Environmental and Resource Economics 11(3 4): 413 428, 1998.
' 1998 Kluwer Academic Publishers. Printed in the Netherlands.
413
Using Choice Experiments to Value the
Environment
Design Issues, Current Experience and Future Prospects
1
NICK HANLEY
1
, ROBERT E. WRIGHT
2
and VIC ADAMOWICZ
3
1
Institute of Ecology and Resource Management, University of Edinburgh, Kings Buildings,
Edinburgh EH9 3JG, Scotland (email: n.d.hanley@ed.ac.uk);
2
Economics Department, University
of Stirling, Scotland;
3
Department of Rural Economy, University of Alberta, Canada
Abstract. This paper we outline the choice experiment approach to environmental valuation. This
approach has its roots in Lancaster’s characteristics theory of value, in random utility theory and in
experimental design. We show how marginal values for the attributes of environmental assets, such as
forests and rivers, can be estimated from pair-wise choices, as well as the value of the environmental
asset as a whole. These choice pairs are designed so as to allow ef cient statistical estimation of the
underlying utility function, and to minimise required sample size. Choice experiments have important
advantages over other environmental valuation methods, such as contingent valuation and travel cost-
type models, although many design issues remain unresolved. Applications to environmental issues
have so far been relatively limited. We illustrate the use of choice experiments with reference to
a recent UK study on public preferences for alternative forest landscapes. This study allows us to
perform a convergent validity test on the choice experiment estimates of willingness to pay.
Key words: choice experiments, cost-bene t analysis, environmental valuation, forest landscapes,
stated preference models
JEL classi cation: Q23, Q26
1. Introduction
Environmental valuation has come a long way since the original work on the
travel cost model and contingent valuation in the USA in the 1960s. This paper
sets out the basic concepts behind a relatively new methodology in environmental
valuation, namely Choice Experiments (CE) , which tries to address some of the
limitations of traditional methods. We provide an account of the theoretical under-
pinnings of the method, and consider the main design issues involved in a CE study,
before reviewing the literature which exists so far in this area. The relationship of
CE to other valuation methods is also touched on. We then give a brief account
of a CE study of forest landscape change in the UK, as an illustration of how
the method can be implemented and the kinds of results which can be achieved.
Finally, we consider likely future developments in CE. For a fuller account of the
CE method, see Adamowicz et al. (1998b).

414 NICK HANLEY ET AL.
The CE technique is an application of the characteristics theory of value
(Lancaster 1966), combined with random utility theory (Thurstone 1927; Manski
1977). It thus shares strong links with the random utility approach to recreational
demand modelling using revealed preference data (Bockstaell et al. 1991). Re-
spondents are asked to chose between different bundles of (environmental) goods,
which are described in terms of their attributes, or characteristics, and the levels
that these take. One of these attributes is usually price. For example, consider a
respondent’s choice of shing location. Assume that utility depends on choices
made from some set C of alternative sites. The representative individual is assumed
to have a utility function of the form:
U
in
=U(Z
in
,S
n
) (1)
where, for any individual n, a given level of utility will be associated with any
alternative shing site i. Alternative i will be chosen over some other option j iff
U
i
>U
j
. Utility derived from any option is assumed to depend on the attributes,
Z, of that option (for example, water quality and the nature of the surrounding
landscape). These attributes may be viewed differently by different agents, whose
socioeconomic characteristics S will also affect utility. Assume now that the util-
ity function can be partitioned into two parts; one deterministic and in principle
observable, and one random and unobservable. Then Equation (1) can be re-written
as:
U
in
=V(Z
in
,S
n
)+ε(Z
in
,S
n
) (2)
and the probability that individual n will choose option i over other options j is
given by:
Prob(i | C) = Prob{V
in
+ ε
in
>V
jn
+ε
jn
,all j ∈ C} (3)
where C is the complete choice set. In order to estimate Equation (3), assumptions
must be made over the distributions of the error terms. The usual assumption made
is that the errors are Gumbel-distributed and independently and identically dis-
tributed (McFadden 1974). This implies that the probability of choosing i is given
by:
Prob(i) =
exp
µν
i
∑
j∈C
exp
µν
j
(4)
Here, µ is a scale parameter, which is usually assumed to be equal to 1 (implying
constant error variance). As µ →∞, the model becomes deterministic. Equation
(4) is estimated by means of a multi-nomial logit regression, which assumes that
choices are consistent with the Independence from Irrelevant Alternatives (IIA)
property.Thisstatesthat ...foranyindividual, theratioofchoiceprobabilities of