Valuing Our Natural Environment Final Report ... - HM Treasury

Valuing Our Natural Environment – Final Report - Annex1
the recreational value of that site. A common response may be to weight cost according to
the proportion of the day’s ‘enjoyment’ to the site in question, although how this is carried
out with likely influence estimates of consumer surplus.
In addition, the inclusion of different factors in the travel cost calculation will also likely
result in different estimates of consumer surplus. Specifically, expenditure on travel may
be estimated in terms of marginal cost (e.g. petrol costs only), full costs (e.g. petrol costs,
insurance, maintenance costs, etc.) or perceived costs as estimated by survey respondents.
Notably, the second formulation will lead to higher estimated costs than the first. Aside
from derivation of travel costs, the way in which the cost of time spent travelling is dealt
with may also lead to different estimates of recreational value. For example, omitting time
costs will ensure that consumer surplus is under-estimated; however, there is no definitive
assessment as to how the value of time should be accounted for. The wage rate approach
sets time costs as some proportion of the visitor’s wage rate. Typically, this will be less
than 100 percent to reflect the fact that most individuals are not completely free to tradeoff
between leisure and work time. Arguably, assumptions concerning the value of time are
largely subjective, and therefore sensitivity of consumer surplus estimates to the value of
time is a key issue in the application of the TCM.
A further issue to consider is the presence of substitute sites. Generally, alternative sites
will depress demand below ‘true’ demand’ and failure to account for this may lead to an
under-estimate of the recreational value of a given site. In particular, if there are several
similar sites within a similar distance, then the demand for each site will be less than
demand for the recreation experience. Alternative sites can be incorporated into analysis
(in the trip-generating function) through specific questions to respondents in survey and GIS
techniques can also be applied to generate data (Brainard et al., 1999), such as distance
between sites.
In addition to the affect of substitute sites, it is also difficult for the TCM to capture fully
the effects of variation in quality of sites and also individual characteristics of sites and
how these influence the demand for visits to a site. In particular, an improvement in site
quality should raise demand for the visits to the site at every level of travel cost.
Accordingly the difference between the original demand curve and the new demand curve
represents the change in consumer surplus. However, there is also the need to account for
changes in other sites and the substitution of visits from one site to another which arise
from improved quality as well as the impact on travel costs which this will have (Freeman,
1993). While the TCM is suited to explaining recreation demand over a given time period
(e.g. the number of visits in a year), it is not suited to considering these effects. Instead,
random utility models (RUM), which are an extension to the standard TCM, are however
suited to the consideration of the effects of the availability of substitute sites and changes
in quality levels of specific site characteristics (Bockstael et al., 1991; Freeman, 1993) (see
separate RUM fiche for further discussion).
A distinct issue concerning practical TCM applications is a lack of theoretical guidance,
concerning the appropriate functional form of the trip-generating function. Moreover,
different studies often use different functional forms. It is often observed that, for a single
dataset, changing the functional form can result in different estimates of consumer surplus
without resulting in significant differences in the statistical fit of different models (see for
example Hanley, 1989). Hence, appropriate specification of functional form is typically a
matter of expert judgement and consequently a potential weakness of the TCM.
Finally, there is also consideration of the comparison between ITCM and ZTCM approaches.
However this is not a straightforward issue, particularly in terms of the degree of
explanation of a given dataset provided by each approach. In most instances, a higher
degree of fit is found via the ZTCM, as observed in terms of the r-squared statistic. While
the ITCM estimates the demand relationship by drawing on all observations and their
variation, the ZTCM amalgamates travel cost into distinct bands, and consequently
estimates demand from a smaller number of observations. Differences in r-squared
statistics are therefore likely to be symptomatic of these differing approaches to estimating
demand. Given this, it is not appropriate to compare these models on the basis of this
eftec A14
December 2006