elsevier theory and methodology choosing realistic values

EUROPEAN JOURNAL

OF OPERATIONAL RESEARCH

ELSEVIER European Journal of Operational Research 107 ( 1998) 542-55 1

Theory and Methodology

Choosing realistic values of indifference, preference and veto thresholds for use with environmental criteria within ELECTRE

Martin Rogers a, * , Michael Bruen b a Department of Engineering Technology, Dublin Institute of Technology, Bolton Street, Dublin 1. Ireland

b Department of Civil Engineering, Uniuersity College Dublin, Earlsfort Terrace, Dublin 2, Ireland

Received 5 November 1996; accepted 22 April 1997

Abstract

The ELECTRE III outranking model is particularly suited to aiding the choice between project alternatives on the basis of mainly environmental criteria. The model requires values of three criterion thresholds, the indifference threshold (q), the preference threshold ( p) and the veto threshold (u). These allow the uncertainties inherent in the criteria valuations to be incorporated into the decision process. There is, at present, a high degree of subjectivity involved in determining these thresholds, which are expressed in terms of the error/uncertainty associated with the valuations of each of the criteria under scrutiny. If, however, the ELECTRE III outranking model is to be used within a formal environmental appraisal system, the thresholds which govern the outranking relationship of one project option over another must take account of the effect on human beings of the difference between any two criterion scores. The authors suggest a new method for applying the standard ELECTRE III model to decision-aid problems within the formal mechanism of environmental impact assessment. This involves a new, more comprehensive approach for specifying realistic limits for p, q and u, within the context of an environmental appraisal, where both criterion error/uncertainty and human sensitivity to differing levels of the criterion are taken into account. Threshold valuations for noise impacts from a highway project are used to illustrate the proposed method. 0 1998 Elsevier Science B.V.

Keywords: Engineering; Environment; Decision-aid; ELECTRE; Thresholds

1. Introduction

The authors have examined a number of decision- aid techniques for use with mainly environmental criteria, and have recommended Outranking Meth- ads/Concordance Analysis as a set of techniques

* Corresponding author. Fax: +353-l-402-3999.

very suited to the environmental appraisal of complex civil engineering projects (Rogers and Bruen, 1995). This conclusion was based on the belief that the approach permits a general ordering of alternatives, while allowing individual pairs of options to remain uncompared where there is insufficient information to distinguish between them. In contrast, any additive method, such as Multi-Attribute Utility The- ory or the Analytical Hierarchy Process, which gen-

0377-2217/98/$19.00 0 1998 Elsevier Science B.V. All rights reserved. PI1 SO377-2217(97)00175-6

M. Rogers. M. Bruen/European Journal of Operational Research 107 (1998) 542-551 543

erates a single score for each alternative, requires that all options be directly comparable with each other, even when such comparisons are questionable because of the lack of suitable data. Also, the desire to have an appropriate technique capable of dealing with the mix of both quantitative and qualitative information obtained within an environmental appraisal was one of the main factors in the authors’ choice of the outranking approach as the most suitable decision-aid model.

The Outranking Approach to multicriteria decision-aid builds a relation, called an ‘outranking relation’, which represents the decision maker’s strongly established preferences, given the information avail- able. It is a multicriterion model which uses various mathematical functions to indicate the degree of dominance of one project alternative over another. Outranking methods facilitate comparison between alternatives by ascribing initial weights to decision criteria, and then varying these weights as part of a sensitivity analysis, if their exact value is not known. Comparison between alternatives proceeds on a pair- wise basis with respect to each decision criterion, and establishes the degree of dominance or ‘outranking’ of one option over another. The result is a ranking of the various options.

The technique is readily applicable to the problems of transport investment choice (Roy and Hugonnard, 1982; Roy et al., 19861, land-use planning, (Guigou, 1971; Massam, 1988) and energy investment (Siskos and Hubert, 1988).

The concept of outranking was formulated by Roy (1968). His methodology, ELECTRE, has been widely applied to problems of environmental management (Simos, 1990; Maystre et al., 1994; Hokka- nen and Salminen, 1994). The method is particularly useful when a large number of alternatives needs to be shortlisted to a smaller number of preferred ones in order to facilitate further detailed consideration. It thus fits neatly within the environmental impact assessment framework, which occurs at the planning stage of the project when a wide range of diverse alternatives are being considered, enabling a ‘core’ of environmentally suitable options to be brought forward to the next stage of evaluation.

There are four basic versions of ELECTRE - I, II, III and IV - each quite distinct from the other in terms of data required and output produced. ELEC-

TRE III, the most sophisticated version, is used in this paper.

ELECTRE III (Roy, 1978) is a complex decision- aid model which evaluates a number of project options using a family of pseudo-criteria for comparison purposes. It uses three distinct thresholds to incorporate the uncertainties that are inherent in most impact valuations. For any given environmental criterion, the three thresholds are as follows:

the indifference threshold, q, beneath which the decision maker is indifferent to two project option valuations, the preference threshold, p, above which the decision maker shows a clear strict preference of one project option over the other, and the veto threshold, u, where a ‘discordant’ difference in favour of one option greater than this value will require the decision maker to negate any possible outranking relationship indicated by the other criteria.

Fixing these thresholds involves a significant subjective input by the decision maker. It is the authors’ contention that, within a process such as Environ- mental Impact Assessment (EIA), use of ELECTRE to aid option selection may be seen as unconvincing if the thresholds are too subjectively based. They contend that there can be a more meaningful interpretation of the thresholds than the present one where both q and p are linked to the margin of error/uncertainty associated with the criterion in question, and u is naturally set at a value noticeably greater than p. The authors believe that the definition of p and q, within the context of EIA, can be defined within relatively strict limits, and are quite different in nature. In addition, for a given impact, the veto threshold, v, which is naturally greater than p, should move closer to p with increasingly adverse human reaction to differences in impact over and above the preference threshold p.

Using noise impacts to illustrate the new approach, the authors discuss, firstly, the nature of this criterion’s assessment, to date, within the ELECTRA model. Then, using the method of assessment of noise required within a formal impact assessment, they outline how it can be utilised as a basis for estimating more realistic threshold values to be used with a quantitative noise model within ELECTRE.

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2. Assessment of the noise criterion within ELEC- TRE

To date, assessment of the noise criterion within the ELECTRE model (Simos, 1990; Maystre et al., 1994) has been qualitative, with each project option assigned a point on an agreed graded scale on the basis of general forecasts of noise level change for each alternative relative to existing levels, for example:

slight increase above existing noise level, no increase/decrease, slight decrease below existing noise levels, etc. (Maystre et al., 1994)

In this situation, p and q are defined in terms of the number of rankings on the qualitative graded scale that must separate the two options under consideration before indifference, weak preference or strict preference is pronounced. The ranking difference required for weak/strict preference reflects the uncertainty associated with the noise valuations in each case.

In a wider context, other pollution impacts or ‘nuisances’ such as levels of greenhouse and acidi- ficative gas emissions have been evaluated quantitatively within the ELECTRE III model (Hokkanen and Salminen, 1994). Again, in this case study, the values of indifference and preference thresholds are based on the error ranges of the relevant air pollution models used to evaluate the relative effects of different project alternatives.

3. Evaluating noise within a highway environmental appraisal

The quantitative estimation of traffic noise is the basis on which all planning policies are based (OECD, 1995). Thus, within the environmental impact assessment process for highway projects, traffic noise is evaluated quantitatively, by the following two different means:

1. measurement, where acoustical instruments such as sound level meters make direct measurements of existing noise levels, and

2. prediction, where acoustical theories of sound emission and propagation are used to calculate future noise levels by simulating predicted situa- tions by means of mathematical models.

Comparison of these predicted levels with the measured existing ones allows the level of noise increase resulting from a proposed highway scheme to be assessed.

In the Design Manual for Roads and Briges (DMRB), the Department of Transport (1993) rec- ommends the use of measurement methods to survey ambient noise levels in the vicinity of a proposed highway. (Ambient noise is defined by the manual as the level of noise in an area before any changes take effect.) In addition, DMRB prescribes the Calcula- tion of Road Traffic Noise (Department of Transport and Welsh Office, 1988) as the predictive method for forecasting noise levels emanating from planned/estimated traffic flows in the future.

Having used both measurement and prediction techniques to evaluate levels of noise increase from a proposed highway project, the human effect of this increase, termed ‘noise nuisance’, must be gauged. Noise nuisance is defined as a feeling of displeasure evoked by noise. The questionnaire based market research survey is the predominant method used for measuring noise nuisance. Such surveys relate an- noyance expressed by people interviewed with some physical measurement of the noise causing the an- noyance. DMRB uses data from surveys by Huddart and Baughan (1994) to relate changes in noise nuisance, measured as the percentage of people both- ered by traffic noise, to changes in noise exposure arising from a proposed highway scheme.

The Calculation of Road Traffic Noise method of noise prediction (CRTN) is outlined in detail directly below, along with the levels of uncertainty associated with its output. Further on, in Sections 6 and 8, these accuracy levels, together with the information connecting predicted noise levels with their consequent nuisance effects on humans, are used to evaluate appropriate indifference, preference and veto thresholds for the noise values evaluated quantitatively, for each of the project options, using CRTN.

M. Rogers, M. Bruen /European Journal of Operarional Reseurch 107 (1998) 542-551 545

Existing methods for estimating the thresholds are outlined in Sections 5 and 7.

4. The highway noise estimation model

Noise levels from any source are, by their nature, fluctuating over time. It is, however, important to be able to characterise them in a simple, single figure index in order to be in a position to incorporate the result into the noise assessment process. One index which takes account of these variations is the Equiv- alent Continuous Sound level, or Leq (Federal Inter- agency Committee On Urban Noise, 1980). This scale is widely used for measuring noise in such contexts as urban planning, transport and industrial development. It is the presumed constant level of acoustic pressure, in which the quantity of acoustic energy emitted during a defined period would be the same as that of the effective fluctuating noise. There can be a Leq of a minute, an hour, a day, etc. In the United States and in Europe, Leq for the high design hour is used (OECD, 1995).

Simple models have been developed for specific types of sources, for example road vehicles, con- struction sites and airports. All these models operate on the basis of identifying the source of noise as being either an impulse, continuous, or a combina- tion of both. Noise from a road vehicle is most often defined as an impulse source. Impulse sources, which are single events, involve noise levels rising with time to a maximum level, and then falling to ambient levels.

The basic equation for an impulse source in terms of Leq is as follows:

Leq( 1 hour) = SEL + lOlog,, N - 35.5 dB(A),

(1)

where N is the total number of vehicles passing the point of noise measurement every hour, and SEL, the maximum sound exposure level, represents the noise energy involved in a vehicle pass-by. It is the constant level which, if maintained for a period of one second, would deliver the same energy to the re- ceiver as the event itself. It equals Leq normalised to a time period of one second (Staunton, 1991).

Eq. (1) assumes that the point of noise measure-

ment is 13.5 metres from the source. If this is not the case, an additive correction, based on the level of distance attenuation, is made as follows:

ADJ = - lOlog,, Y/X, (2)

where Y is the actual measured distance from the source to the reception point, and X is the initial assumed distance (13.5 metres in Eq. (1)).

Eqs. (1) and (2) form the basis for the calculation of road traffic noise as outlined in the ‘Calculation of Road Traffic Noise’ (CRTN) published in 1988 by the Department of Transport, UK (Department of Transport and Welsh Office, 1988).

Although more comprehensive models exist for noise prediction, the above ‘simple model’ procedure is widely used, and will form the basis for an examination of the uncertainties involved in noise impact estimation.

4.1. Uncertainty in noise prediction

The calculation formulae used in CRTN are very similar to most other prediction models used within Europe (OECD, 1995). Basically, a reference noise level corresponding to a single vehicle running under standard conditions at a reference distance is obtained experimentally, and incorporated into the for- mula as a constant. Correction factors are used to allow for the influence of the types of vehicles, traffic flow, average speed, distance, type of pave- ment, ground absorption, road cross section, screen- ing effect of obstacles, etc. The number and values of these factors vary from one country to another.

The OECD Report on Roadside Noise Abatement (OECD, 1995) emphasised the value of knowing the accuracy of computer programs based on these semi-empirical methods of traffic noise assessment. The Report gave an accuracy in normal use for these programs of f2 dB(A). In RT389 (Staunton, 1991) which outlines the computer package ‘SOUNDTRACK’ for predicting road traffic noise on Irish roads, prediction accuracy with programs predicting both LlO and Leq were found to be often better than + 1 dB(A) where propagation was unob- structed, and k2 dB(A) where obstruction was significant. The programs in RT389 are based on the UK CRTN (Department of Transport and Welsh Office, 1988). Thus a general accuracy level of +2

546 M. Rogers, M. Bruen/European Journal of Operational Research 107 (1998) 542-551

dB(A) can be reasonably assumed for this type of noise prediction procedure. The model ‘SOUNDTRACK’ is widely used within Ireland to predict road traffic noise within an Environmental Impact Assessment of a highway project.

4.2. Sensitivity analysis of ‘SOUNDTRACK’

Not all inputs to the computer program on noise prediction are known with absolute certainty. There- fore, a preliminary sensitivity analysis of the package was undertaken by the authors in order to gauge the effect of changes in the major variables on the final noise level. It concluded that, where the traffic flow <Q> and the traffic speed (v> are in error by 10% in the same direction, the likely error induced in the final result will be 1 dB(A). Given the prediction accuracy of the program at +2 dB(A), it would, given the potential uncertainty in estimating the values of Q and v to be input to the program (particularly Q), it would be reasonable to assume an overall accuracy level for ‘SOUNDTRACK’ of somewhere between f2 and +3 dB(A). The parameter estimation error and the program error would need to be at their maximum and acting in the same direction before the value would approach 3. Also, while an error of 10% in Q is possible, an error of this magnitude in estimating v, the traffic speed, is less likely.

5. Existing indifference and preference thresholds within environmental decision making

The pseudo-criterion used in ELECTRE III requires specified indifference, preference and veto thresholds. It differs fundamentally from the ‘true’ criterion used within the ‘traditional’ preference structure, where no thresholds exist, and the decision maker assumes strict preference of one option over another once any difference exists in their respective criterion scores.

Roy et al. (1986) believe that the fixing of thresholds involves not only the estimation of error in a physical sense, but also a significant subjective input by the decision-maker himself. Assuming that the thresholds p and q can be constant values or can take the linear form (r + /3x, where x is the criterion

valuation and (Y and /3 are constants, they state that ‘common sense’ is the predominant factor in the choice of representations for q and p, with p being set at a significantly greater value than q. Both the choice of the form in which the threshold is expressed (i.e. as a constant value or as a value directly related to the absolute value of the option valuations), and the numerical values characterising it, lead to final values for p and q which are, by their nature, significantly subjective. Roy et al. conclude that, in order to verify that this subjective input does not significantly affect the final ranking of options considered, a sensitivity analysis using extreme values of q and p for each criterion in question is required.

Bouyssou (1990) derived mathematical equations defining p and q. Assuming the case in which two alternatives a and b are being assessed on the basis of a given criterion, with c(u) and c(b) denoting the best guess evaluation of both options, c + (a> and c + (b) denoting optimistic evaluations, and c - (a) and c - (b) denoting pessimistic evaluations, they expressed c + (a) and c - (a) as follows:

c+(a) =c(u) +n+(u), (3)

c-(a)=c(u)+n-(a), (4)

where n’(u) and n-(u) are, respectively, the posi- tive and negative dispersion thresholds, which, when combined, constitute the model imprecision range for criterion a. Roy et al. (1986) assumed that the thresholds were symmetrical, and denoted as ‘e’. Thus, if N was defined as the best estimate for the criterion, They assumed that the value could vary over the interval N + e, N - e, where e is of the form (CX + PN). Assuming that g(u) is the valuation of alternative a on criterion g, and that (Y and /3 are constants, They derived the following equations for the preference and indifference thresholds:

d&41 = f-x+ PtT(a)* (5) where p E [ - l,l], cz E 8, Ly + pg(a> > 0;

PM41 =2(~+kww(l -PI. (6) Substituting Eq. (5) into Eq. (61, the following relationship is obtained:

PM41 = GIM41/0 - 0 (7) This method of expressing p and q in terms of a

linear equation, due to Roy et al. (1986), has been

M. Rogers, M. Bruen / European Journal of Operational Research 107 (1998) 542-551 547

utilised by Hokkanen and Salminen (1994) in their application of ELECTRE III to the choice of a solid waste management system in Finland.

Maystre et al. (1994) expressed definitions of 4 and p in slightly different terms. They interpreted the indifference threshold as the minimum margin of uncertainty associated with a given criterion, and the preference threshold as the maximum margin of error associated with the criterion in question.

There is a certain consistency between the above interpretations of the indifference and preference thresholds. In each case, the thresholds are directly related to a factor, be it imprecision, error or uncertainty, which adversely affects the accuracy of the criterion valuation.

6. A new interpretation of p and q within EL4 decision making

Roy et al. (1986) believe that there should be room for a substantial element of flexibility/subjectivity in the estimation of p and q. However, particularly within Environmental Impact Assessment, just as the traditional model is ‘unconvincing’ because of the effectively zero valuation of both p and q, so the double threshold model may be seen as unconvincing in a practical decision making context if the thresholds are too subjectively based, and derived totally on the basis of the model imprecision range of each of the criteria in question. It is thus impera- tive that p and q be chosen in a rational and defendable manner, and that we explicitly estimate them, rather than pick some arbitrary values whose effect must be examined later by a sensitivity analysis. Particularly within a sometimes contentious process such as Environmental Impact Assessment, defining thresholds in terms of both model and data error and uncertainty alone is too restrictive. A threshold should be developed in a wider context.

The European Directive on Environmental Impact Assessment (Council of The European Communities, 1985>, in naming environmental impacts on human beings as the most important consideration within EIA, emphasised the centrality of the individual, and his/her perception of the effects of these impacts, to the assessment process. In this context, human perception should play a central role in our definitions

of p and q for use within EIA. In the case of q, it should define the point at which one option is mea- surably distinguishable from the other, assuming that human beings can perceive such a difference, and, in the case of p, it should define the point at which one option is perceived to be clearly preferable to the other.

It seems appropriate to estimate the indifference threshold primarily in terms of the model imprecision range of the criterion in question. For the noise model, it can be seen above that an overall accuracy level of approximately 2-3 dB(A) seems appropriate. Since, according to Staunton (19911, this is also the threshold at which a difference in noise levels becomes perceptible to humans, it can be taken as the value for q. If the human perception threshold and model sensitivity values do not coincide, the higher value will determine q.

Roy et al. (1986) define p as being a value substantially greater than q. In the simplified case where the model imprecision is symmetrical about its mean value, and the range then goes from +e to -e, p is shown to be at least twice q (i.e. p = (2/l - p)q>. In a wider context than error estimation alone, however, there seems no logic in connecting the two thresholds so directly. Valuing p at some multiple of q has no physical basis. It seems more logical to define p in terms of the point at which the difference between criterion valuations produces, in humans, a clear preference for one option over the other. In the case of noise, an increase of 5-6 dB(A) is clearly perceptible (Staunton, 1996). The UK Re- port on Community Response to Noise (CAAS Ltd., 1994) stated that, at increases of greater than 5 dB(A) over a given base noise level, people become aware of a noise increase, and, as a result, com- plaints begin to arise. The accuracy of this figure is reinforced by the findings of Huddart and Baughan (1994) who showed that, when noise levels changed by more than 5 dB(A), a significant proportion of the people surveyed by them suffered severe nuisance as a result.

Thus, in the context of noise impact assessment, one can value the thresholds as follows:

- q = 2-3 dB(A), the threshold at which human beings can perceive differences in noise levels, and

548 M. Rogers, M. Bruen/ Europeun Journal of Operational Research 107 (1998) 542-551

. p = 5-6 dB(A), th e noise difference at which clear preference can be expressed for one option over another.

As noted in Section 4 above, the sensitivity of the ‘SOUNDTRACK’ model used to calculate future noise levels for a highway project is estimated at 2-3 dB(A). Thus, both the perception threshold of noise in humans and the model sensitivity, in this case, coincide. If they had not, the higher of the two values would have determined q.

It seems logical that p and q must be defined in wider terms than the model imprecision of the criterion in question, and that the level of subjectivity/flexibility involved in their estimation should not be significant, in order to avoid their valuations becoming both technically and politically unacceptable.

7. The veto threshold within EIA decision making

The veto threshold, u, characterises, in a simple way, the conditions under which a discordant criterion can, on its own (without having to take account of other possibly discordant criteria) exert a veto on an outranking relationship. The introduction of this threshold into ELECTRE III is central to the concept of discordance (Simos, 1990). It conveys the idea that any outranking of b by a can be vetoed if a performs very much worse than b for any criterion. If the difference in value between a and b for all criteria is less than p, then the discordance is zero. It goes above zero as the criterion valuation difference exceeds p. When the difference in values of the criteria becomes large, it can veto any outranking of a by b. The magnitude of criterion difference at which outranking can be vetoed is the veto threshold.

At a minimum, the value of uj will be set at least equal to pj, in the unlikely event that the point where a criterion difference becomes clear and sub- stantive coincides with the point at which the criterion difference is adjudged extreme. In all probabil- ity, uj will be noticeably greater than pi, i.e., for a

given criterion j, the usual relative values of the three relevant thresholds are as follows:

s; < P, -c u,

(Maystre et al., 1994). Because, for a given criterion, the discordance

index begins to register above zero at the preference threshold, and reaches its maximum value at the veto threshold, the higher threshold uj is sometimes expressed in terms of the lower one p,. Thus, for example, uj is often valued at three times, five times or ten times the value of pj.

This point is emphasised by Roy et al. (19861, who, while describing the veto threshold as a ‘don- nee volontariste’, that is, a figure or piece of data whose value can be freely chosen by the decision maker, they nonetheless qualify this definition by stating that it is natural to have its value fixed by reference to the preference threshold p. Furthermore, they state that, except where a particular set of circumstances dictate otherwise, the ratio should be held constant for each criterion j, with the value of the constant vj/pj normally all the larger as kj, the importance weighting for criterion j, gets smaller. This has the effect of neutralising the mechanism of veto for the criteria of lesser importance while making it an important factor in the decision process for the most important ones. The nearer v is to p, the lower the criterion valuation difference is at which the veto is imposed. The more elevated u is above p, the less the veto threshold will affect the overall outranking of one option over another. Thus, u is set at an elevated value, relative to p, for the less important criteria, whereas v is set relatively close to p for the more important ones. By doing this, Roy is allowing the veto threshold to become a critical factor for only the most important criteria in the analysis, since the introduction of the threshold increases the sensitivity of the degree of outranking to the relative criterion valuations of the project options. He is thus, albeit in an indirect way, connecting the veto threshold valuation of a criterion to its importance rating.

Let us explore the connection between the veto threshold chosen for a criterion and its overall importance within an outranking framework.

Roy and Bouyssou (1993) believe the aspects of importance dealt with through the veto threshold do

M. Rogers, M. Bruen / European Journal of Operational Research 107 (1998) 542-551 549

not have the same significance as criterion weighting values. There is, however, as noted above, a perceived connection between the veto threshold of a criterion u,, and its importance weighting kj. Within the ELECTRE III system, for example, the veto threshold u,, along with the importance coefficient k,, are, according to Roy and Bouyssou, indicators of the overall importance of the criterion j. They believe that u, moves closer to pj as its importance within the overall framework increases, that is, the proximity of one to the other relates to the importance of that criterion. Roy and Bouyssou argue, though, that this approach to the notion of reflecting a criterion’s importance through its veto threshold valuation, and its consequent effect on the discordance index, is fundamentally different from that which prevails when the estimation of outranking is based purely on the Concordance index which de- pends for its value on the importance coefficients kj. They believe that it permits a differentiation of the role allotted to each criterion according to its own importance rating by fixing, in a precise way, the conditions giving criterion j the power to put its veto on the outranking of one option by another. They also believe this characteristic of a criterion infers, to a certain extent, the importance attached to it by the decision maker. However, while Roy and Bouyssou outline the connection between uj and ki through the concept of criterion importance, which implies a correlation between increasing values of kj and de- creasing values of (u,, - pi), they also emphasise that their origins are distinctly different, as are their respective roles in the outranking adjudication process. The former feeds into concordance values while the latter only affects discordance indices.

The relationship between u, and kj is therefore both complex and ill-defined rather than rigid and formal (Roy and Bouyssou, 1993). They represent two different and distinct facets of the ‘importance’ of criterion j, facets which the concordance and discordance indices bring to the fore. In other words, the veto threshold can only affect the outranking process in a negative way, by disallowing an outranking relation between two options because of extreme contra-indications on one criterion.

Bouyssou (1990) believes that using the thresholds p and q within a threshold model is preferable to using a model with only ‘true’ criteria, because

comparisons inferred by the threshold model make criticism arising from comparisons between project options less likely. It is logical to extend this belief to the veto threshold, use of which will, in a likewise manner, also help reduce criticism arising from project comparison.

8. A new interpretation of v within EIA decision making

Within the context of EIA, it seems appropriate to define the veto threshold as the point at which human reaction to the criterion difference becomes so adverse that it places an ‘environmental stop’ on the project option in question. This definition would exclude any direct link with either p or q. Therefore, the linkage, as outlined above, between criterion importance and its veto threshold is one which should be questioned. Setting the value of u, relative to pj on the basis purely on the perceived importance of that criterion would seem incorrect. There seems no logical justification for relating p to u, as no actual connection exists between the initial perception point at which one notices a clear difference between the impact valuations of two options, and the point at which the valuation difference becomes so extreme as to be ‘veto inducing’ on an otherwise favourable option.

It would seem more logical to assume that the more sensitive human beings are to increases in a given impact, the nearer u, should be to p,. One could say that the impacts to which humans are particularly sensitive are the ones that will be given high importance ratings within the framework. The original linkage between kj and u,/pj put forward by Roy et al. (1986) is, then, only a subsidiary one.

For example, returning to noise impacts, the value of noise increase at which a large proportion of people in the vicinity would suffer extensive disamenity is thought to be in the region of 15-20 dB(A). Echoing this estimate, the UK Report on Community Response to Noise (CAAS Ltd., 1994) stated that, at increases of 15-20 dB(A) over a given base noise level, community response becomes very strong, and vigorous community action against the project causing the disamenity would be inevitable. The research of Huddart and Baughan (1994) also

550 M. Rogers. M. Bruen / European Journal of Operational Research 107 (1998) 542-551

indicated that noise increases of this magnitude will result in the overwhelming majority of people in the vicinity experiencing severe nuisance. Because this is an impact to which humans are highly sensitive, the value of u is approximately three times the value of the preference threshold which we have already set at 5-6 dB(A). Coincidentally, Roy et al. (1986) also set u/p = 3 for the most important criteria in their study. For an impact to which the human population might be much less sensitive, the level causing significant disamenity may be an order of magnitude greater than its preference threshold, at which point those affected begin to sense an impact change.

This approach to setting values for the veto threshold therefore goes against Roy’s conviction that there can be a high level of subjectivity associated with choosing its value, and only a very loose connection between it and the overall importance of the impact within the decision framework. Just as the indifference and preference thresholds p and q have a distinct connotation within Environmental Impact Assessment, so too should the veto threshold. The more sensitive human beings are to increases in this impact over the point at which changes become noticeable (the preference threshold) the nearer u is to p.

Thus, in the context of noise impact assessment, one can value the veto threshold as follows:

. u = 15-20 dB(A), the threshold at which an individual suffers such a high level of disamenity resulting from the relative noise increase that the option causing the high noise level becomes environmentally unacceptable, regardless of its perfor- mance on the other relevant criteria.

9. Conclusion

Within the context of EIA, it is not sufficient to determine criterion thresholds for use within ELEC- TRE III either subjectively or on the basis of model and estimation error alone. The authors contend that the methodology for their valuation should be widened to take account of the human sensitivity to, and perception of, the impact measured by the criterion in question. They believe that a more compre-

hensive approach to threshold valuation, as illus- trated above by the estimation of p, q and u for the highway noise criterion, will lead to a greater accept- ability of the ELECTRE III method for aiding deci- sions within EIA, for major engineering projects.

References

Bouyssou, D., 1990. Building criteria: A prerequisite for MCDA in readings in MCDA. In: Bana e Costa (Ed.), Springer Verlag, Germany, pp. 58-80.

CAAS Ltd., 1994. EIS evaluation handbook for local authorities institute of public administration. Dublin.

Council of The European Communities, 1985. On the assessment of the effects of certain public and private projects in the environment. Official Journal L175, 40-48 (85/337/EECJ.

Department of Transport, 1993. Design manual for roads and bridges, Volume 11 - Environmental assessment. Department of Transport, June.

Department of Transport and Welsh Oftice, 1988. Calculation of road traffic noise. HMSO, London.

Federal Interagency Committee On Urban Noise, 1980. Guide- lines for considering noise in land use planning and control. Federal Interagency Committee on Urban Noise. Washington D.C., June.

Guigou, J.L., 1971. On French location models for production units. Regional Science and Urban Economics 1 (2), 107- 138.

Hokkanen, J., Salminen, P., 1994. Choice of a solid waste management system by using the ELECTRE III Method. In Paruc- cini, M. (Ed.), Applying MCDA for Decision to Environmen- tal Management, pp. Ill- 153.

Huddart, L., Baughan, C.J., 1994. Units for exposure and response to traffic noise exposure. TRL research report RR363. Depart- ment of Transport, Transport Research Laboratory, Crowthome.

Massam, B.H., 1988. Multi-criteria decision-making techniques in planning. Progress in Planning 30 (I), l-84.

Maystre, M., Pictet, J., Simos, J., 1994. Methodes muhicriteres ELECTRE. EPFL Presses Polytechniques et Universitaires Romandes, Lausanne.

OECD, 1995. Road transport research, Roadside noise abatement. OECD, Paris.

Rogers, M., Bruen, M., 1995. Non-monetary based decision-aid techniques in EIA - An overview. In: Proc. Instn. Civ. Engr., Mun. Engr. 109, pp. 98-103.

Roy, B., 1968. Classement et choix en presence de points de vue multiples (la methode ELECTRE). Revue Francaise d’Auto- matique Information et Research Operationelle 8, 57-75.

Roy, B., 1978. ELECTRE III: Algorithme de classement base sur une representation floue des preferences en presence de criteres multiples. Cahiers de CERO 20 (11, 3-24.

Roy, B., Bouyssou, D., 1993. Aide muhicritete a la decision: Methodes et cas. Collection Gestion, Economica, Paris.

Roy, B., Hugonnard, B., 1982. Ranking of suburban line exten-

M. Rogers. M. Bruen/ Europeun Journal of Operational Research 107 (1998) 542-551 551

sion projects on the Paris metro system by a multi-criteria method. Transport Research Record 16A (4), 301-312.

Roy, B., Present, M., Silhol, D., 1986. A programming method for determining which paris metro stations should be renovated. European Journal of Operational Research 24, 3 1 S-334.

Simos, J., 1990. Evaluer L’Impact sur L’Environment. Presses Polytechniques et Universitaires Romandes, Lausanne.

Siskos, J., Hubert, P., 1988. Multi-criteria analysis of the impacts

of energy alternatives: A survey and a new approach. Euro- pean Journal of Operational Research 13, 278-299.

Staunton, M.M., 1991. SOUNDTRACK: A computer package for predicting road traffic noise. RT389, Environmental Research Unit, Dublin.

Staunton, M.M., 1996. Traffic noise and vibration. Master’s De gree Course Notes. Department of Civil Engineering, Univer- sity College Dublin, January.

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