Integrated Value Mapping
for Sustainable River BasinManagement: Economics,
Ethics and Social
Psychology

Kevin C. Urama, Wendy Kenyon,
Rob Burton and Jackie Potts

ATPS SPECIAL PAPER SERIES No. 22

Published by the African Technology Policy Studies Network, P.O. Box 10081, 00100
General Post Office, Nairobi, Kenya.

© 2006 African Technology Policy Studies Network (ATPS)

PUBLISHED BY ATPS COMMUNICATIONS DEPARTMENTPRINTED BY ZEALOT PRINTERS

ISBN: 9966-916-94-6

ABOUT THE AFRICAN TECHNOLOGY POLICY STUDIES NETWORKThe African Technology Policy Studies Network (ATPS) is a multi-disciplinary network of researchers,
policy makers, actors in the private sector and other end users interested in generating, promoting
and strengthening innovative science and technology policies in Africa. With a regional secretariat in
Nairobi, the network operates through national chapters in 23 countries, with an expansion plan tocover the entire sub-Saharan Africa.
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Papers published under the ATPS Special Paper Series are those commissioned as concept
papers, think pieces, leading conference papers and keynote addresses. In keeping with
our knowledge brokerage function, ATPS publishes, with permission of the author(s) or
journal editors, papers (published or unpublished) produced by distinguished academics/
researchers for a different purpose but judged by ATPS to be of excellent quality. Thesepapers address significant policy questions relevant to the work of ATPS and/or support
the Southern voice or an African perspective. We also consider theoretical papers that
advance our knowledge of science and technology policy issues.
ATPS is supported by a growing number of donors including the International Development ResearchCentre (IDRC), the Carnegie Corporation of New York, the Rockefeller Foundation, the World Bank,
the OPEC Fund, Ford Foundation, Coca-Cola Eastern Africa, the African Development Bank, InfoDev,
and the Royal Dutch Government.

Table of Contents

List of Tables and Figures
List of Abbreviations
Abstract

iii
iii

Chapter One:
Chapter Two:

Introduction
Theoretical Frameworks of Contending Models andRationale for Integration
Chapter Three: Model Specification and Estimation Procedure
Chapter Four: Case Study and Survey Design
Chapter Five: Model Results and Discussion
Chapter Six:
Conclusions and Policy Recommendations

1

Appendices
Bibliography

34
36

3
9
13
20
32

List of Tables and Figures

List of Tables
Table 1
Table 2
Table 3
Table 4
Table 5

Variables in the models
Ethical positions / positions statements specified in survey
WTP principle category of response
Ethical position on species protection compared with WTP Principle category
Logistic regression of the probability that respondents are disposed to pay in principle
using the single domain models
Table 6 Logistic regression of the probability that respondents are disposed to pay in principle
using the integrated model alternatives
Table 7 Marginal contributions to the explanatory power of the socio-economic model
Table 8 Multinomial logistic regression of the probability of positive response compared to No,
Refused and Don’t Know using IVM4.

List of Figures
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

The theory of planned behaviour (Ajzen, 2002)
The underlying assumptions of the Selected Models
Location of the Tummel catchment and the hydropower structures
Coverage of the focus groups, pre-tests and main surveys
Logistic regression of the probability that respondents are disposed to pay in principle
using the integrated model alternativesiList of Abbreviations and Acronyms

Attitude towards behaviour
CBA
CVM
DK
ECOMODETHIMOD
GAC
GIS
IVM
LR
MLP
PBC
PSYCHOMOD
SN
RA
TPB
TRA
WFD
WTP
WTPP
ZB

Cost benefit analysis
Contingent valuation method
Don’t know
Economic model
Ethical model
General awareness of consequences
Geographic information systems
Integrated value mapping
Likelihood ratio
Modified lexicographic preferences
Perceived behaviour control
Social psychology model
Subjective norms
Refused to answer
Theory of planned behaviour
Theory of reasoned action
Water framework directive
Willingness to pay
Willingness to pay principle
Zero bidsii

ATbAbstract

Recent studies in social psychology and in environmental ethics suggest alternative models ofenvironmental behaviour that challenge the rational expectation assumptions of CBA and, in turn theuse of CBA in environmental policy making. This paper integrates some of the alternative models
and tests their relative strengths and complementarities in explaining human dispositions to pay forbiodiversity restoration using logistic models. Primary data were collected from face-to-face interviewsof a stratified random sample of 1012 individuals across Scotland. The results of the analysis provide
empirical evidence of the long-standing concerns for using single domain models to aid
environmental decision making and illustrate the case for considering integrated value mapping
alternatives.

iii

1.

Introduction

Critics of the neo-classical welfare economic theory find the use of economic costs and benefits asa main criteria for setting environmental priorities morally objectionable (see for example: Niemeyer
and Spash, 2001; Spash, 2002). One central theme of the criticism of the neo-classical model isbased on the concept of value pluralism, i.e. values associated with environmental entities arecomplex, incommensurable and ultimately cannot be reduced to a single metric, such as pecuniary
values. Critics argue therefore that environmental values cannot adequately be assessed under the
standard neo-classical framework. Empirical evidence suggesting that non-economic values exist
and often inform the environmental priorities of significant proportions of the public has continued tochallenge the use of cost benefit analysis (CBA) in setting environmental priorities in the past decade(see for example, Stevens et al., 1991; Spash and Hanley, 1995; Spash, 2002 and Urama 2003). Yet,
CBA practitioners still proceed as if values are commensurable and ultimately reducible to a single
metric (for details see Rosenberger, et al., 2003). Dealing with non-economic value categories in
the analysis of CBA data is therefore problematic. Indeed, the standard practice in the estimation ofenvironmental demand models such as WTP is to ignore them (see for example, Mitchell andCarson, 1989, Arrow et al., 1993, and Bateman et al., 2002). Thus, all respondents who refuse to“play the economic game” and instead are guided by ethical, moral or other principles are excluded
from the analysis.
Despite these problems CBA continues to be promoted as a tool for informing environmental policy,
such as the EU Water Framework Directive (WFD)1. For CBA to be useful for decision-making under
the WFD their underlying assumptions must, in principle, be seen as valid by individuals who mustalso be prepared to play the economic game – i.e. trade environmental entities for money. Empiricalevidence from previous studies show that this is often far from true (Burgess, Clark and Harrison
1995; Burgess, Clark and Harrison 1998). Alternative decision-making models exist in the fields ofsocial psychology and environmental ethics that are not based on these assumptions and challenge
the use of economic valuation methods, especially the CBA, to aid environmental policy choices.
The epistemological debate regarding the limitations of applying single disciplinary models to informcomplex environmental policy decisions (and hence the call for integrated modelling alternatives)
has peppered the environmental valuation literature in the past decade (Urama, 2003, Urama andHodge, in press), but has gone largely unheeded.
1

WFD is an EC water legislation requiring all inland and coastal waters to reach “good status” by 2015 (EEC, 2000)

1

2

ATPS SPECIAL PAPER SERIES NO. 22

This paper integrates three contending models: neo-classical welfare economics; environmental
ethics; and social psychology; tests their relative contributions to understanding public dispositions
to pay for biodiversity restoration, and examines the potential synergies and/or complementarities
between them.
The paper is presented in the following format. First, the theories behind the contending models andthe rationale for integrating them are discussed briefly. Second, the integrated model is specified
followed by a brief discussion of the case study and survey design. Finally, the model results arepresented and discussed, critically examining the relative contributions to the explanatory power of
each model, the precision of parameter estimates, and their overall statistical robustness.

2.

Theoretical Frameworks of Contending Models
and Rationale for Integration

Learning from Environmental Ethics: The Role of Fundamental Ethical Beliefs
The dominant economic theory of decision-making requires a fundamental philosophicalassumption; that individuals believe the net utility from the consequences of an action determines
whether that action should be undertaken. Thus, CBA and its tools, such as contingent valuation(CVM), assume that individuals are able and willing to consider trade-offs in relation to the quantity
and/or quality of public goods. Debates in environmental ethics have raised the issue of individuals
refusing to make these trade-offs and so questioned the applicability of economic efficiency
arguments (Sagoff 1998; Stevens et al., 1991). One aspect of refusal can be a basis of belief ininviolable rights so that actions are intrinsically of value or deontological (Spash 2002). Other
motivations for non-compensatory preference expressions may include dual, non-reducible utility
functions (Sen, 1977; Etzioni, 1998, Sagoff, 1988, 1998); ambivalence between hard-to-compare
values (Opaluch, and Segerson, 1989); inability to commodify environmental goods (Vatn, 2000); theessentiality of a good (Lockwood, 1996; Schmidtz, 2000), or lack of environmental knowledge (Urama,2003).
Micro-economic welfare theory recognises the fundamental differences in utility functions for thesedifferent types of goods and their associated preference structures, (see for example, Koutsoyiannis,
1991). Yet some CBA critiques still reject the neoclassical paradigm on the basis of deontologicalarguments (see for example, Spash, 1998, Niemeyer and Spash, 2001, Spash 2002). “Suchpreferences mean that utility functions including environmental aspects which are to be protected at
all costs are undefined for an individual (since the axiom of continuity is violated), and the indifference
curves collapse to single points (denying the principle of gross substitution). These preferences are
termed lexicographic by neo-classical economics because they give absolute priority to one
commodity over all others and therefore imply a strict ordering, as in a lexicon…Economists havetended to regard the denial of continuity and violation of gross substitution as of little relevancebecause lexicographic preferences as unrealistic and unlikely to occur” (Spash, 1998: 60)2.

2

In other words, such preferences mean that utility functions including environmental aspects which are to be protected at all cost
are undefined for an individual since the axiom of continuity is violated, and that indifference curves collapse to single points,
denying the principle of substitution (Spash, 1998).

3

4

ATPS SPECIAL PAPER SERIES NO. 22

Existing empirical evidence on the role of lexicographic preferences in environmental valuation ismixed. While some studies suggest that a significant proportion of respondents hold lexicographic
preferences towards environmental attributes such as biodiversity (Stevens, et al., 1991, Spash andHanley 1995; Spash 2002), others suggest that such rights-based preferences are not strictly
lexicographic (Urama 2003) and/or are sometimes internally inconsistent (Rosenberger et al., 2003).
If respondents perceive an environmental entity as an “essential good”, their WTP for that goodincreases since the axiom of rationality suggests that economic agents’ disposition to pay for a goodincreases with the essentiality of the good. This fundamental principle of the neo-classical welfare
theory seems to be ignored or addressed fleetingly in the lexicographic preference literature. Spash,(1998) had argued that the extreme lexicographic position does indeed, seem likely to be uncommon
because of the overriding ranking of a good above even the individuals own life (Spash, 1998: 60)3.
However, he went on to argue that “a range of broadly right-based or deontological positions do,
however, appear to be relevant to the general public when considering the environment (Spash,1997) and have important implications for the application of monetary valuation to environmental
policy” (cited in Spash, 2000: 196). Tracing the theoretical history of lexicographic preferences
back to Georgescu-Roegen (1954) and Lockwood (1996), a set of modified lexicographic preference(MLP) positions based on thresholds has therefore been developed (for details see Spash, 2000;
Rosenberger, et al., 2003: 64)4. Spash, (2000) defines two MLP positions: the strong MLP and weak
MLP signifying beliefs in rights to life for species regardless of considerations for one’s minimum
living standards, and rights to life for species qualified by considerations for one’s minimum living
standards, respectively (Spash, 2000). While these modifications are conceptually more compatible
with the neoclassical model than the extreme positions, i.e. allowing for different types of utility
preferences, it has been recognised that empirical estimation of these modifications can beproblematic (Urama, 2003). The conceptual problems associated with defining “the minimum
standards of living” dates back to Sen’s work on the concept of “functionings” (Sen, 1987)5.
We therefore hypothesise that respondents holding strong ethical positions regarding rights to life for
species would in principle be prepared to pay more for biodiversity restoration than those believing
in human rights to life vis a vis species. As noted by Rosenberger, et al., (2003: 65) “we must also
accept that some people seemingly expressing lexicographic preferences are actually being
inconsistent in their expressions”. Overall, the propositions regarding the role of these ethicalpreference expressions on WTP models still require further validation. These preference categories
were therefore replicated in this study to test the hypothesis of their relative contribution to people’s
dispositions to pay for biodiversity restoration in Scotland.

3

A martyr would fit this category, but it might be rare to find individuals choosing to die to protect environmental entities.
There exist certain thresholds, or minimum levels of a good that are necessary and prior to other choices of goods (for details see
Rosenberger, et al., 2003: 64).
5
The various living conditions one can achieve and one’s capabilities (i.e. abilities to achieve them) are essential parts of one’s living standard rather than commodities. Hence different respondents may have different standards in different circumstances,making it difficult to establish a common metric for relating responses to MLP questions to standard economic variables likeincome.
4INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

5

Learning from Social Psychology: the Role of Attitudes, Social Norms, and Perceived
Behaviour Control
Proponents of CBA now increasingly recognise the benefits of cross fertilisation between the neoclassical model and those developed by cognitive psychologists and sociologists (see for example:
Mitchell and Carson, 1989; Ajzen and Driver, 1991; Bateman et al., 2002, and Pouta and Rekola,
2001 and 2003). “Of particular interest are the attitude-behaviour models that look at the links between
people’s attitudes and intended behaviour as revealed by surveys, and subsequent actual behaviour”(Bateman et al., 2002: 113). Examples of such models, which have obvious implications for CBA
include Fishbein and Ajzen’s (1975) theory of reasoned action (TRA) and Ajzen’s (1991) theory ofplanned behaviour (TPB)6.
Micro-economic welfare theory assumes that individuals hold complete, invariant and transitive
preferences, which they seek to maximize (see Kahneman, 1986) often in static equilibrium
conditions. Conversely, Ajzen’s TPB model postulates three conceptually dependent determinantsof human actions: specific beliefs about the likely consequences of the behaviour (behavioural
beliefs); beliefs about normative expectations of others (normative beliefs); and beliefs about thepresence or absence of factors that may further or hinder performance of the behaviour (control
beliefs). In their respective aggregates, behavioural beliefs produce a favourable or unfavourable
attitude toward the behaviour ( ATb ) 7; normative beliefs result in perceived social pressure or
subjective norm (SN); and control beliefs give rise to perceived behavioural control (PBC), theperceived ease or difficulty of performing the behaviour.” (Hrubes, et al., 2001: 166). In combination,

ATb , SN , & PBC , lead to the formation of a behavioural intention, such as WTP. A schematic
representation of Ajzen’s (1991) TPB is shown in Figure 1.
As a general rule, “the more favourable the ATb & SN , and the greater the PBC , the stronger
should be the individual’s intention to perform the behaviour in question” (Ajzen 1991:188). Relying
on the expectancy-value theory, the TPB also specifies the nature of relationships between beliefs
and attitudes: people’s overall attitude toward a behaviour is determined by the subjective values ofthe outcomes associated with the behaviour and by the strength of these associations (Fishbein,
1963; 1967; Fishbein & Ajzen, 1975)8. A stated intention, such as WTP Principle is therefore assumed
to be the immediate antecedent of behaviour, such as making actual payments to support the policy
option specified in the WTP question.
6

The TPB is a product of further development of TRA and has been widely tested and accepted in the Social Psychology field andin CVM literature as a useful model for understanding the relationship between specific attitudes and behavioural intentions (seeMitchell and Carson, 1989, Ajzen, 1991, Ajzen and Driver, 1991, Pouta, 2001, Bateman et al., 2002, and Pouta, 2003) to mention
just a few studies.
7
8

In this case ( ATb ) is used to signify specific attitude towards a referent behaviour “B”.

Further details on the meaning of ATb, SN and PBC and how to estimate them in empirical surveys can be found in Ajzen, (1991,
and 2002), respectively.

ATPS SPECIAL PAPER SERIES NO. 22

6

Behavioural
Beliefs

Attitude
Toward the
Behaviour

Normative
Beliefs

Subjective
Norm

Control
Beliefs

Perceived
Behaviour
Control

Intention

Behaviour

ActualBehaviouralControl

Figure 1: The theory of planned behaviour (Ajzen, 2002)
There is now a growing body of evidence suggesting that learning from Ajzen’s TPB model could
improve economic valuation experiments (see for example Mitchell and Carson, 1989; Ajzen andDriver, 1991; Blamey, 1996, Bateman et al., 2002, and Pouta and Rekola 2001, and Pouta 2003).
However, the application of the TPB in CV experiments poses a specific design challenge. The
referent behaviour (B) of interest in the model must “be defined in terms of Target, Action, Context,
and Time (TACT)” elements, for the model assumptions to hold (Ajzen 2002: 2; also cited in Eagly
and Chaiken 1993: 164). Mitchell and Carson, (1989) therefore identify three important connections
between the earlier version of Ajzen’s model (i.e. Fisbein and Ajzen’s, 1975) theory of reasoned
action and the design of CV surveys: (i) Correspondence: asking questions about attitudes to public
goods is not as powerful a predictor of underlying values as eliciting attitudes towards paying for
public goods: (ii) Proximity: attitudes are poorer indicators of likely payment behaviour than arestatements regarding valuation intentions, that is, willingness to pay, and (iii) Familiarity: “the morefamiliar the behaviour, the more likely the respondent’s attitude and/or behavioural intentions will
predict that behaviour” (Mitchell and Carson, 1989: 186, also cited in Batemen et al., 2002: 114 -5).
This means that specific attitudes toward payment to support a specific policy measure, to achievea specific goal, within a specific context, in a specified period are required for TPB model application
as opposed to general environmental attitudes/awareness of consequences (GAC) scales9. This isnecessary to maintain the level of specificity required by the TPB component of the integrated
model10.
9The contending propositions regarding the role of GAC Scales in understanding WTP by (Stern, Dietz and Kalof, 1993;
Guagnano, Dietz and Stern, 1994; Stern et al., 1995; and Spash, 1998) were therefore not considered in this paper.
10
The Spash, (1998) study also recognised that specific attitudes and social/subjective norms may operate differently as
explanatory factors and could perform better (than GAC scales) in explaining WTP.

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

7

In what follows the three models are tested drawing on the relevant theories and assumptions in theirrespective disciplines: neo-classical economics, ethics, and social psychology. The aim was toretain the assumptions of the individual model applications as much as possible, and assess thecontributions of each domain model and the various combinations of the models to public willingness
to invest in biodiversity restoration in Scotland (Figure 2).
As shown in Figure 2, seven different modelling alternatives are considered: (i) maintaining the strictassumptions of each domain11; (ii) bilateral integration between economics and social psychology(IVM1), economics and ethics (IVM2), and ethics and social psychology (IVM3), and (iii) full integrationof the three models (IVM4).
Despite their differences in disciplinary perspectives, standard models for WTP Principle models,
Ajzen’s TPB model, and the ethical models share a common focus on individuals’ specific behaviouralintentions. However, the standard neo-classical economic WTP models make cognitive processesof choice endogenous, and exclude social norms, individual beliefs and attitudes, their perceptionof peer pressure and other control factors. Conversely, social psychology models treat individual
behaviour as the result of interaction between social norms, attitudes and perceived behaviour
control factors. On the other hand ethical models regard human behaviour toward environmental
entities as a product of rights-based beliefs, some of which are inviolable, leading to a collapse of a
key assumption of the neo-classical model. The modified lexicographic preferences categories(discussed above) relaxed the strict incommensurability assumption, paving the way for testing the
ethical model within the neo-classical WTP framework.

11

ECOMOD = the single domain model maintaining the underlying assumptions of neo-classical welfare theory;
ETHIMOD = the single domain model maintaining the underlying assumptions of environmental ethics; andPSYCHOMOD = the single domain model maintaining the underlying assumptions of social psychology, in this caseAjzen’s TPB.

ATPS SPECIAL PAPER SERIES NO. 22

8

Alternative Models of Human Behaviour

Social Psychology

Neo-classical economics

Environmental Ethics

Process models where theassumption is that beliefs
and preferences are formed
or learnt as information is
acquiredStatic models where theassumption is that
individuals hold both preexisting preferences andperfect information

Rights-based models
where the assumption isthat deontologicalpositions prevail overutility preferences

Human preferences are
fallible and complex –driven by attitudes, norms
and behaviour control
factors

Human preferences driven
by rational utility
maximising objectives

Human preferences are
driven by rights-based
ethical beliefs about
environmental entities

ECOMOD

IVM2

IVM1
IVM4
PSYCHOMODIVM3ETHIMOD

Figure 2: The underlying assumptions of the selected models

3.

Model Specification and Estimation Procedure

Model estimation focused on mapping out the significant drivers of an individual’s disposition to pay
for biodiversity restoration in principle (WTPP) from a vector of consistently significant variables
suggested by the relevant theories underpinning the separate models12. The dependent variable isassumed to be binomial, taking the value “1” if the respondent is willing to pay in principle and “0” ifotherwise. We therefore use the logistic regression model proposed by Wrigley (1985):[1] 13where P1i = P(WTPPi = 1) ranges from 0 to 1 as β ′x i ranges from.

∞linearcontending
theories are therefore specified as in′x2iUNDBIOD
x +=Thex−i∞β ′toexp(
βmodels) for eachi of
α 0 +generalized
α 1 KNBIODα+ αthe3 INCREFi +i + α 4 AGECLi
P1i = Pi (WTPP
i = 1) =equation
1, whereanspecific vector of regressors in the logistic function for each
′x individual
1 +i exp(+ α 5 EDUGP+ αis6βGENDi ) i + α 7 LOCSCOTi
modelling alternative, and β is a vector of estimated parameters in the function for each modelling
alternative. β ′ x i is the log of the odds that the ith respondent is in principle willing to pay for biodiversity
restoration. The dependent variable remains the same for all the modelling alternatives while thevectors of regressors and the parameters change as specified below14:
For the neo-classical model (ECOMOD):[2]

12

These variables were selected from relevant state of the art manuals and empirical tests of each model component (Mitchell andCaron, 1989, Arrow et al. 1993, and Bateman et al., 2002 for the standard economic model; Spash, 1998, 2000, 2002 andRosenberger et al., 2003, for the ethical model; and Aizen, 1991, Ajzen and Driver, 1991, Pouta and Rekola 2001, Ajzen 2002, andPouta 2002, 2003, for the social psychology model).
13
Equation 1 is linear logit model where for each individual “i“, the probability of “being willing to pay in principle” or “not willingto pay in principle” depends on a vector of explanatory variables, “xi“ and a vector of associated parameters β . The parameters
β are estimated using the maximum likelihood method (MLM), which maximizes the probability of obtaining the sample actuallyobserved.
14
See Table 1 for definition of the variables in the models.

9

ATPS SPECIAL PAPER SERIES NO. 22

10

For the ethical model (ETHIMOD):β ′x i = φ 0 + φ1 KNBIODi + φ 2UNDBIODi + φ 3 SMLPi + φ 4WMLP i+ φ 5CONSEQ 1i + φ6 CONSEQ 2 i + φ 7 HUMRGTi[3]

For the social psychology model (PSYCHOMOD):β ′x i = λ0 + λ1 KNBIODi + λ 2UNDBIODi + λ 3 ATB i + λ 4 SN i + λ5 PBC i[4]
Using the neo-classical WTP model as the baseline, the functional forms of the integrated
models (IVM1 – IVM4) are specified in equations 5 -8, respectively:

β ′x i = γ 0 + γ 1 KNBIODi + γ 2UNDBIODi + γ 3 INCREFi + γ 4 AGECL + γ 5 EDUCLi+ γ 6 GENDi + γ 7 LOCSCOTi + γ 8 ATBi + γ 9 SN i + γ 10 PBC i[5]β ′x i = ϑ0 + ϑ1 KNBIODi + ϑ2UNDBIODi + ϑ3 INCREFi + ϑ4 AGECLi + ϑ5 EDUCLi+ ϑ6 GENDi + ϑ7 LOCSCOTi + ϑ8 SMLP i +ϑ9WMLPi + ϑ10 CONSEQ1i+ ϑ11CONSEQ 2 i + ϑ12 HUMRGTi[6]β ′x i = ϖ 0 + ϖ 1 KNBIODi + ϖ 2UNDBIODi + ϖ 3 SMLPi + ϖ 4WMLPi + ϖ 5 CONSEQ 1i+ ϖ 6 CONSEQ 2 i + ϖ 7 HUMRGTi + ϖ 8 ATBi + ϖ 9 SN i + ϖ 10 PBC i[7]β′x i = Ω0 + Ω1 KNBIODi + Ω 2UNDBIODi + Ω3 INCREFi + Ω 4 AGECLi + Ω5 EDUCLi+ Ω 6GENDi +Ω 7 LOCSCOTi + Ω8 SMLPi + Ω9WMLPi + Ω10 CONSEQ1i+ Ω11CONSEQ2 i + Ω12 HUMRGTi + Ω13 ATBi + Ω14 SN i + Ω15 PBCi
[8]
where α, φ, λ, γ , ϑ ,ϖ , and Ω are the parameters to be estimated.
For specific values of the independent variables, the corresponding estimated value ofthis theprobability of the event that the i individual answered “Yes” to the WTP principle question. Therefore,
alternative values of the regressors can be used in the estimated model to predict the probability of
respondents being willing to pay for biodiversity restoration (in principle) under the conditions specified
in the survey.

( P1i )

11

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

Each estimated coefficient reflects the effect of a unit change in the corresponding regressor on thelogarithm of the odds15 of the event to occur, certris paribus. These coefficients are therefore difficult
to interpret because the magnitude of the increase in probability depends on the original probability,
which is determined by the individual values of all independent variables and their coefficients16. The
effect of individual explanatory variables was therefore assessed by estimating their marginal effects
model:∂P1 ∂x k = f (β ′ x) β k
where f is the derivative of F (β ′x) =[9]exp(β ′x)with respect to β ′ x .
1 + exp(β ′x)

These marginal effects correspond to changes in the probability of a respondent being willing to pay
in principle, given an infinitely small change in the characteristic explanatory variable in the vector of
regressors.
The definitions of variables in the models are presented in Table 1. Respondents’ knowledge of
biodiversity and understanding of the impact scenario were included in all the models to test therationality assumption common to all the contending models. Initial exploration of the income data
found the responses unreliable. There were many non-responses and some of the responses were
substantially below expected income levels vis a vis stated occupational classes and education.
Following Spash, (1998) a dummy variable INCREF was applied to cover respondent refusal to giveincome level17. In addition, respondent’s level of education was used as a proxy to income. The
occupation of the respondent was tried as an alternative proxy for income, but this was significantly
correlated with education. The rest of the variables and how they were generated are discussed in
the following sections.

15

The odds of an event to occur is the ratio of the probability that the even will occur over the probability that it will not occur. Thiscan be stated mathematically as follows: log of the odds of an event = log {Prob(event)/Prob(no event)} = β0 + β1x1+β2x2+,…,+βnxn
16
One important point about the logistic transformation is that while the probabilities are bounded between 0 and 1, the logits areunbounded with respect to the values of the explanatory variables X. The predicted logit values are likewise unbounded, but thepredicted probabilities can be found by substituting the estimated parameters into equation 1.
17
The aim was to test the significance of people refusing to respond to income questions on WTP.

ATPS SPECIAL PAPER SERIES NO. 22

12

Table 1: Variables in the models
Variables

SymbolDescription of variable

Log WTP

LnWTP

Natural logarithm of actual WTP with zero values
assigned a value of £ 0.0001.

WTP
Corr*

Variables on respondent’s cognitive processing of information
Knowledge of biodivesity KNBIOD
Understanding ofvaluation scenario

Knowledge of biodiversity, 1 if level of knowledge of biodiversity
is greater than 3, zero otherwise.
+ve
Understanding of information on impacts presented in the valuation
scenario. 1 if level of understanding is greater than 3, zero otherwise. +ve

Variables from neo-classical economics
Income Refused
Age Class
Education Class
GenderLocation

INCREF
AGECL
EDUCL
GEND
LOCSCOT

Refused to give income level, 1 if refused to give income level
Age class, 1 if age class is greater than 34 years, zero otherwise.
Education class, 1 if educated for more than 16 years
Gender, 1 if male, zero if female
Location in Scottish council area, I if urban, zero if rural-ve+ (-) ve+ve
+ (-) ve
+ (-) ve

Variables from environmental ethics
Strong MLP

SMLP

Weak MLP

Consequentialist 1
Consequentialist 2
Strong Human rights

Strong modified lexicographic preference, (i.e. strong species rights),
1 if prepared to protect rights to life for species at the expense of living
standards(-) veWeak modified lexicographic preference (i.e. weak species rights),
1 if rights to life for species is qualified by considerations for living
standards(-) +ve
Consequentialists favouring animals, 1 if relative utility favouring
species first. Zero otherwise+ve
1 if relative utility favouring humans first. Zero otherwise+(-ve)
Strong human rights, 1 if prefers to commit resources to help
humans rather than wildlife.
-ve

Variables from social psychology – the theory of planned behaviour
ATB Scale
SN ScalePBC Scale*expected correlation with WTP.

ATB Scale computed as shown in section 4
SN Scale computed as shown in section 4
PBC scale computed as shown in section 4+ (-) ve
+ (-) ve
+ (-) ve

4.

Case Study and Survey Design

Case Study
The CVM experiment was carried out in the Tummel catchment located in the Grampian
Highlands of Scotland. The river Tummel is part of a freshwater system made up of an intricate
network of rivers, streams and lakes with 1,253 km of river channel and 77 km2 of standing waters
covering an area of 1,713km2. The area includes eight reservoirs and pre-existing lakes which areused for hydro-power generation, along with the rivers and streams draining into and connecting
them. The Tummel area has a very low population density (less than 0.10 persons per hectare) andthe area is classified as a less remote rural zone (Black et al., 2002). Figure 3 shows the location of
the Tummel catchment and the hydro-power structures in the catchment.

Figure 3: Location of the tummel catchment and the hydropower structures

13

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ATPS SPECIAL PAPER SERIES NO. 22

In order to comply with the EU Water Framework Directive (WFD) a previous study by Black et al.,(2002) had identified measures which might be taken in the catchment. One of these was theintroduction of a compensation flow regime from the dammed lochs to mimic the natural flow insome of the rivers within the catchment. The aim was to restore the diversity and abundance ofspecies and habitats in the river catchment. Grounded in real policy concerns, the Tummel provided
a scenario of reduced biodiversity in the rivers downstream of the hydro-dam due to low flow. This
was a suitable basis for the development of a practical, feasible and believable scenario for a WTP
question. Increasing river flows from the hydro-system would potentially reduce electricity generationand increase costs for the hydropower companies. Such costs would then be (wholly or partially)transferred to electricity consumers. Eliciting willingness to accept increments in household electricity
bills in principle was therefore seen as a realistic bid vehicle suiting the scenario (see Spash et al.,2004 for details).
Survey Design
The development of the CV questionnaire followed five stages: (i) gathering scientific information on
the current level of biodiversity in the Tummel catchment and the potential impacts of thecompensation flow regime if introduced; (ii) scenario setting/framing of the hypothetical market, (iii)
initial design of the questionnaire, focus groups and consultation with relevant experts, (iv) pretesting of the survey questionnaires, and (v) final re-design of the survey questionnaires in line withlessons learned.
The survey questionnaire- The survey questionnaire had five sections (A to E): introductory
framing and information; monetary valuation and follow-up; questions eliciting information on
ethical positions, ATb , SN , & PBC ; socio-economic data; and interviewee and interviewer
evaluation.
Section A: Framing concern for public policy issues and the environment: The first section of thequestionnaire asked the respondents to rank their concern for the quality of selected public services
such as education, road transport and the National Health Service, and then to rank their concern for
selected environmental policy issues, including biodiversity restoration, on the same scale. Suchframing has been suggested to avoid embedding problems by making explicit the context of the
WTP question as only one area of potential expenditure.
The final questions in Section A related to respondents’ familiarity with the case study area, theirunderstanding of biodiversity and the impacts described in the survey. Respondents were asked
whether they were familiar with the area and then provided with information about the water courses,
the hydropower schemes and informed about the associated changes in biodiversity. Information
provided was based on previous scientific reports concerning physical characteristics of the water
bodies in the Tummel catchment, land and water use in the area, the hydro-schemes and their likely
impacts on biodiversity, current ecological status, and the proposed mitigation actions (see forexample, Black et al., 2002).

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

15

Three types of information were used in the final survey. First, information about biodiversity ingeneral, aquatic biodiversity and the web of life provided background on the decline in biodiversity
due to hydropower, and the potential increases in biodiversity as a consequence of compensations
flows. Second, information about the Tummel catchment, the hydro-power schemes in the area
along with details of the decline in biodiversity specific to those schemes. Third, information thatfamiliarised respondents with the two scenarios was provided. The “business as usual” scenario
described biodiversity at 14% of natural levels due to reduced water flow in the catchment. The
alternative scenario involved an increase in water flows and an associated increase in biodiversity inthe catchment to 70% of natural levels at the expense of reduced energy generation and increased
electricity bills. The neo-classical model assumes that respondents understand this perceived change
in the pre-and post-payment quality of the environmental good and make a rational choice of thepreferred scenario that maximize their individual utility. A follow-up question asked respondents how
well they had understood the scenario described.
Section B: The hypothetical market scenario: After describing the pre-and post payment scenarios,
the respondents were told that “in order to achieve the described increase in river flow, electricity
generation will be reduced and hydropower companies will incur extra costs“. Three types of valuationquestions were then asked. First, the payment in principle (WTP Principle) question: “Would you be
willing to accept an increase in your energy bill in order to increase biodiversity?” This WTP principle
question addressed whether respondents found acceptable the idea of paying an increase in theirenergy bill to improve biodiversity (without reference to the Tummel). Only those who answered“YES” to the WTP Principle question were asked the specific WTP question where the 14% to 70%
change in biodiversity in the Tummel area is specified as the object of value. The specific WTP
question was open-ended with provisions for zero bids (ZB), refuse to answer (RA) and don’t know
(DK). The WTP Principle and WTP Specific questions had follow-ups eliciting the reason(s) behindeach response category. The third valuation question elicited information on how valuable therestoration of biodiversity in the River Tummel was to the respondent, ranked from “1 = not at all
valuable” to “7 = extremely valuable”. The question was included to explore the consistency between
qualitative and quantitative preference expression.
The open-ended WTP question format was selected as the most easily understood by respondents
and as avoiding leading respondents to a desired answer (as occurs with some other methods due
to starting point bias and anchoring effects). The scenario also presented a suitable payment vehicle
in the form of respondent’s willingness to accept an increase in energy bills in a given time frame. In
the context of neo-classical economics, increasing river flows from the lochs in the hydro system
would reduce electricity generation and increase costs for the hydropower companies. Such costs
would then be (wholly or partially) transferred to electricity consumers. Eliciting WTP for increments
in household electricity bills was therefore seen as an appropriate bid vehicle. This is a credible
scenario familiar to energy consumers in Scotland, providing an effective payment vehicle. From thesocial psychology perspectives, the framing also ensured that the referent behaviour of interest was
defined in terms of a specific target, action, context, and time (TACT) elements and achieved thelevel of specificity required by the TPB model (Ajzen 2002: 2; also cited in Eagly and Chaiken
1993:164).

ATPS SPECIAL PAPER SERIES NO. 22

16

Section C: Environmental ethics and social psychology: This section comprised questions on ethicalbeliefs, attitudes towards behaviour ( ATb ), subjective norms (SN) and perceived behavioural control

ßá
“ë£ßá

(PCB). Section C1 aimed to obtain measures of the underlying ethical motives for respondents WTP
using questions adapted from Spash, (1998). Specifically, respondents were told that: “A major aim
of maintaining natural flows of water is to provide habitat for endangered wildlife species such as
otters and kingfishers”. They were then asked which one of four statements (presented on a showcard) most closely matched their opinion about the scheme to get hydropower companies to release
more water to the rivers to mimic a natural flow (in the Tummel). The respondents also had a fifth
choice “can’t answer – this is too complicated”. The set of statements on the show card measured
the extent to which the respondents saw rights as relevant to the Tummel case study. Those making
a specific attribution of rights were then probed further to elicit the strength of these ethical positions.
The questions and method of analysis replicated the original study by Spash (1998) to ensure
consistency with the original model. The aim was to identify the different categories of non-economic
ethical positions including the modified lexicographic preference categories to test the hypothesison their relative contribution to WTP Principle. The different ethical categories and the position
statements used in the survey are presented in Table 2. Section C2 aimed at creating suitable scales
to test the hypothesis on Ajzen’s TPB model. The questions and scales followed Ajzen’s guidelines
for constructing a TPB questionnaire (Ajzen 1991: 91; and 2002: 10).
Specifically, ATb was determined by the total set of accessible behavioural beliefs linking the
behaviour to various outcomes and other attributes. The ATb scales therefore involved pairs ofquestions eliciting the strength of each belief (b) and the evaluation (e) of the outcome or attribute. An
example of the ATb pair of questions was:Table 2: Ethical Positions / Positions Statements Specified in Survey
Variables

Symbols

Position Statements

Strong species rights

SMLP

Weak species rights

WMLP

Consequentialist favouring
species

CONSEQ 1

Consequentialist favouring
peopleCONSEQ 2

Humans first

HUMRGT“I would protect the right to life for species at the expense of
my standard of living.”“I would be prepared to see some species become extinct if
protecting endangered wildlife would mean I had to incur apersonal cost which reduced my standard of living to what I
regard as a minimum.”“Protection of such endangered species must be weighed against
economic considerations, but in this case, the endangered
species should come first.”“Protection of such endangered species must be weighed against
economic considerations, and in this case, people’s livelihoods
come first.”“Too much concern is shown for wildlife and not enough forhumans, so I would rather see the resources used to help
humans.”17

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT(i) Eliciting behavioural beliefs strength (bi ) :“Paying more for electricity to restore biodiversity will increase the diversity and abundance ofplant and animal species in the Tummel area.” This was scored from “1 = extremely likely” to “7= extremely unlikely”.
(ii) Eliciting evaluation of outcome (ei ) :“Increasing the diversity and abundance of plant and animal species in the Tummel area is…”.
This was also scored from “1 = extremely bad” to “ 7 = extremely good”.
A total of 13 pairs of questions were asked. Following Ajzen, (2002: 10), the ATb scale was createdusing equation 10:
13

ATBr ∝ ∑ bir eir

[10]

i =1

where r is the respondent,

the strength of each salient belief (the subjective probability) that the

behaviour will produce outcome i ,the respondent’s evaluation of the outcome i and the sum istaken over all accessible outcomes.

beir

Similarly, the subjective norm ( SN scale) comprised six pairs of questions eliciting the respondent’s
accessible normative beliefs (n) concerning making payments to restore biodiversity in the Tummel
catchment and their motivation to comply (m) with that referent norm in question. An example pair of
SN scale questions was:(i) Eliciting normative belief (n):“My spouse / partner would think that I … pay more for electricity to preserve biodiversity in theTummel area.” This was scored from “1= should” to “7 = should not”, and(ii) Eliciting motivation to comply (m):“Generally speaking, how much do you want to do what your spouse/partner thinks you should
do?” This was scored from “ 1= not at all” to “7 = very much”.
The normative belief (n) is multiplied by the relevant motivation to comply (m), and the resulting
products are summed over all accessible behavioural outcomes to create the SN scale18:
13

SN r ∝ ∑ nir mir
i =1

18

Respondents who had no spouse/partner did not respond to the question.

[11]

ATPS SPECIAL PAPER SERIES NO. 22

18

where r is the respondent,

is a belief in a referent norm i ,is the respondent’s motivation tocomply to the referent norm i , and sum is taken over all accessible norms.
Finally, perceived behavioural control (PBC) was determined by the total set of accessible control
beliefs, i.e. beliefs about the presence of factors that may facilitate or impede performance of the
behaviour. A total of five PBC questions were asked. An example is: “I can easily afford to pay more formy electricity.” This was scored from “1 = strongly agree to 7 = strongly disagree”. Control belief
strength is generally multiplied by control belief power “the extent to which the control factors presence
has the power to impede performance of the behaviour” (Ajzen, 2002:5). The results of the focus
groups, however, showed that the hypothetical referent behaviour in this case involved three linked
behavioural control items: the individual’s ability to (i) pay higher energy bills, (ii) trust in the ability andwillingness of the hydro-power companies to ensure that the money went to biodiversity restoration,(iii) believe in the efficacy of the proposed compensation flow scheme (when implemented) todeliver the proposed improvements in biodiversity. Belief power was therefore too obscure forrespondents to assess within the context of the survey. The perceived behaviour control questions
therefore measured control belief strength only.
Section D covered relevant socio-economic data including gender, age groupings, education levels,
income groupings, actual disposable incomes, standard educational classifications, household
location, etc. The categories used corresponded with the 2001 Scottish census (General Register
Office for Scotland, 2001). Finally, Section E asked for interviewer evaluation of the context of theinterviews, difficulties encountered by the respondents in responding to particular sections of thequestionnaire, and how seriously they seemed to take the survey. This information was necessary forvalidating survey results.
The final version of the survey as discussed above was informed by a series of reviews which
involved expert consultation with aquatic ecologists, social psychologists, economists, geographic
information systems (GIS) specialists, relevant end users, and presentations at stakeholder
conferences / project meetings; two public focus groups and 81 pre-test interviews. Details of these
exercises can be found in (Spash et al, 2004).
The same multi-stage probability proportional to size quota sampling technique was employed forthe focus groups, the pre-tests and the main survey19. The main survey sample consisted of 1,012
face-to-face interviews that took place between August and December 2003 in urban and rural
council areas in Scotland (Figure 4). The sample provided a good representation of the Scottish
population based on the socio-economic statistics gathered (Appendix 1).

19

As noted above aim was to get a good representation of each strata of the Scottish population and ensure ease of comparisonof data between the different social groups, at each stage of the survey.

nmi rirINTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

Figure 4: Coverage of the focus groups, pre-tests and main surveys

19

5.

Model Results and Discussion

Sample and Descriptive Statistics
Table 3 presents the distribution of responses to the payment in principle (i.e. WTP Principle). About
38% of the sample (387 respondents) were explicitly willing to pay in principle, 43% (437 respondents)
were explicitly not willing to pay, 18% (180 respondents) were undecided and 0.8% (8 respondents)
explicitly refused to respond to the WTP Principle question.
Table 3: WTP Principle Category of Response
Response category

Number% of Sample

Yes in Principle
No in Principle
Don’t know in Principle
Refuse in Principle

387
437
180
8

38.24
43.18
17.79
0.79

TOTAL

1012

100.00Analysis of the follow-up questions found that the reasons for don’t know responses and the refusals
to offer a positive bid were diverse as were the reasons for positive bids (see Spash et al., 2004).
About 31 percent of the refusals, don’t knows and zeros were related to the payment vehicle used in
the survey while 18% were related to income constraint. About 88% of the respondents who werewilling to pay something stated that it was because they wanted to help nature or restore biodiversity.
Only about 4% of the payments were not directly related to biodiversity restoration. Thus the behaviouralintention (WTP Principle) and actual referent behaviour (i.e. WTP Specific) meets the Ajzen’s (TACT)
requirements (see Ajzen, 2002: 2), providing suitable dependent variables for testing the TPB model.
Scale reliability tests found that the ATb , SN , & PBC scales were reliable (Cronbach’s alpha≥ 0.93)20. There were also significant partial correlations between the predictor variables (i.e.

scales) and WTP Principle, fitting a priori expectations and the assumptions of
the TPB model.
Descriptive comparison of respondent’s ethical beliefs and WTP Principle categories also foundthat ethical categories do have a significant influence on WTP Specific (Table 4). The chi-squared20

Ten out of the 13 ATb scale items with the highest Cronbach’s apha were included in the model.

20

ATb , SN , &21

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

results are highly significant. However, the analysis also suggests a deviation from the proposition by
Spash, (1998: 60) that ethical beliefs necessarily suggest a violation of the axiom of continuity andthat indifference curves collapse to single points, denying the principle of substitution. The strong
rights (SMLP) and consequentialist favouring species (CONSEQ1) are over-represented in the
category of respondents who are willing to pay in principle (i.e. the Yes category) andunderrepresented in the “No, Don’t Know, and Refused” categories. If the SMLP category signifies
that preferences are strictly lexicographic, it is expected that respondents holding such beliefs are
more likely to reject the trade-off, falling in the “No”, “Don’t know” and “Refused” categories. In thiscase, Consequentialist favouring people (CONSEQ2) and the human right group (HUMRGT) weremore likely to reject payment in principle while those with strong rights for species and those whoheld ethical rights favouring species were more disposed to accept the idea of payment in principle.
As shown in Appendix 2 those in the strong rights position also had the highest mean WTP and, withthe consequentialist favouring humans, the highest median WTP.
Table 4: Ethical Position on Species Protection Compared with WTP Principle Category
Ethical Positions

SMLP

WTP Principle
Yes
No

Don’t
Know

Refusedto answer

Total

1
1.8
0.4
-0.7
0
0.4
0
-0.7
0
2.2
0
-1.8
1
1.7
0.5
-0.6
0
0.6
0
-0.8
6
1.2
3.9
4.7

230
230
100

ActualExpected% within ethical positions
Adjusted Residual
ActualExpected% within ethical positions
Adjusted Residual
ActualExpected% within ethical positions
Adjusted Residual
ActualExpected% within ethical positions
Adjusted Residual
ActualExpected% within ethical positions
Adjusted Residual
ActualExpected% within ethical positions
Adjusted Residual

138
88.2
60.0
7.7
18
21.1
32.7
-0.9
161
106.6
57.9
7.9
49
80.5
23.3
-5.0
4
31.0
4.9
-6.4
16
58.6
10.5
-7.7

66
99.4
28.7
-5.1
28
23.8
50.9
1.2
79
120.1
28.4
-5.8
131
90.7
62.4
6.3
57
35.0
70.4
5.1
74
66.1
48.4
1.4

25
40.7
10.9
-3.1
9
9.7
16.4
-0.3
38
49.1
13.7
-2.1
29
37.1
13.8
-1.7
20
14.3
24.7
1.7
57
27.0
37.3
6.9

Total Number
Chi-Square tests

386
Value

435
df

178
8
Asymp. Sig.

Pearson Chi-square
Likelihood Ratio
Linear-by-Linear Association

250.61
257.66139.08

15
15
1

WMLP

CONSEQ 1

CONSEQ 2

Human
Rightscan’t answer

Excludes refusals to answer questions on ethical categories: missing N= 5.

55
55
100
278
278
100
210
210
100
81
81
100
153
153
100
1007
<0.001<0.001<0.001

22

ATPS SPECIAL PAPER SERIES NO. 22

The analysis shows that 74.33% of those holding strong beliefs in rights to life for species werewilling to play the economic game (i.e. WTP e” 0). The mean WTP for the SMLP sub-group is £10.58(min £0.00, max £120.00). The median was £5.00 which means that more than half of the respondents
who held strong beliefs in rights for species (i.e. the SMLP category) were willing to pay ≥ £5.00 for
biodiversity restoration in the Tummel. In contrast, the mean WTP for the extreme human rights
group (i.e. HUMRGT category) was £0.54 (min £0.00, Max, £3.91). The median WTP for this groupwas £0.00 which means that more than half of the respondents who believe that “too much concern
is shown for wildlife and not enough for humans” were not willing to pay for biodiversity restoration in
the Tummel catchment. Although this analysis questions the concept of “lexicographic preferences”
signifying a collapse of the neo-classical welfare theory, it does suggest that ethical considerations
do significantly affect WTP principle for biodiversity restoration in this case study. Instead of rejecting
the trade-off, respondents holding strong ethical rights positions were more willing to pay forbiodiversity restoration in the Tummel than any of the other sub-categories in this case. Belief instrong rights to life for species can, in this case, be seen as signifying increasing values placed uponbiodiversity rather than a total collapse of the neo-classical welfare theory as currently argued bysome critics of the neoclassical model. Hence ethical factors do not necessarily explain negative
responses to WTP principle questions in CV surveys. An unknown proportion of the public may holdstrong ethical beliefs as well as legitimate dispositions to pay for improvements in aspects of theenvironment which they hold dear.
This finding corroborates an earlier suggestion by Rosenberger et al., (2003: 65) that “… somepeople seemingly expressing lexicographic preferences are actually being inconsistent in theirexpressions”. In an earlier study, Urama (2003) finds that lexicographic preference expressions arein fact inconsistent and weakly held preferences which change with further information on thewelfare impacts of proposed changes in the valuation scenario. There may therefore be otherfactors that inform human valuation of environmental entities that are excluded in single domain
models, be it neo-classical economics or environmental ethics, furthering the case for the use of
integrated models in environmental valuation. This result does not however suggest any paradigm
shift within the single domain models. Instead the results strongly question some fundamental
assumptions underpinning the contemporary critique of CBA. It shows that occurrence of strong
ethical beliefs neither signify “a collapse of indifference curve to single points, denying the principleof gross substitution” (Spash, 1998: 60) nor “a departure from the usual economic paradigm” (Spash,
2000: 195). This however, is an area that requires further research.
Comparing Single Domain Models
In this section, the contributions of factors taken from three different single domain models to publicdisposition to pay for biodiversity restoration, in this case study are compared. The aims of theanalyses are threefold: (i) to test the hypothesis that human dispositions to invest in biodiversity
restoration schemes are significantly driven by multiple factors including ethical values, social
psychology factors and socio-economic ones; (ii) to test the hypothesis that each of the single
domain models provide complementary insights to our understanding of human valuation of
biodiversity consistent with their respective theoretical assumptions and; (iii) to test the hypothesis

23

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

that integrated models would, ceteris paribus, explain public willingness to invest in biodiversity
restoration in principle better than the single domains models.
Table 5: Logistic Regression of the Probability that Respondents are Disposed to Pay in Principle
Using the Single Domain Models
Coefficients

Single domain models
Economic model

Ethics modelSocial
Psychology model

Variables
Constant
Knowledge ofdiversity
Understanding of
impacts
Income refused
Age class
Education class
GenderATB

Location in Scottish
council area
Strong MLP-1.69(7.82)***
0.18(1.16)
1.25(6.95)***-0.28(1.94)**
0.23(1.65)*
0.56(3.20)**
0.12(0.87)
0.01(0.04)

Weak MLP
Consequentia lists
favouring animals
Consequentialists
favouring humans
Human rights

0.42(1.15)
0.26(8.16)***-0.06(1.96)**
0.05(1.66)*
0.13(3.13)***
0.02(0.87)
0.00(0.04)-2.39(8.85)***
0.18(1.19)
0.73(3.80)***

0.04(1.18)
0.16(4.12)***

2.10(7.13)***
1.12(2.88)***
2.01(6.96)***
0.57(1.84)*-0.95(1.66)*

0.48(8.31)***
0.27(2.91)***
0.46(7.86)***
0.13(1.79)*-0.17(2.10)**Scale

SN ScalePBC Scale
LR Chi2(Significance)
PseudoN

98.57(<0.01)***0.08
982.00233.79(<0.01)***0.18
998.00-4.67(9.26)***
0.27(1.56)
0.51(2.42)**

0.06(1.53)*
0.11(2.57)**

-0.23

-0.05

(10.96)***(11.42)***-0.08X-0.02(10.96)***(6.35)***

-0.46

-0.10

(5.27)***
405.01(<0.01)***0.31
973.00(5.24)***

The absolute z values associated with the underlying coefficients reported in brackets.
* coefficient significant at 10% level; ** coefficient significant at 5% level, *** coefficient significant at 1% level. Dependent
variable = WTP principle. The correlations between the predictor variables were all £0.36, suggesting that multicollinearity was
not a serious problem. The pseudo R2 is the difference between the log-likelihoood for the full model and the log-likelihood for theintercept only model divided by the log-likelihood for the intercept only model.

24

ATPS SPECIAL PAPER SERIES NO. 22

The estimated WTP principle models, i.e. the standard economic model, the ethical model, and thesocial psychology model are presented in Table 5. Their relative contributions to the probability ofpositive dispositions to pay for biodiversity within their respective theoretical assumptions are discussed
below.
The model results show that the standard economic model explains 8% (Pseudo R2 = 0.08) of
people’s dispositions to pay for biodiversity improvement, in this particular context21. This is relatively
low compared to the ethical and social psychology models, which explain 18% and 31% of WTP
principle respectively.
The levels of significance of the individual models followed similar patterns with the psychologicalmodel attaining the highest level of stability and Chi2 value ( LR χ 2 = 405.01, P < 0.01) followedby the ethical model ( LR χ 2 = 233.79, P < 0.01) and lastly the neo-classical economic model( LR χ 2 = 98.75, P < 0.01) . Understanding of the impacts of the proposed scheme on biodiversity
was consistently significant in explaining WTP Principle in all three models corroborating earlier
findings by Urama, (2003). This also suggests that the responses to the WTP principle question
involved a cognitive activity instead of a “yea saying” exercise.
The implications of these findings are two-fold. First, the social psychology factors (i.e.

ATb , SN , & PBC ) outperform the ethical and socio-economic variables in explaining publicdisposition to pay for biodiversity restoration in the case study. Second each of the three single
domain models meets a priori expectations and is consistent with the assumptions of their underlying
theories. The neo-classical model suggests that understanding of the impact scenarios and level of
education are the most significant determinants of WTP principle for biodiversity restoration in thecase study. Holding all other factors constant, the log of odds ratio in favour of getting a positive
response to the WTP principle question goes up by 1.25 and 0.56 units per unit changes inunderstanding of impacts and education levels, respectively. A unit change in the respondents’
understanding of the impact scenario and in his/ her level of education results in an increase in theprobability of a positive disposition to pay for biodiversity restoration of 0.26 and 0.13, respectively.
Refusal to give income data was also significant but negative corroborating earlier findings by Spashet al., (2004). As noted by Spash et al., (2004:27), “This may be interpreted as those refusing to pay
wishing to avoid being seen as miserly and so refusing to admit their income level”. Overall, theestimated model based on socio-economic factors is consistent with the neoclassical welfare
theory. The results of the analyses do not therefore suggest any fundamental contradictions to the21

Following Mitchell and Carson, 1989, and Gujerati, 1995, adjusted R2 values were used as a test of explanatory power of themodel. Only the variables predicted by the underlying theories were included in the individual models. KNBIOD * andUNDBIOD * were included in all the individual models because each model requires that the respondent knows what he/she is valuing and understands the questions clearly.

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

25

ontological assumptions of the standard neo-classical welfare model as some critiques of CBA haveargued (see for example, Niemeyer and Spash, 2001).
The results of the WTP principle model based on ethical positions suggest that belief in species
rights, i.e. both strong and weak species rights positions (SMLP & WMLP), and belief inconsequentialism favouring animals (CONSEQ 1) were the most significant drivers of publicdispositions in principle to pay for biodiversity restoration, in the case study. All had positive signs,
indicating that the stronger the belief in rights to life for species, the higher the probability of positive
dispositions to pay for biodiversity restoration. SMLP and CONSEQ1 had the highest marginalcontributions to the probability of positive disposition to payment while human rights beliefs had asignificant negative marginal effect at the 5% level. While this finding contradicts the suggestion thatholding strong ethical beliefs signifies the collapse of neoclassical welfare theory (Spash, 1998,
Spash, 2000)22, we argue that it is consistent with neo-classical welfare theory (see Koutsoyiannis,
1991). Holding ethical beliefs in rights to life does not necessarily lead to a rejection of making
payments towards protection of species. Instead, this analysis suggests that people’s disposition to
pay for biodiversity restoration increased with the strength of their belief in rights to life for species.
The analyses of the ethical model do not therefore suggest a rejection of the ontological assumptionsof the standard neo-classical welfare theory. Instead, strong species rights can be seen as signifying
the essentiality of restoring biodiversity (see Lockwood, 1996; Schmidtz, 2000) as reflected in thehigh WTP bid amounts discussed above.
Finally, all three factors taken from the TPB were the most significant determinants of WTP in thecase study. Consistent with the underlying assumptions of the TPB model, specific attitude toward
payment to restore biodiversity in the specific context of the Tummel catchment was the mostsignificant driver of WTP Principle within the social psychology model (significant at the 1% level).
Subjective norms and perceived behaviour control factors were also significant at the 1% level.
Together with the respondent’s knowledge of biodiversity and understanding of the impacts describedin the survey, ATb , SN , & PBC explained more of WTP principle than the ethical and socioeconomic variables combined. ATb , SN , & PBC had negative marginal contributions to theprobability that the respondents are disposed to pay for biodiversity restoration in principle. A unit
increase in the strength of specific attitudes towards payments for biodiversity restoration reduced
public disposition to pay by 0.05. In the same way, each unit change in perception of factors that arelikely to inhibit payment (i.e. PBC) reduced the willingness to pay in principle by 0.1. These findings
are consistent with a priori expectations. The attitude scales used in the study measured specificattitudes towards payments for restoring biodiversity to meet the “TACT” requirements of the TPB
model.

22

Respondents holding lexicographic preferences are expected to reject the trade-off, hence bidding zero, refused, or don’t know (see for example Spash, 1998).

26

ATPS SPECIAL PAPER SERIES NO. 22

Overall, the results of the single domain models suggest that the social psychology model outperforms
the ethical and socio-economic factors in terms of explaining the probability of positive responses to
WTP Principle question, while the ethical variables had the largest marginal contributions to theprobability of positive responses. Judging by the explanatory power of each model and the level ofsignificance of their individual predictor variables, it can be argued that social psychology andethical factors should have even more weight amongst the criteria for justifying environmental policyin the Tummel catchment, than the socio-economic ones. Applying the neo-classical economics
approaches (e.g. CEA and CBA) as the only criteria for justifying environment policy would, in thiscase, leave out a significant proportion of the factors driving public dispositions to pay for biodiversity
restoration in the Tummel catchment that could be explained with insights from social psychology
and environmental ethics. In the same way, using any of the single domain alternatives (SPYCHOMOD
or ETHIMOD) as the sole criterion for environmental decision making would also lose the perspectives
from the contending models. This led to the second part of the analysis examining the relevance ofintegration. Will integration enable us to explain public dispositions to pay for biodiversity restoration
better than single domain models? Will integration of models reduce the precision of model parameter
estimates? These questions are examined in the subsequent sections.
The Value of Integrated Modelling in Environmental Valuation
The integrated value-mapping models (IVM) are presented in Table 6. Four models are estimatedconsidering the different possible combinations of the individual domain models (see IVM1 – IVM4
in Figure 2). The analysis finds that combining all three models (IVM4) explains about 36% of WTP
Principle in the case study, outperforming alternative combinations of models (IVM1, IVM2 and IVM3)
respectively. IVM4 fits significantly better than the next best model IVM3 (P = 0.005).
Respondent’s age and education class were the only socio-economic variables significant in the
fully integrated model (IVM4). On the contrary, all the social psychology variables, and all the ethicalvariables remained significant except for the human rights category. A unit increase in the belief inconsequentialism but favouring species (CONSEQ1) increased disposed to pay for biodiversity
restoration in the Tummel catchment by 0.42. This was followed by the strong modified lexicographic
preference (SMLP) group with a 0.41 marginal increase in the probability of giving a “Yes response”.
Overall, all the ethical factors had a positive contribution to the probability that respondents will givea positive response to the WTP Principle question, contradicting the current perception that ethicalpositions lead to negative bidding in CVM experiments. The results further suggest that the strengthof ethical positions reflect measures of essentiality of biodiversity, not a rejection of the neo-classicalwelfare theory. Those who hold strong beliefs favouring species (SMLP) and those who consider
human welfare but favouring species were most disposed to pay than any of the other ethicalcategories.
Consistent with a priori expectations, all the social psychology factors had a negative contribution
to the probability of a positive response. In other words, the stronger the specific attitudes toward
monetary payments for the restoration of biodiversity, the existing subjective norms and the perception

27

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

Table 6: Logistic Regression of the Probability that Respondents are Disposed to Pay in Principle
Using the Integrated Model Alternatives
Variables

Economics &Economics &Social Psychology ethics combined

Combined (IVM1) (IVM2)

Constant
Knowledge of
biodiversity
Understandingof impacts
IncomeRefusedAge Class
Education
Class
GenderLocation in
ScottishCouncil Areas
Strong Species
RightsWeak Species
RightsConsequentialists
favouring animals
Consequentialists
favouring humans
Human rights

ATB Scale

Ethics & Social All Three Models
Psychology
combined(IVM4)
Combined(IVM3)

4.38
2.66
3.66
(7.98)***(8.21)***(6.29)***
0.09
0.02
0.04
0.01
0.19
0.39
(0.45)(0.45) (0.23)(0.23)(1.01) (1.00)
0.46
0.09
0.71
0.15
0.03
0.01
(2.09)** (2.20)** (3.51)*** (3.82)*** (0.12) (0.12)
-0.11
-0.02
-0.23
-0.05
(0.59)(0.60) (1.49)(1.51)(0.36) (0.36)
0.40
0.09
0.26
0.06
(2.26)** (2.30)** (1.68)* (1.70)*
0.57
0.13
0.46
0.11
(2.73)*** (2.63)*** (2.43)** (2.36)**
0.21
0.04
0.16
0.03
(1.20)(1.20) (1.03)(1.03)
0.17
0.04
0.06
0.01
(0.95)(0.96) (0.36)(0.36)

3.37(5.39)***
0.01(0.05)-0.10(0.39)-0.07

0.00(0.05)-0.02(0.39)-0.01

0.40(2.15)**
0.49(2.17)**
0.20(1.14)
0.17(0.89)

0.08(2.19)**
0.10(2.04)**
0.04(1.13)
0.03(0.90)

2.16
0.49
1.70
0.38
1.82
(7.15)*** (8.43)*** (4.89)*** (4.98)*** (5.00)***
1.18
0.28
0.91
0.21
1.02
(2.95)*** (3.01)*** (1.99)** (1.88)* (2.16)**
2.06
0.47
1.76
0.39
1.93
(6.92)*** (7.88)*** (5.17)*** (5.32)*** (5.42)***
0.59
0.14
0.87
0.19
0.97
(1.85)* (1.80)* (2.39)** (2.28)** (2.56)**
-0.85
-0.16
-1.10
-0.18
-0.87
(1.46)(1.80)* (1.72)* (2.38)** (1.34)
-0.25

-0.05

-0.21

-0.04

-0.23

% changein precision
due to integration

27.67
30.10
26.85
31.02
29.00

0.41
24.30
(5.16)***
0.23
20.88
(2.04)**
0.42
23.37(5.67)***
0.21
23.07
(2.44)**
-0.14
13.78
(1.71)**
-0.05

12.51

(11.14)*** (11.64)***(9.42)*** (9.81)*** (9.74)*** (10.20)***SN Scale

-0.08
-0.02
(5.99)*** (6.06)***

-0.09
-0.02
-0.08
-0.02
5.15
(6.31)*** (6.37)*** (6.05)*** (6.09)***PBC Scale

-0.46
-0.10
(5.18)*** (5.14)***

-0.44
-0.09
-0.45
-0.09
8.84
(4.83)*** (4.77)*** (4.79)*** (4.73)***

LR Chi2(Significance)
PseudoN422.18(<0.01)***
0.33
957

247.80(<0.01)***0.19
982.00471.71(<0.01)***
0.36
973

488.48(<0.01)***0.38
957.00

The absolute z values associated with the underlying coefficients are reported in brackets. * coefficient significant at 10% level;** coefficient significant at 5% level, *** coefficient significant at 1% level. Percentage change in precision due to integration
was computed as the percentage reduction in the standard errors of IVM 4 parameter estimates relative to those of the individual
single domain models.

ATPS SPECIAL PAPER SERIES NO. 22

28

of the presence of factors that may impede payment, the less likely that the respondent will bewilling to pay in principle.
To examine the source of improvement in the model, the different permutations of possible integration
were examined: economics and social psychology (IVM1), economics and ethics (IVM2), and ethics
and social psychology (IVM3). Again, integration of ethics and social psychology significantly explained
the probability of getting a positive response to the WTP principle question better than otheralternatives. The pattern of marginal contributions of variables is also consistent with the fully
integrated model (IVM4).
Using the single domain models as base line models, we also find a consistent improvement in the
precision of individual parameter estimates due to integration (see column 9 of Table 6). Again theleast gain in precision is observed in the social psychology variables suggesting that the TPB
model was the most robust of all the single domain models. The marginal contributions of eachvariable to the fit of the model are presented in Table 7.
Table 7: Marginal Contributions to the Explanatory Power of the Socio-economic Model
Variables

Marginal contribution to LR Chi2 if
added to economic model

Ethics variables
40.24
0.41
37.16
32.19
32.93
Social psychology variables

ETHIMODPSYCHOMODETHIMOD + PSYCHOMOD

238.51116.1785.72
149.23343.61389.91Baseline model = Standard neo-classical model for WTP Principle.

Overall, the analyses strongly suggest integration increased both the precision of the parameter
estimates and the fit of the WTP Principle models. However, the social psychology model had thebest fit of all the single domain models and all possible combination of the ethical and neo-classicaleconomic predictor variables. Of all the components of the three single domain models, the social
psychology factors were also the most precise.
To examine if disaggregating the negative outcomes to their individual categories would lead todifferent conclusions, each of the models discussed above was re-estimated using multinomial logit
regression techniques. The analyses yield consistent conclusions. Table 8 presents the fully integrated
multinomial logistic regression model (IVM4) only. This models the probability of a positive outcome

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

29

against all other possible negative outcomes including No, Don’t Know (DK) and Refused to Answer(RA)23.
The analysis found that those holding strong modified lexicographic preferences (SMLP) and those
who believe in consequentialism but favouring species were more likely to give a positive responseto the WTP principle question than any other group. The probability of getting a “NO” response alsosignificantly decreased with age of respondents and their level of education. The probability ofgetting a DK response significantly decreased by age of respondents and strength of belief in species’
rights to life. On the other hand, all the social psychology variables significantly increased the probabilityof getting a NO or DK response.

Further Tests for Sensitivity, Specificity and Predictive Power of the Integrated Logistic
Regression Models
For a particular classification threshold, the sensitivity of the logistic regression models wasestimated as the fraction of observed positive-outcomes cases that are correctly classified while
the model specificity was estimated as the fraction of observed negative-outcome cases that are
correctly classified (StataCorp. 2003: 310). The predictive power of the model was estimated using
the ROC curve (Peterson, 1954, Green and Swets, 1974)24.
As shown in ROC curve (Figure 5 panel A), the model is of high predictive power (area under the
curve = 0.89). As a general rule, ROC curve for a model with no predictive power would be a 450 line
(StataCorp. 2003: 310).
The greater the predictive power of the model, the more bowed the ROC curve, and hence the area
beneath the curve is often used as a measure of predictive power25. Figure 5 panel B measures thedecrease in the Pearson χ 2 goodness-of-fit statistic that would be caused by deleting an observation.
The points going from the top left to the bottom right correspond to positive outcomes, i.e. theprobability of a Yes response; the points on the other curve correspond to zero outcomes, i.e. theprobability of a No response (see Hosmer and Lemeshow, 2000: 198 -179; also cited in StataCorp.
2003: 318). These figures in conjunction with further tests including the test of deviance residuals
against the probability of a positive outcome and sensitivity curves (see StataCorp, 2003: 311-318)
show the IVM4 model has a good fit.

23

Further details on the implications of these findings for the longstanding debate on the use of ethical positions in setting public
environmental choices can be found in a companion paper (Urama et al., in preparation).
24
ROC curve was first discussed in signal detection theory developed by Paterson, et al. (1954) and then was quickly introduced
into psychology and medicine (Tanner and Swets, 1954; Metz, 1978; and Green and Swets, 1974).
25
A perfect model would have a predictive power of 1 and hence the area under the ROC curve would be 1.

ATPS SPECIAL PAPER SERIES NO. 22

30

Table 8: Multinomial logistic regression of the probability of positive response compared to No,
Refused and Don’t Know using IVM4.
Variables

No

DK

RA

Marginal contribution to the probabilityof outcomescoefficientscoefficientscoefficients

Yes

No

DK

Constant-4.85(7.08)***
0.01(0.05)
0.15(0.56)
0.24(1.23)-0.35(1.73)*-0.84(3.39)***-0.20(0.97)
0.19(0.99)-1.42(3.65)***-0.52(1.04)-1.61(4.22)***-0.46(1.17)
1.32(1.97)**-2.8
1(3.83)***-0.04(0.17)
0.00(0.01)-0.28(1.17)-0.53(2.33)**
0.07(0.25)-0.28(1.24)-0.14(0.59)-2.28(5.44)***-1.62(2.84)***-2.20(5.54)***-1.73(4.00)***
0.42(0.61)-10.22(2.96)**
0.05(0.05)
0.04(0.36)-0.62(0.57)
0.67(0.63)
1.06(0.89)
1.08(0.93)
1.35(1.08)-4.13(2.82)**-34.76(0.00)-35.01(0.00)-2.87(2.16)**-32.04(0.00)

-

-

-

0.00
(0.03)-0.02(0.40)-0.02(0.47)
0.08(2.20)**
0.11(2.12)**
0.04(1.18)
0.04(0.97)
0.38(4.55)***-0.18(1.50)
0.40(5.09)***
0.17(1.93)*-0.18(2.30)**

0.01(0.15)
0.04(0.71)
0.09(2.14)**-0.02(0.71)-0.21(4.33)***-0.02(0.44)-0.04(0.83)-0.18(2.14)**-0.02(0.18)-0.21(2.73)***
0.02(0.23)
0.26(3.22)***-0.00(0.22)-0.02(0.38)-0.07(2.14)**-0.05(1.63)
0.10(2.06)**-0.02(0.87)-0.00(0.06)
0.20(5.36)***-0.16(3.94)***-0.19(5.14)***-0.19(5.25)***-0.08(1.83)*

0.24

0.21

0.23

-0.05

0.04

0.01

(9.74)***(7.80)***(2.37)(9.19)***(6.93)***(3.26)***

0.10

0.06

-0.25

-0.02

0.17

0.00

(6.67)***(3.64)***(1.94)*(5.79)***(6.14)***(0.03)

0.51

0.31

1.53

-0.09

0.10

-0.00

(5.60)***(2.76)***(2.82)***(4.60)***(4.43)***(0.03)

579.41(<0.01)

Pseudo0.29

N957.00Knowledge of biodiversity
Understanding of impacts
Income Refused
Age Class
Education Class
GenderLocation in Scottish Council Areas
Strong Species Rights
Weak Species Rights
Consequentialists favouring animals
Consequentialists favouring humans
Human rights

ATB Scale
SN ScalePBC Scale
LR Chi2 (Significance)

The absolute z-values associated with the underlying coefficients reported in brackets.
* coefficient significant at 10% level; ** coefficient significant at 5% level, *** coefficient significant at 1% level.

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

Panel A: ROC Curve
1.00

0.75

Sensitivity
0.50

0.25

0.00
0.00

0.25

0.50
1 - Specificity

0.75

1.00

Area under ROC curve = 0.8859

Panel B: contributions of individual observations to Pearson χ 2 goodness-of-fit statistic

80

60

H-L dX^2
40

20

Figure 5: Tests of the Sensitivity, Specificity and Predictive capacity of IVM4 model
0
0

.2

.4
.6
Pr(wtpprin1)

.8

1

31

4.

Conclusions and Policy Recommendations

Alternative models of human behaviour from social psychology and environmental ethics challenge
the axiomatic claims made for the assumptions underlying CBA, suggesting that mainstream static
economic models of behaviour are limited in their capacity to explain human dispositions to pay forenvironmental improvement. In social psychology behaviour is seen as a process where the emphasis
is on how beliefs and preferences are formed or learnt, and how information is acquired (Green and
Tunstall, 1999) while in the field of environmental ethics, certain positions (e.g. lexicographic
preferences) suggest a rejection of the principle of trade-offs (Spash, 1998). Urama (2003) andUrama and Hodge (in press) argued that no single disciplinary approach is likely to provide acomprehensive valuation of environmental entities or a logical conclusion to the longstanding debate
on the use of neo-classical economic tools to aid environmental policy. Critiques of CBA that draw on
single disciplinary assumptions (such as environmental ethics or social psychology) might therefore
be purely speculative (see Urama and Hodge, in press). Following recommendations by Urama(2003) and Urama and Hodge (in press) this paper examines the case for considering integration ofmodels and different perspectives as a virtuous path for future environmental decision making.
The results of this study lead to three main conclusions. First, each of the single domain models
significantly explained WTP Principle at the 5% level. Each of them also fits its own a priori expectations
and ceteris paribus assumptions, suggesting they are all theoretically valid. Each provides valuable(and complementary) insights to the diverse factors that inform public dispositions to pay for biodiversity
restoration in the Tummel catchment.
Second, integrating all three models (IVM4) explained WTP principle better than any of the single
domain models or any other bilateral integration possibilities (IVM1- IVM3). This also improved theprecision of individual parameter estimates. Integrating factors from social psychology, environmental
ethics and neo-classical economics therefore reduces the stochastic error in our current
understanding of people’s environmental preferences, without compromising the underlying
assumptions of the component models. This finding validates the recommendation by Urama (2003)
and Urama and Hodge, (in press): “integration of models from different perspectives is the virtuous
path for future”. Despite the statistical robustness and theoretical validity of the single domain models
within their own disciplinary silos, integration of models would improve our understanding of public
environmental priorities without losing the required precision in parameter estimates or robustnessof models.

32

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

33

Third, the results of these analyses questions the growing critique of CBA based on the assumptionthat respondents holding strong ethical positions (e.g. those in the SMLP category) are more likely toreject the trade-offs, leading to a collapse of indifference curves to single points (see for example
Spash, 1998, 2000). The analyses find that the probability of positive dispositions to pay for biodiversity
increases with the strength of belief in rights to life for species. This is consistent with neo-classicalwelfare theory (see Koutsoyiannis, 1991). This is though, an issue that requires further research.
Overall, the results of the analyses provide empirical evidence to: corroborate the long-standing
concerns that using single domain models to aid environmental decision making might beunnecessarily limiting; question the growing proposition that deontological positions constitute a
rejection of the neoclassical paradigm; and illustrate the case for considering integrated value
mapping alternatives in environmental policy planning.

Appendices

Appendix 1: Comparing the Sample Data with the Scottish Census Population
Socio-economics

GenderMale
FemaleAge class
Under 25
25-34
35-44
45-54
55+
Occupational classifications
Managers and senior officials
Professional occupations
Associate professional and technical occupations
Administrative and secretarial occupations
Skilled trades occupations
Personal services
Sales and customer service occupations
Process, plant and machine operatives
Elementary occupations
Housewife/husband
RetiredStudent
Unemployed
No response
Rural – Urban Location by council areas
Rural
Aberdeenshire
Argyll & Bute
Highland
Perth & KinrossDumfries & Galloway
Urban
Aberdeen
Edinburgh
Glasgow
Inverness
North Ayrshire

34

Sample(n=1012)% of Total

Scottish Popn
(N= 5,062,011)% of Total

48
52

48
52

20
21
19
18
22

30
14
15
14
27

5.2
6.5
3.7
5.1
8.8
9.3
6.9
3.0
5.4
9.6
13.3
9.9
9.9
3.4
.

8.0
7.1
9.2
8.4
8.0
4.7
5.7
6.3
8.3
4.4
8.2
15.5
6.2
N/a

10.4
4.4
7.6
6.4
6.7

10.0
4.0
8.0
6.0
7.0

9.7
20.3
26.2
1.9
6.4

10.0
21.0
26.0
2.0
6.0

35

INTEGRATED VALUE MAPPING FOR SUSTAINABLE RIVER BASIN MANAGEMENT

Appendix 2: Ethic beliefs and WTP Specific bids
Ethical Positions

Strong Species rights
Weak Species rights
Consequentialists
favouring animals
Consequentialists
favouring humans
Human rights

Position Statements

WTP Statistics
Min

Mean

Median M a x

Std. Dev. No of% response
responses rate

Strong MLP
Weak MLP
Relative utility, species first

0
0
0

10.58
4.44
7.29

5
0
5

120
50
60

19.51
10.71
10.31

170
41
201

74.23
74.45
72.30

Relative utility, Humans first

0

2.53

0

50

7.36

167

79.90

Humans first

0

0.54

0

30

3.91

59

72.84

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LongmanAcknowledgement

Dr Kevin C. Urama1, Dr Wendy Kenyon and Dr. Rob Burton are members of staff at the Macaulay
Institute. Dr Jackie Potts is on the staff of Biomathematics and Statistics, Scotland. This research
was carried out under the auspices of a European Commission-funded project entitled: ‘Integrated
Evaluation for Sustainable River Basin Governance’ (ADVISOR)- Contract EVK1-CT-2000-00074,
Energy, Environment and Sustainable Development RTD. The UK component of the project wascontracted to the University of Aberdeen, and was led by Prof Clive Spash in collaboration with the
Macaulay Institute. The overall project was co-ordinated by Prof Paula Antunes of the University of
Lisbon. The authors are also grateful to the Scottish Executive Environment and Rural Affairs
Department (SEERAD) for their support during the implementation of the project. The authors gratefully
acknowledge the inputs of Prof Clive Spash, Mr Peter Shannon, Dr David Miller, Dr Benoit Demars,
Dr Kirsty Blackstock during the design and implementation of the survey. The authors would also liketo thank Prof Lynn Mytelka, Prof David Elston, Dr E. J. Milner-Gulland, Dr Osita Ogbu, Dr Gerald
Schwarz, and Dr Anke Fischer for their valuable comments on drafts of the paper.

1

Corresponding author: Dr Kevin C. Urama, e-mail, k.urama@macaulay.ac.uk

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