The role of regulation in the improvement of

The role of regulation in the improvement of

business environmental performance: a case

study of US fossil fuel-fired electric utilities

D. Tyteca*, C. Gallez

Centre Enterprise - Environment

"IAG - School of Management, Universite Catholique de

Louvain, B-1348 Louvain-la-Neuve, Belgium

Email: tyteca@qant. ucl ac. be

SME and Information Systems Department, Facultes

Universitaires Catholiques de Mons, B- 7000 Mom, Belgium

Email: [email protected]

Abstract

In order to improve the public policy of environmental management, it is ofprime importance to assess the influence that regulation can have on businessenvironmental performance. In our research, performance is quantified throughuse of benchmarking indicators, that incorporate all relevant information on theenvironmental impacts of companies, as well as their use of inputs andproduction of outputs. The methodology is illustrated by the analysis of a smalldata set of 39 U.S. fossil fuel-fired electric utilities, for which data were availableover three different years (1987, 1991, 1994). Whereas no significant changesoccurred in environmental performance between 1987 and 1991, this is not thecase between 1991 and 1994. The main factor that allows to explain thesignificant improvement observed is the change in regulation (i.e., Clean AirAct amendments). Additionally, while the utilities generally show a high levelof efficiency in the use of fuels (mainly due to their cost), this is not (yet) thecase as regards the polluting behaviour. The main explanatory factor might bethe lack of internalisation of external costs, i.e. the lack of (regulatory) incentiveto be efficient in environmental terms.

1 Introduction

Due to international competition and the imperatives of commercialsuccess, most private business companies behave efficiently as regardsthe use of inputs and effectively with respect to the production of

Transactions on Ecology and the Environment vol 32, © 1998 WIT Press, www.witpress.com, ISSN 1743-3541

90 Environmental Engineering and Management

outputs. Those firms that are either inefficient in inputs and/orineffective in outputs would in most situations be eliminated by thelaws of free market. However, efficiency and/or effectiveness are not(yet) absolutely required when one considers factors that are notpurely economic. Such is the case, e.g., for environmental dimensions,due to the fact that regulatory and economic incentives, or ethicalpressures, are not yet powerful enough, although recent research tendsto indicate that market more and more values firms that show apositive behaviour with respect to the environmental 1.

Incentives for environmental efficiency can take various forms.From a neoclassical economic viewpoint, definite efficiency would beattained when environmental costs will be fully internalised. However,we are still far from that stage, mainly because the information aboutenvironmental costs is very fragmentary. Presently, incentives arebeing progressively implemented in various ways. Political decision-makers have at their disposal either environmental regulations, oreconomic instruments. The latter include taxes, subsidies, as well astradeable permits^. From the business standpoint, environmentalinternalisation is possible, at least in theory, through variousenvironmental assessment tools such as life cycle assessment, eco-auditand environmental accounting, within the scope of an environmentalmanagement system. The main problem is the current lack ofknowledge about the way in which such regulations, instruments andmanagerial techniques influence the behaviour of companies withrespect to the environment, although significant steps have been madein that direction^. This issue appears even more complex when onerealises that there are various additional factors, internal or external,that are likely to affect the environmental performance of firms 1AThe external factors mainly include the influence of stakeholders(among whom the public decision-makers who set up regulations),while internal factors are mainly concerned with the actions thecompany can take as regards, e.g., investments and technology.

In a previous paper^ we showed how indicators could be used toassess the influence of internal factors on business environmentalperformance. Based on a study of fossil fuel-fired electric utilities, thefactors that appeared most significant in this regard (and for whichdata were available) were identified, in decreasing order, as: theinvestments toward air pollution control in previous year of operation,the total investments for environmental protection in previous year,the initial year of operation, the fuel mix, the number of units, theoperation and maintenance expenses per kWh, and the expendituresfor environmental protection during the same year. In this paper ourgoal is to investigate the influence of external factors such asregulation and economic incentives, using the same environmentalperformance indicators and based on the same case study of U.S.


Environmental Engineering and Management 91

fossil fuel-fired electric utilities. In addition, in this situation, withrespect to the previous study, to properly describe the intervention ofregulation, we will have to exploit data collected at different periods oftime, to allow for the assessment of performance progress (or regress)induced by regulatory changes.

2 External influences on environmental

performance: the U.S. Clean Air Act

Various studies attempted to clarify the influence of stakeholders onbusiness environmental performance. Some inquiries analysed theperceived relative importance of stakeholders among a particulargroup of firms^6,18. Generally, their results showed a great pressurefrom legislation and weak influence from financial stakeholders. Inother words, presently, the regulatory pressure is the key factorexplaining business environmental behaviour. Companies react to thatlegislative constraint by establishing a strategy of actions to whichthey devote resources (money, time, man-power, natural resources, rawmaterials and machinery).

Our research will take the electricity production sector in the USAas a case study. Several environmental legislative texts regulate thesector. The main pollution of the fossil-fueled steam-electric plants isatmospheric. The first version of the Clean Air Act was introduced in1963. In 1970, the Congress passed some amendments. As requiredby the Act, the Environmental Protection Agency established uniformprimary national ambient air quality standard (NAAQS) for six majorpollutants: particulate matter, carbon monoxide, sulfur dioxide (SO]),nitrogen oxides (NO%), ozone, and lead. Each State then submitted aState Implementation Plan (SIP) that outlined how existing pollutionsources would be regulated so that the NAAQS would be achieved byJuly 1975. At that time, coal-fired power plants were the source ofmore than 50% of the nation's SO] emissions and as a result were oneof the first pollution sources to be regulated. Now, as then, the stateplans vary greatly in both the stringency of the regulations and themethod of regulation. Besides these NAAQS and the SIPs, new ormodified sources face unique national emission standards: the NewSource Performance Standards of 1971 (NSPS). The NSPS setspecific limits to SO] and NO% emissions of coal-fired utility boilersbuilt after August 17, 1971.

In 1977, the CAA was again amended. The compliance dates of theNAAQS were postponed and the standards were restricted. All regionsnot complying with the NAAQS were declared "non attainmentregions" as opposed to the other, "attainment regions". The nonattainment regions were subject to more stringent control. New



sources were allowed within their borders only if equipped with thebest technology insuring a minimum emission rate. A permit systemwas implemented for these new sources located in a non attainmentregion. For attainment regions, increments and ceilings were fixedunder the Prevention of Significant Deterioration of Air Quality title,in order to avoid any worsening of the air quality. Following theamendments of 1977, the EPA also implemented the Revised NewSource Performance Standards in 1979, setting slightly modifiedgoals as regards SC>2 emissions, and a minor revision of the categoriesconsidered in NO% standards.

The last amendment of the Clean Air Act occurred in 1990. Itsmain feature was the introduction of an innovative marketableemission allowance program for SO] taking place in two phases.Phase 1 began in 1995, with specific restrictions for the mostimportant 110 electric utilities. Phase 2, begining in 2000, will lead toa reduction of the total annually allowed emissions, and will alsoestablish restrictions on smaller coal or gas fueled production units.The objective concerning electricity generators is a 10-million-tonreduction in SO] emissions and a 2-million-ton reduction in NO%emissions with respect to 1980 levels.

3 Methods

In previous papers we reviewed several literature surveys aboutindicators of environmental performance and sustainable developmentat the firm Ievel4>l3»l4,l5 We need not reproduce them once morehere, and simply give a short explanation on the design and functionof the indicators we used in this research.

Indicators based on productive efficiency start from the principleof best observed behaviour. Initially designed to compare similarproduction units by their (technico-economic) efficiency inproducing outputs using appropriate levels of inputs, they wererecently extended to incorporate the efficiency in the release ofundesirable outputs (i.e., pollutants). Thus we have three broadcategories of factors: inputs, desirable outputs, and undesirableoutputs. The latter behave in some respect as inputs, in the sense thatone should minimize their quantity if one wishes to improveefficiency.

In previous research we used and tested several versions ofenvironmental performance indicators based on productiveefficiency. In this paper the focus is to relate performance withpotential explanatory factors; therefore, based on the experiencegained in previous research (especially Gallez & Tyteca ), we will useonly one indicator, termed "undesirable output - oriented"



(abbreviated as UO). The central idea is that, while accounting for allavailable information on inputs and (un-)desirable outputs, thequantification of efficiency will concentrate specifically on thepossible improvements in terns of undesirable outputs. Thus, theindicator will yield a value 1 for those producing units that performefficiently with respect to pollutants, in the present (observed) state oftechnology, while the indicator value will be less than 1 for those unitsperforming inefficiently, i.e., those for which improvements are stillpossible, compared to other existing, similar production units. A lastpoint worth mentioning is that in the scope of productive efficiency,the weight factors used to aggregate the impacts are self-defined bythe method, which is often considered an advantage but may entailsignificant objections as discussed, e.g., by Callens & Tytecat

Because we wish to compare the performance of production unitswhile accounting for the time period, a specific procedure had to beadopted as to the way in which the performance evolution over timeshould be described. Methods exist in the framework of productiveefficiency, that allow to quantify the changes in technical andeconomic productivity, based on Malmquist indexes^. In this paper weexploited the same method already used by Ball et aU and ourselves^;i.e., one unique production frontier is generated, incorporating alldata from several succesive years and for all producing units;although simplified, this method allows to identify whetherimprovements over time have taken place or not.

Additionally, we also made use of an alternative environmentalperformance indicator, where the weights are arbitrarily fixed. Thiswas inspired by Jaggi & Freedman^ and extensively used in previousresearch as mentioned in our aforementioned references. Theirphilosophy is quite comparable to productive efficiency methods, inthe sense that for a given set of analogous units devoted to a giventype of production, and characterised by a few variables reflectinginputs and (un-) desirable outputs, reference is made to the units thatperform best. The essential difference is that here, the variables arecompared one by one to their best possible value among the observedset, instead of being taken together; afterwards, the individual scoresare summed using an arbitrary weight of 1. Dividing the valueobtained by the number of variables will yield a standardised indicatorwhose value will be less than or equal to 1. Details about the methodand formulations can be found elsewhere^.

With respect to previous research, and because we wanted tocompare specific pollution efficiency with input (i.e., fuel) efficiency,we also used so-called "sub-indicators", strictly concentrating on onegiven aspect (i.e., pollution or fuels), without accounting for theinformation on the other variables, but with the same meaning asbefore (i.e., less than or equal to 1 means inefficient or efficient,



respectively). Because these (very simple) indicators were not usedbefore, we briefly describe here how they work. Assuming we haveone quantity, poll , designating the pollution, or undesirable output,the "pollution indicator" of production unit indexed k can be definedin a first step as

I(poll) = (poll) J (output produced)^ k = 1, ... , K (1)

and this for the whole set of K units whose efficiency has to bequantified. In a second step, we identify the unit that performs bestand quantify the pollution performance relatively to that referenceunit:

(pollution index), = — - — - - - - (2)

which obviously takes the value 1 only for the best unit and values lessthan 1 for other units. Similarly, a fuel index can be successivelydefined as

I(fuel) - (fuel) /(output produced)^ k = 1, ... , K (3)

m'mluel.

The initial idea of Jaggi-Freedman indicators is easily retraced fromthese equations: they are simply defined as arithmetic means ofquantities analogous to those appearing in Equations (2) and (4).

4 Case study

In this research we used the same study case as in previous research onindicators8,9,14,15^ with some additional data. The data usedpreviously and the context of electricity production in the USA, basedon fossil fuel consumption, was described in the references listedabove. Essentially, the data exploited so far included annual values ofinputs used (three fuels: coal, oil, and gas; labour; capital in the formof installed capacity), output produced (electricity in kWh), andundesirable outputs (atmospheric pollutants: sulfur dioxyde, nitrogenoxydes, and carbon dioxyde), for year 1991. In one of theaforementioned studies data available for the same plants in 1987



were also exploited^, while information on internal explanatoryfactors was used by Gallez & Tyteca^.

For the present research we collected additional data that wasavailable in 1996, i.e., data on the aforementioned productionvariables and explanatory factors, up to year 1994. This allowed us tobuild a pannel of 39 electric utilities for which all data were availablefor years 1987, 1991, and 1994. This would enable us to studytemporal trends in the environmental performance and to study theinfluence of the potential external explanatory factors listed in Section2, i.e., regulation in the form of amendments to the Clean Air Act, and(the absence of) economic incentives.

Table 1. - Descriptive statistics of the variables used (n = 117).

OutputElectricity generation(MkWh)

InputsAggregated fuels(tons oil equivalent)

Labour (numberof employees)

Installed capacity(MW)

WastesAggregated airpollution (103 $)

Yr.

879194

879194

879194

879194

879194

Mean

11740.12115.10737.

2852.72991.62663.4

455.33446.82369.08

2200.42199.71949.7

233180.242201.213874.

Std.dev.

3303.43268.34731.4

886.28981.661294.3

219.13212.40159.61

605.06609.52834.08

98818.95953.82565.

Min.

7008.48166.8606.9

1532.1796.141.

133.132.57.

1305.51304.0158.0

95432.103089.104071.

Max.

21504.21883.21443.

5409.5937.6213.

1112.1050.686.

3952.83953.03952.8

456395.501502.410836.

As in previous research^, the input fuels were merged into oneunique input fuel, using energetic equivalents, while the pollutants(SC>2, NOx, CO2) were merged into one synthetic pollution variable,using cost equivalents supposed to reflect harmful effects. Table 1gives a few descriptive statistics for all variables used in our data set.As to the explanatory variables, no additional information is requiredherein: we do not need any specific values of, e.g., standards applied,or taxes per unit pollutant, since the influence of regulatory andeconomic instruments will be tested by investigating the evolution of



performance over time, and through comparison of the efficiency ininput use with that observed in pollution removal,

5 Results and analysis

In the following, we will refer to the indicators as JF and UO for Jaggi& Freedman and undesirable output - oriented productive efficiency,respectively. The results obtained with the "global" indicators revealthat performance is best in 1994 for 21 plants with JF and 22 plantswith UO (the plants often coincide but not systematically), i.e., formore than half of the total number of plants. This is sometimesaccompanied by a decrease in performance between 1987 and 1991(in seven cases over 21 with JF, nine cases over 22 with UO). Figs. 1and 2 illustrate how performance evolved between the three years(only for the UO results). It can be seen that between 1987 and 1991the changes are only minor (most points are concentrated along thediagonal), while between 1991 and 1994, there is much morespreading: a few plants significantly dropped, but most plants haveimproved their performance and depart somewhat more from thediagonal (in the upper-left direction) than between 1987 and 1991.

Although statistical tests applied do not allow to detect anysignificant trend, due to the counterbalancing effect of a few (very)bad plants, regulation, expressed in terms of the successiveamendments to the Clean Air Act, seemed to play a visible role in theimprovement of environmental performance. Between 1987 and 1991nothing special happened; the most significant amendments to theCAA (over the period reported here) began in 1990 and alreadyproduced a clear improvement in the performance of most plants. Anexplanation, then, should be sought for the (few) plants thatsignificantly decreased their performance between 1991 and 1994. Asindicated in Section 2, the 1990 amendments to the CAA introduced asystem of tradeable permits, besides strengthened standards. Thusthere may be two kinds of explanations to the observed decrease inperformance: the first is that some of the utilities already boughtadditional permits that would allow them to pollute more; this,however, is not a good solution in the long term, and these plantsmight well face a situation that we will propose as our secondexplanation, i.e., due to harsh competition and reinforced standards,the least performing plants are close to leaving the market and theirbad performance is a first signal towards this evolution. Indeed, Table1 indicates a trend in that direction: as can be seen, while the input andoutput quantities do not vary much between 1987 and 1991, asignificant change took place between 1991 and 1994, with severalplants seriously reducing their input use and output production.



Fig. 1. - Comparison of produc- Fig. 2. - Comparison of produc-tive efficiencies obtained for the live efficiencies obtained for the39 plants in years 1987/91. 39 plants in years 1991/94

30 40Row Numbers

Fig. 3. - Partial fuel indicatorsfor the 39 plants in years 1987,91 and 94 (cumulated).

Fig. 4. - Partial pollution indic-ators for the 39 plants in years1987, 91 and 94 (cumulated).

The second explanatory factor we wished to investigate was aboutthe economic incentives towards better environmental performance.Figs. 3 and 4, based on the simplified sub-indicators derived fromJaggi & Freedman as described in Section 3, provide us with someuseful information in this regard. The figures illustrate a verydramatic trend, in the sense that, on the one hand, the economic



(price) incentives seem to play a powerful effect as far as the fuels areconcerned. Indeed, the spreading observed with respect to the bestperformer is rather moderate (the average lies between 82 and 83percent of the best performer; see Fig. 3). On the other hand (Fig. 4),the spreading is much more pronounced for the pollutionperformance, the average lying between 50 and 51.5 percent of thebest performer.

Thus, we have not yet reached a situation where incentives, eithereconomic or other (ethical, stakeholders ...) are powerful enough tojustify additional efforts towards improved environmentalperformance. The reason is that, clearly, the external costs electricutilities impose on society and the environment are not yet(sufficiently, if at all) internalized. Perhaps this will happen with theprogressive adaptations electric companies will have to implement tomeet the requirements and amendments of the Clean Air Act.However, we are still far from the point where the price of electricityincorporates all environmental externalities.

6 Conclusions

This paper illustrated the potential use of environmental performanceindicators in the assessment of the efficiency of regulatory and/oreconomic incentives in meeting environmental quality goals. Based onthe very limited amount of information that was available in our casestudy of US fossil fuel-fired electric utilities, two kinds of behaviourseemingly prevailed as an answer to strengthening environmentalregulation. Most utilities turned out to be adequately prepared to thenew amendments to the US Clean Air Act passed in 1990 andaccordingly increased their environmental performance. A few otherplants were apparently not prepared: their environmental performancedramatically dropped while input use and output production werereduced; these plants are likely to close down in the near future.Parallely, we also illustrated that we are still far from the situationwhere economic incentives are sufficient to adequately translatecomplete internalisation of external costs. For the utilities that werepart of our data set, the price incentives in the purchase of fuelsappeared to be much more effective in terms of rational use ofenergy, than the (economic and/or ethical) incentives towards betterenvironmental behaviour. Indeed, the observed spreading of pollutionefficiencies was much larger, and their average values considerablylower, than was the case with fuel efficiencies.

Thus the effectiveness of environmental regulation in phasing outthe worst performers, and favouring those well equiped withappropriate technology, which provides them with sufficient capacity



to increase their environmental efficiency, was substantiated by ourempirical findings. However, our results are still very limited, becauseof both the limited scale of the example and the impossibility to takeprofit of adequate, robust statistical procedures. Further researchshould obviously concentrate on the collection of additional, adequatedata, that would describe in deeper detail both the technologicaloptions and the implementation of the Clean Air Act requirementsand amendments. Our research should not be seen as more than amethodological exercise, showing the potentialities of indicators as anaid to public deciders towards the elaboration and implementation ofappropriate environmental regulations and incentives.

7 Acknowledgements

This paper benefited from valuable discussions with, among othersLeon Bolle, Ivan Sintzoff and Olivier Squilbin, from the Universitycathohque de Louvain. Thanks are also due to Geoffrey Lauwers forhis help in the construction of the data set and the analysis of theresults. For data availability we thank the Energy InformationAdministration (EIA), the Federal Energy Regulatory Commission(both in Washington, DC), and more especially John Colligan andWilliam R. Jeffers at EIA.

8 References

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SECTION 2

Life Cycle Assessment (LCA)


The role of regulation in the improvement of

Documents

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