Science of the Total Environment 409 (2011) 1305–1314

Contents lists available at ScienceDirect

Science of the Total Environment

j ourna l homepage: www.e lsev ie r.com/ locate /sc i totenv

What weight should be assigned to future environmental impacts? A probabilisticcost benefit analysis using recent advances on discounting

Carmen Almansa a,⁎, José M. Martínez-Paz b

a Department of Business Management, Public University of Navarra, Spainb Department of Applied Economics, University of Murcia, Spain

⁎ Corresponding author. Campus de Arrosadía s/n. 31.0948 169425; fax: +34 948 169404.

E-mail addresses: carmen.almansa@unavarra.es (C. A(J.M. Martínez-Paz).

0048-9697/$ – see front matter © 2010 Elsevier B.V. Aldoi:10.1016/j.scitotenv.2010.12.004

a b s t r a c t

a r t i c l e i n f o

Article history:Received 6 October 2010Received in revised form 1 December 2010Accepted 1 December 2010Available online 16 January 2011

Keywords:Social discount rateEnvironmental discountingCost benefit analysisDesalination plantDelphiMonte Carlo simulation

Cost–benefit analysis is a standard methodological platform for public investment evaluation. In highenvironmental impact projects, with a long-term effect on future generations, the choice of discount rate andtime horizon is of particular relevance, because it can lead to very different profitability assessments. Thispaper describes some recent approaches to environmental discounting and applies them, together with anumber of classical procedures, to the economic evaluation of a plant for the desalination of irrigation returnwater from intensive farming, aimed at halting the degradation of an area of great ecological value, the MarMenor, in South Eastern Spain. A Monte Carlo procedure is used in four CBA approaches and three timehorizons to carry out a probabilistic sensitivity analysis designed to integrate the views of an internationalpanel of experts in environmental discounting with the uncertainty affecting the market price of the project'smain output, i.e., irrigation water for a water-deprived area.The results show which discounting scenarios most accurately estimate the socio-environmental profitabilityof the project while also considering the risk associated with these two key parameters. The analysis alsoprovides some methodological findings regarding ways of assessing financial and environmental profitabilityin decisions concerning public investment in the environment.

06, Pamplona, Spain. Tel.: +34

lmansa), jmpaz@um.es

1 The use of ECBAappropriateness of tclimate change poligeneral. Some of theand Bromley (1994(1997), Goulder anduse of ECBA are, am(2006). Studies suchthe practical relevan

l rights reserved.

© 2010 Elsevier B.V. All rights reserved.

is not exempt from debate. Thus, some authors question thehe CBA technique as the right approach in decisions concerningcies, and problems with significant intergenerational impact in

1. Introduction

1.1. Context and research objetives

The incorporation of the sustainability objective has renderedtraditional cost–benefit analysis (CBA) approach obsolete for theevaluation of certain types of projects, particularly those with a largenumber of environmental externalities and effects extending over along period. The literature is proposing a series of changes in CBA inorder to adapt the analytical context to the demand for sustainability,resulting in what is alternately denominated Extended or Environ-mental Costs Benefits Analysis (ECBA).

From an analytical point of view, CBA is changing in two ways. Thefirst is the development of new tools for the economic evaluation ofenvironmental externalities traditionally omitted from the analysis.The second is an in-depth revision of the theoretical foundationsunderlying the traditional approaches to discounting, since therepercussions of current decisions will extend into the distant future(in some cases for centuries), in contrast to classical CBA, which dealtwith a few decades at best.

Against this background, this paper seeks to explore how cost–benefit analysis should discount or, in effect, weight environmentalimpacts. This economic evaluation platform therefore tries to find thebest way to integrate current society's level of sustainability orintergenerational ethics, accepting the limitation of ECBA as a “mono-criterion” decision approach,1 while still considering CBA a usefulvaluation tool, obviously needing to be applied in a broader contextjointlywithother criteria and theecological constraints of theecosystem.

This paper achieves its objective by means of a combination ofstrategies. Firstly, after a brief review of the literature on environ-mental discounting, it singles out three discounting approaches fortheir particular qualities. Secondly, it polls the opinions of aninternational panel of experts on environmental discounting usingthe Delphi Method. Thirdly, it applies ECBA in a real practical case: theeconomic evaluation of a plant for the desalination of irrigation returnwater from intensive farming, which, among other benefits, prevents

authors that question the use of ECBA are Bromley (1990), Vatn), Munda (1996), Goulder and Kennedy (1997), Joubert et al.Kennedy (1997) and Prato (1999). Researchers that favour theong others, Navrud (1992), Hanemann (1994) and Pearce et al.as those of Brouwer and Van Ek (2004) and Gulli (2006) illustratece of this tool.

2 Numerous authors, including those cited in this section, defend the reduction ofdiscount rates for environmental reasons, conventionally determined as a rationaladjustment in standard discount rates, while Philibert (1999), Pearce and Turner(1990) and Lind (1997), among others, are critical of the approach.

3 The hyperbolic discount factor has the form of a general equation:

DFh1

ð1 + atÞa=b

;a; b N 0:

Environmental applications (see for example Almansa and Calatrava, 2007) have used,among others, the specification proposed by Poulos and Whittington (2000), where theparameters a and b are defined as a=b=2r, and r is the applied discount rate. Someuncertainty still remains, however, as to the precise values these parameters should take.

4 According to the cited works, the Weitzman discount factor can be expressed asfollows:

RðtÞ = α

1 + tβ2.

α

� �

where α and β denote the mean and standard deviation of the Gamma distribution.Weitzman proposes two rounded off average values as parameters for the gammadistribution: α=0.04 per annum and β=0.03 per annum.

5 Problems arising from the use of declining discount rates are discussed in thelight of “time inconsistency” theories by Heal (1998) and Hepburn (2005), amongothers. Hansen (2006) concludes with the view that the fear of introducing a timeinconsistency in long-term planning when using hyperbolic discounting isexaggerated. Groom et al. (2005), in an analysis of the consequences of the useof the various approaches to and formats for Declining Discount Rates, concludethat this set of approaches does indeed have implications, such as a time-inconsistency problem in the decision-making process. Furthermore, the use ofDDRs may actually create the risk of placing more weight upon potentially richerindividuals in the far distant future than is placed on potentially poorer individualsin the present. Their conclusion is that, “however, admitting a time-varyingdiscount rate at least provides another degree of freedom.”

1306 C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

the degradation of an area of great ecological value (the Mar Menor,Murcia, Spain) by halting the flow of nutrients into this lagoon andthus its increasing eutrophication. Fourthly, it analyses uncertaintyand risk using Monte Carlo techniques to incorporate expert opinionsregarding the choice of the most suitable probabilistic distributionsand the determination of appropriate limits (intervals). Thus, insteadof profitability indicator scores for the different scenarios, it producesan output probabilistic distribution.

1.2. Monte Carlo techniques for risk and uncertainty analysis in CBA

While the standard and desirable practice in CBA is to include asensitivity analysis in order to incorporate uncertainty, as Belli(1996) points out, the Monte Carlo technique provides a majorimprovement.

Monte Carlo simulation is a computational algorithm designed toevaluate the variability or uncertainty of the input variables of amodel. It is used to evaluate the outcome uncertainties in riskassessment, and also has uses in finance, business, etc. It can be usedto model the effects of key variables on the NPV of a given proposal.The approach provides a systematic assessment of the combinedeffects of multiple sources of risk in key variables.

The process involves, first, the identification and assessment of thekey variables. For each key variable, we assign the probabilisticdistribution that best describes the range of uncertainty around theexpected value. The model including these variables is then calculatedusing randomly-generated input values taken from the underlyingprobabilistic distribution function. The computermodel combines theseinputs to generate an estimated outcome value (for example an NPV).The process is repeated (thousands of times) to generate a probabilisticdistribution of possible outcomes. Analysis of this distribution providesan indication of the variability or robustness of the NPV analysis.

The technique is recommendable in analyses involving several keyvariables with significant and/or correlated uncertainties and in caseswhere simpler sensitivity analysis approaches fail to provide anaccurate description of the resulting variation in net benefits. Forfurther technical details, see, for example, the guide to Monte Carlosimulation by Vose (2002).

The importance of the use of Monte Carlo Techniques for RiskAnalysis in social CBA has been pointed out by, among others, Balcombeand Smith (1999) and more recently by Vining and Weimer (2010),who stress the need to recognize and explicitly address the greatuncertainty in the prediction and valuation involved when applyingsocial CBA: “Indeed, becauseof themanyuncertainties in the applicationof CBA to social policy, an explicit consideration of uncertainty, throughMonte Carlo simulation or equivalent methods, is essential.”

Since it is necessary to explicate the underlying hypotheses in thecalculation of the key variables, this technique has the added advantageof helping to minimize bias from over-optimistic or over-pessimisticscenarios. Thus, for example,Monte Carlo simulationshavebeenused toaddress the uncertainty associated with the Ramsey rule (Ramsey,1928; see footnote 8), in the context of long-term environmentaldiscounting, as in the SternReview, a report on theEconomicsof ClimateChange, by HM Treasury, UK (Stern et al. 2006).

For the purposes of this paper, although a sensitivity analysis usingspecific values might be sufficient to illustrate the importance of thechoice of discounting approach, the use of the Monte Carlo techniqueis advisable for several reasons. The main advantage of this techniquein the case that concerns is that it enables us to use the probabilisticdistribution of discount rates for different time horizons. Thus, itfollows the suggestions (described later) of the experts on environ-mental discounting in the Delphi interviews, by incorporating theuncertainty associated with the choice of discount rate. It has theadditional advantage of simulating the potential price distribution ofthe main marketable output of the case study project, that is, the priceof irrigation water in a water-deprived area in which water is an

essential constraining input for the region's economic development(Martínez-Carraso et al., 2009).

1.3. Discounting in environmental cost–benefit analysis

Many authors stress the need for modification of the assumptionstraditionally taken for granted and applied in the calculation of thesocial discount rate (SDR). For an overview of the various approachesto discounting, see, for example, Portney and Weyant (1999), Pearceet al. (2003), Guo et al. (2006) and Almansa and Calatrava (2007).

In addition to the use of discount rates below the standard socialdiscount rate (SDR) to address environmental concerns (EuropeanCommission, 1998; Rabl, 1996; Stern et al. 2006; Gollier, 2010),2 inwhat follows we discuss two further proposals of particular interestfor the context that concerns us.

1.3.1. Declining discount rates (DDR)This discounting framework uses declining discount rates based on

different rationales. One is to replace the exponential discount factorwith a hyperbolic function (the theoretic justification for hyperbolicdiscounting is provided by Chichilnisky's (1996) criterion). Ahyperbolic discount function tends to improve the viability of projectsin which the costs occur early in the time horizon and the benefits donot appear until the end, but it tends to reduce the estimated viabilityof projects with costs to be met at the end of the time horizon.Hyperbolic discounting3 causes the penalty in the future to tendasymptotically towards zero over time, thus providing a verypromising option in projects with time horizons stretching overcenturies (Loewenstein and Prelec, 1992; Sterner, 1994; Hendersonand Bateman, 1995; Azqueta, 1996; Frederick et al., 2002).

Alternative declining functions have been explored, among others,by Weitzman (2001), who finds that in a climate of uncertainty aboutthe economic future, the discount rate follows a gamma distribution(giving rise to the term gamma-discounting). Newell and Pizer (2003),an attempt to operationalise the findings of Weitzman (2001), isfurther developed by Groom et al. (2004).4 Guo et al. (2006) presentsa review of different types of declining discount rates.5

1307C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

According to Pearce et al. (2003), the “political” argument for usingtime-varying discount rates is that “in one swoop this would help toresolve the long-standing tension between those who believe thedistant future matters and those who want to continue discounting thefuture in the traditional way.” These authors therefore predictincreasing application of DDR schemes. The UK government has infact recommended it for its public policies (HM Treasury, 2003, “TheGreen Book: Appraisal and evaluation in Central Government”), usingthe following time-variant scheme: 3.5% for 0–30 years; 3% for31–75 years; 2.5% for 76–125; 2% for 126–200 years; 1.5% for 201 a300 years; and 1% for over 301 years.

1.3.2. Dual-rate discounting (DRD)This discounting strategy uses different discount rates for tangible

and intangible goods, in the same CBA application (dual-ratediscounting), given their different characteristics (Almansa andCalatrava, 2007; Erhun and Evans, in press; Kögel 2009; Tol, 1994;Rabl, 1996; Hasselmann et al., 1997; Hasselmann, 1999; Philibert,1999; and Yang, 2003). Under this approach,6 Net Present Value isgiven by the following equation:

NPV = ∑t=n

t=0

Ft1 + STPRð Þt

� �+ ∑

t=n

t=0

N0

1 + EDRð Þt� �

ð1Þ

where Ft denotes the annual net financial cost or benefit (usually theshadow price of the tangible effects), and N0 is the annual netenvironmental cost or benefit (usually of the intangible effects) asassessed by the current generation in year 0,7 using natural resourcevaluation methods drawn from Environmental Economics. Thediscount rate varies, with the appropriate STPR (Social TimePreference Rate)8 being used for the economic effects and a lowerenvironmental discount rate (EDR) for the environmental effects.

As a complement to dual-rate discounting, two environmentalprofitability indicator concepts have been tested (Almansa andCalatrava, 2007):

a) Intergenerational Transfer Amount (ITA)

6 This paper applies the same dual-rate discounting approach as in Almansa andCalatrava (2007) and Erhun and Evans (in press). Recently Kögel (2009) enables theconclusion that the non-substitutability of environmental and man-made capitaljustifies the dual discounting approach. The proposal discussed in Tol (1994), Rabl(1996), Hasselmann et al. (1997), Hasselmann (1999) and Philibert (1999), is tomaintain the standard discount rate but increase the value of the good over time, inline with the approach proposed by Krutilla and Fisher (1975). A similar, though notidentical, approach is presented in Yang (2003), where the two approaches justmentioned are applied simultaneously, that is, using different rates and increasing thevalue of the environmental good over time.

7 Yang (2003) proposes a similar equation except that it considers Nt instead of N0.

However, as discussed in Almansa and Calatrava (2007), this option would be, inpractice, difficult to implement and, more importantly, would result in doubleaccounting, since the rationale for the use of EDR already includes the hypothesis of adeclining consumption of marginal utility for environmental goods.

8 In practice, the STPR formula works as follows (Ramsey equation (Ramsey, 1928),referred in Pearce and Turner, 1990):

STPR = ce + p

where c=the real per capita consumption rate; e=the elasticity of the marginalutility of the consumption function; and p=the interest rate of pure time preference.While component p reflects impatience, parameter e reflects the expected utility fromextra units of consumption, and, for the sake of analytical convenience, thisrelationship is expressed as an elasticity, that is, the change in marginal utilityresulting from a change in marginal consumption. The component ce, therefore,represents the idea that, since future societies are likely to be richer, we assign lessweight to their earnings, and should therefore discount those future earnings. This isknown as the decreasing consumption marginal utility principle. It is a straightforwardprinciple, amounting to the following: the more we have of something, the less extrasatisfaction we derive from each additional unit of it. Generally speaking, the better offwe are, the less extra satisfaction we get out of a small improvement. Thus, accordingto this logic, if, as the historical evidence suggests, in fifty years' time, people are betteroff in welfare terms, they will be less affected by any potential deprivation than thecurrent population, who are worse off and have more urgent needs to meet.

The Intergenerational Transfer Amount (ITA) is a criterion for theestimationof environmental profitability in absolute terms. It is definedasthe difference between the Net Present Value (NPV) obtained using thegeneral STPR for public investments, the NPV (STPR%), and the NPV inwhich an intergenerational equity adjustment has been carried out on thediscount rate applied to the environmental effects, effects, the NPV (STPR%, EDR%). See Eq. (2),whereNdenotes the environmentalflow, calculatedin year 0.

This indicator represents what the current generation passes on tofuture generations by incorporating a certain degree of intergenerationalequity in the analysis. It may therefore be interpreted as an approxima-tion to the degree of sustainability chosen by the current generation.Although expressed in monetary units, it is less important in absoluteterms than as a means to compare different investment proposals.Logically, this value will be higher or equal to zero, andwhen comparingalternative environmental restoration projects, a higher ITA value willindicate a more environmentally profitable project.9

ITA STPRð Þ = NPVdual STPR;EDRð Þ−NPVstandard STPRð Þ

= ∑t=n

t=0

Ftð1 + STPRÞt

� �+ ∑

t=n

t=0

N0

ð1 + EDRÞt� �� �

−ð∑t=n

t=0

Ftð1 + STPRÞt

� �

+ ∑t=n

t=0

N0

ð1 + STPRÞt� �Þ = ∑

t=n

t=0

N0

ð1 + EDRÞt� �

−∑t=n

t=0

N0

ð1 + STPRÞt� �

ð2Þ

b) Critical Environmental Rate (CER)

The Critical Environmental Rate (CER) is defined as the discountrate that, when applied to the environmental effects after discountingthe market effects from the usual STPR, gives a Net Present Value(NPV) equal to zero. The CER is obtained from Eq. (1), where STPR is apreselected value, Ft is the annual financial flow and N0 theenvironmental flow in year 0 (see Eq. (3)).

NPV = ∑t=n

t=0

Ft1 + STPRð Þt

� �+ ∑

t=n

t=0

N0

1 + CERð Þt� �

= 0 ð3Þ

It is, therefore, a criterion for assessing the environmentalprofitability of a project in relative terms. It must be interpreted incomparison to the social environmental discount rate (SEDR); that is,the environmental discount rate that adequately represents the levelof intergenerational equity a society is willing to assume. Another wayof viewing this criterion is to considerer it as an indicator of“environmental profitability generated by the financial cost.”10

2. Expert opinion of environmental discounting: an internationalDelphi survey

The key methodology in this part of the study is a two-round Delphisurvey of a panel of experts, allowing a feedback process. The Delphimethod is a group-forecasting technique that draws on the opinions ofexperts. Its aim is to develop a framework through which individualspecialists from different disciplines may contribute their own opinionsabout the problem under discussion. It is therefore, as noted by Parisca(1995), “a way of using collective human intelligence.”

The standardprocedure for thismethod is to consult a panel of expertson a given topic by questioning them, individually and anonymously.

9 In projects with negative environmental effects, that is, projects with a positivefinancial flow but a negative environmental flow (e.g., the activation of a nuclearpower plant where the negative effect is given by radioactive residue), the ITA wouldbe negative as a result of a sustainability adjustment assigning more weight to theenvironmental damage to future generations.10 As in the previous criterion, for projects with a positive financial flow but anegative environmental flow, we would rank the projects also aiming at selectingthose with higher CER. In this case, a higher CER would be interpreted as lessenvironmental damage associated with the financial benefit.

Table 1Technical details of the Delphi process.

Interview format: e-mail.Interviewmaterial: a letter of presentation, an information pack and a questionnaire.Panellists are encouraged to add their own comments, as well as answering theclosed questions in the questionnaire.

Number of rounds: 2Starting date for distribution of the 1st round: June 2007Number of questionnaires distributed (pre-selected panel): 174Number of experts participating in the 1st round: 112Percentage of participation in the 1st round: 64.37%Maximum number of experts expected in the 2nd round: 112Starting date for distribution of the 2nd round: September 2007Number of replies received from the 2nd round (February 2008): 98Percentage of participation in the 2nd round: 87.50%Consensus: higher level of consensus among panel members in the second round.Significant reduction in variance (verified with Levene's test of homogeneity ofvariances at 5%) of the replies in 7 items after the second round; 1 item with nochange in the variance.

Stability: response stability is low overall. 70.2% of the experts changedhalf ormore oftheir replies after the second round. Mean change of response: 36.5%.

1308 C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

Using a series of questionnaires, it creates channels of communication,allowing participants to give their opinions and later receive the feedbackof other panel members' views on the same issues, finally allowing themthe opportunity to review their original responses (Dalkey and Helmer,1963).

According to Linstone and Turoff (1975), the Delphi method isparticularly recommendable for use in areas where ethics or moralsoverride economic and technological issues. There is no question ofthe high level of uncertainty surrounding long-term environmentalissues or of the need to determine an appropriate social discount ratefor their evaluation. At the same time, they also carry direct or indirectethical implications, which, in the case in hand relate specifically tointergenerational ethics. This provides the main justification for theuse of this methodology in this study, particularly in view of thelimited number of experts in environmental discounting.

The quality of the results depends primarily on the care taken in thepreparation of the questionnaire and the selection of experts. It is alsoimportant for the survey designers to be experts in the issue underinvestigation, or at least to be well informed about it. There is no need toconsult a very large number of experts. Dalkey (1969), investigating theoptimum number of experts needed to conduct a Delphi survey, foundthat average group error drops exponentially as the number in the Delphigroup increases to about 17, afterwhich it continues to fallmore gradually(linearly). Adler and Ziglio (1996) recommended an expert sample ofbetween 17 and 50 individuals, the required minimum being 7.

The main features of the Delphi method are: a) anonymity (noindividual expert is aware of the identity of the other members of thedebating panel); b) iteration (there is no upper limit on the number ofrounds, the minimum is two); c) controlled feedback (eachparticipant receives the results of the previous round and thus learnsthe different points of view put forward by the rest and is able tomodify his/her own); d) statistical results (the group response in eachsuccessive round can be represented by means of various descriptivestatistics including means, modes and frequencies, among others);and e) a heterogeneous sample (experts from various branches ofactivity contribute from their own perspectives).

11 This variation, which is more pronounced for the intermediate horizons, might bedue to the use of different discount rate estimation methods (Guo et al., 2006). Sincethe panel discount rates are slightly higher than those proposed by Weitzman (2001)in his step decline schedule to approximate gamma discounting, they can be said tostrike a balance between the two proposals.

2.1. Technical details of the Delphi process

The survey was conducted by e-mail and the main criterion for theselection of the Delphi panel was that the panellists should havepublished research in international scientific journals on the specifictopic of environmental discounting and the overall theme of theevaluation of public projects with environmental impact. The searchdrew on the Web of Knowledge (WoK) platform provided by theSpanish Ministry of Science and Technology, which includes, amongothers, reference bases such as the Web of Science, Current ContentsConnect, ISI Proceedings, Derwent Innovations Index and Medline.This enabled the construction of an initial panel of 174 experts.

The questionnaire consisted of ten questions with space foradditional comments. In questions 1 to 8, panel members were askedto indicate their level of agreementwith eight statements (see Table 2),using a standard Likert-type scale (from −2 to 2), where −2 indicates“complete disagreement”; 2 indicates “complete agreement”; and 0indicates indifference. Question 9 (only for those who agreed withstatement 5) was designed to elicit opinions regarding the mostappropriate discount rate for a series of time horizons: 0–30, 31–75,76–125, 126–200, 201–300 andmore than 300 years. Togetherwith thequestionnaire, participants received an information pack explaining thedifferent discounting strategies. The questionnaire for the second roundgave the overall statistics for thefirst round (the percentage frequenciesin questions 1 to 8; and themean and standard deviation in question 9),allowing participants to consider the initial replies of each panelmember, thus providing the feedback process that makes the Delphimethod so attractive.

Table 1 shows the technical details of the survey. Althoughparticipation was purely voluntary and altruistic, the suitability of thepanel members and their knowledge of the subject are evident in thefinal participation rate, which, at 56%, is well over the 35% thresholdconsidered acceptable in Delphi surveys (Adler and Ziglio, 1996).

Two further points are worth noting regarding the final size of thepanel, which comprised 98 experts. The first is that this figure is nearlytwice thehighest recommendedminimumof50alreadymentioned in theprevious section. The second is that, although the Delphi survey is aprospecting techniqueand thusnotdesigned todrawstatistical inferences(Dalkey, 1969), it is worth estimating the representativeness of thesample in theworst-case scenario of apopulation tending towards infinity(a very large lumber of experts). Even then, the maximum error in theestimation of a binary variable would range somewhere between 9.9%(maximumvariance) and 5.9% (minimumvariance), for a 95% confidencelevel. Thus, the results of this studyallow inferential interpretationwithanacceptable margin of error, thus precluding the need for the bootstraptechniques required with smaller Delphi panels (Akins et al., 2005).

The last two rows in Table 1 summarize the validation of thefeedback process (Linstone and Turoff, 1975). The main observation isthat the second round yields two desirable outcomes: a higher degreeof consensus and a lower level of stability, both of which providejustification for the use of the Delphi method.

2.2. Results of Delphi survey

Tables 2 and 3 give the basic statistics for each question. Significantly,for this paper, Table 2 reveals the panel's unanimous full support for themodification of the discounting approach in the intergenerational context(question 2), almost unanimous support for the reduction of discountrates (question 3), and ample support for DDR approaches (questions 5and 7). Although DRD (question 8) receives a somewhat lower level ofsupport, it still meets with the approval of more than half the panelmembers, which is a relatively high proportion, taking into account thatthis is a more novel approach and therefore less well known.

The discount rates proposed by the panel members for thedifferent time horizons (Table 3), are similar although somewhatlower in average terms than the UK Government proposal (HMTreasury, 2003).11 The modes reveal that the experts prefer threeintervals: up to 75 years; 75 to 200 years and over 200 years, insteadof the six originally proposed.

Table 2Frequencies (%) of questions 1 to 8 (2nd round).Authors' own calculations based on survey results.

−2 −1 0 1 2

1. Cost–benefit analysis is the appropriate method to use for the economic appraisal of public investment projects, including those withintergenerational impact.

5.7 13.2 1.9 32.1 47.2

2. The social discount rate should be reconsidered in intergenerational settings with time horizons spanning centuries, because the standarddiscount rate is only appropriate in the case of projects with time horizons of a few decades.

0.0 0.0 0.0 24.5 75.5

3. A lower social discount rate should be used in intergenerational contexts, in order to assign more weight to long- and very long-term benefitsand costs (sustainability).

0.0 0.0 2.0 38.0 60.0

4. The only valid discount rate in intergenerational contexts is 0%. 52.8 17.0 13.2 7.5 9.45. The value of the social discount rate should be linked to the project time horizon (a “Weitzman” or “Green book” scheme, or similar). 3.8 0.0 9.4 39.6 47.26. The value of the social discount rate should be linked to the characteristics and type of natural resource affected. 7.5 20.8 9.4 37.7 24.57. In time horizons spanning centuries, it is better to use a time variable discount factor (such as hyperbolic discounting) rather than a constantfactor (exponential).

4.0 4.0 6.0 36.0 50.0

8. One option to bear in mind is the simultaneous use of different discount rates for tangible and intangible effects in the same CBA. 11.3 15.1 18.9 24.5 30.2

Maximum frequencies in bold.

1309C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

3. Economic appraisal of a desalination plant to treat farm drainagewater flowing into the mar menor (Spain)

3.1. Description of the study area and its environmental problems

The Mar Menor is a hypersaline coastal lagoon located in Murcia,SE-Spain. Its surface area of 135 km2 makes it the largest lagoon of itskind in the western Mediterranean. It has five islands and a 24 kmlong sandbar (La Manga) that separates it almost completely from theopen sea, several natural canals being its only direct links to theMediterranean Sea. The area is one of the enormous ecological andnatural values that comes under multiple national and internationalnature protection orders (RAMSARwetlands since 1994; ZEPA, amongothers) (Martínez-Fernández et al., 2005).

The natural drainage basin of theMar Menor (see Fig. 1), known asthe Campo de Cartagena, has gradually developed into an area ofirrigated farmland, much of it intensively cultivated, using waterspumped from the underground water table or diverted from the Tajo-Segura transfer. The run-off from the nutrient-rich fertilizers used onthis land flows directly into the Mar Menor, causing severe alterationsin its hydrological functioning and ecological integrity (Martínez-Fernández et al., 2007), and leading to the eutrophication of thelagoon. To comply with the Water Framework Directive (Bouleau,2008) intervention is needed to stem the flow of contamination fromnutrient-laden agricultural run-off into the lagoon.

The main source of this agricultural run-off into the Mar Menor is theAlbujón watercourse (Lloret et al., 2005). This 42 km long wadi collectsdrainage from an area of over 44.000 ha, accounting for no less than onethird of the total nutrient load flowing into the lagoon. Removal of thissourceof run-off is clearlyoneof themainundertakingsneeded tohalt theeutrophication process of the Mar Menor. A brief outline of the plan isgiven below.

Table 3Basic statistics for question 9.Authors' own calculations based on survey results.

9. If you agree that the time horizon for the project is a variable that that should beconsidered when selecting the intergenerational discount rate (that is, if you gavea rating of 0, +1 or +2 in question 5), please indicate the rates you considerreasonable in projects with different time horizons (score or interval):

Interval Mean Median Standard deviation

0–30 years 3.47 3.5 1.4631–75 years 2.84 3.0 1.0476–125 years 1.88 2.0 1.05126–200 years 1.55 1.5 1.04201–300 years 1.05 1.0 0.82Over 300 years 0.87 1.0 0.79

3.2. The desalination plant for treatment of agricultural drainage water

One of the proposals for dealing with drainage via the Albujón isthe construction of a desalination plant to collect and treat thedrainage water to remove the dissolved salts that are responsible forpolluting the lagoon.

The technical/project details published by the ConfederaciónHidrográfica del Segura (Segura Water Board) (CHS, 2005), the agencydesignated tomanage theproject, indicate amaximumwater treatmentcapacity of 9 mln m3/year. This is more than enough for a normal year,given that the AlbujónWatercourse has a normalflow rate of 6 mln m3/year, and treats a large proportion of the excess overland flow of stormrainfall, which can double the river's normal flow rate (Velasco et al.,2006). As well as removing salts, the plant produces a flow ofmarketable fresh water for irrigation purposes in an area heavilyburdened in economic and ecological terms by a fresh-water shortage.Since the rate of freshwater production from this process is set at 75% ofthe total in-flow, the 6.75 mln m3/year generated would be enough tofully serve the needs of over 800 ha. of the region's irrigatedhorticultural lands (Colino and Martínez-Paz, 2007).

The same technical project plan also includes the plant construc-tion cost estimates shown in Table 4.12

The benefits of the desalination plant are shown below under twoheadings:

• Market benefits (MB). Total income from the sale of the fresh watergenerated is estimated at 1,687,500€/year, based on an initial priceof 0.25€/m3, which is the standard price of water from alternativesources in the area (Colino and Martínez-Paz, 2007).

• Environmental benefits (EB). One of the main objectives of the plantis to prevent the ecological degradation of the Mar Menor by haltingthe flow of a large volume of salt-laden run-off into its waters. Thisservice to the environment is a non-market service and, as such, hasno direct price, but can be evaluated by indirect methods drawnfrom Environmental Economics (Stemplewski et al., 2008). Thispaper uses the Avoided Cost Method, valuing the impact (benefit) interms of the cost of the cheapest available alternative treatment(Markandya et al., 2002). For this case in hand, the cheapest,technically viable, alternative measure to prevent the flow of thesedrainage waters into the Mar Menor is to construct an artificialwetland (El-Sheikh et al., 2010) at the mouth of the watercourse toact as a green filter on the drainage waters (Vymazal, 2007) Aprevious, complete study can be found inMartínez-Paz et al. (2007),which, after a detailed analysis of the project, obtains an estimatedcost of 138,100€/mln m3 for the treatment of drainage water andrun-off. The resulting environmental benefit of the desalination

12 All monetary figures are expressed in real terms (2010 Euros) (Kokoski, 2010).

Fig. 1. The Mar Menor (Spain).Source: Authors' own elaboration using European Environment Agency Info Viewer.

1310 C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

plant is estimated in monetary terms as 1,242,900€/year, given atreatment capacity of 9 mln m3/year.

3.3. Description of probabilistic variables used in the CBA model

The variables selected for the Monte Carlo simulation in this studywere as follows:

a) The price of water, a variable that is subject to uncertainty in thestudy region. We use a triangular distribution, taking 0.25€/m3 asthe most likely value; and 0 and 0.60€/m3 as the minimum andmaximum values, respectively. Theminimumvalueof 0€ is selectedfor the worst-case scenario that the water generated by the plantcannot be sold for irrigation purposes (negative affectation of theregional farming sector due to consequences arising from govern-mental agricultural policy decisions, for example). The maximumvalue of 0.60€/m3 represents the best-case scenario—understood inthe present evaluation context—in which the only availablepossibility is to purchase it frommore expensive alternative sources,sea-water desalination. The most-likely-case scenario, 0.25€/m3, istheone inwhichwater fromtheTajo-Segura transfer continues tobeavailable at the current price, although the transfer is underdiscussion, hence the uncertainty.

b) The environmental discount rate (EDR) to be appliedper timehorizon,is determined according to thedistributionmatching theDelphi data(question 9), the main statistics of which are shown in Table 5.

Table 4Construction and operating costs (€2010).Authors' own calculations based on Martínez-Paz et al. (2007).

Chapter Cost

Initial investment (€) 6,124,560Staff (€/year) 382,000Maintenance (€/year) 388,720Membrane and filters (€/year) 400,274Power (€/year) 1,676,824

Normal truncated distributions are used in all three time horizons(Table 6) because they fit the data well; with the sample mean andstandard deviation and distributions truncated at the minimum andmaximumvalues. Prior to this, normal, normal truncated, uniform, beta,chi-squared, lognormal, gamma, logistic, exponential, t-student, andWeibull and Rayleigh distributions were also tested (Winston, 2004).

The choice of distribution type in this study is also based on therecommendations of Vose (2002), in which the non-parametrictriangular distribution is the optimal choice when there is littleinformation regarding the underlying distribution, as in the case ofthe price of water in this region, about which specific values areknown but not their frequency. A normal, parametric distribution isappropriate in the context of a medium to high knowledge level, suchas this one, where the data are drawn from a panel of experts.

We performed 10,000 iterations for each scenario using R (Robertand Casella, 2010) and Excel (Winston, 2004).

4. Results and discussion

Table 6 presents the average values of both the classic profitabilityindicators (NPV and IRR), and the environmental profitabilityindicators (ITA and CER), the latter for the dual-rate discountingapproach. This is done across all three of the considered time horizons(40, 100 and 200 years) and the four cost–benefit analysis approaches(options A to D) detailed below.

Table 5Statistics of the EDR distribution by time horizon (question 9).Authors' own calculations based on survey results.

Statistics Time horizon (in years)

31–75 76–125 126–200

Mean 2.84 1.88 1.55Standard deviation 1.04 1.05 1.04Maximum 5 4 4Minimum 1 0 0

Table 6Summary statistics for the traditional and environmental profitability indicators, using Monte Carlo simulation.

⁎Except for the CER, where we show themedian because themean is strongly influenced by the effect of outliers, some of which are very high for this indicator. aCBA=standard CBAwithout including environmental externalities. bECBA=environmental cost–benefit analysis CBA (including environmental xternalities). cDD=distribution of the results from theDelphi study of discounting experts. dSDR=social discount rate. eEDR=environmental discount rate. fNPV=Net Present Value. gProb (NPVb0)=probability of NPV being negativeand, thus, the project unprofitable. hITA=Intergenerational Transfer Amount. iIRR=Internal Rate of Return. jCER=critical enviromental rate.

1311C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

■ From the standard cost–benefit approach, not including non-marketintangibles, which, in this case, are environmental benefits(standard CBA) (option A).

■ From the environmental cost–benefit approach (environmental CBA),including both market flows (tangibles) and non-market flows(intangibles). In this case, three different discount rates are used:

- Option B: a standard social discount rate (standard SDR) of 5.5%.For the period 2007–2013, the European Commission (2008)suggests two benchmark social discount rates: 5.5% and 3.5%. Weselect the higher rate for comparison of the scenarios, because itis more graphic in the results and nearer to the traditional rate of5% as suggested in the guidelines of the European Commission(2003).

- Option C: a declining social discount rate by time horizon (decliningSDR), according to the distribution given by the Delphi study.

- Option D: the dual discount rate, with a standard SDR of 5.5% fortangibles (market flows) and a declining rate (also according tothe distribution given by the Delphi study) for intangibles (non-market flows). The 40-year time horizon was omitted from thisoption which we understand to be most suited to long-termhorizons with an intergenerational impact.

From the resulting data, displayed in Table 6, we are able toconclude that:

Fig. 2. Means of NPV in the time horizons considered (1,000€).

In the standard CBA scenario (option A)where environmental valuesare not included, the average indicator score shows that theproject isjust on the border of profitability. For the 40-year time horizon, it hasan NPV slightly greater than zero, an IRR below the SDR used tocalculate the NPV, and only a 44.5%13 probability of having a positiveNPV. This greatly strengthens the rationale for the use ofMonte Carlosimulation in this case, since it enables us to analyse the uncertaintyof the key parameters in the economic appraisal.In option A, we also show the values for the 100-year and 200-yeartime horizons, although, strictly speaking, their use is less justified inthis case, where environmental benefits are not included in theanalysis.

Next, we present our conclusions from the results of the com-parison of options B to D, the ECBA (environmental cost benefitanalysis) scenarios, where, once converted into monetary terms,environmental benefits are included in the analysis using differentdiscounting strategies. The following observations were made:

AverageNPV (Net Present Value) increaseswith the length of the timehorizon and over options B to D (see Fig. 2), ranging between 6,198(optionB, 40 years) and62,653(optionD,200 years) thousandsof€.14

Furthermore, viewed in the context of a Monte Carlo procedure, it isinteresting to note the potential conclusions when there is aprobability of NPV being less than zero (that is, of the project beingunprofitable). Thus, the top and bottom values of the sensitivityanalysis show (see Fig. 3) that the probability of the project beingprofitable (NPVN0) increases from 78.8% (for option B, 40 years) to96.8% (for option D, 200 years).

13 In fact, the actual NPV value, without the Monte Carlo simulation, is negative(−4,938 thousands of €).14 In this particular case study, the fact that in options B to D, the mean value of theNPV is greater than zero might have fewer implications for public decision-makers.However, it must be borne in mind that, in projects where the nature of theenvironmental impact means that current generations bear the bulk of the cost andfuture generations enjoy most of the benefits, the NPV values will differ much morenotably across the various scenarios, for example, in CBA of projects involving climatechange. This study uses annual estimates of both environmental and market benefits,making the findings less apparent on the graph.

Fig. 3. Probability of NPVN0 in the time horizons considered (%).

Fig. 5. NPV kernel (option D, 200 years).

1312 C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

Figs. 4 and 5 depict the Gaussian kernel-smoothed probabilisticdistribution of the NPV in both cases. The smoothing process turns thehistogram of the statistical distribution into a continuous graphassigning less weight to the observations furthest from the point ofevaluation and facilitating interpretation of the data structure. BothNPV values are mono-polar distributed around the mean with rightskewness resulting from the skewness of the distribution of the priceof water.

Moreover, the reason for the choice of time horizons (40, 100 and200 years) was to exemplify the impact of this variable in ECBA.Strictly speaking, the useful lifetime or time horizon of a project withenvironmental impact is the period during which the impact persists.In the case of impacts with potentially irreversible effect (the loss of aunique natural setting), the time horizon is theoretically infinite; and,in discounting practice, would be the length of time for which theupdated environmental impact estimate were zero, in relation to thechosen discounting approach and discount rate. In this practical case,it is very clear that, since the residual value of the environmental goodis unknown, a longer time horizon is a more appropriate choice inECBA, which is the option most closely linked with intergenerationalethics and sustainability.

Meanwhile, the environmental profitability indicator scoresproposed for the dual-rate discounting approach (option D) showthat:

a) In terms of environmental profitability, this project can beestimated as having a CER (Critical Environmental Rate) of 4.6%.As explained in Subsection 1.3, this is a relative value that must becompared with the level accepted by society, using either aprescriptive approach (by deducting it from the returns to publiclyfinanced environmental projects, as in the Social Opportunity Cost

Fig. 4. NPV kernel (option B, 40 years).

approach); or a normative approach, through institutional supportfor a certain level of CER in public projects. Obviously, however,this would not be a unique value and would depend, among otherthings, on the nature of the environmental impact.The institutionally set standard might be, for example, the oneused in HM Treasury (2003). That is, the proposed SDRs of 2.5% for100 years and 2% for 200 years could be taken as a reference here.The project could then be considered environmentally profitable(under the methodological logic considered here).

b) The Intergenerational Transfer Amount (ITA), meanwhile, sug-gests that if this project were to take place, it would mean a legacyto future generations in the form of benefit amounting to anoverall monetary value of 36,041 (100 years) and 62,653(200 years) thousand €.15 The fact that the value increases forlonger time horizons is in line with the fact that the longer theperiod, the more generations are affected.

To describe the above findings in more graphic terms, based onthis case study, the methodological issue analyzed in this paperreduces to a decision between two extremes: (1) presenting theproject as being of doubtful profitability with a 44.5% probability ofNPVN0 and mean NPV of 109 thousand € (option A, 40 years, withMonte Carlo simulations) or even negative NPV of−4,938 thousand €

(option A, 40 years, exact value); or (2) presenting it as a sociallybeneficial option, generating a mean net gain of 58,939 thousand € inthe probabilistic context under analysis, with a 96.8% probability ofpositive NPV (NPV, option D, 200 years) and benefits worth 62,653thousand € passed on to future generations.

Obviously, it is not a case of presenting a single extremealternative, nor of selecting all available discounting approaches;it is rather a case of selecting the ones best suited to thecharacteristics of the project. The nature and time scale of theenvironmental impact must be taken into account. In other words,there can be no universal discounting strategy for all types ofproject. Some have a moderate, non-irreversible impact and some“market-priceable” benefit that makes the baseline NPV positive.Others have considerable environmental impact, as observed in the

15 Note that the exact value (without Monte Carlo simulation) of this indicator for200 years is given by the equation:

ITA 5:5%ð Þ = NPV SDR = 5:5%;EDR = 1:55ð Þ−NPV SDR = 5:5ð Þ

where 1.55% is the mean value for this time horizon according to the Delphi survey. Anegative NPV(SDR=5.5%) means that, in this particular case, the ITA (5.5%) is greaterthan the NPV(SDR=5.5%, EDR=1.55). The same effect is observed in the Monte Carlosimulations; hence, the value of ITA in table a is greater than that of NPV (option D,both time horizons).

1313C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

CBA of projects involving climate change, and the choice of strategycan mean the difference between leaving a positive or a negativelegacy to future generations. In the latter case, the sensitivityanalysis might contemplate options including a hyperbolic EDR forhorizons spanning centuries.

We must also caution against interpreting the central issue of thispaper as “the higher the NPV, the better the methodological approachused in the evaluation.” It is not a case of finding the formula thatassigns most weight to sustainability (in the tricky choice betweeneconomic growth and sustainability). The aim in the methodologicalsense is that the indicator scores should reflect society's real option.That is, by opting to carry out the desalination plant project inquestion, public decision-makers are showing that the collectiveoption is to consider this project economically and environmentallyworthwhile, otherwise they would not carry it out. It is difficult toincorporate the sustainability objective into the methodologicalcontext of traditional CBA. Thus, our proposal aims to bring the CBAapproach up to date, by integrating methodological changes to allowthe profitability indicators to capture society's real options moreaccurately.

5. Conclusions

Application of the ECBA (extended or environmental cost benefitanalysis) evaluation platform in the context of high environmentalimpact projects can be improved in various ways:

(1) By selecting a time horizon to suit the environmental impact,such that if the latter is probably irreversible, a time horizonspanning several centuries will be better than the traditionalone of a few decades when it comes to integrating thesustainability objective into the analysis.

(2) By selecting several different discounting platforms orapproaches in the sensitivity analysis, rather than the tradi-tional option which is to vary the discount rate within a givendiscounting approach.

(3) Besides the reduction of the conventional discount rate, theapproach in which the discount rate is set to the time horizon isalso supported by most of the experts in discounting consultedin the Delphi survey. This strategy is already being pioneeredafter recommendation by the UK Government (HM Treasury).

(4) As well as supporting the specific rates used under thisapproach, the international panel of experts, furthermore,believes that discount rates could be revised downwards fortime horizons spanning several centuries.

(5) The relatively novel dual-rate discounting approach, which isstill under study, may be useful. This approach supported byempirical research and grounded upon arguments arising fromthe re-assessment of Ramsey's formula for the case of ECBA, isalso supported by a modest but significant percentage of thepanel of experts. The case study exemplifies the potentialpractical implications of this approach, which not onlyincreases the probability of NPV being greater than zero, butalso makes the intergenerational ethics explicit throughenvironmental profitability indicators.

(6) ECBA based on probability rather than exact criteria is anuncommon, albeit desirable, practice. It presents the results,including the uncertainty associated with key variables, interms of their probability, thus minimizing bias from over-optimistic or over-pessimistic expert opinions. It also providesa more appropriate way to include experts' opinions, whichare so important in Environmental Economics, where there isgreat uncertainty at all levels regarding possibilities for thefuture.

The following are the main conclusions drawn specifically fromthis case study:

(7) It has illustrated the usefulness of theMonte Carlo procedure toassess the economic valuation of projects with environmentalimpacts. First, it includes uncertainty (in this case the price ofwater) among the key variables of the model; it also enablesthe use of environmental discounting schedules in which it ispossible to incorporate the uncertainty regarding the discountrate to be applied in long term.

(8) The results yielded by the Monte Carlo procedure also enrichthe discussion surrounding project profitability estimation byshowing both the average value and the probability distribu-tion of the profitability indicators. Thus, for example, thetraditional CBA method (option A), estimates the probability ofthe desalination plant project being non-profitable (ProbVNPb0) at 55.5% (for a 40-year time horizon) and averageNPV at 109 thousand €, versus the classic methodology whichyields only a specific NPV value of −4938 thousand €.

(9) This economic evaluation of the irrigation return water desalina-tion plant to prevent the eutrophication of the Mar Menor (byhalting the flow of nutrients into the lagoon) illustrates theimportance of including (in this case positive) environmentalexternalities when assessing the economic viability of projectswith major environmental impact. The only inclusion of marketflows results on average in very low investment return estimates,as shown in the point above, whereas the inclusion of environ-mental benefits has enabled us to show that the environmentalproject considered in this analysis is highly profitable, for anydiscounting approaches or time horizon (options B to D).

(10) In this case, however, average NPV increases with longer timehorizons and the use of recent advances on discounting(Declining Discount Rates and Dual-Rate Discounting, options Cand D respectively). Bearing in mind that the Local Administra-tion has in fact carried out the project, these discountingapproaches can suitably accommodate the sustainability prefer-ence of the local community. In concrete terms, for option D (the200-year time horizon) the estimated profitability of the projectis a 96.8% probability of a positive NPV of 58,939 thousand €; anda legacy of 62,653 thousand € to future generations.

References

Adler M, Ziglio E. Gazing into the oracle: the Delphi method and its application to socialpolicy and public health. London: Jessica Kingsley Publishers; 1996. 252 pp.

Akins R, Tolson H, Cole B. Stability of response characteristics of a Delphi panel:application of bootstrap data expansion. BMC Med Res Methodol 2005;5:37.

Almansa C, Calatrava J. Reconciling sustainability in cost benefit analysis: amethological proposal. Ecol Econ 2007;60(4):712–25.

Azqueta Oyarzun D. Valoración económica de la calidad ambiental. Madrid: McGrawHill; 1996.

Balcombe K, Smith L. Refining the use of Monte Carlo techniques for risk analysis inproject planning. J Dev Stud 1999;36(2):112–34.

Belli P. Is economic analysis of projects still useful? Policy Research Working PaperSeries, 1689. The World Bank; 1996.

Bouleau G. The WFD dreams: between ecology and economics. Water Environ J2008;22:235–40.

Bromley D. The ideology of efficiency: searching for a theory of policy analysis. J EnvironEcon Manage 1990;19:86-107.

Brouwer R, Van Ek R. Integrated ecological, economic and social impact assessment ofalternative flood control policies in the Netherlands. Ecol Econ 2004;50(1–2):1–2.

Chichilnisky G. An axiomatic approach to sustainable development. Soc Choice Welf1996;13(2):219–48.

CHS, 2005. Informe de los artículos 5, 6 y 7 de la Directiva Marco del Agua.Confederación Hidrográfica del Segura y Ministerio de Medio Ambiente CHS-MMA(Spain).

Colino J, Martínez-Paz J. Productividad, disposición al pago y eficiencia técnica en el usodel agua: la horticultura intensiva de la Región de Murcia. Economía Agraria RecurNaturales 2007;7:109–25.

European Commission. Externalities of Energy. “ExternE” Project. Report on ClimateChangeDamageAssessment. DirectorateGeneral XII Brussels: EuropeanCommission;1998.

European Commission. Guide to cost–benefit analysis of investment projects —

Structural Funds-ERDF, Cohesion Fund and ISPA, prepared for the EvaluationUnit. Bruxelles: DG Regional Policy; 2003.

1314 C. Almansa, J.M. Martínez-Paz / Science of the Total Environment 409 (2011) 1305–1314

European Commission. Guide to cost–benefit analysis of investment projects —

Structural Funds-ERDF, Cohesion Fund and ISPA, prepared for the EvaluationUnit. Bruxelles: DG Regional Policy; 2008.

Dalkey N. Analyses from a group opinion study. Futures 1969;1(6):541–55.Dalkey N, Helmer O. An experimental application of the Delphi method to the use of

experts. Manage Sci 1963;9(3):458–67.El-Sheikh M, Saleh H, El-Quosyb D, Mahmoudc A. Improving water quality in polluted

drains with free water surface constructed wetlands. Ecol Eng 2010;36(10):1478–84.

Erhun K, Evans D. Dual discounting in cost-benefit analysis for environmental impacts.Environmental Impact Assessment Review; in press doi:10.1016/j.eiar.2010.06.001.

Frederick S, Loewenstein G, O'Donoghue T. Time discounting and time preference: acritical review. J Econ Lit 2002;40(2):351–401.

Gollier C. Ecological discounting. J Econ Theory 2010;145:812–29.Goulder LH, Kennedy D. Valuing ecosystem services: philosophical bases and empirical

methods. In: Daily GC, editor. Nature's services: societal dependence on naturalecosystems. Washington, DC: Island Press; 1997. p. 23–47.

Groom, B., Koundouri, P., Panopoulou, E., Pantalidis, T., 2004. Model selection forestimating certainty equivalent discount rates. University College London Discus-sion Paper 04-02.

Groom B, Hepburn C, Koundouri, Pearce D. Declining discount rates: the long and theshort of it. Environ Resour Econ 2005;32:445–93.

Gulli F. Social choice, uncertainty about external costs and trade-off betweenintergenerational environmental impacts: the emblematic case of gas-basedenergy supply decentralization. Ecol Econ 2006;57(2):282–305.

Guo J, Hepburn CJ, Tol RSJ, Antoff D. Discounting and the social cost of carbon: a closerlook at uncertainty. Environ Sci Policy 2006;9:205–16.

Hanemann WM. Valuing the environment through contingent valuation. J EconPerspect 1994;8(4):19–43.

Hansen AC. Do declining discount rates lead to time inconsistent economic advice? EcolEcon 2006;60:138–44.

Hasselmann K. Intertemporal accounting of climate change: harmonizing economicefficiency and climate stewardship. Climate Change 1999;41:333–50.

Hasselmann K, Hasselmann S, Giering R, Ocana V, Storch H. Sensitivity study of optimalCO2 emissions using a simplified structural integrated assessment model (SIAM).Climate Change 1997;37:345–86.

Heal G. Valuing the future: economic theory and sustainability. New York: ColumbiaUniversity Press; 1998.

Henderson N, Bateman I. Empirical and public choice evidence for hyperbolic socialdiscount rates and the implications for intergenerational discounting. EnvironResour Econ 1995;5:413–23.

Hepburn, C., 2005. Concerning the future: declining discount rates and intergenerationalequity. PhD thesis. Oxford University.

Joubert AR, Leiman A, de Klerk HM, Katua S, Aggenbach JC. Fynbos (fine bush)vegetation and the supply of water: a comparison of multi-criteria decisionanalysis. Ecol Econ 1997;22:123–40.

Kögel, T., 2009. On the Relation between dual-rate discounting and substitutability.Economics Discussion Papers, No 2009-10.

Kokoski M. Getting real: price deflation of nominal values. J Benefit Cost Anal 2010;1(1)Article 7.

Krutilla JV, Fisher AC. The economics of natural environments. Baltimore: JohnsHopkins University Press; 1975.

Lind RC. Intertemporal equity, discounting, and economic efficiency in water policyevaluation. Climate Change 1997;37:41–62.

Linstone H, Turoff M. The Delphi method. Techniques and applications. Reading, Mass.:Addison Wesley Publishing; 1975

Lloret J, Marin A, Marin-Guirao L, Velasco J. Changes in macrophytes distribution in ahypersaline coastal lagoon associated with the development of intensivelyirrigated agriculture. Ocean Coast Manage 2005;48:828–84.

Loewenstein G, Prelec D. Anomalies in intertemporal choice: evidence and interpretation.Q J Econ 1992;107:573–97.

Markandya P, Harou L, Cistulli V. Environmental economics for sustainable growth: ahandbook for practitioners. Cheltenham: Edward Elgar; 2002.

Martínez-Carraso F, Schwentesius R, Martínez-Paz JM, Gómez-Cruz M. Characteristicsand comparative of organic food producers in southeastern Europe: the case of theMurcia Region, Spain. Agrociencia 2009;43:649–57.

Martínez-Fernández J, Esteve-Selma M, Robledano-Aymerich F, Pardo-Sáez M, Carreño,Fructuoso M. Aquatic birds as bioindicators of trophic changes and ecosystemdeterioration in the Mar Menor lagoon (SE Spain). Hydrobiologia 2005;550:221–35.

Martínez-Fernández J, Esteve M, Martínez-Paz J, Carreño F, Robledano F, Ruiz M, AlonsoF. Simulating management options and scenarios for the sustainability of marMenor (Southeast Spain). Transitional Water Monogr 2007;1:34–56.

Martínez-Paz J, Martínez-Fernández J, Esteve MA. Evaluación económica del tratamientode drenajes agrícolas en el Mar Menor (SE España). Rev Esp Estud AgrosocialesPesqueros 2007;215(216):211–32.

Munda G. Cost benefit analysis in integrated environmental assessment: somemethodological issues. Ecol Econ 1996;19(2):157–68.

Navrud S. Pricing the European environment. Oslo: Scandinavian University Press;1992.

Newell RG, Pizer WA. Discounting the distant future: how much do uncertain ratesincrease valuations? J Environ Econ Manage 2003;46:52–71.

Parisca S. El Método Delphi. Gestión tecnológica y competitividad. In: Parisca S, editor.Estrategia y filosofía para alcanzar la calidad total y del éxito de la gestiónimpresional. La Habana: Academia; 1995. p. 129–30.

Pearce DW, Turner RK. Economics of natural resources and the environment.Hertfordshire: Harvester Weats Leaf; 1990.

Pearce DW, Groom B, Hepburn C, Koundouri P. Valuing de future: recent advances insocial discounting. World Econ 2003;4(2):121–39.

Pearce D, Atkitson G, Mourato S. Cost–benefit analysis and environment: recentdevelopments. Paris: OECD; 2006.

Philibert C. The economics of climate change and the theory of discounting. EnergyPolicy 1999;27:913–29.

Portney PR, Weyant JP. Discounting and intergenerational equity. Washington:Resources for the Future; 1999.

Poulos C, Whittington D. Time preferences for life-saving programs: evidence from sixless developed countries. Environ Sci Technol 2000;34:1445–55.

Prato T. Multiple attribute decision analysis for ecosystem management. Ecol Econ1999;30(2):207–22.

Rabl A. Discounting of long term cost: what would future generations prefer us to do?Ecol Econ 1996;17:137–45.

Ramsey FP. A mathematical theory of saving. Econ J 1928;38:543–59.Robert C, Casella G. Introducing Monte Carlo methods with R. Berlin: Springer; 2010.Stemplewski J, Krull D, Wermter P, Nafo II, Palm N, Lange C. Integrative socioeconomic

planning of measures in the context of the Water Framework Directive. WaterEnviron J 2008;22:250–7.

Stern N, Peters S, Bakhshi V, Bowen A, Cameron C, Catovsky S, Crane D, Cruickshank S,Dietz S, Edmonson N, Garbett S-L, Hamid L, Hoffman G, Ingram D, Jones B, PatmoreN, Radcliffe H, Sathiyarajah R, Stock M, Taylor C, Vernon T, Wanjie H, Zenghelis D.Stern review: the economics of climate change. London: HM Treasury; 2006.

Sterner T. Discounting in a world of limited growth. Environ Resour Econ 1994;4:527–34.

Tol RSJ. The damage costs of climate change: a note on tangibles and intangibles,applied to DICE. Energy Policy 1994;22:436–8.

Treasury HM. The green book: appraisal and evaluation in central government. London:HM Treasury; 2003.

Vatn A, Bromley DW. Choices without prices without apologies. J Environ Econ Manage1994;26:129–48.

Velasco J, Lloret J, Millán A, Marín A, Barahona J, Abellán P, Sánchez-Fernández D.Nutrient and particulate inputs into the Mar Menor lagoon (SE Spain) from anintensive agricultural watershed. Water Air Soil Pollut 2006;176:37–56.

Vining A, Weimer D. An assessment of important issues concerning the application ofbenefit–cost analysis to social policy. J Benefit Cost Anal 2010;1(1) Article 6.

Vose D. Risk analysis: a quantitative guide. 2 ed. London: John Wiley & Sons; 2002.Vymazal J. Removal of nutrients in various types of constructed wetlands. Sci Total

Environ 2007;380:48–65.Weitzman M. Gamma discounting. Am Econ Rev 2001;91(1):261–71.Winston W. Microsoft® Excel® data analysis and business modeling. Microsoft Press;

2004.Yang Z. Dual-rate discounting in dynamic economic–environmental modelling. Econ

Model 2003;20:941–57.