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Journal of Environmental M anagement (1996) 46 , 281–308

On the Measurement of the Environmental Performance of Firms—A Literature Review and a Productive Efciency Perspective

Daniel Tyteca

Institut d’Administration et de Gestion, Universite ´ Catholique de Louvain,Place des Doyens, 1, B-1348 Louvain-la-Neuve, Belgium

Received 23 M arch 1995

We dene environmental performance indicators as analytical tools that allowone to compare various plants in a rm, or various rms in an industry, witheach other and with respect to certain environmental characteristics. We startfrom a reection about the kind of information that should be incorporatedinto such indicators, and the extent to which that information should beaggregated. Existing approa ches a re b oth rare and dissimilar. They range fromoversimplied indicators to more sophisticated ones, in which there is a trendto take somewhat arbitrary viewpoints. In the scope of the theory of productive efficiency, three categories of factor are taken into account, i.e.inputs, desirable production outputs and pollutants in the form of ‘‘undesirable’’ out puts. Non-parametr ic e fficiency measures easily an d usefullylend themselves to the derivation of environmental performance indicators.They are the duals of indicators that can be obtained in the traditionalframework of data envelopment analysis. We use data envelopment analysis todene standardised, aggregate environmental performance indicators, that is,quantities comprised between 0 (bad performance) and 1 (good performance).Such indicators do not require the specication of any a priori weight on theenvironmental impacts that are being aggregated. A discussion is proposed onsuch topics as an analysis of the nature and causes of environmentalinefficiencies, and the relationship b etween ‘‘environmental performance’’ asdened in this paper and the actual global e ff ect of industrial activities onhealth (toxicity) and the environment.© 1996 Academic Press Limited

Keywords : environmental performance, indicators, measurement, DEA (dataenvelopment analysis), literature review, productive e fficiency.

1. Introduction

Among the human activities imposing a heavy burden on our environment, industrialactivities are in the foreground. While, in the previous two or three decades, thebehaviour of industries in that respect was mainly dictated by impositions originatingfrom governmental decisions (in the form of, e.g. regulation, standards, or taxes), somecompanies have recently realised the huge potential benets they could obtain by

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Measuring the environmental performance of rms282

adopting more conscious and pro-active behaviour towards the environment. Parallelto that evolution, there is an increasing need for tools that would allow for proper andobjective quantication or measurement of the performance of rms with respect to theenvironment. ‘‘Demands on companies to measure, document and disclose informationabout environmental performance will become more invasive—i.e. as the result of pressures from employees, neighbours, the general public, environmental groups andregulatory agencies. In the same way that public companies are measured by theirnancial results, environmental performance will increasingly become a critical factorto scrutinise.’’ (Greeno and Robinson, 1992).

James (1994) identied ve main driving forces in the pressure for business en-vironmental performance measurement: the biosphere, nancial stakeholders, non-nancial stakeholders, buyers and the public. A similar argument was made by Haines(1993), who added one important aspect ‘‘to attain leadership in dening industry-wideenvironmental excellence’’. Regarding the nancial stakeholders, a change seems indeedto have taken place in the attitude toward the environment: for example, based on

1978 data, Jaggi and Freedman (1992) found t hat ‘‘. . . the markets are not rewardinggood pollution performance by rms . . . in the short run the rm’s protability will benegatively a ff ected by pollution abat ement activities involving heavy expenditures’’,while, starting from 1986–88 data, Cormier et al. ’s results (1993) ‘‘weakly suggest thata rm’s [bad] pollution performance negatively a ff ects its market valuation’’ (thussupporting an ‘‘ethical investor hypothesis’’) but may also nd an explanation in thefact that ‘‘investors perceive that rms which are not meeting current environmentalstandards may only get in worse nancial shape in the future’’.

Various authors have located environmental performance measurement in the centreof a conceptual framework for business environmental management (e.g. Eckel et al .,1992; Taylor, 1992; Welford and Gouldson, 1993; James, 1994). Several companies havealready worked out various kinds of environmental performance indicators (see, e.g.Greeno and Robinson, 1992; Jackson and Chynoweth, 1992; Snyder, 1992; Coulter,1993; Fitzgerald and Fox-Penner, 1993; Hocking and Power, 1993; Wolfe and Howes,1993; Azzone and Manzini, 1994; James, 1994), but these are seldom disclosed explicitlyin the literature and are more likely to be specically orientated towards the company’sobjectives. Additionally, there is obviously no attempt toward standardisation, whichwould allow, for example, for comparisons among rms and over time (Hocking andPower, 1993). Fortune ’s recent attempt to rank some of the biggest American companiesaccording to their environmental performance (Rice, 1993) can be considered as a stepin that direction, but it is based on a somewhat arbitrary choice of 20 criteria with arather subjective procedure (from 0–10 points).

From a political economy’s point of view, what we would require is an indicator(or several indicators) allowing us not only to perform that kind of comparison in anobjective way but also to study the e ff ect of various kinds of regulations or economic

tools, such as pollution standards, taxes or tradable permits, on environmental per-formance. Conversely, the information derived from environmental performancemeasurement can provide the public deciders with meaningful guidelines in order toimplement relevant economic and/or regulatory instruments.

It should be made clear at this point what is our purpose in this paper. We look for one (or a few) instrument(s) that would allow us to account for the various possibleenvironmental impacts of industrial activities, and to compare in this respect analogousunits in a set, i.e. either plants in a rm, or rms in an industry, or even industrialsectors in an economy, or to monitor the behaviour of any of these units over time. In



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T 1. Sample list of environmental impa cts of industrial activities

Resources Raw materials uptakeEnergy consumption

Wastes Water: BOD and/or COD (biochemical and chemical oxygen demand, respectively)TSS (total suspended solids)pHnitratesphosphatesheavy metals . . .

Air: CO 2

NO x

SO 2

Solid wastesToxic wastes: e.g. TRI—Toxics R elease In ventory (EPA 1989, 1992)Noise

Further impacts: Impacts on landscape

Impacts on biodiversityContribution to the greenhouse e ff ectContribution to the ozone layer depletionContribution to acid rain deposition

doing so, there is a crucial stage of aggregating the impacts: the option taken in thispaper is to suggest an indicator as a unique gure, in which all the information on theimpacts is supposed to be captured. This might require the adoption of adequate weightcoefficients, which is the app roach adopt ed by several authors in the literature. However,there is an alternative perspective, which we will try to adopt, that consists of exploitingthe ideas of the productive e fficiency theory.

2. Standpoints adopted in developing environmental performance indicators

2.1. .

In order to adequately reect the impact an industry may have on the environment,we have to dene at the outset various measurements, as exemplied in the (non-exhaustive) list of Table 1. The indicators in Table 1 can be considered as ‘‘simple’’ inthe sensethat they measure onlyone aspect of the impact of activities on the environment.There is, of course, some level of aggregation in a few of them, since, for example, BODmeasures global impact caused by var ious kinds of substances, while the contr ibution tothe greenhouse e ff ect will obviously result from computations that account for variouskinds of interactions. In the same manner, the impact of polluting substances on human

health combines primary data on single e ffluents or emissions with critical toxicitylevels (Mar tin et al ., 1991).

In a second step of the denition of indicators, we might want to normalise themto some extent, i.e. control them for some quantity or quantities reecting a rm’sactivity, such as the annual tons of a given produced output (see, e.g. Martin et al .,1991; Haines, 1993) or the level of employment (e.g. Templet, 1993a,b). But, mostimportantly, in order to produce simple yet meaningful indicators reecting the overallimpact on the environment, we should aggregate the information gathered so far. Thisis of course a critical step; the key problem of environmental performance measurement




is converting large amounts of data into managerially useful information via appropriatemetrics (James, 1994).

2.2.

Many of the quantities listed in Table 1 are naturally expressed in physical, chemicaland biological units. Normalisation can introduce an economic dimension to themeasurement. However, attempts to aggregate several indicators will result in unitlessor dimensionless measures. On doing that exercise, we might require that the resultingglobal indicator be standardised in order to allow for proper and easy comparisons.Thus, for example, we could dene an indicator such that

v (0,1) (1)

with 0 and 1 being the worst and best possible values, respectively, while the openinterval means, on the one hand, that it is always possible to do worse than the observedsituation and, on the other hand, that the absolute perfection (i.e. zero-waste, zero-impact) cannot really be achieved. To be more practical, we could dene, instead,

v [0,1] (2)

where this time the closed interval refers to worst and best observed practices. Thelower bound would correspond to rms that show no consideration for the environment,while the upper bound would indicate that a rm has adopted the best availabletechnology (BAT) for its industrial sector. This is, of course, a relative concept and theupper bound would have to be adapted each time a new, more environmentally friendlytechnology was invented.

2.3.

An indicator can be relative or absolute. The quantities listed in Table 1 are absolutein t he sense that they show absolute amounts of waste or resources without anypart icular reference point. Aggregate variables can a lso be absolute for t he same reason.We shall dene a relative quantity as being the result of the comparison of a givenabsolute variable with some predened level, such as, for example, worst or bestpractice, or some standard coming from legislation, or any specied target. Thecompliance indicator dened by Haines (1993), i.e. the percentage of control samplesthat complied with a given qua lity standard for a given period, enters the same category.Other examples of relative and absolute indicators will be reviewed in the next sections.

An indicator will generally be static in the sense that it is computed from pastobservations and simply reects the past behaviour of a given rm with respect to theenvironment. In that case, it would allow, e.g. for various kinds of inter-plant, inter-rm or inter-industry comparisons. However, we might also want an indicator to be prospective , in which case it would allow us to follow the environmental performanceof a given rm or industry over time or to monitor its environmental behaviour insome way (e.g. improving it through implementation of cleaner technologies). Here theproblem is data availability, since it will generally be easier to react with respect toprevious observations than to make plans based on often unpredictable events (markets,



D. Tyteca 285

demand, competition, t echnology, characteristics o f the natural environment) or regu-latory standards. Though we must recognise that prospective indicators would reallyprovide us with valuable management tools, there are several obstacles to their im-plementation, some of which may also originate from the presently available methodsused to compute them, as we shall see in the next two sections.

3. Existing approaches toward environmental performance indicators

3.1.

This is the most detailed level for dening environmental performance indicators. Theso-called LCA (where the ‘‘A’’ stands for either ‘‘analysis’’ or ‘‘assessment’’) is a toolfor studying the impacts of a given product over all stages of its life cycle (resourceextraction, energy use, production, distribution, use and ultimate disposal), rather thanthe impacts of a given plant or rm (e.g. Kirkpatrick, 1992a,b, 1993a,b; SETAC, 1992).The LCAnalysis mainly aims at identifying the impacts and quantifying them in

‘‘natural’’ units such as physical, chemical or biological. Methodologies are now wellestablished for that rst level. At a second level, the LCAssessment of a product aimsat aggregating the impacts, which implies various kinds of characterisation, evaluation,classication and weighting between sometimes conicting objectives before one cancome to a meaningful conclusion as to the global environmental impact of the product.This second level appears to be much less stud ied and settled as r egards the terminologyand scientic bases, for example; no standard methodology exists although signicanteff orts are being made in that direction (Kirkpatrick, 1992b, 1993a; Sullivan andEhrenfeld, 1993). Although some authors insist on the impracticability and tremendouscost implied by LCAs (e.g. Arnold, 1993), these should, in the end, provide a companywith the denitive t ools for studying and monitoring the impacts its various activitiescan have on the environment. As such, they should be considered from the very startof product design (Thayer, 1992). They would also be useful as a means of improvingthe price system in a way that would more appropriately reect the environmentalimpact of products in nancial or monetary terms (Popo ff and Buzzelli, 1993; Sullivanand Ehrenfeld, 1993; Portney, 1993, 1994).

However, even when such a methodology eventually becomes well understood andeff ective, it does not tell us how to integrate the impacts of the various products intoone or a few plant-level indicators, which is the goal we are pursuing. Therefore, itwould be better, at least with the present state of knowledge, to go on quantifyingimpacts at the plant level without attempting to separate the contributions of thevarious products or services a given plant or rm is delivering.

Environmental impact assessment (EIA, e.g. Devuyst, 1993) tends to generalise theLCA concept and methodology to study the impact of a proposed development projectwhich may or may not include industrial components. It is to be viewed as an instrumentfor a preventive environmental policy. Like LCA, EIA is not, as such, well adapted tocompare a large number of (existing or possible) process units or rms. Now we takea closer look at other proposed methodologies that better t our initial purpose, asdescribed in the introduction.

3.2. - —

A few company-specic approaches toward dening environmental performance in-dicators have been listed in the introductory section. James (1994) and Azzone and




Manzini (1994) gave good syntheses of existing approaches. As mentioned above, theseare seldom if at all published in the scientic literature; additionally, they are very likelyto be specic to the company’s management objectives and would probably notlend themselves to any inter-rm or inter-industry comparison, due to a lack of standardisation. Nevertheless, we mention here one such initiative which is amongthose that could be most easily (at least in theory) generalized to any kind of company.

BSO/Origin is a Dutch software rm which developed and published, for the rsttime in 1991, comprehensive environmental accounting results in the framework of theannual account balance sheet (BSO/Origin, 1992; Huizing and Dekker, 1992; Gray,1993). Roughly speaking, the principle is as follows. We rst dene VA as being the‘‘gross’’ annual value added in the usual sense. Then let V L designate what we shallcall the value ‘‘lost’’, consisting of the annual sum of values of the damages caused tothe environment by the rm’s activities minus the annual sum of pollution abatementinvestments. Since BSO/Origin is essentially a service rm, there is not much wasteproduction or environmental damage in the usual sense; these consist mainly of pollution

caused and energy consumed by individual cars used by the rm’s consultants to visittheir clients, wastewater, electricity, heating, paper consumption etc. Then we denethe ‘‘net’’ value added as:

NVA= VA − VL (3)

which can produce a ( nancial ) environment performance indicator in the followingway:

= NVA/VA (4)

It should be noticed that, as such, the indicator is not necessarily standardised as inEquation (1) or (2); generally speaking, it could be negative (if the damages caused tothe environment are so important that their monetary value exceeds the rm’s valueadded) or larger than 1 (if the damages are insignicant and the rm devoted manyexpenses toward pollution abatement). In the latter case, one should of course payattention to the fact that the abatement expenses should be really e ff ective, i.e. thatthey actually reduce pollution.

3.3.

Research conducted outside the business community, i.e. at universities or independentresearch centers, is more likely to pr ovide us with examples of indicators that correspondmore closely to the goal we have specied, i.e. a general approach allowing forperformance comparison and monitoring. However, such approaches seem to be rather

rare in the literature. We give a short account of the existing (all quite recent) studieswhich were found to be meaningful for our purpose.

Cormier et al. (1993) investigated the impact of pollution performance of rms ontheir market valuation. For this purpose, they developed an empirical model in whicha so-called ‘‘pollution performance index’’ takes part as an explanatory variable and isdened as follows:

Pollution performance index of a firm = plants

A P / plants

PS (5)



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in which A P= actual pollution levels recorded by the Environment Ministries for agiven plant and PS = pollution standard set by the Environment Ministries for a givenplant. The quantity supposed to reect the pollution level and standard depends onthe industry. For pulp and paper, the quantity is BOD (biochemical oxygen demand),while for chemicals, oil reneries, steel, metals and mines industries, the selected quantityis TSS (concentration of suspended solids). This is a rst example of a very roughrelative, standardised (in the sense that it meets the requirements of Equation 1 or 2)performance indicator.

Martin et al. (1991) and Beede et al. (1993) dened an index that reects the toxicintensity of pollutants selected according to the TRI (Toxics Release Inventory) scheme(EPA, 1989, 1992). For a given plant or rm,

Pollutant risk = pollutant × toxicity weight (6)

Then they computed the pollutant intensity index of a given plant or company (or even

a whole industrial sector) by dividing by the total manufacturing activity, which yieldsan example of a normalised indicator:

Pollutant intensity index = Total pollutant risk/Total manufacturing activity (7)

The quantity supposed to reect the manufacturing activity was taken as the shipmentvalue. Martin et al. (1991) and Beede et al. (1993) used this index t o assess the var iationin the generation and management of industrial waste (which appeared quite signicant,especially across rms of a given industrial sector!) or to rank the U .S. industrial sectorsaccording to their pollutant intensities.

Fo r t he same kind of purpose as Cormier et al. (i.e. studying the impact of pollutionperformance on economic and market performance), Jaggi and Freedman (1992)used a somewhat more elaborate indicator. The impact is investigated here through

computation of the association (in terms of Pearson correlation coe fficients) betweenpollution performance and economic and market performances. The pollution indicatoris essentially a combination of three components, BOD, TSS and pH, scaled by ton of production, thus providing us with an example of an aggregate, normalised indicator.Suppose we have dat a on t he t ot al annual BOD and TSS quant it ies(concentration × volume of water) released by each rm i in the sample. We rstnormalise these data in the following way:

IBOD i= BOD i /tons produced (8)

ITSS i= TSS i /tons produced (9)

The contribution of pH is modelled through the assumption that a value outside therange of 6–8·5 is considered pollution, which yields a pH indicator designated by IpH i.The latter does not need to be normalised since it does not represent an accumulativequantity as do BOD and TSS. All three indicators are then standardised by dividingeach of them by their largest observed value in the sample and multiplying by 100;summing the three results obtained yields the overall pollution indicator for rm i:

(Overall po llution index) i= 100× IBOD i

maxi

{ IBOD i}+ ITSS i

maxi

{ ITSS i}+ IpH i

maxi

{ IpH i}(10)




which thus takes a value comprised between 0–300. The model has been applied to 13pulp and paper companies in the United States.

Showing about the same level of sophistication, the approach by Wehrmeyer (1993)attempted to take more factors into account, i.e. the output production of the company,the energy factors (electricity, gas, oil) and the e ffluent factors (BOD, COD—chemicaloxygen demand—and nitrates). The approach can be qualied as aggregate and relative,in the sense given above (section 2). Wehrmeyer considered that the environmentalperformance of a given company includes two components:

Environmental performance = 8 (general component) 2+ (local component) 2 (11)

The ‘‘general component’’ takes into account the ambient concentration of each of thesubstances(the factors listed above) and its legal limit. The ratio between both quantities,through use of a (unspecied) function f , is supposed to reect the likelihood that theratio may cause damage to the environment. Fo r the ‘‘local component’’, a ratio istaken between the actual discharge of each of the substances by the rm and the legallimit applicable to it. Then the overall environmental performance of that rm isobtained by summing t hese two components over all substances:

Company score =n

i= 1( f concentration i

limit i

2+ corporate discharge i

local limit i

2(12)

This equation was used to compare the environmental performance scores of ve pulpand paper rms, between themselves and over time, which correspond more closely to ourobjectives (section 1) than the previous approaches. One peculiar aspect of Wehrmeyer’sapproach with respect to others is that it takes explicit account of the output productionof the rm, and also of important production factors such as energy. However, thepublished paper does not indicate how these so-called ‘‘substances’’ are treated withrespect to the ‘‘true’’ polluting ‘‘substances’’. We shall return to that problem in section4.

3.4.

The approaches reviewed so far are both rare and dissimilar. They range from over-simplied indicators to more sophisticated ones, in which there is a trend to take

somewhat arbitrary viewpoints. As Wehrmeyer (1993) stated, ‘‘Science has not yet comeforward with a universally accepted and absolute measure of how to compare andevaluate di ff erent environmenta l impacts’’. In t his paper, we will not discuss the var iousattempts to attach a monetary weight to environmental impacts, which is both anextremely active research eld in many departments of economics around the world(see e.g. Pearce and Turner, 1990; Kopp and Smith, 1993; Pearce, 1994) and a highlycontroversial problem area, that would lead us far beyond the scope of this paper.Instead, in the next section, we will look for methods that do no t require the specicationof any a priori weight on the variables.



D. Tyteca 289

4. Deriving environmental performance measures in the scope of the theory of production efciency

4.1.In this section, we investigate t he possibilities of obtaining the environmental per-formance indicator (or indicators) from an approach that would be analogous to thatclassically used to quantify output, input or overall productive e fficiency. There existsa large amount of literature on this subject; reference books include Shephard (1970),F a re et al. (1985), F a re (1988), or Fried et al. (1993). Rather recently, there havebeen several attempts in the literature toward measuring productive e fficiency whileaccounting for pollution, essentially in the form of ‘‘undesirable outputs’’ (see referencesin the next two sections). However, in most of these studies, the emphasis is on overallproductive efficiency and no attempt is made toward the denition or quanticationof pollution or environmental efficiency. One of the main ideas used here starts from aperspective that considers pollutants as peculiar outputs , in the sense that they are the

outputs on which attention is actually centered (and which we shall seek to minimise).However, ‘‘traditional’’ outputs, the quantities of goods produced, are considered inthe same way as inputs, rather as making up part of the factors that contribute topollution generation. The same kind of ideas have been sometimes used in someapplications where the distribution of various factors into inputs, outputs or exogeneous(in)efficiency factors had to be decided upon (Deprins and Simar, 1989; Miliotis, 1992).

As usual in the production literature, we shall consider two broad classes of approaches, namely the parametric and the non-parametric approaches. In the formercase, we shall only try to modify existing published models in order to generate thekind of information we are looking for, while in the case of non-parametric approacheswe shall not only do that but also investigate alternative ways of deriving the kind of measures we are searching for. In all subsections that follow, we shall seek to denean aggregate, standardised performance indicator, that is, a quantity comprised between0 (bad performance) and 1 (good performance) (see section 2.2).

4.2.

One of the earliest studies toward incorporat ing environmental preoccupation intoproduction efficiency was that by Pittman (1983) who generalised previous work byCaves et al. (1982a,b) for coping with pollution, taken as an undesirable output. Thisapproach resulted in a so-called multilateral productivity indicator which includedmeasures of undesirable as well as desirable outputs. For the approach to be feasible,the former outputs have to be valued by their shadow prices, whose values may turnout to be rather arbitrary because, as stated by Pittman (1983), ‘‘they are not estimatedat the plant level’’ but rather come from estimates at the state level, and may therefore

not reect the actual conditions encountered.More recently, Fa¨ re et al. (1993), followed by Hetema¨ki (1993), used a parametric

specication of a distance function as dened by Shephard (1970). In contrast with theprevious approach, the shadow prices of undesirable outputs are calculated here fromthe model. Fa re et al. obtained the required specication through the solution of alinear programming problem where the shadow prices of the ‘‘bads’’ (the undesirableoutputs) were imposed to be non-positive, while Hetema ¨ki used a stochastic distancefunction without requiring non-positive shadow prices and nally obtained positivevalues.




Both of these approaches could be modied in two ways in order to deriveenvironmental performance indicators. F irst, let NPI (‘‘net productivity index’’) des-ignate either of the quantities dened above for reecting the overall productivitymeasure, i.e. Pittman’s multilateral productivity index, or the inverse of the distancefunction:

NPI = NPI ( y,w x) (13)

in which y, w and x designate vectors of desirable outputs, undesirable outputs andinputs, respectively. The higher the productivity, the larger the value taken by NPI orthe lower the value of the distance function (which measures the distance from a givenobservation to the e fficiency frontier). If we consider a more classical way of measuringthe overall productivity, i.e. without taking account of the undesirable outputs, weobtain a quantity that we could call ‘‘gross productivity index’’ ( GPI ):

GPI = GPI ( y x) (14)

This quantity is likely to exhibit larger values than NPI , because it does not includeparameters (i.e. undesirable outputs) that tend to reduce productivity. This e ff ect,however, is likely to be small for ‘‘pollution-e ff ective’’ rms and higher for ‘‘pollution-negligent’’ rms. Therefore, we could simply dene an environmental performanceindicator as the ratio between both indicators:

= NPI/GPI (15)

The value of that indicator would be close to 1 in rms that are ‘‘pollution-e ff ective’’and signicantly less than 1 for ‘‘pollution-negligent’’ rms.

The approach adopted by Bra¨nnlund et al. (1994) for estimating the cost of environmental regulation starts from a somewhat similar, but dual, viewpoint sincethey maximise a short-term prot function in two versions, one accounting for theactual regulation on e ffluent quality and the other without any consideration of theregulation. The cost of regulation, which reects in some way its e fficiency, is thenmeasured by the ratio between the regulated and the unregulated prots.

As a second possibility of adapting the parametric models, we could implement theidea advocated above (section 4.1), i.e. view the production outputs on the same sideas the inputs that contribute to pollution and simply change the specication of theproductivity index or distance function accordingly. The resulting value would directlyyield a measure of the pollution e fficiency:

= NPI = NPI (w x, y) (16)

4.3. -

In the following, we review the few published non-parametric approaches to themeasurement of productive e fficiency that account for the presence of pollutants in theform of undesirable outputs. We try to work out these models in a sense that meetsour objective, i.e. the measurement of environmental performance. We also show thatthese approaches are equivalent to those that would be considered in the classicalframework of data envelopment analysis (DEA). We consider three alternative DEA



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Undesirable outpu t ( w )

D e s i r a b l e o u

t p u t

( v ) "Ideal" frontier

Actua l frontier

Figure 1. Data envelopment analysis (convex envelope in the case of varying returns to scale) and freedisposal hull (‘‘staircase’’) with one desirable output and one undesirable output (waste). Points inside the

frontier represent ine fficient observations (e fficiency <1).

models that diff er by their complexity level and the way in which they describeenvironmental performance. In an additional subsection we comment on the use of t heFDH (free disposal hull) which can be considered as an extension of the DEA.

4.3.1. Nonparametric undesirable output-orientated model

Let x n(n= 1, . . . , N ), vm(m= 1, . . . , M ), and w j( j= 1, . . . , J ) designate the inputs, thedesirable outputs and the undesirable outputs, respectively. The latter can be any simple(i.e. BOD, TSS, CO 2, NOx, . . .) or aggregate indicators such as those considered inTable 1. Next, suppose we have observations k (K = 1, . . . ,K ) on all of these variables

(k is most likely to designate a rm or a plant but could indicate a time period as well).The non-parametric approach adopted by F a ¨re et al. (1989) started from Farrell’s(1957) measure of technical e fficiency. They dened the reference t echnology set orproduction set as

P w( x)= {(v,w):vΖ Vz,w= W z, XzΖ x, zv RK + } (17)

where x , v and w are vectors of inputs, desirable outputs and undesirable outputs,respectively, z is a vector of intensity variables, and X , V and W arematricesincorporatingobservations on inputs, desirable outputs and undesirable outputs, respectively. Theconstraints state that the desirable outputs are strongly disposable (i.e. their quantitiescan be reduced at no cost), while the undesirable outputs are only weakly disposable(i.e. their production can be reduced only at the expense of a reduction in the otheroutputs or an increase in the use of inputs), which is expressed by the equality sign of the second set of constraints. An ‘‘enhanced hyperbolic output e fficiency measure’’ canbe dened for producer 0 as (Fa¨re et al ., 1989)

H 0(v0,w0, x0)= max{ :( v0, − 1w0)v P w( x0)} (18)

Solving that problem yields the value of producer 0’s e fficiency with respect to thefrontier of the production set, which is determined by those producers who are themost efficient among the available observations. Figure 1 illustrates the simplied case




where we consider one desirable output and one undesirable output. The value of inEquation (18) indicates the extent to which it is possible to move from the consideredpoint to the frontier, upwards and to the left, with values equal to or larger than 1corresponding to e fficient or inefficient points, respectively. For ine fficient points,Equation (18) would show that improvements are possible both in terms of desirable(v) and undesirable ( w) outputs. To compute the e fficiency score of all producers in thedata set, we have to solve Equation (18) for each producer in turn. This approachresults in nonlinear programming problems, which Fa ¨re et al. (1989) solved throughconversion into linear programming approximations. It is possible, however, to de-compose the problem stated in Equations (17) and (18) according to the desirable andundesirable components of the outputs, as shown by Fa ¨ re (1992), which results inlinear programming formulations. A similar idea was exploited by Klein and Yaisawarng(1993), Nestor and Pasurka (1993) and Ball et al. (1994).

Because we are interested in the measurement of environmental e fficiency, andbecause we regard the desirable outputs in the same way as the inputs, we could take

the same standpoint as these authors and transform the problem stated in Equations(17) and (18) in the following manner.

P w( x,v)= {w:vΖ Vz,w= W z, XzΖ x, zv RK + } (19)

H 0(v0,w0, x0)= max{ : − 1w0v P w( x0,v0)} (20)

which is equivalent to

H 0(v0,w0, x0)= min{ : w0v P w( x0,v0)} (21)

In this case, the efficiency improvement is on the bad outputs only; this corresponds

to a move towards the left in Figure 1. The problem stated in Equations (19) and (21)results in the following linear programming problem:

min ( Ζ 1) s. t. v0Ζ Vz0 (22)

w0= W z0

Xz0Ζ x0

This problem can be stated more explicitly as

min ( Ζ 1) s. t.K

k = 1

zk vk m[ v0

m, m= 1, . . . , M (23)

K

k = 1 zk wk

j = w0 j , j= 1, . . . , J

K

k = 1 zk xk

nΖ x0n, n= 1. . . . , N

zk [ 0, k = 1, . . . ,K



D. Tyteca 293

with M , N and J being the numbers of desirable outputs, inputs and undesirableoutputs, respectively. The dual of Equation (23) can be written as

max g0= M

m= 1mv0

m− N

n= 1n x0

n (24)

s.t. M

m= 1 mvk m−

N

n= 1n xk

n− J

j= 1 jwk

j Ζ 0

J

j= 1 jw0

j = 1

m , n[ 0, j free

This problem in turn is equivalent to the following one:

min h0=

J

j= 1c jw0

j

M

m= 1a mv0

m− N

n= 1bn x0

n

(25)

s.t.

J

j= 1c jwk

j

M

m= 1a mvk

m− N

n= 1bn xk

n

[ 1 k = 1, . . . ,K

a m,bn[ 0, c j free

The variables of Equations (24) and (25) are linked together through the followingrelationships:

t = J

j= 1c jw0

j− 1 (26)

n= ta n, m= tb m, j= tc j (27)

Equation (25) corresponds to the classical formulation of DEA (data envelopmentanalysis) as introduced by Charnes et al. (1978) which is closely connected to previous

approaches to productive e fficiency, such as those by Fa rrell (1957) and Shephard(1970). We refer to Seiford and Thrall (1990) or to Lovell (1993) for a review of variouspossible DEA formulations, including primal and dual ones. With respect to the classicalDEA formulation, Equation (25) shows one important di ff erence, in the sense thatinstead of minimising a ratio of inputs to outputs (or maximising a ratio of outputs toinputs) we minimise a ratio of undesirable outputs to a weighted sum of desirableoutputs and inputs. In that way, we exploit the idea stated above, i.e. we view theundesirable outputs as peculiar outputs, which we try to minimise with respect to theother production factors (inputs and desirable outputs). The minus sign appearing at




the denominators of E quation (25) has the meaning that these quantities provide somemeasurement of the net production (weighted outputs − weighted inputs) of a rm.

Seiford and Thrall (1990) reviewed the various advantages of non-parametricapproaches (including DEA) over parametric approaches. Among these advantages arethe robustness of the linear programming methods used to solve DEA problems andthe new insights and additional information it provides with respect to conventionaleconometric methods. Charnes et al. (1985) also noted that a variable that is neitheran economic resource nor a product, but is an attribute of the environment or of theproduction process, can be included easily in a DEA-based production model. To thebest of our knowledge, only one DEA model—in the strict sense as dened above, i.e.starting from ratios of inputsto outputs—has been applied until now to the measurementof environmental performance (Haynes et al ., 1993). H owever, these authors considereda special case of the general situation in which three categories of factors are incorporat ed(inputs, desirable outputs, undesirable outputs), since they regarded the pollutiongenerated a s inputs (and, a ctually, the only inputs) of their production processes.

The variable in Equation (23), or the objective function g0 in Equation (24) or1/ h0 in Equation (25), provides us with a standardised indicator of environmentalperformance. As such, t he problems assume constant returns to scale (i.e. in pollutionterms, for ‘‘efficient’’ rms—those showing a value of or g0 equal to 1—a givenincrease in outputs and/or inputs would result in a proportional increase in undesirableoutputs: Fa re, 1992), but they easily accommodate varying returns to scale by theadjunction of one additional constraint (Klein and Yaisawarng, 1993; Lovell, 1993).This is meaningful since varying returns to scale are likely to be observed in actualsituations (see, e.g. Beede et al ., 1993). The constraint to be added to Equation (23)would be the following:

K

k = 1 zk = 1 (28)

However, what is perhaps more important is that, with the approach dealt with in thissection, all undesirable outputs have to vary according to the same proportion becauseof the uniqueness of the factor aff ecting all undesirable output components (seeequality constraints in Equation 23). This can be a serious limitation as indicated byKopp (1981), e.g. for analogous situations.

4.3.2. Variant: input—undesirable output-oriented model

As a possible alternative to the previous model, as stated in Equation (25), we mightwant to solve a problem in which the quantity to be minimised is the ratio of a

weighted sum of inputs and undesirable outputs over the desirable outputs. From anenvironmental performance viewpoint, this means that, while most privately ownedrms are likely to operate near points where output productivity (ratio of inputs todesirable outputs) is optimal, there can be some slack as regards the environmentalperformance (due to lacks of incentives—see section 5.2 for further discussion) thatwill give the environmentally most e fficient rms the smallest possible ratio (i.e. 1) andwill prevent less efficient rms with higher levels of pollution reaching that frontier.Another possible interpretation of that formulation would be that at least part of theinputs can be viewed as valuable resources (e.g. fuels, raw materials) whose uptake can



D. Tyteca 295

exert a threat on the environment (see Table 1). Equation (25) can be easily convertedto reect that approach:

min h0=

N

n= 1bn x0

n+ J

j= 1c jw0

j

M

m= 1a mv0

m

(29)

s.t.

N

n= 1bn xk

n+ J

j= 1c jwk

j

M

m= 1a mvk

m

[ 1 k = 1, . . . , K

a m,bn[ 0, c j free

Transformations similar to those e ff ected in Equations (23) and (24) to obtain linearprogramming problems readily apply here (see Tyteca, 1995 for details).

4.3.3. Normalised undesirable output approach

This is a simplied version of the DEA formulation in which the desirable outputs andthe inputs ar e no longer considered explicitly, but are rather implicitly a ccounted for.Here the quantities w j( j= 1, . . . , J ) will designate the same waste components aspreviously, divided by a quantity measuring the rm’s activity (e.g. total annual tonsproduced, . . .). This approach is inspired by a analogous study on railroad productivity(Adolphson et al ., 1989) while the variables obtained in this way are similar to thoseof the Jaggi and Freedman (1992) study which was reviewed above (Equations 8, 9).

The problem can be stated as

min h0= J

j= 1c jw0

j (30)

s.t. J

j= 1c jwk

j [ 1 k = 1, . . . , K

c j free

This formulation has the advantage over the previous ones that it is linear from theoutset (see details in Tyteca, 1995).

4.3.4. FDH ( free disposal hull) approach

This approach, rst introduced by Deprins et al. (1984), was recently extended andplaced in the context of data envelopment analysis (Thiry and Tulkens, 1992; Tulkens,1993). With respect to DEA, the fundamental change is that there is no assumptionrequired about the convexity of the production set: the observations are comparedbetween themselves and considered e fficient as soon as there exists no dominatingobservation (one that would require less of inputs to produce more of outputs). This




results in the staircase-like frontier depicted in Figure 1. It can be seen that this methodwill produce more efficient points (that is, more points are located on the frontier).Some of the advantages over DEA methods are that (1) no reference is made to anabstract frontier, which would represent linear, non -existent combinations of actualobservations, (2) FDH is less sensitive to outliers than DEA, and (3) the method iswell adapted to situations in which reference has to be made to ‘‘good practice’’(Tulkens, 1993). The latter advantage makes FDH an attractive method for studyingthe efficiency of public organisms and rms. Additionally, its mathematical (linearprogramming) formulation is quite similar to that of DEA methods, the only di ff erencebeing the inclusion of constraints incorporating integer variables (the problem shouldincorporate Equation 28, with zk values restricted to 0 and 1).

There is, however, one specic reason that would lead us to prefer the DEA methodsover FDH, namely that for environmental performance measurement we do not worryabout the fact that we compare existing points to an abstract, ‘‘articial’’ frontier sincewe would instead refer to a frontier that is eventually even located outside the convex

envelope of the existing points (which is depicted in Figure 1 as the ‘‘ideal’’ frontier).The latter would correspond to a reference situation that could be taken either as thebest available technology or as a quasi zero-waste state. This will be further discussedin the next section.

5. Discussion

5.1.

5.1.1. Best practice frontier, why and how to reach it

At this point it would seem appropriate to look back to the initial purpose we had inmind in the introduction (end of section 1). The DEA approach can provide us withan object we were looking for, namely, a unique indicator that associates to each DMU(decision making unit, i.e. a plant, a rm, . . .) a value that reects its good ( = 1) orbad (<1) environmental performance without requiring any a priori , arbitrary as-sumption about how to weight the various impacts. In this way, we compare eachDMU with a frontier based on the observations, which is therefore a best practicefrontier. But such a methodology raises three kinds of questions. F irst, the problem of outliers is well known in da ta envelopment analysis (e.g. F ried et al ., 1993); it is perfectlypossible for a DMU to be declared e fficient even though it is very bad in one or a fewrespects, provided it is highly e fficient on others. This outcome can be overcome in twoways: by simply ignoring these outlying observations, which is unsatisfactory in ouropinion, or by replacing the ‘‘best practice’’ frontier by another one located outside therst (this is what we will investigate in section 5.1.2). Second, why would it be benecialfor a DM U to join the frontier (whatever it is)? The answer is clear whenever the

frontier represents a goal to be achieved (section 5.1.2) but is less evident when we usethe best practice frontier. More will be said about this in section 5.2; let us mentionhere that we can use the best practice frontier not for the value of the indicator in itself but rather as a means to rank the various DMUs between themselves. Third, how dowe reach the frontier for a DMU computed as ine fficient? I think this is outside thescope of this paper, since the aim was to look for a methodology to quantify theenvironmental performance of rms. Let us simply state that environmental regulationhas an important part to play here, although in some instances this would be likely tocreate some distortions in environmental performance (section 5.2.1). On the other



D. Tyteca 297

hand, simply disclosing the gures of environmental performance or ranks may in itself be a sufficient incentive for a rm to make adequate e ff orts to improve its efficiencyand, hence, its brand image.

5.1.2. Toward a denition of the ‘‘ideal’’ frontier

As the discussion of sections 4.3.4 and 5.1.1 has suggested, it would be meaningful tohave a denitive reference at our disposal to assess environmental performance. Thisis mainly because a best-practice frontier is a relative concept, likely to be unceasinglyrevised as regulation strengthens, technological advances are made and new datareecting these circumstances become available. Here we discuss the possible ways of dening and calculating an ‘‘ideal’’ frontier. In our opinion, there should be two suchapproaches. The rst is the ‘‘technological denition’’ and would be based both on aninventory of best existing, commercially available technologies for the processes underconsideration and experts’ opinions on what will become available or possible in thenext few years. Such an approach may still be too pragmatic and not reect the true,ultimate possibilities of improving the technology. The second approach could betermed the ‘‘thermodynamic denition’’ and is based on the fundamental p rinciplesgoverning the processes considered. It incorporates basic thermodynamics and mass-balance relationships in much the same way as the ‘‘industrial metabolism’’ approach(Ayres, 1989; Ayres et al ., 1989; Graedel et al ., 1993) and therefore also incorporatesmany aspects of the life cycle analyses (section 3.1).

Reaching the denition of the ideal frontier in one or the other way is, of course,a huge task and would imply the same kind of theoretical and practical di fficulties aslife cycle analyses (section 3.1), among which would be the collection and processingof an enormous amount of data. At this point, it should be emphasised that dataavailability is really the bottleneck as it is in all similar studies on productive e fficiency

that rely heavily on empirical or fundamental work. However, even partial informationwould be useful and it could be incorporated easily into the DEA formalism (Seifordand Thrall, 1990). In this sense, a third, less ambitious, more pragmatic, yet usefuldenition of the ideal frontier might correspond to quality targets (and thereby tolegislation), specied for the undesirable outputs and accounting for the local char-acteristics of the receiving bodies (atmosphere, rivers, etc.). Such a frontier might becalled a ‘‘target’’ frontier.

5.1.3. A taxonomy of frontiers and technologies

Frontiers obtained from applying DEA or FDH models, or one of the models of section 4.3, reect best-practice among the observed set of rms or plants. This in turn

is inuenced by the state of technology at the time of data gathering. Wheeler et al.(1993) identied three types of technologies that have succeeded each other during thelast few decades: (1) ‘‘obsolete’’ technologies, which were not as e ff ective as they aretoday in terms of desirable output production but presented moderate levels of pollution;(2) ‘‘mainstream’’ technologies, which improved signicantly over the latt er in terms o f productive e fficiency but a lso implied a signicant increase of pollution; and (3) ‘‘cleaner’’or contemporary technologies, in which the emphasis was on reducing the environmentalimpact while maintaining at least the same levels of output production. Each of thesetechnologies is likely to correspond to a di ff erent frontier in the space of inputs,




Undesirable output ( w )

D e s i r a

b l e o u

t p u

t ( v )

IdealBATNEEC

CleanerMainstream

Obsolete

Figure 2. Temporal succession of frontiers and technologies, from ‘‘obsolete’’ to ‘‘ideal’’, in the case of variable returns to scale. The shape of frontiers is not meant to correspond to any actual situation and is

given only for illustrative purposes.

desirable outputs and undesirable outputs. For example, F igure 2 gives a possibleoutline of frontiers in a two dimensional representation of desirable and undesirableoutputs.

As regards future prospects, we would also consider at least one other type of frontier, t he ‘‘ideal’’ front ier discussed above, corresponding to the ultimate possibilitiesof pollution reduction. However, in a pragmatic way, we may ask whether such afrontier would ever correspond to reality. That is, would the benets in terms of improved environmental quality resulting from the adoption of ‘‘ideal’’ technologiesalways be higher (marginally speaking) than the costs of implementing such technologies?The answer is probably no, but this is highly controversial and depends on the techniqueswe have at our disposal for assessing the benets of a cleaner environment. Wewould therefore like to consider some intermediate level of technology and frontier,corresponding fairly well to the notion of BATNEEC (Best Available Technology NotEntailing Excessive Cost, e.g. Cairncross, 1993; Smith, 1993; Welford and Gouldson,1993). This, however, is quite a relative concept because, as time elapses and astechnological advances are made, it is very likely that the cost of ‘‘ideal’’ technologieswould decrease and that the benets from improved environmental quality wouldincrease which would unceasingly move the BATNE EC frontier toward the ‘‘ideal’’one.

5.1.4. Temporal aspects

The discussion above was intended to draw attention to the relative character of ‘‘environmental performance’’ and to the fact that frontiers and technologies are likelyto evolve over time. Temporal aspects have been seldom dealt with in the relevantliterature. Nestor and Pasurka (1993) reviewed some of the possible approaches thatcan be taken in that respect. If we have data from several successive years at ourdisposal we could construct a single frontier enveloping all dat a, as did Ball et al.(1994). The technological progress (or regress) can then be measured for each decision



D. Tyteca 299

making unit (i.e. each rm or each plant in the sample) by comparing the successiveperformance values for that unit. Another approach to assess global technologicalprogress, as used by Nestor and Pasurka (1993), is to construct the frontier fromobservations of a given period t taken as a reference, and to compare posteriorobservations to that frontier; in this way one can separate the e ff ects of improvementsin efficiency from those of technological change. The approach followed by T hiry andTulkens (1992) and Tulkens (1993), in a FD H framework, is based on the comparisonof the period t + 1 frontier with that of period t ; the volume of space between bothgives a measure of annual technological progress. A fourth possibility is to use Malmquistindices for measuring productivity growth (Fa ¨re, 1992; Chambers et al ., 1994; F a re et al ., 1994a).

5.2.

Regardless of the tools used to measure performance, several studies suggest that thereactually exist highly signicant di ff erences among decision making units as regardsenvironmental e fficiency. This is observed not only among industrial sectors (Nestorand Pasurka, 1993), which is not very surprising since di ff erent types of industries usequite diff erent levels of inputs and technologies, but also among rms in the sameindustry, even when concentrating on given types of wastes or receiving bodies (air,water, land). It even appears that variation within (sub-) sectors is considerably largerthan variation across sectors (Beede et al ., 1993)! In this section we consider, in aspeculative way, the factors that could explain these variations, and especially theenvironmental ine fficiencies, a s detected by low values given by the environmentalperformance indicators. If correctly interpreted, these factors would in turn determineconditions for improvement. We can imagine three broad classes of factors.

5.2.1. The economic context

One could, of course, advocate the present bad economic context and other externalfactors such as energy prices, international trade competition or various categories o f taxes, to explain why environmental preoccupations do not come as the main prioritiesand why eff orts are primarily devoted to restoring economic growth through com-petitiveness of rms. Though it can be argued that competitiveness and environmentalperformance are not necessarily incompatible (e.g. Porter, 1990, 1991), that point issomewhat outside the scope of this paper and we shall not discuss it any longer(excellent discussions on that topic can be found in Oates et al ., 1993 or in Jaff ee et

al ., 1994). What could also be advocated are the di ff erences between physical (i.e.environmental) and economic aspects of productive e fficiency; depending upon the levelof factor prices, ‘‘relative ine fficient inputs in a physical sense can be relatively e fficientin a cost sense’’ (Kopp, 1981). H owever, what we will concentrate on a little moreherein is the apparent paradox that environmental regulations can, at least partly,explain and even provoke environmental ine fficiencies such as dened in this paper.

Let us rst consider one of the most obvious cases, i.e. tradable pollution permits.Suppose we have two rms A and B in a given industry, producing approximately thesame kinds and amounts of outputs but with di ff erent technologies. As illustrated in




Undesirable output

D e s i r a b l e o u

t p u

t AB

Figure 3. Example of two rms A and B in a given industry, producing the same kinds and amounts of desirable outputs, with technologies d i ff ering by their environmental e fficiency (data envelopment analysis).

Pollution redu ction

M a r g i n

a l a b a t e m e n

t c o s t

( M C ) MC A

t a x B

A

MC B

Figure 4. Example of two rms A and B in a given industry, producing the same kinds and amounts of desirable ou tputs, with technologies di ff ering by their marginal abatement costs.

Figure 3, it is obvious that if a rm A, possessing an environmentally high-leveltechnology, sells its permits to rm B, the latter is likely to be much less pollution-efficient than the former, and this in perfect conformity with the regulations. Second,

if we consider taxes on polluting e ffluents or emissions, we could have the situationdepicted in Figure 4, with the same two rms A and B responding by equating theirmarginal abatement costs to the tax level. The result of this would be pretty much thesame situation as in Figure 3, and this again even though both rms operate inconformity with the legislation.

In cases where environmental regulation takes the form of e ffluent or emissionstandards, then most rms should lie in the neighbourhood of the ‘‘best-practice’’ ormore appropriately, ‘‘command-and-control’’ frontier. This will happen of course if thestandards have been properly elaborated. However, even in such situations, there could



D. Tyteca 301

be signicant departures due to either of the following two possible attitudes that wouldresult from strategic choice: (1) non-compliance, if the probability of being penalisedis very low, or (2) over-compliance, which is observed in actual situations and may beexplained by several factors such as an anticipation of stricter regulations, an attemptto modify the legislation in order to acquire a competitive advantage (barriers to entry)or the outgrowth of consumer consciousness (Smart, 1992; Arora and Cason, 1994;Arora and Gangopadhyay, 1994).

5.2.2. X-inefciency

This concept was introduced by Leibenstein (1966, 1978) to designate any ine fficiency,due to non-maximising behaviour or to some slack in the managerial activity of a rm,that cannot be dealt with in the scope of neoclassical economic theory. The origins of X-inefficiency can be diverse (Frantz, 1990). For our purpose, independent of thecircumstances originating from the economic context, we could advocate as possible

explanations for environmental ine fficiencies the fact that the production function isnot completely specied or known (i.e. that given amounts of inputs producing givenamounts of outputs will generally not result in pr edetermined quantities of wastesreleased to the environment), or that there exists no particular internal motivationtoward environmental e fficiency which could take the form of internalisation of en-vironmental costs, for example. While Darwinian selection is likely to eliminate any(private) rm that would not be e fficient in terms of inputs used to attain some levelof desirable output production, the same cannot be said about environmental factorsbecause of a lack of internal mot ivation and an absence of competitive pressure applyingto environmental performance.

5.2.3. Technological aspects

Up to now we have considered the rm or the plant as an integral black box on whichexternal (section 5.2.1) or internal (section 5.2.2) pressures can exert various kinds of eff ects. There are also, obviously, causes coming from inside the box that explaindiff erences in environmental performance. First, there can be di ff erences between theprocesses themselves, even though they are designed for producing the same outputsstarting from the same inputs, depending on the degree of internal recycling and theextent t o which the production of wastes has been reduced at source during t he designstage. Second, if we take a sample of rms or plants observed at a given period, t , theywill include various categories of age. Therefore, the processes compared a re in d i ff erentstages of their lifetime: older equipment is likely to be less e fficient t han newer equipment.Third, even if prevention and ‘‘end-of-pipe’’ technologies 1 may appear to yield t he sameresults as regards one given category of pollutants, they could be quite di ff erent with

respect to another category. There are obviously substitutions between certain classesof polluting substances that are more likely to inuence the performance of end-of-pipe technologies than those of prevention technologies. An example of this is thereduction of SO 2 emissions at electric utilities using gas scrubbers at the expense of

1 By end-of-pipe technology, we mean any technology that treat the wastes after they have been pro duced inthe production process; by contrast, prevention technologies, or clean(er) technologies, are implemented toreduce or even suppress the pollution at source through adequate design of the production process and/orchanges in the input materials (see e.g. F ischer and Schot, 1993 or Thayer, 1992).




increased solid wastes (South et al ., 1990). This again stresses the importance of accounting for all relevant aspects of pollution if we are to produce meaningfulperformance indicators. If prevention technologies are to be preferred over end-of-pipetechnologies from an environmental standpoint, this should be reected in the valuestaken by the environmental performance indicator.

5.3. ‘‘ ’’

In this section we discuss some of the important issues raised by the connectionsbetween ‘‘environmental performance’’, as quantied by any of the models of previoussections, and the actual e ff ects of industrial activities on the human health and theenvironment.

To begin with, because we have aggregated all impacts into one single gure, itshould be realised that identical values of the environmental performance indicator

computed for two di ff erent rms or plants would mean that they are ‘‘equally e fficient’’,whereas such a value can be obtained by various kinds of combinations betweenundesirable outputs, and in some of the aforementioned models, desirable outputs aswell as inputs. To take a very simple example, two rms can be declared equivalent,or equally efficient, when one of them releases 100 mg/l of BOD into the water and10 Mt/yr of SO 2 into the air while the other discharges 500 mg/l of BOD and 1 Mt/yrof SO 2. Clearly, there can be many reasons why these two rms might not be consideredquite equivalent. Similarly, if we take an approach based on the toxics release inventory(EPA, 1989, 1992) some rms would develop signicant e ff orts to reduce those substancesthat are easiest to eliminate and at the same time not too toxic (which may eventuallybe accompanied by slight increases in other, more toxic substances). This could resultin a dramatic improvement of their ‘‘environmental performance’’ but no progress atall from a public health or environmental quality standpoint.

A solution to these dilemmas can be found in the proper weighting of the factors.That is, we would have to weight the aforementioned variables (BOD, TSS, SO 2 orother toxics) by some factor reecting their relative toxic impact on human health orthe environment (e.g. Klein et al ., 1988; Ko nemann and Visser, 1988; Timmer et al .,1988; O’Bryan and Ross, 1988; Caldwell and Ortiz, 1989) before calculating the ratiosused to quantify overall environmental performance. Such an approach has alreadybeen taken by researchers dealing with the toxics release inventory, as mentioned insection 3.3.2 (Martin et al ., 1991; Beede et al ., 1993). However, it is not evident thatderiving individual weights would properly quantify the combined e ff ect of all possiblemixtures of toxics in all possible site-specic contexts, and there seems to be a lack of consensus in this regard (see the discussion at the life cycle analysis level in section3.1). Moreover, it is even less evident that the combined e ff ects of toxics would result

in linear interactions, which is a basic assumption of all models discussed so far. Forexample, there are such complex interactions between reactive organic gases (ROG)and nitrogen oxides (NO x) that, in some instances, reducing the concentration of NO x

will actually result in an increase in ozone concentration and thus of the overallpollution level; furthermore, these e ff ects are likely to vary as a function of thegeographical site considered (Milford et al ., 1989).

As an alternative to the toxicity weights mentioned above, we could use costcoefficients that reect the damage caused by the e ffluents to the environment whenthese are available. This would be the case for emissions of SO 2, CO 2, NOx and total



D. Tyteca 303

suspended particles at electric utilities (Naill and Van den Berg, 1992; Cifuentes andLave, 1993). However, the gures given in these studies are valid only at an aggregatestate or nation level and cannot be taken as representing the impacts at any site-speciclevel, which are likely to vary in considerable proportions due to such factors astopography, meterology and population distribution (L. Cifuentes, pers. comm.).

A last point to be raised is about the number of waste components that should beincorporated into the analysis of environmental e fficiency. There is a crucial trade-o ff

in this regard; while it is essential to retain as many aspects as possible in order tocover the global impact of industrial activities on the environment (e.g. Sullivan andEhrenfeld, 1993), increasing the number of variables in data envelopment analysis willresult in an increase of the number of e fficient decision making units, up to the pointwhere all rms or plants are e fficient (when their number equals the number of variables),making the interpretation of results meaningless. Selection of the variables that shouldtake part in the analysis can incorporate at least the following two steps: (1) removevariables whose toxicity or environmental impact is minor compared to other variables;

(2) if some variables are signicantly correlated, include only a part of them or aggregatethem. Appropr iate statistical too ls, such as factor analysis, principal component analysisor discriminant analysis, can be useful in the selection of meaningful variables. However,in view of the paucity of data usually available this is not likely to cause seriouslimitation to the productive e fficiency approach.

6. Conclusions

Two important conclusions can be drawn at this stage.1. A few papers listed in the previous sections, including research by the author

and others (Fa re et al ., 1994b; Tyteca, 1995) have demonstrated the feasibility of productive efficiency approaches similar to those dealt with in this survey. Now, whatare the potential uses and who are the potential users of environmental performanceindicators such as reviewed and developed in this paper? From the outset, we haveadopted an aggregate concept, aiming at comparing various decision making units(DMUs) between themselves from the point of view of their global, aggregate en-vironmental performance. Therefore, we exclude the use of models such as thosedescribed herein at the process or product level, as would be done in life cycle analyses.The models would also be useless for a company that simply wishes to report on itsenvironmental impacts, e.g. in the scope of an environmental audit. Various companieshave already developed measurement tools that are well adapted to their specicpurposes, as we briey reviewed in the introduction.

In contrast, our analysis makes sense in any situation where one can identify asignicant set of DMU s and where improvements over time can be observed andmonitored. There are three di ff erent levels at which this would be the case. As a rst

example, a large company that possesses many similar plants installed in variouslocations might nd it useful to nd out how its plants compare to each other withrespect to environmental performance. The information obtained can be exploited toidentify which DMUs are performing poorly, what are the causes of the weaknessesand what a ctions can be taken in the scope of t he company’s objectives with regard toenvironmental management and to regulation. In that case, the users would be themanagers of the company. At a second level, we can consider various plants or rmsfrom diff erent companies in a given industrial (sub-)sector which perform analogouskinds of production. In this case the users would be public decision-makers who could




use the results to identify which rms and plants are the leaders and the laggers andaccordingly take regulatory measures to improve the situation. Ideally speaking, forsuch an analysis to be comprehensive, it should incorporate the actual e ff ects on humanhealth and the environment as we discussed in section 5.3. The third, most aggregatelevel is the one in which the DMUs are the industrial sectors themselves; here againthe users of the models are the public decision-makers but, in order to performappropriate comparisons, the ‘‘desirable outputs’’ must be quantied on a comparablebasis, such as the total value added. N estor and Pasurka (1993) have shown thefeasibility of such an approach. However, at this level even more than the other two,all aspects of pollution must be taken into account since di ff erent sectors pollutediff erently and appropriate weighting or cost factors should ideally be used.

2. At a few places in the text we have addressed the crucial question of dataavailability. If the models reviewed and presented in this paper are to be used as anaid to decision-making to ward t he improvement of environmental quality, it is obviousthat we will require large amounts of data. It is commonplace to say that models as

such are only empty shells and need to be quantied by adequate amounts of information. Relevant data are publicly available only for public or regulated rms,such as electric uility companies. F or other kinds of companies we are typicallyconfronted by either non-availability of data, e.g. in developing countries, or con-dentiality problems. If we are to work toward the improvement of environmentalquality, then public decision-makers should realise that adequate data have to beeither collected or made available to the scientic community, not only to derivemeaningful and operationa l results, but also as a way to develop and perfect modellingtools that are well adapted to the reality we are facing. This can be an interactiveprocess since the researcher can indicate what kind of information he or she requires,while the specicity of certain data and situations would imply some adjustments inthe model formulation. There is another crucial aspect that should be mentionedhere, about the quality of data. Once again it would be commonplace to stress thatmodel reliability is largely conditional on data reliability.

An important part of this research was developed during a sabbatical visit to the Departmentof Economics, University of Umea˚ (Sweden), in October–December 1993 and May–June1994. I would like to gratefully thank all members of the department for comments anddiscussions, and more especially M ats Bergman, R unar Bra ¨nnlund, Lars Hultkrantz (whowas the rst to suggest me to exploit DEA) and Karl-Gustav Lo ¨ fgren. Subsequent versionsof the paper were prepared during my sabbatical visits (January–April 1994) to Resourcesfor the Future (RFF, Washington D.C.) and the Department of Engineering and PublicPolicy, Carnegie Mellon University (CMU, Pittsburgh, PA). They strongly beneted fromdiscussions with, among others, Seema Arora, Dallas Burtraw, Raymond Kopp, DavidSimpson and Michael Toman (RFF ), Luis Cifuentes, N oellette Conway-Schempf, H adiDowlatabadi, Chris Hendrickson, A´ rpad Ho rvat h, Lester Lave, F rancis McMichael, Gra ngerMorgan, Ted Rubin, Jhih-Shyang Shih, Stuart Siegel and Tse-Sung Wu (CMU), as well as

Carl Pasurka (U.S. Environmental Protection Agency), M ala Hettige (World Bank) andRolf F a re and Shawna Grosskopf (Department of Economics, Southern Illinois Universityat Carbondale). Additional thanks to Lars Hultkrantz (Umea ˚ ), Arp a d Horva th and Tse-Sung Wu (CMU) for re-reading the paper. Financial support from CIM (IntercollegiateCenter for Management Science, Brussels) is gratefully acknowledged.

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