Download Public Preferences and Aquaculture Site Selection

Public preferences and aquaculture site selection
A Briefing Note for ECASA Project Partners
David Whitmarsh, University of Portsmouth
1. Introduction
Marine fish farming in Europe is commonly regulated using the EQO/EQS
(environmental quality objectives / environmental quality standards) system
(Fernandes et al., 2000; Read and Fernandes, 2003), which in the case of
Scotland is implemented at site level through the framework of controls on
location and effluent discharge (Henderson and Davies, 2000). However,
while the EQSs are relatively well specified, the corresponding EQOs are less
so. The site-specific approach adopted for Scottish salmon requires SEPA to
identify local uses of a water body, existing and potential, and to adopt a
standard that ensures that where multiple uses are involved, all are protected
(Ibid.) This system has the merit of minimising the risks to established
activities (e.g. fishing, recreation, etc.) and amenity values (e.g. water quality,
landscape) from fish farming, but there appears to be no formal mechanism
for evaluating these various uses in terms of their importance. In setting
EQOs for aquaculture, the following questions are central: Whose objectives
count ? How are these objectives prioritised and traded off ? How can they be
factored into the decision to allow (or not) an aquaculture project to go ahead
? This Briefing Note proposes a methodology which may go some way to
answering these, and is specifically aimed at the problem of choosing an
appropriate location for new fish farm developments.
2. The site selection problem
Planners may be faced with a site-selection decision where there could be
(say) three feasible locations for a fish farm of a given size and specification.
The three alternative sites might be in quite different coastal regions, and as
such the environmental impact in terms of water quality, visual amenity, etc.
will be dissimilar. The challenge to the environmental scientists is to predict
what that impact will be in quantitative terms, and this may involve deriving
empirical indicators by which the sites can be compared. The challenge from
a socio-economic perspective is establish the acceptability of such a project to
the particular coastal communities affected by the decision to locate in their
area. The essence of the problem is identifying the trade-off between the
socially beneficial effects of fish farm deployment (job creation etc.) and the
possible negative effects associated with environmental deterioration. Multicriteria analysis based on preference elicitation methods can be used to
measure the relative priority that a given community attaches to the positive
versus the negative effects, and these can be expressed as numerical
'importance' weights. These weights can then be applied to the site- selection
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decision. The key point is this: Community A may attach a relatively low
importance to possible adverse environmental impacts of fish farm
deployment, since theirs is a region of high unemployment and they would
therefore welcome a new employer. Community B may have quite the reverse
attitude, if theirs is a well-off community and/or they are more environmentally
conscientious. The measured strength of these preferences can therefore be
set alongside the environmental indicators for the different site options. It may
be that the optimal location for a proposed fish farm is at Site A, even though
(say) the discharge of nutrients is higher there than at Site B or C, if the local
community at Site A attaches a low priority to such effects compared to the
socio-economic gains.
3. Methodology
3.1 Multi-criteria analysis. MCA is an approach to decision-making
involving alternative options that accomplish several objectives, typically nonmonetary as well as monetary. As applied to site selection, the decision
problem could be characterised as follows:
Goal
e.g. Choose optimal site
Criterion 1
e.g. Maximising socioeconomic benefits
Option 1
e.g. Site A
Criterion 2
e.g. Minimising
environmental damage
Option 2
e.g. Site B
Option 3
e.g. Site C
The key steps in MCA are:
(i) identifying the options (e.g. the alternative sites)
(ii) identifying the objectives and criteria (e.g. socio-economic, environmental,
etc.)
(iii) Scoring the options (i.e. describe the consequences of each location
choice by assessing their performance against the criteria)
(iv) Weighting the criteria (i.e. assess the relative importance of the socioeconomic v. environmental objectives)
(v) Deriving an overall value and ranking by combining the scores and weights
for each option (= site)
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Here we are concerned with steps (ii) and (iv), which is essentially an exercise
in preference elicitation.
3.2 Analytic hierarchy process (AHP)
3.2.1 Brief Outline
AHP is a multi-criteria decision method which enables qualitative judgements
about the relative importance of different objectives to be converted into
quantitative information in the form of numerical scores. (Saaty, 1977;
DTLR, 2001). This is achieved by asking respondents to make comparisons
between pairs of objectives, where the intensity of preference is
conventionally measured on a 9-point scale: 1 indicating that the two
objectives are of equal importance, 9 indicating that one objective is of
absolute importance compared to the other (see below).
How important is objective A relative to
objective B ?
AHP scale
Equal importance
Moderate importance
Strong importance
Very strong importance
Absolute importance
1
3
5
7
9
Intermediate values of importance are scaled as 2, 4, 6 and 8
These responses form the basis of the pairwise comparison matrix [A],
which has the form:
w1/w1 w1/w2
…
…
w1/wn
w2/w1 w2/w2
…
…
w2/wn
:
:
:
:
:
:
wn/w1 wn/w2
…
…
wn/wn
The vector W (i.e. w1, w2, … wn) denotes the weights or importance attached
to a set of attributes or objectives. Hence wi/wj measures the importance of
attribute i relative to alternative j in a given paired comparison. With n
attributes, there will be [n(n-1) / 2] paired comparisons.
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To illustrate, consider the answers given by a single respondent (e.g. a
marine scientist) using the 9-point scale to questions concerning the
seriousness of the following environmental impacts of aquaculture within a
given area:





Organic and chemical pollution
Visual intrusion
Habitat degradation
Impacts on wild stocks
Competition for water space
With 5 attributes (n = 5), there will be [n(n-1) / 2] = 10 paired comparisons.
Arranged in the form of the A matrix, the results might be as follows:
Pollution
Pollution
Intrusion
Habitats
Wild stocks
Competition
Intrusion
1
1/7
1/2
1/5
1/8
Habitats
7
1
5
1
1/5
2
1/5
1
1/4
1/7
Wild
stocks
5
1
4
1
1/2
Competition
8
5
7
2
1
The number in a given cell measures the respondent’s judgement about the
seriousness of impact wi in relation to wj. In this example, the scientist has
judged pollution to be ‘very strongly more important’ than visual intrusion –
evidenced by the number 7 in row 1 column 2 (or equivalently, the reciprocal
1/7 in row 2 column 1).
Deriving a set of weights which gives the ‘best fit’ to the relativities stated in
the matrix can be done in a number of ways, the method proposed by Saaty
(1977) involving matrix algebra to derive the eigenvector associated with the
maximum eigenvalue of the matrix [A]. Specialist computer software is
available for this (e.g. Expert Choice), though an alternative is to use a
spreadsheet program such as Excel which has matrix commands (Mardle,
2005, Pers. Comm.). If we apply the eigenvalue method to the data in our
example, we obtain the following normalised priority weights:
Pollution
Visual intrusion
Habitat degradation
Impacts on wild stocks
Competition for water space
47.4%
9.6%
31.0%
8.2%
3.9%
This result gives us not only an ordinal ranking of impacts in terms of their
importance (pollution being the most serious, competition for water space the
least), but numerical scores on a ratio scale.
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Further steps are normally undertaken in AHP, including (i) testing to see how
far individual responses to pairwise comparison questions are consistent, (ii)
deriving group weights to reflect to preferences of particular stakeholders.
These will not be dealt with in detail here, but a useful discussion of the issues
and examples of their application to fisheries management can be found in
Mardle and Pascoe (2003) and Mardle et al. (2004).
3.2.2 Applying AHP to the site selection problem: Scottish salmon
The environmental impacts of fish farming, and the concerns to which those
impacts give rise, will vary according to species and location. To illustrate how
AHP can be applied to site selection, we consider the case of salmon farming
in Scotland. Here there are some quite specific environmental issues and
controversies surrounding aquaculture, but it is generally acknowledged that
salmon farming has also brought benefits in the form of jobs and incomes.
How are these negative and positive effects perceived by the public, and how
does this perception vary between communities and stakeholder groups ?
A hierarchy of performance indicators that could apply to Scottish salmon
farming is shown in Figure 1, and it these that make up the objectives and
sub-objectives (= criteria) used in the analysis. Their selection is based on
policy documents concerning the EU and national government strategy
towards aquaculture (CEC, 2002; OECD, 2003; Scottish Executive, 2003), as
well as other publications detailing the major issues concerning marine fish
farming in general and salmon farming in particular (Muir et al., 1999; Black,
et al, 2001; Burbridge et al., 2001).
Though AHP can be applied to just one decision-maker, here the intention is
to use it as a social survey instrument for eliciting group preferences. The
groups should be representative of stakeholders, and as such are likely to
include the following:
Residents (i.e. householders living in a given coastal area where the
prospect of a fish farm being sited nearby would be of more than academic
interest)
Policy-makers (i.e. local councillors and SMPs who can be expected to have
some knowledge of salmon farming and the trade-off between benefits and
costs)
Experts (i.e. marine scientists and representatives of bodies such as SEPA
with specialist knowledge of the environmental impact of salmon farming)
Fishing interests (i.e. commercial and recreational fishermen whose
activities within the coastal zone may conflict with aquaculture)
Visitors (i.e. non-residents whose recreational experience may be affected
either positively or negatively by the presence of salmon farms)
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Two points need to be emphasised. Firstly, the objectives and criteria shown
in Figure 1 are meant to reflect the benefits and costs of aquaculture to
society as a whole rather than those of the commercial fish farmer. The
essence of the problem is this: if an aquaculture facility is established in a
given area, what are the wider impacts of such a project and how are these
evaluated by the community at large or its stakeholders ? Though the
attributes listed in the Figure are not all externalities in the strictly defined
economic meaning of the term (i.e. spillover effects that impinge on the costs
or benefits of others), they nevertheless each represent an aspect of social
performance which is outside the purely financial performance of the fish
farm. Secondly, the description of the objectives and criteria needs to be
simple enough for them to be understood by non-experts. Whereas marine
scientists would have a detailed knowledge and understanding of the impacts
of fish farming in terms of objectively measurable indicators (e.g. N and P
discharge, Secci depth, parasitic infestation, etc.), the general public would
comprehend these in a relatively common-sense manner that relates to their
everyday experience. Descriptive terms such as ‘water quality’ therefore have
more meaning to non-experts than ‘eutrophication’, and the questionnaire is
accordingly constructed using the simpler terminology even though this may
be at the expense of scientific precision.
3.2.3 Survey design and data collection
The format of the AHP questionnaire will be similar for each of the stakeholder
groups, but the numbers of people surveyed can be expected to vary. For
some groups (e.g. policy makers) it may only be possible to obtain information
from one respondent, but provided his/her judgement is considered
representative of the group’s preferences then that should be sufficient. In
other cases, notably where residents are being surveyed, this assumption is
untenable and a sampling strategy would be required to obtain a statistically
representative number of responses from the group.
There are a number of ways in which data could be collected from a sample
of residents in a coastal area, but an important determinant will be the
geographical dispersal of households and the scale of the survey. Given the
comparative simplicity of the AHP format (see below), a self-administered
questionnaire is considered preferable to an interview-administered one on
grounds of cost-effectiveness. Mail surveys are suitable where the sample is
geographically widespread, but experience suggests that the response rate is
typically low (e.g. less than 20%). However, here it is envisaged focussing on
perhaps three separate locations (= sites), and if these are centred on coastal
towns the addresses to be sampled may not be so dispersed. In these
circumstances it may still be preferable to use self-administered
questionnaires but to hand-deliver these using staff employed specifically for
the purpose.
While the questionnaire may seek to obtain some general information from
respondents, including their general attitude towards aquaculture
development, the key questions are those based on the pairwise comparisons
of objectives and attributes. The structure and rationale of the questionnaire is
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presented in Appendix 1, based on the pairwise comparisons derived from the
hierarchy shown in Figure 1.
3.2.4 Illustrative results
The calculation of priority weights for the various objectives and criteria is
similar to the example we considered earlier, except that here we are
considering a rather a more complex situation. Appendix 2 shows the
pairwise comparison matrices obtained from the responses given by a
‘representative’ respondent, with the priority weights computed using the
eigenvalue method. To prevent the number of pairwise comparisons from
becoming onerously large, the decision problem is split up between two levels
in the hierarchy. The respondent is first required to make a judgement
between the importance of maximising socio-economic benefits and
minimising environmental damage; then, within each of those broadly defined
objectives, a judgement has to be made about the importance of the different
sub-objectives (criteria). The results can be summarised as follows:
Goal
Maximise
net
benefits
from
salmon
farming
Objective
Maximise
socioeconomic
benefits
Minimise
environmental
damage
Objective
weight
0.25
0.75
Criteria
Criterion
weight
(within
objective)
Criterion
weight
(overall)
Employment
and
livelihoods
0.8
0.2
Fish supply
0.2
0.05
Pollution and 0.634
water quality
0.476
Visual
intrusion
0.218
0.164
Impact on
wild stocks
0.111
0.083
Fishmeal
demand
0.037
0.028
The results show that this particular respondent attached higher importance to
minimising environmental damage (0.75) than to maximising socioeconomic benefits (0.25). Of the two criteria making up the socioeconomic
objective, ‘sustaining employment and livelihoods’ (0.8) was judged to be
much more important than ‘contributing to edible fish supplies’ (0.2). Within
the environmental objective, the four criteria were scored as follows:
‘minimising pollution and water quality impacts’ (0.634), ‘minimising visual
intrusion and landscape impacts’ (0.218), ‘minimising impacts on wild salmon
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stocks’ (0.111), and ‘minimising demand for fishmeal based salmon feed’
(0.037). The overall criterion weights are given in the final column, and are
computed by multiplying the objective weights in column 3 with the ‘within
objective’ criterion weights in column 5. Out of the six total criteria,
‘minimising pollution and water quality’ ranks highest with an overall priority
score of 0.476, followed by ‘sustaining employment and livelihoods’ with 0.2.
3.2.5 Operational considerations
The basic methodology illustrated above can in principle be applied to the site
selection problem, and at the very least should seek to obtain results which
can enable a comparison to be made between (i) different coastal areas and
sites (ii) different stakeholder groups. The main practical steps in
implementing this approach are: (i) designing the AHP hierarchy so that the
objectives and criteria realistically characterise the decision problem (ii)
agreeing who are the relevant stakeholders (iii) piloting the AHP questionnaire
on a small scale to eliminate problems and ensure its suitability as a selfadministered survey instrument.
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Figure 1: Hierarchy of objectives for salmon aquaculture
Goal (Level One)
Objectives (Level Two)
Maximise
socioeconomic
benefits
Maximise net
benefits from
salmon farming
Criteria (Level Three)
Sustaining
employment and
livelihoods
Contributing to edible
supplies of fish
Minimising pollution
and water quality
reductions
Minimise
environmental
damage
Minimising visual
intrusion and
landscape impacts
Minimising impact on
wild salmon stocks
Minimising demand
for fishmeal-based
salmon feed
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Appendix 1: Structure and rationale of the AHP questionnaire
There would be an introductory section to the questionnaire in which
respondents would be told of the importance of salmon aquaculture to
Scotland but informed also of the concerns raised about its environmental
impact. The different attributes or qualities making up the positive effects (=
socio-economic benefits) and negative effects (= environmental damage)
would be defined and explained. Guidance on how to use the scale of relative
importance would be given (see below).
The pairwise comparisons would be made according to the hierarchy of
objectives shown in Figure 1, i.e at two levels.
(i)
(ii)
The relative importance of maximising socio-economic benefits
compared with minimising environmental damage
The relative importance of the specific attributes (criteria) within each
of those objectives.
The advantage of this is that it prevents the number of pairwise comparisons
from becoming too great. As shown below, each respondent is required to
make a total of just 8 comparisons; the alternative would be to ask
respondents to make paired choices between all of the criteria at the second
level in the hierarchy, which would come to a total of 28.
How to use the importance scale:
Objective A
Objective B
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
Significantly
more important
8
9
Significantly
more important
Much
more important
Much
more important
More important
More important
Moderately
more important
Moderately
more important
Equally important
In the example below, if you think that minimising environmental damage is "moderately more important"
than maximising socio-economic benefits , circle the relevant point on the scale as shown:
Maximising socio-economic benefit
9
8
7
Minimising environmental damage
6
5
4
3
2
1
2
3
4
5
6
7
8
9
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Part One
The relative importance of socio-economic benefits compared with environmental damage
Maximise socio-economic benefits
9
8
7
Minimise environmental damage
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Part Two
The relative importance of different socio-economic benefits
Employment and livelihoods
9
8
Fish supply
7
6
5
4
3
2
1
2
3
4
5
6
7
8
9
Part Three
The relative importance of different environmental impacts
Pollution and water quality
9
8
Visual intrusion
7
6
5
4
3
2
1
2
3
4
5
6
7
7
6
5
4
3
2
1
2
3
4
5
6
7
Pollution and water quality
9
8
8
9
Impact on wild salmon
Pollution and water quality
8
9
Fishmeal demand
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
9
8
7
6
5
4
3
2
1
2
3
4
5
6
7
8
7
6
5
4
3
2
1
2
3
4
5
6
7
Visual intrusion
8
9
Impact on wild salmon
Visual intrusion
8
9
Fishmeal demand
Impact on wild salmon
9
8
9
Fishmeal demand
8
9
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Appendix 2: Illustrative results of the AHP paired comparisons
Relative importance of socio-economic benefits compared with
environmental damage
Objective
Socio-economic
benefits
Environmental
damage
Priority
weight
Socio-economic
benefits
1
1/3
0.25
Environmental
damage
3
1
0.75
Relative importance of different socio-economic benefits
Criterion
Employment and
livelihoods
Fish supply
Priority weight
Employment and
livelihoods
1
4
0.8
Fish supply
1/4
1
0.2
Relative importance of different environmental impacts
Criterion
Pollution
and water
quality
Visual
intrusion
Impact on
wild
salmon
Fishmeal
demand
Pollution
and water
quality
Visual
intrusion
Impact on
wild
salmon
Fishmeal
demand
Priority
weight
1
5
6
9
0.634
1/5
1
3
7
0.218
1/6
1/3
1
5
0.111
1/9
1/7
1/5
1
0.037
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