slide12: 

METHODOLOGY (SINGLE SITE) If a single site is chosen, a standard demand function approach estimating recreation demand could be appropriate. The frequency of visits for person ‘i’ to the site can be given by: Where Pi is the “price” to visitor ‘i’ of visiting the site and Zi is a vector of the visitor’s characteristics. If more than one site is chosen, however, a Random Utility Maximisation (RUM) approach may be more appropriate.

Slide13: 

METHODOLOGY (MULTIPLE SITES) Random utility theory considers utility to be a random variable, partly observable and partly not. The utility gained by visitor ‘i’ to site ‘j’ can be represented as follows: Where the observed component contains a vector of the characteristics of the site (X) and of the individual (Z): The probability that site ‘j’ will be visited by visitor ‘i’ is equal to the probability that the utility gained from visiting ‘j’ is greater than or equal to that gained from visiting any other site ‘k’ in a finite set ‘C’.

Slide14: 

A number of possibilities, for example Jakus and Shaw posit two functions for a visitor’s demand for a recreational site as follows: and, Where P represents the “price” of visiting a site, C is congestion, A a vector of site attributes and Y the visitor’s recreation income. In the former, the congestion term has as a direct utility effect, and therefore must appear in the visitor’s utility function. In the latter, congestion is viewed as part of the “price” of visiting the site (for example by increasing the amount of time needed to recreate). METHODOLOGY (INCORPORATING CONGESTION)

Slide15: 

In addition to addressing a real ‘on the ground’ policy issue in Queensland, I hope to make a contribution to the fields of non-market valuation and natural resource management by doing the following: Reconsidering the definition and treatment of congestion in revealed preference models. An area of significant debate and of increasing importance to the management and conservation of protected natural areas. Providing a systematic analysis of alternative rationing tools. Although the role of pricing has received extensive attention, other methods deserve further examination. Finally, the issue of rationing access to natural areas is often an emotive one, it is hoped that this research will promote a more rational debate on the subject. MY CONTRIBUTION

Slide16: 

Adamowicz, W., Louviere, J. and Williams, M. (1994) Combining revealed and stated preference methods of valuing environmental amenities. Journal of Environmental Economics and Management 26, 271-292. Bateman, I., Lovett, A., Brainard, J. and Jones, A. (2003) Using Geographical Information Systems (GIS) to estimate and transfer recreation demand functions. In Hanley, N., Shaw, W., and Wright, R. (2003) The New Economics of Outdoor Recreation, Edward Elgar, Cheltenham, 191-220. Boxall, P., Rollins, K. and Englin, J. (2003) Heterogeneous preferences for congestion during a wilderness experience, Resource and Energy Economics 25, 177-195. Brown, W. and Nawas, F. (1973) Impact of aggregation on the estimation of outdoor recreation demand functions, American Journal of Agricultural Economics, 55, 246-249. Cited in Hanley, N., Shaw, W., and Wright, R. (2003) The New Economics of Outdoor Recreation, Edward Elgar, Cheltenham. Cameron, T. (1992) Combining contingent valuation and travel cost data for the valuation of nonmarket goods. Land Economics 68, 302-317. Carson, R., Mitchell, R., Hanemann, W., Kopp, R., Presser, S. and Rudd, P. (1995) Contingent valuation and lost passive use: Damages from the Exxon Valdez, Department of Economics, University of California, San Diego, Discussion Paper 95-02, January. Cited in Perman, R., Ma, Y., McGilvray, J. and Common, M. (1999) Natural Resource and Environmental Economics, 2nd ed, Pearson Education, New York. Cesario, F. (1980) Congestion and the valuation of recreation benefits, Land Economics 56(3), 329-338. REFERENCES

Slide17: 

Cicchetti, C. and Smith, K. (1973) Congestion, quality deterioration and optimal use: Wilderness recreation in the Spanish Peaks Primitive Area, Social Science Research 2(1), 15-30. Cicchetti, C. and Smith, K. (1976) The Costs of Congestion: An Econometric Analysis of Wilderness Recreation, Ballinger, Cambridge, MA. Clawson, M. (1959) Methods of measuring the demand for and the value of outdoor recreation, reprint No 10, Washington D.C: RFF. Cited in Hanley, N., Shaw, W., and Wright, R. (2003) The New Economics of Outdoor Recreation, Edward Elgar, Cheltenham. Clawson, M. and Knetsch, J. (1966) Economics of Outdoor Recreation, Washington D.C.: RFF. Cited in Herriges, J. and Kling, C. (2003) Recreation Demand Models. In Folmer, H. and Tietenberg, T., (eds.) The International Yearbook of Environmental and Resource Economics 2003-2004: A Survey of Current Issues , Edward Elgar, Cheltenham. Davis, R. (1963) Big game hunting in the Maine Woods, Natural Resources Journal, 3, 239-249. Cited in Hanley, N., Shaw, W., and Wright, R. (2003) The New Economics of Outdoor Recreation, Edward Elgar, Cheltenham. Fisher, A. and Krutilla, J. (1972) Determination of optimal capacity of resource-based recreation facilities, Natural Resources Journal 12, 417-444. Freeman, A. and Haveman, R. (1973) Congestion, quality deterioration and heterogeneous tastes, Journal of Public Economics 8, 225-232. Grijalva, T., Berrens, R., Bohara, A., Jakus, P. and Shaw, W. (2002) Valuing the loss of rock climbing access in wilderness areas: A national-level random-utility model, Land Economics 78, 103-120. REFERENCES

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Hanemann, W. (1978) A Methodological and Empirical Study of the Recreation Benefits for Water Quality Improvement, Ph.D. dissertation, Department of Economics. Cited in Herriges, J. and Kling, C. (2003) Recreation Demand Models. In Folmer, H. and Tietenberg, T., (eds.) The International Yearbook of Environmental and Resource Economics 2003-2004: A Survey of Current Issues , Edward Elgar, Cheltenham. Hanley, N., Alvarez-Farizo, B. and Shaw, W. (2002) Rationing an open-access resource: Mountaineering in Scotland, Land Use Policy 19, 167-176. Hicks, J. (1941) The rehabilitation of consumers’ surplus. Review of Economic Studies 8, February, 108-116. Jakus, P. and Shaw, W. (1997) Congestion at recreation areas: Empirical evidence on perceptions, mitigating behaviour and management preferences, Journal of Environmental Economics and Management 50, 389-401. Kling, C. (1997) The gains from combining travel cost and contingent valuation data to value nonmarket goods, Land Economics 73, 428-439. McConnell, K. (1977) Congestion and willingness to pay: A study of beach use, Land Economics 53(2), 185-195. McConnell, K., Weninger, Q. and Strand, I. (1999) Testing the validity of contingent valuation be combining referendum responses with observed behavior. In Herriges, J. and Kling, C. (eds.), Valuing Recreation and the Environment: Revealed Preference Methods in Theory and Practice, Edward Elgar, Cheltenham, 65-120. McFadden, D. (1974) Conditional logit analysis of qualitative choice behavior. In Zarembka, P. (ed), Frontiers in Econometrics, Academic Press, New York, 105-142. REFERENCES

Slide19: 

Marshall, A. (1890) Principles of Economics, Macmillan, London. Cited in Perman, R., Ma, Y., McGilvray, J. and Common, M. (1999) Natural Resource and Environmental Economics, 2nd ed, Pearson Education, New York. Mills, J. (1863) Utilitarianism. Fontana Library, Collins, London. Cited in Perman, R., Ma, Y., McGilvray, J. and Common, M. (1999) Natural Resource and Environmental Economics, 2nd ed, Pearson Education, New York. Office of National Tourism. (1997) Ecotourism Snapshot: A focus on recent market research. Pigou, A. (1920) The Economics of Welfare, Macmillan, London. Cited in Perman, R., Ma, Y., McGilvray, J. and Common, M. (1999) Natural Resource and Environmental Economics, 2nd ed, Pearson Education, New York. Shelby, B. (1980) Crowding models for backcountry recreation, Land Economics 56, 43-55. Stankey, G. (1973) Visitor perceptions of wilderness recreation carrying capacity, Research paper INT-142, USDA Forest Service. Cited in Stewart, W. and Cole, D. (2001). Stankey, G., Cole, D., Lucas, R., Peterson, M., Frissel, S. and Washburne, R. (1985) The limits of acceptable change (LAC) system for wilderness planning. USDA Forest Service General Technical Report INT-176. Cited in Manning, R. (2003) What to do about crowding and solitude in parks and Wilderness? A reply to Stewart and Cole, Journal of Leisure Research 35(1), 107-118. Stewart, W. and Cole, D. (2001) Number of encounters and experience quality in Grand Canyon backcountry: Consistently negative and weak relationships, Journal of Leisure Research 33(1), 106-120. Wagar, J. (1964) Recreational carrying capacity reconsidered, Journal of Forestry 72, 274-278. REFERENCES