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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 1 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) 2 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. 3 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. 4 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) 5 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 6 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 7 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. 8 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 9 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 10 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 11 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 12 References Black KD. (ed.) 2001. Environmental Impacts of Aquaculture. Academic Press, Sheffield, UK Burbridge P, Hendrick V, Roth E, Rosenthal, H. 2001. Social and economic policy issues relevant to marine aquaculture. Journal of Applied Icthyology 17: 194 - 206 Commission of the European Communities. 2002. A Strategy for the Sustainable Development of European Aquaculture. Communication from the Commission to the Council and the European Parliament. COM(2002) 511 final. Brussels DTLR 2001. Multicriteria analysis: a manual. http://www.dtlr.gov.uk/about/multicriteria/index.htm Fernandes TF, Miller KL, Read PA. 2000. Monitoring and regulation of marine aquaculture in Europe. Journal of Applied Icthyology 16: 138 - 143 Henderson AR and Davies IM. 2000. Review of aquaculture, its regulation and monitoring in Scotland. Journal of Applied Icthyology 16: 200 - 208 Mardle S, Pascoe S. (eds.) 2003. Multiple objectives in the management of EU fisheries: Preference elicitation. CEMARE Report 64, University of Portsmouth, UK. Mardle S, Pascoe S, Herrero I. 2004. Management objective importance in fisheries: an evaluation using the Analytic Hierarchy Process. Environmental Management 33(1): 1-11 Muir JF, Brugere C, Young JA, Stewart JA. 1999. The solution to pollution ? The value and limitations of environmental economics in guiding aquaculture development. Aquaculture Economics and Management 3(1): 43-57 OECD 2003. Review of Fisheries in OECD Countries: Policies and Summary Statistics. Organisation for Economic Cooperation and Development, Paris, France. Read P, Fernandes T. 2003. Management of environmental impacts of marine aquaculture in Europe. Aquaculture 226: 139-163 Saaty TL. 1977. A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology 15(3): 234-281. Scottish Executive. 2003. A Strategic Framework for Scottish Aquaculture. Scottish Executive Rural Affairs Department, Edinburgh. 13