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Economic analysis of environmental preferences: progress and prospects
Computers, Environment and Urban Systems 25 (2001) 131±139 www.elsevier.com/locate/compenvurbsys
Editorial
Economic analysis of environmental preferences: progress and prospects The purpose of this editorial is to review recent developments in the economic valuation of environmental variables Ð such as noise levels, air pollution and visual amenity. We begin by outlining the main assumptions that underpin valuation of environmental characteristics, and the types of techniques that are used for this purpose. Today, the availability of GIS is transforming many aspects of valuation practice, and here we discuss such issues as enhanced variable speci®cations, larger sample sizes, and the greater replicability of analyses. These developments, in turn, have increased the transferability of bene®t parameters from one location to another. GIS also creates new opportunities to enhance spatial representations of the environmental impacts of policy decisions, something that is of particular relevance given the growing interest in matters of environmental equity as a component of sustainable development. 1. Economic value and the environment When discussing the economics of the environment it is useful to begin by making a distinction between market price and value. Relatively few of the services provided by the environment have a readily observable, speci®c price, while it has long been recognised by economists that both market priced and non-marketed commodities can yield utility (i.e. increase the welfare of the individual using them). More recently, particularly from the 1980s onwards, such use (or instrumental ) values have been viewed as but one element of the total economic value (TEV) of a commodity (Pearce & Turner, 1990). This approach has been especially important with respect to environmental commodities, many of which can have diverse TEV components. Fig. 1 illustrates the concept of TEV; the examples in brackets relate to the types of value associated with forestry. The majority of economic analyses concentrate upon the instrumental values of a commodity. Prominent amongst these are the direct use values generated by private goods that are often re¯ected in market prices, and those indirect use values associated with public goods which are generally unpriced (Turner, Pearce, & Bateman, 1994). A unifying aspect of these values is that they are all generated via the present use of the commodity by the valuing individual. An extension of the temporal frame 0198-9715/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0198-9715(01)00008-4
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A. Lovett, I. Bateman / Comput., Environ. and Urban Systems 25 (2001) 131±139
Fig. 1. Elements of the total economic value of forestry (Bateman, 1999).
allows for individuals valuing the option of future use (Krutilla & Fisher, 1975). Related to this is the notion of bequest value where the valuing individual gains utility from the provision of use or non-use values to present and/or future others. Pure non-use values are most commonly associated with preferences for the continued existence of entities such as species of ¯ora and fauna or even whole ecosystems. This generally involves both intra- and inter-generational values, and because of the lack of an instrumental element, has proved problematic to measure (Pearce & Barbier, 2000). Nevertheless, the theoretical case for the `existence of existence value' is widely accepted (e.g. Young, 1992). An important issue in the debates regarding wider de®nitions of value concerns the extent of the `moral reference class' in decision making (Turner et al., 1994). A `deep ecology' ethic would place animal, plant and ecosystem interests on an equal footing with human preferences (O'Riordan, 1976; Singer, 1993) and argue that these entities possess an intrinsic value separate from anthropocentric existence values. This, in turn, leads to an emphasis on concepts such as the `precautionary principle' and `ecological resilience' that are central to decision making under an `ecological economics' paradigm (Costanza & Daly, 1992; Pearce & Barbier, 2000). From this discussion it is evident that economists cannot, by de®nition, estimate the `value of the environment', or even the full value of an environmental commodity, because intrinsic value cannot be translated into human terms. However, estimating the size (in monetary or other terms) of individuals' preferences for environmental goods and services is theoretically feasible, and in some instances quite possible. In particular, a number of methods exist for placing monetary values on preferences for the non-market environmental goods that people are familiar with. These techniques have been widely employed in the context of cost-bene®t analyses and are summarised in Fig. 2 (for further details see Bateman, 1999; Garrod & Willis, 1999). Approaches to the monetary valuation of environmental preferences can be divided into formal valuation (or demand curve) methods and ad-hoc environmental pricing techniques. In theoretical terms, the valuation and pricing methods are quite distinct. Whereas the former are based upon individuals' preferences and yield conventional
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Fig. 2. Methods for the valuation of environmental preferences (Bateman, 1999).
welfare measures (as de®ned by the standard, or `neoclassical', economic theory of value; hence the term valuation methods), the pricing techniques are much more akin to market price observations. For example, a `shadow project' pricing approach examines hypothetical environmental asset replacement, restoration or transplantation schemes to generate prices for the environmental costs of a proposed project (Buckley, 1989). Such methods can provide useful information for appraisal purposes, but they re¯ect only the costs of protecting or providing environmental assets and not the bene®ts involved. This is inadequate for a complete cost-bene®t analysis and generally valuation methods are preferable to pricing techniques. Valuation methods all ultimately rely upon the preferences of individuals, but can be divided into two main categories. These are revealed preference approaches, in which values are obtained from examining individuals purchases of market priced goods that possess associated environmental characteristics, and expressed preference techniques where values are elicited through questionnaire surveys. Revealed preference methods focus on surrogate markets (e.g. those in¯uenced by the environmental characteristics) to determine values of non-marketed commodities. For example, the hedonic pricing (HP) method is most commonly applied to in¯uences on property sales and is based on the idea that a good such as a house can be regarded as a bundle of characteristics. Each of these characteristics has its own implicit price, but some (such as noise levels or visual amenity) may have no direct market. Individuals are regarded as expressing their preferences for particular non-market attributes through their selection of speci®c bundles of characteristics, and ultimately via the market prices paid for properties (Orford, 1999; Pearce & Barbier, 2000). For instance, a house with a scenic view of lakes and woodland will usually, all other things being equal, be more expensive than one overlooking a gasworks. In practical terms the HP method uses econometric techniques (typically multiple regression analysis) to relate data on house prices to details of property attributes. With the resulting predictive equation it is possible to identify how overall prices change in response to increases in an environmental characteristic and so derive an implicit price for the non-market commodity concerned. The travel cost (TC) method is another revealed preference technique and has been widely used to assess values of recreational sites (Bockstael, 1995; Clawson &
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Knetsch, 1966). It focuses on the costs incurred by visitors to recreation sites (e.g. in terms of travel time, journey expenditure or entry fees) and relates these to the number of visits undertaken to ultimately derive estimates of site value. There are two main variants of the TC method, one concentrating upon the costs faced and associated number of visits enjoyed by individuals, while the other is based on per capita characteristics for zones around a site and the visit rates exhibited by those zones. Both variants have strengths and weaknesses (Bateman, 1993), but share a methodology where regression techniques are used to predict visit rates from travel costs, socio-economic characteristics of the population, indicators of site quality and the availability of alternative (i.e. substitute) destinations. A demand curve for visits can be derived from the resulting regression equation, integration of which gives the consumer surplus (i.e. the value of the site to visitors in excess of the direct cost which they have to pay for its use). Expressed preference approaches to valuation are based on questionnaire surveys. The most common approach is contingent valuation (CV) where a hypothetical market for a particular environmental commodity is described. Respondents may then be asked questions regarding their willingness to pay for a particular improvement in the provision of a commodity (e.g. a reduced level of air pollution or enhanced scenic amenity). Questions may also focus on loss of an environmental asset and consequently ask about the amount respondents would be willing to accept as compensation to oset the welfare change. The results from such surveys are subsequently assessed and processed via econometric techniques to derive monetary valuation measures (Bateman & Willis, 1999; Mitchell & Carson, 1989). CV has the advantage of being a very ¯exible valuation methodology and has been widely applied in both developed and developing countries (Pearce & Barbier, 2000). Nevertheless, the reliability of results from such studies has been questioned (Diamond & Hausman, 1994) and in certain instances may be very controversial (e.g. the valuation of oil spill damage caused by the grounding of the Exxon Valdez tanker in Alaska). This has led to a general recognition that great care needs to be taken with the design and implementation of CV surveys to avoid various sources of bias (Arrow, Solow, Portney, Leamer, Radner, & Schuman, 1993; Bateman & Willis, 1999). More recently, there has been increased interest in other questionnairebased approaches such as the contingent ranking or stated preference (also known as conjoint analysis) methods. These techniques essentially involve rating sets of attributes and avoid some (although not all) of the elicitation problems associated with CV, but still require care in survey design to ensure that respondents do not ®nd choosing between combinations of characteristics too dicult or fatiguing (Hanley et al., 1998; Johnson & Desvouges, 1997). 2. Applying GIS techniques The use of GIS in environmental economics essentially dates from the mid 1990s and in many respects this innovation is very overdue. The unrealistic assumptions, implicit or otherwise, made by economists in order to implement their analyses have
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often attracted critical comment, but the application of GIS has provided a means of avoiding some of the worst simpli®cations in valuation methodologies. For instance, studies using TC techniques to estimate the recreational value associated with openaccess rural sites have often assumed that all trips take place in straight lines between origins and destinations, and based analyses on concentric surrounding zones (e.g. English & Bowker, 1996). With a GIS, travel costs can be calculated in a manner which is far more sensitive to the nature of the available road network, and research has demonstrated that this results in lower consumer surplus estimates than are derived with a straight line simpli®cation (Bateman, Garrod, Brainard, & Lovett, 1996; Bateman, Brainard, Lovett, & Garrod, 1999a). Another recognised problem in TC analyses has been the de®nition of substitute recreation opportunities (Kling, 1989). While this has been tackled using various ad-hoc approaches (Ozuna, Jones, & Capps, 1993; Willis & Benson, 1989), the integrative capabilities of GIS make it much easier to quantify access to alternative destinations in a consistent and replicable manner (Bhat & Bergstrom, 1997; Brainard, Lovett, & Bateman, 1999; Lovett, Brainard, & Bateman, 1997). Even more important, perhaps, are issues of origin speci®cation and zone de®nition. Many TC studies have de®ned visitor origins using centroids of administrative areas (e.g. US counties) without recognising that this may misrepresent the underlying distribution of population and ultimately lead to in¯ated welfare estimates (Bateman et al., 1999a). The availability of GIS, however, has made it practical to work with ®ner resolution visitor and other demographic data (e.g. Boxall, McFarlane, & Gartell, 1996), and to experiment with more theoretically appropriate origin zones (utilising geographical concepts arising from the modi®able areal unit problem: Brainard, Lovett, & Bateman, 1997; Brainard et al., 1999). There have also been applications of GIS in HP studies that aim to isolate the in¯uence of environmental characteristics on property prices. One straightforward use has been to calculate distances from properties to facilities such as parks or woodland areas (Metz & Clark, 1997; Powe, Garrod, Brunsdon, & Willis, 1997), while other analyses have generated more sophisticated indices of neighbourhood characteristics (e.g. Geoghegan, Wainger, & Bockstel, 1997; Orford, 1999). Further innovations have included the use of viewshed techniques to quantify views of water features or industrial areas which in the past (e.g. McLeod, 1984) have required considerable ®eldwork and/or involved appreciable subjectivity (Lake, Lovett, Bateman, & Langford, 1998). It has also proved possible to extract property characteristics from high resolution digital map databases to compensate for information that was not readily available from other sources (Lake, Lovett, Bateman, & Day, 2000a). Overall, these developments have helped to improve the speci®cation of HP equations and increase the sample size that can be analysed to the extent that much more speci®c environmental in¯uences on property prices can now be distinguished. This, in turn, has considerable implications for impact assessment and compensation procedures associated with developments such as new road schemes (Lake, Bateman, Day, & Lovett, 2000b). Both of the types of valuation methodologies discussed above are revealed preference techniques. The use of GIS with expressed preference approaches has been
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much less common, but this does not mean that it will not occur in the future. In particular, there may be opportunities to integrate current developments in the generation of 3D or virtual landscapes from GIS databases (Limp, 2000; Lovett et al., 2001) with stated preference valuation methods. There are already studies using colour landscape photographs as one element in choice experiments (e.g. Hanley et al., 1998) and research in the USA is incorporating virtual reality representations of consumer products within a broader conjoint analysis methodology (see www.rti.org/vr/w/conjoint.cfm). It would therefore appear increasingly feasible to use visualisations of a modi®ed landscape generated from a GIS database as part of the environmental economic assessment of construction projects or proposed land use changes. 3. Transferring and extending valuations As experience with undertaking revealed and expressed preference valuations has grown, so there has been increasing interest in issues of meta-analysis and the scope for bene®t transfer. The latter has been de®ned by Smith (1993, p. 7) as the process of ``adapting existing models or value estimates to construct valuations for resources that are dierent in type or location from the one originally studied''. From a practical decision making perspective this is an attractive option because of the costs involved in implementing a series of separate valuation studies. The use of GIS can contribute to bene®t transfer because, given suitable data, it is relatively straightforward to reproduce variables included in the original model for new locations and then derive comparable value estimates (Eade & Moran, 1996). Perhaps the most obvious example concerns the transfer of TC models (where measures of proximity are so important for the prediction of visitor numbers), and several studies have demonstrated the possibilities in the context of woodland recreation value (Bateman, Lovett, & Brainard, 1999b; Brainard et al., 1999). It is fair to note, however, that a similar GIS-assisted methodology could be implemented with other valuation techniques, and current research at the University of East Anglia is investigating the scope for transferring hedonic pricing models between two UK cities. The use of GIS has also enhanced valuation research and cost-bene®t analysis by providing a much greater ability to represent spatial variations in welfare estimates and outcomes in such studies. This trend has been most apparent in research on land use, where the integration of data on factors such as agricultural productivity, timber yield, recreational demand and biodiversity has allowed improved multidimensional assessment of policy scenarios (O'Callaghan, 1996; Bateman, Ennew, Lovett, & Rayner, 1999c; Bateman, Lovett, & Brainard, forthcoming). In addition, a GIS-based analysis can highlight where certain changes in land use (e.g. from farming to forestry) would be particularly advantageous or detrimental. This geographical speci®city is important in enabling greater consideration of equity issues (e.g. in access to environmental amenities or other resources) and the formulation of policies that support sustainable development (Pearce & Barbier, 2000; Pearce, Markandya, & Barbier, 1989).
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4. Prospects From the above discussion it should be evident that the recent use of GIS has contributed to substantial improvement in the economic valuation of environmental preferences. Some of these advances are essentially more sophisticated or reliable measurement of variables that had previously been treated in a rather simplistic manner, but others open up signi®cant new avenues for valuation research. This development can be seen as part of a broader growing awareness regarding geographical factors and spatial dynamics within economics (e.g. Bockstael, 1996), a situation that suggests there may be considerable opportunities for future multidisciplinary studies. References Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R., & Schuman, E. H. (1993). Report to the National Oceanic and Atmospheric Administration, Panel on Contingent Valuation. Federal Register, 58(10). Bateman, I. J. (1993). Valuation of the environment, methods and techniques: revealed preference methods. In R. K. Turner, Sustainable economics and management: principles and practice. London: Belhaven Press. Bateman, I. J. (1999). Environmental impact assessment, cost-bene®t analysis and the valuation of environmental impacts. In J. Petts, Handbook of environmental impact assessment, volume 1 Ð environmental impact assessment: process, methods and potential. Oxford: Blackwell Science. Bateman, I. J., & Willis, K. G. (1999). Contingent valuation of environmental preferences: assessing theory and practice in the USA, Europe, and developing countries. Oxford: Oxford University Press. Bateman, I. J., Brainard, J. S., Lovett, A. A., & Garrod, G. D. (1999a). The impact of measurement assumptions upon individual travel cost estimates of consumer surplus: a GIS analysis. Regional Environmental Change, 1(1), 24±30. Bateman, I. J., Ennew, C., Lovett, A. A., & Rayner, A. H. (1999c). Modelling and mapping agricultural output values using farm speci®c details and environmental databases. Journal of Agricultural Economics, 50(3), 488±511. Bateman, I. J., Garrod, G. D., Brainard, J. S., & Lovett, A. A. (1996). Measurement, valuation and estimation issues in the travel cost method: a geographical information systems approach. Journal of Agricultural Economics, 47(2), 191±205. Bateman, I. J., Lovett, A. A., & Brainard, J. S. (1999b). Developing a methodology for bene®t transfers using geographical information systems: modelling demand for woodland recreation. Regional Studies, 33(3), 191±205. Bateman, I. J., Lovett, A. A., & Brainard, J. S. (forthcoming). Applied environmental economics: a GIS approach to cost bene®t analysis. Cambridge: Cambridge University Press. Bhat, G., & Bergstrom, J. C. (1997). Integration of geographical information systems based spatial analysis in recreation demand analysis. Faculty Series 96±26. Athens, GA: Department of Agricultural and Applied Economics, University of Georgia. Bockstael, N. E. (1995). Travel cost models. In D. W. Bromley, The handbook of environmental economics. Cambridge MA: Blackwell. Bockstael, N. E. (1996). Modeling economics and ecology: the importance of a spatial perspective. American Journal of Agricultural Economics, 78, 1168±1180. Boxall, P. C., McFarlane, B. L., & Gartrell, M. (1996). An aggregate travel cost approach to valuing forest recreation at managed sites. The Forestry Chronicle, 72, 615±621. Brainard, J. S., Lovett, A. A., & Bateman, I. J. (1997). Using isochrone surfaces in travel cost models. Journal of Transport Geography, 5(2), 117±126.
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Brainard, J., Lovett, A., & Bateman, I. (1999). Integrating geographical information systems into travel cost analysis and bene®t transfer. International Journal of Geographical Information Science, 13, 227±246. Buckley, G. P. (1989). Biological habitat reconstruction. London: Belhaven Press. Clawson, M., & Knetsch, J. L. (1966). Economics of outdoor recreation. Baltimore: Johns Hopkins Press (for Resources for the Future). Costanza, R., & Daly, H. (1992). Natural capital and sustainable development. Conservation Biology, 6, 37±46. Diamond, P. A., & Hausman, J. A. (1994). Contingent valuation: is some number better than no number? Journal of Economic Perspectives, 8(4), 45±64. Eade, J. D. O., & Moran, D. (1996). Spatial economic valuation: bene®ts transfer using geographical information systems. Journal of Environmental Management, 48, 97±110. English, D. B. K., & Bowker, J. M. (1996). Sensitivity of whitewater rafting consumers surplus to pecuniary travel cost speci®cations. Journal of Environmental Management, 47, 79±91. Garrod, G., & Willis, K. (1999). Economic valuation of the environment. Cheltenham: Edward Elgar. Geoghegan, J., Wainger, L. A., & Bockstel, N. E. (1997). Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS. Ecological Economics, 23, 251±264. Hanley, N., MacMillan, D., Wright, R. E., Bullock, C., Simpson, I., Parsisson, D., & Crabtree, B. (1998). Contingent valuation versus choice experiments: estimating the bene®ts of environmentally sensitive areas in Scotland. Journal of Agricultural Economics, 49(1), 1±15. Johnson, F. R., & Desvousges, W. H. (1997). Estimating stated preferences with rated-pair data: environmental, health and employment eects of energy programs. Journal of Environmental Economics and Management, 34, 79±99. Kling, C. L. (1989). A note on the welfare eects of omitting substitute prices and qualities from travel cost models. Land Economics, 65, 290±296. Krutilla, J. V., & Fisher, A. C. (1975). The economics of natural environments: studies in the valuation of commodity and amenity resources. Baltimore: Johns Hopkins University Press (for Resources for the Future). Lake, I. R., Bateman, I. J., Day, B., & Lovett, A. A. (2000b). Improving land compensation procedures via GIS and hedonic pricing. Environment and Planning C: Government and Policy, 18, 681±696. Lake, I. R., Lovett, A. A., Bateman, I. J., & Langford, I. H. (1998). Modelling environmental in¯uences on property prices in an urban environment. Computers, Environment and Urban Systems, 22, 121±136. Lake, I. R., Lovett, A. A., Bateman, I. J., & Day, B. (2000a). Using GIS and large-scale digital data to implement hedonic pricing studies. International Journal of Geographical Information Science, 14, 521±541. Limp, F. (2000). Put the `®zz' into `data viz'. GEOWorld, 13(9), 40±45. Lovett, A. A., Brainard, J. S., & Bateman, I. J. (1997). Improving bene®t transfer demand functions: a GIS approach. Journal of Environmental Management, 51, 373±389. Lovett, A. A., Kennaway, R., Sunnenberg, G., Cobb, R., Dolman, P., O'Riordan, T., & Arnold, D. (2001). Visualising sustainable agricultural landscapes. In P. Fisher, & D. Unwin, Virtual reality in geography. London: Taylor and Francis. McLeod, P. B. (1984). The demand for local amenity: an hedonic price analysis. Environment and Planning A, 16, 389±400. Metz, W. C., & Clark, D. E. (1997). The eect of decisions about spent nuclear fuel storage on residential property prices. Risk Analysis, 17, 571±582. Mitchell, R. C., & Carson, R. (1989). Using surveys to value public goods: the contingent valuation method. Washington DC: Resources for the Future. O'Callaghan, J. R. (1996). Land use: the interaction of economics, ecology and hydrology. London: Chapman and Hall. O'Riordan, T. (1976). Environmentalism. London: Pion. Orford, S. (1999). Valuing the built environment: GIS and house price analysis. Aldershot: Ashgate. Ozuna, T., Jones, L. L., & Capps, O. (1993). Functional form and welfare measures in truncated recreation models. American Journal of Agricultural Economics, 75, 1030±1035.
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Pearce, D. W., & Barbier, E. B. (2000). Blueprint for a sustainable economy. London: Earthscan. Pearce, D. W., Markandya, A., & Barbier, E. B. (1989). Blueprint for a green economy. London: Earthscan. Pearce, D. W., & Turner, R. K. (1990). Economics of natural resources and the environment. Hemel Hempstead: Harvester Wheatsheaf. Powe, N. A., Garrod, G. D., Brunsdon, C. F., & Willis, K. G. (1997). Using a geographic information system to estimate an hedonic price model of the bene®ts of woodland access. Forestry, 70, 139±149. Singer, P. (1993). Practical ethics. Cambridge: Cambridge University Press. Smith, V. K. (1993). Nonmarket valuation of environmental resources: an interpretive appraisal. Land Economics, 69, 1±26. Turner, R. K., Pearce, D. W., & Bateman, I. J. (1994). Environmental economics: an elementary introduction. Hemel Hempstead: Harvester Wheatsheaf. Willis, K. G., & Benson, J. F. (1989). Recreational values of forests. Forestry, 62, 93±110. Young, M. D. (1992). Sustainable investment and resource use. Carnforth, UK: UNESCO/Parthenon.
A. Lovett, I. Bateman School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK E-mail address: [email protected] PII: S0198-9715(01)00008-4
Editorial
Economic analysis of environmental preferences: progress and prospects The purpose of this editorial is to review recent developments in the economic valuation of environmental variables Ð such as noise levels, air pollution and visual amenity. We begin by outlining the main assumptions that underpin valuation of environmental characteristics, and the types of techniques that are used for this purpose. Today, the availability of GIS is transforming many aspects of valuation practice, and here we discuss such issues as enhanced variable speci®cations, larger sample sizes, and the greater replicability of analyses. These developments, in turn, have increased the transferability of bene®t parameters from one location to another. GIS also creates new opportunities to enhance spatial representations of the environmental impacts of policy decisions, something that is of particular relevance given the growing interest in matters of environmental equity as a component of sustainable development. 1. Economic value and the environment When discussing the economics of the environment it is useful to begin by making a distinction between market price and value. Relatively few of the services provided by the environment have a readily observable, speci®c price, while it has long been recognised by economists that both market priced and non-marketed commodities can yield utility (i.e. increase the welfare of the individual using them). More recently, particularly from the 1980s onwards, such use (or instrumental ) values have been viewed as but one element of the total economic value (TEV) of a commodity (Pearce & Turner, 1990). This approach has been especially important with respect to environmental commodities, many of which can have diverse TEV components. Fig. 1 illustrates the concept of TEV; the examples in brackets relate to the types of value associated with forestry. The majority of economic analyses concentrate upon the instrumental values of a commodity. Prominent amongst these are the direct use values generated by private goods that are often re¯ected in market prices, and those indirect use values associated with public goods which are generally unpriced (Turner, Pearce, & Bateman, 1994). A unifying aspect of these values is that they are all generated via the present use of the commodity by the valuing individual. An extension of the temporal frame 0198-9715/01/$ - see front matter # 2001 Elsevier Science Ltd. All rights reserved. PII: S0198-9715(01)00008-4
132
A. Lovett, I. Bateman / Comput., Environ. and Urban Systems 25 (2001) 131±139
Fig. 1. Elements of the total economic value of forestry (Bateman, 1999).
allows for individuals valuing the option of future use (Krutilla & Fisher, 1975). Related to this is the notion of bequest value where the valuing individual gains utility from the provision of use or non-use values to present and/or future others. Pure non-use values are most commonly associated with preferences for the continued existence of entities such as species of ¯ora and fauna or even whole ecosystems. This generally involves both intra- and inter-generational values, and because of the lack of an instrumental element, has proved problematic to measure (Pearce & Barbier, 2000). Nevertheless, the theoretical case for the `existence of existence value' is widely accepted (e.g. Young, 1992). An important issue in the debates regarding wider de®nitions of value concerns the extent of the `moral reference class' in decision making (Turner et al., 1994). A `deep ecology' ethic would place animal, plant and ecosystem interests on an equal footing with human preferences (O'Riordan, 1976; Singer, 1993) and argue that these entities possess an intrinsic value separate from anthropocentric existence values. This, in turn, leads to an emphasis on concepts such as the `precautionary principle' and `ecological resilience' that are central to decision making under an `ecological economics' paradigm (Costanza & Daly, 1992; Pearce & Barbier, 2000). From this discussion it is evident that economists cannot, by de®nition, estimate the `value of the environment', or even the full value of an environmental commodity, because intrinsic value cannot be translated into human terms. However, estimating the size (in monetary or other terms) of individuals' preferences for environmental goods and services is theoretically feasible, and in some instances quite possible. In particular, a number of methods exist for placing monetary values on preferences for the non-market environmental goods that people are familiar with. These techniques have been widely employed in the context of cost-bene®t analyses and are summarised in Fig. 2 (for further details see Bateman, 1999; Garrod & Willis, 1999). Approaches to the monetary valuation of environmental preferences can be divided into formal valuation (or demand curve) methods and ad-hoc environmental pricing techniques. In theoretical terms, the valuation and pricing methods are quite distinct. Whereas the former are based upon individuals' preferences and yield conventional
A. Lovett, I. Bateman / Comput., Environ. and Urban Systems 25 (2001) 131±139
133
Fig. 2. Methods for the valuation of environmental preferences (Bateman, 1999).
welfare measures (as de®ned by the standard, or `neoclassical', economic theory of value; hence the term valuation methods), the pricing techniques are much more akin to market price observations. For example, a `shadow project' pricing approach examines hypothetical environmental asset replacement, restoration or transplantation schemes to generate prices for the environmental costs of a proposed project (Buckley, 1989). Such methods can provide useful information for appraisal purposes, but they re¯ect only the costs of protecting or providing environmental assets and not the bene®ts involved. This is inadequate for a complete cost-bene®t analysis and generally valuation methods are preferable to pricing techniques. Valuation methods all ultimately rely upon the preferences of individuals, but can be divided into two main categories. These are revealed preference approaches, in which values are obtained from examining individuals purchases of market priced goods that possess associated environmental characteristics, and expressed preference techniques where values are elicited through questionnaire surveys. Revealed preference methods focus on surrogate markets (e.g. those in¯uenced by the environmental characteristics) to determine values of non-marketed commodities. For example, the hedonic pricing (HP) method is most commonly applied to in¯uences on property sales and is based on the idea that a good such as a house can be regarded as a bundle of characteristics. Each of these characteristics has its own implicit price, but some (such as noise levels or visual amenity) may have no direct market. Individuals are regarded as expressing their preferences for particular non-market attributes through their selection of speci®c bundles of characteristics, and ultimately via the market prices paid for properties (Orford, 1999; Pearce & Barbier, 2000). For instance, a house with a scenic view of lakes and woodland will usually, all other things being equal, be more expensive than one overlooking a gasworks. In practical terms the HP method uses econometric techniques (typically multiple regression analysis) to relate data on house prices to details of property attributes. With the resulting predictive equation it is possible to identify how overall prices change in response to increases in an environmental characteristic and so derive an implicit price for the non-market commodity concerned. The travel cost (TC) method is another revealed preference technique and has been widely used to assess values of recreational sites (Bockstael, 1995; Clawson &
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Knetsch, 1966). It focuses on the costs incurred by visitors to recreation sites (e.g. in terms of travel time, journey expenditure or entry fees) and relates these to the number of visits undertaken to ultimately derive estimates of site value. There are two main variants of the TC method, one concentrating upon the costs faced and associated number of visits enjoyed by individuals, while the other is based on per capita characteristics for zones around a site and the visit rates exhibited by those zones. Both variants have strengths and weaknesses (Bateman, 1993), but share a methodology where regression techniques are used to predict visit rates from travel costs, socio-economic characteristics of the population, indicators of site quality and the availability of alternative (i.e. substitute) destinations. A demand curve for visits can be derived from the resulting regression equation, integration of which gives the consumer surplus (i.e. the value of the site to visitors in excess of the direct cost which they have to pay for its use). Expressed preference approaches to valuation are based on questionnaire surveys. The most common approach is contingent valuation (CV) where a hypothetical market for a particular environmental commodity is described. Respondents may then be asked questions regarding their willingness to pay for a particular improvement in the provision of a commodity (e.g. a reduced level of air pollution or enhanced scenic amenity). Questions may also focus on loss of an environmental asset and consequently ask about the amount respondents would be willing to accept as compensation to oset the welfare change. The results from such surveys are subsequently assessed and processed via econometric techniques to derive monetary valuation measures (Bateman & Willis, 1999; Mitchell & Carson, 1989). CV has the advantage of being a very ¯exible valuation methodology and has been widely applied in both developed and developing countries (Pearce & Barbier, 2000). Nevertheless, the reliability of results from such studies has been questioned (Diamond & Hausman, 1994) and in certain instances may be very controversial (e.g. the valuation of oil spill damage caused by the grounding of the Exxon Valdez tanker in Alaska). This has led to a general recognition that great care needs to be taken with the design and implementation of CV surveys to avoid various sources of bias (Arrow, Solow, Portney, Leamer, Radner, & Schuman, 1993; Bateman & Willis, 1999). More recently, there has been increased interest in other questionnairebased approaches such as the contingent ranking or stated preference (also known as conjoint analysis) methods. These techniques essentially involve rating sets of attributes and avoid some (although not all) of the elicitation problems associated with CV, but still require care in survey design to ensure that respondents do not ®nd choosing between combinations of characteristics too dicult or fatiguing (Hanley et al., 1998; Johnson & Desvouges, 1997). 2. Applying GIS techniques The use of GIS in environmental economics essentially dates from the mid 1990s and in many respects this innovation is very overdue. The unrealistic assumptions, implicit or otherwise, made by economists in order to implement their analyses have
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often attracted critical comment, but the application of GIS has provided a means of avoiding some of the worst simpli®cations in valuation methodologies. For instance, studies using TC techniques to estimate the recreational value associated with openaccess rural sites have often assumed that all trips take place in straight lines between origins and destinations, and based analyses on concentric surrounding zones (e.g. English & Bowker, 1996). With a GIS, travel costs can be calculated in a manner which is far more sensitive to the nature of the available road network, and research has demonstrated that this results in lower consumer surplus estimates than are derived with a straight line simpli®cation (Bateman, Garrod, Brainard, & Lovett, 1996; Bateman, Brainard, Lovett, & Garrod, 1999a). Another recognised problem in TC analyses has been the de®nition of substitute recreation opportunities (Kling, 1989). While this has been tackled using various ad-hoc approaches (Ozuna, Jones, & Capps, 1993; Willis & Benson, 1989), the integrative capabilities of GIS make it much easier to quantify access to alternative destinations in a consistent and replicable manner (Bhat & Bergstrom, 1997; Brainard, Lovett, & Bateman, 1999; Lovett, Brainard, & Bateman, 1997). Even more important, perhaps, are issues of origin speci®cation and zone de®nition. Many TC studies have de®ned visitor origins using centroids of administrative areas (e.g. US counties) without recognising that this may misrepresent the underlying distribution of population and ultimately lead to in¯ated welfare estimates (Bateman et al., 1999a). The availability of GIS, however, has made it practical to work with ®ner resolution visitor and other demographic data (e.g. Boxall, McFarlane, & Gartell, 1996), and to experiment with more theoretically appropriate origin zones (utilising geographical concepts arising from the modi®able areal unit problem: Brainard, Lovett, & Bateman, 1997; Brainard et al., 1999). There have also been applications of GIS in HP studies that aim to isolate the in¯uence of environmental characteristics on property prices. One straightforward use has been to calculate distances from properties to facilities such as parks or woodland areas (Metz & Clark, 1997; Powe, Garrod, Brunsdon, & Willis, 1997), while other analyses have generated more sophisticated indices of neighbourhood characteristics (e.g. Geoghegan, Wainger, & Bockstel, 1997; Orford, 1999). Further innovations have included the use of viewshed techniques to quantify views of water features or industrial areas which in the past (e.g. McLeod, 1984) have required considerable ®eldwork and/or involved appreciable subjectivity (Lake, Lovett, Bateman, & Langford, 1998). It has also proved possible to extract property characteristics from high resolution digital map databases to compensate for information that was not readily available from other sources (Lake, Lovett, Bateman, & Day, 2000a). Overall, these developments have helped to improve the speci®cation of HP equations and increase the sample size that can be analysed to the extent that much more speci®c environmental in¯uences on property prices can now be distinguished. This, in turn, has considerable implications for impact assessment and compensation procedures associated with developments such as new road schemes (Lake, Bateman, Day, & Lovett, 2000b). Both of the types of valuation methodologies discussed above are revealed preference techniques. The use of GIS with expressed preference approaches has been
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much less common, but this does not mean that it will not occur in the future. In particular, there may be opportunities to integrate current developments in the generation of 3D or virtual landscapes from GIS databases (Limp, 2000; Lovett et al., 2001) with stated preference valuation methods. There are already studies using colour landscape photographs as one element in choice experiments (e.g. Hanley et al., 1998) and research in the USA is incorporating virtual reality representations of consumer products within a broader conjoint analysis methodology (see www.rti.org/vr/w/conjoint.cfm). It would therefore appear increasingly feasible to use visualisations of a modi®ed landscape generated from a GIS database as part of the environmental economic assessment of construction projects or proposed land use changes. 3. Transferring and extending valuations As experience with undertaking revealed and expressed preference valuations has grown, so there has been increasing interest in issues of meta-analysis and the scope for bene®t transfer. The latter has been de®ned by Smith (1993, p. 7) as the process of ``adapting existing models or value estimates to construct valuations for resources that are dierent in type or location from the one originally studied''. From a practical decision making perspective this is an attractive option because of the costs involved in implementing a series of separate valuation studies. The use of GIS can contribute to bene®t transfer because, given suitable data, it is relatively straightforward to reproduce variables included in the original model for new locations and then derive comparable value estimates (Eade & Moran, 1996). Perhaps the most obvious example concerns the transfer of TC models (where measures of proximity are so important for the prediction of visitor numbers), and several studies have demonstrated the possibilities in the context of woodland recreation value (Bateman, Lovett, & Brainard, 1999b; Brainard et al., 1999). It is fair to note, however, that a similar GIS-assisted methodology could be implemented with other valuation techniques, and current research at the University of East Anglia is investigating the scope for transferring hedonic pricing models between two UK cities. The use of GIS has also enhanced valuation research and cost-bene®t analysis by providing a much greater ability to represent spatial variations in welfare estimates and outcomes in such studies. This trend has been most apparent in research on land use, where the integration of data on factors such as agricultural productivity, timber yield, recreational demand and biodiversity has allowed improved multidimensional assessment of policy scenarios (O'Callaghan, 1996; Bateman, Ennew, Lovett, & Rayner, 1999c; Bateman, Lovett, & Brainard, forthcoming). In addition, a GIS-based analysis can highlight where certain changes in land use (e.g. from farming to forestry) would be particularly advantageous or detrimental. This geographical speci®city is important in enabling greater consideration of equity issues (e.g. in access to environmental amenities or other resources) and the formulation of policies that support sustainable development (Pearce & Barbier, 2000; Pearce, Markandya, & Barbier, 1989).
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4. Prospects From the above discussion it should be evident that the recent use of GIS has contributed to substantial improvement in the economic valuation of environmental preferences. Some of these advances are essentially more sophisticated or reliable measurement of variables that had previously been treated in a rather simplistic manner, but others open up signi®cant new avenues for valuation research. This development can be seen as part of a broader growing awareness regarding geographical factors and spatial dynamics within economics (e.g. Bockstael, 1996), a situation that suggests there may be considerable opportunities for future multidisciplinary studies. References Arrow, K., Solow, R., Portney, P. R., Leamer, E. E., Radner, R., & Schuman, E. H. (1993). Report to the National Oceanic and Atmospheric Administration, Panel on Contingent Valuation. Federal Register, 58(10). Bateman, I. J. (1993). Valuation of the environment, methods and techniques: revealed preference methods. In R. K. Turner, Sustainable economics and management: principles and practice. London: Belhaven Press. Bateman, I. J. (1999). Environmental impact assessment, cost-bene®t analysis and the valuation of environmental impacts. In J. Petts, Handbook of environmental impact assessment, volume 1 Ð environmental impact assessment: process, methods and potential. Oxford: Blackwell Science. Bateman, I. J., & Willis, K. G. (1999). Contingent valuation of environmental preferences: assessing theory and practice in the USA, Europe, and developing countries. Oxford: Oxford University Press. Bateman, I. J., Brainard, J. S., Lovett, A. A., & Garrod, G. D. (1999a). The impact of measurement assumptions upon individual travel cost estimates of consumer surplus: a GIS analysis. Regional Environmental Change, 1(1), 24±30. Bateman, I. J., Ennew, C., Lovett, A. A., & Rayner, A. H. (1999c). Modelling and mapping agricultural output values using farm speci®c details and environmental databases. Journal of Agricultural Economics, 50(3), 488±511. Bateman, I. J., Garrod, G. D., Brainard, J. S., & Lovett, A. A. (1996). Measurement, valuation and estimation issues in the travel cost method: a geographical information systems approach. Journal of Agricultural Economics, 47(2), 191±205. Bateman, I. J., Lovett, A. A., & Brainard, J. S. (1999b). Developing a methodology for bene®t transfers using geographical information systems: modelling demand for woodland recreation. Regional Studies, 33(3), 191±205. Bateman, I. J., Lovett, A. A., & Brainard, J. S. (forthcoming). Applied environmental economics: a GIS approach to cost bene®t analysis. Cambridge: Cambridge University Press. Bhat, G., & Bergstrom, J. C. (1997). Integration of geographical information systems based spatial analysis in recreation demand analysis. Faculty Series 96±26. Athens, GA: Department of Agricultural and Applied Economics, University of Georgia. Bockstael, N. E. (1995). Travel cost models. In D. W. Bromley, The handbook of environmental economics. Cambridge MA: Blackwell. Bockstael, N. E. (1996). Modeling economics and ecology: the importance of a spatial perspective. American Journal of Agricultural Economics, 78, 1168±1180. Boxall, P. C., McFarlane, B. L., & Gartrell, M. (1996). An aggregate travel cost approach to valuing forest recreation at managed sites. The Forestry Chronicle, 72, 615±621. Brainard, J. S., Lovett, A. A., & Bateman, I. J. (1997). Using isochrone surfaces in travel cost models. Journal of Transport Geography, 5(2), 117±126.
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A. Lovett, I. Bateman School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK E-mail address: [email protected] PII: S0198-9715(01)00008-4