Decision Analysis Energy

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Energy 31 (2006) 2604–2622

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Review

Decision analysis in energy and environmental modeling:An update

P. Zhou�, B.W. Ang, K.L. Poh

Department of Industrial and Systems Engineering, National University of Singapore,

10 Kent Ridge Crescent, Singapore 119260, Singapore

Received 19 October 2004

Abstract

The 1995 survey on decision analysis (DA) in energy and environmental modeling by [Huang et al. Decision analysis in

energy and environmental modeling. Energy 1995; 20: 843–855] lists 95 publications. We updated the survey and found

that the number of publications has almost tripled to 252. We also extended and refined this earlier survey by classifying

the 252 studies by source of publication, DA method, application area, and several new attributes. Statistical analyses

using hypothesis testing and a multiple attribute analysis on the suitability of different DA methods in each application

area were conducted. It was found that the importance of multiple criteria decision-making methods and energy-related

environmental studies has increased substantially since 1995. We also describe some new developments that have taken

place since the last survey.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Decision analysis; Multiple criteria decision making; Energy modeling; Environmental modeling

Contents

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2605

2. Decision analysis methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2605

3. Classification of studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2606

4. Main features observed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2608

4.1. Non-temporal features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2608

4.2. Temporal features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609

4.3. Comparisons with the earlier survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2611

5. Statistical tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2612

6. A multiple attribute analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2613

7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614

e front matter r 2005 Elsevier Ltd. All rights reserved.

ergy.2005.10.023

ing author. Tel.: +65 6874 2203; fax: 65 6777 1434.

ess: [email protected] (P. Zhou).

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ARTICLE IN PRESSP. Zhou et al. / Energy 31 (2006) 2604–2622 2605

1. Introduction

As mentioned in Huang et al. [1], decision analysis (DA) was first applied to study problems in oil and gasexploration in the 1960s and its application was subsequently extended from industry to the public sector. The1991 study by Corner and Kirkwood [2] lists 86 DA studies that appeared in operations research and relatedjournals from 1970 to 1989. They found that DA was very suitable to address strategic or policy decisions fullof uncertainties and multiple conflicting criteria. In a more recent study, Keefer et al. [3] surveyed 85 articlesappearing in 1990–2001 and found that the use of DA for strategic and tactical decisions was growing.

Energy and environmental (E&E) issues are generally complex and conflict with multiple objectives (in thisstudy, we confine environmental issues to only energy-related environmental issues). These issues generallyinvolve many sources of uncertainty, long time frame, capital intensive investment and a large number ofstakeholders with different views and preferences, which make the application of DA methods particularlysuitable [1,4]. It is not surprising that in the surveys by Corner and Kirkwood [2] and Keefer et al. [3], over aquarter of the studies dealt with energy-related issues.

So far, the most comprehensive survey on DA in E&E modeling was conducted by Huang et al. [1]. It coversa wide spectrum of DA methods and E&E application areas. Some literature surveys with a more specificfocus have also been reported. For instance, Greening and Bernow [4] reviewed the application of multiplecriteria decision making (MCDM) methods to the analysis and formulation of E&E policies. Pohekar andRamachandran [5] reviewed more than 90 MCDM studies in sustainable energy planning. Janssen [6] reviewedmultiple criteria analysis in environmental impact assessment analysis in the Netherlands.

The study by Huang et al. [1] reported a total of 95 studies that appeared before 1995. These studies wereclassified by DA method, and general and specific E&E application areas. For each application area, they alsoconducted a multiple attribute analysis to assess the suitability of each DA method in E&E studies. Their workprovides a useful guide to researchers and practitioners. Since 1995, the interest in E&E issues has risen as aresult of the growing emphasis on environmental protection and sustainable development worldwide. Theliterature has expanded substantially with at least 150 new journal publications. There is, therefore, a need torevisit the area and provide an up-to-date literature survey.

In the sections that follow, we shall first refine the classification of DA methods in Huang et al. [1]. We thenclassify a total of 252 studies published from 1975 to 2004 by source of publication, DA method, applicationarea, and several other attributes. We present the main features observed and report on new findings. Finally,we conduct a series of statistical tests and a multiple attribute analysis similar to that in Huang et al. [1].

2. Decision analysis methods

We shall classify DA methods into the three main groups as shown in Fig. 1: single objective decision-making (SODM) methods, MCDM methods, and decision support systems (DSS).

SODM comprises a class of methods for evaluating the available alternatives with uncertain outcomesunder a single objective situation. A classical approach is the decision tree (DT). Another approach, theinfluence diagram (ID), provides a simpler and more compact representation of decision problems [7].

MCDM allows decision makers to choose or rank alternatives on the basis of an evaluation according toseveral criteria. Decisions are made based on trade-offs or compromises among a number of criteria that are inconflict with each other [8,9]. Multiple objective decision making (MODM) and multiple attribute decisionmaking (MADM) are the two main branches of MCDM [10].

MODM methods are multiple objective mathematical programming models in which a set of conflictingobjectives is optimized and subjected to a set of mathematically defined constraints. The purpose is to choosethe ‘‘best’’ among all the alternatives [11]. A special case of MODM is the multiple objective linearprogramming (MOLP) where the objective functions and constraints are linear functions.

MADM refers to making preference decisions by evaluating and prioritizing all the alternatives that areusually characterized by multiple conflicting attributes. Fig. 1 shows the more popular MADM methods inE&E studies. Multiple attribute utility theory (MAUT) allows decision makers to consider their preferences inthe form of multiple attribute utility functions [12]. A special case of MAUT is multiple attribute value theory(MAVT) where there is no uncertainty in the consequences of the alternatives. The analytic hierarchy process

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Decision analysis methods

Decision support systems (DSS)

Multiple criteria decisionmaking (MCDM)

Single objective decisionmaking (SODM)

Decision tree (DT)

Influence diagram (ID)

Multiple attribute decision making (MADM)

Multiple objective decisionmaking (MODM)

Analytic hierarchy process (AHP)

ELECTRE PROMETHEE Multiple attribute utility theory (MAUT)

OMADM

Fig. 1. Classification of decision analysis methods.

P. Zhou et al. / Energy 31 (2006) 2604–26222606

(AHP) is a methodology consisting of structuring, measurement and synthesis, which can help decision makersto cope with complex situations [13,14]. The elimination and choice translating reality methods, includingELECTRE I, II, III and IV methods, are a family of outranking methods [10,15]. The preference rankingorganization methods for enrichment evaluation (PROMETHEE) are also a class of outranking methods[16,17]. Other multiple attribute decision making (OMADM) methods such as conjunctive and disjunctivemethods, TOPSIS are also popular in practice [10]. However, they have not been as widely adopted in E&Emodeling and as such are lumped together as OMADM.

DSS refer to any interactive, flexible and adaptable software systems that integrate models, databases andother decision aiding tools, and package them in a way that decision makers can use [18]. A DSS supports thesolution of complex and unstructured decision problems that are difficult to handle. In traditional DSS, theusers must often depend on their expertise knowledge in order to choose the appropriate parameters andmodels. Recent advances in artificial intelligence have led to the development of intelligent DSS which providemore flexibility to the users in dealing with different situations by incorporating a knowledge base thatcontains heuristic knowledge from domain experts.

3. Classification of studies

The 252 studies [19–270] are collected and classified according to the following attributes: year ofpublication, source of publication, country/region, problem level, application area, energy type, and DAmethod.1 The last attribute is based on the classification presented in Section 2. Since some studies use morethan one DA method, we further classified the methods used into major or minor, where the minor method isoften used as the auxiliary tool of the major method. The definitions of the other attributes are describedbelow.

In the case of ‘‘source of publication’’, we define six sources and the notations used are as follows. Source 1is journals focusing primarily on energy or natural resources issues, e.g. Energy, Energy Policy, Energy

Economics, and Energy Sources. Source 2 is journals focusing primarily on energy engineering issues, e.g.Energy Conversion and Management, Electric Power Systems Research, IEEE Transactions on Power Systems,

1A table which lists all the studies surveyed with their attributes specified, is too big to be included in this paper. It is available from the

corresponding author upon request.


and Electric Power and Energy Systems. Source 3 is journals covering the broad areas of environment, ecologyor climate change, e.g. Journal of Environmental Management, Environmental Modeling and Assessment,Ecological Economics and Journal of Industrial Ecology. Source 4 includes operations research, managementscience, and decision science journals, e.g. Management Science, Operations Research, Interfaces, IEEE

Transactions on Systems, Man and Cybernetics, and Decision Sciences. Source 5 refers to journals that cannotbe classified under any of the above four sources, such as Fuzzy Sets and Systems. Source 6 is non-journalpublications such as conference papers and book chapters. It should be noted that the surveyed studies after1995 are primarily journal papers.

In terms of ‘‘application level’’ the publications are broadly divided into two groups: strategic/policy (S/P)and operational/tactical (O/T). The S/P level mainly deals with issues related to macro issues such as energypolicy analysis, energy investment planning and energy conservation strategies. The O/T level deals with issueswhich are operational and related to short-term development goals such as bidding, pricing and technologychoice.

The following seven ‘‘application areas’’ are specified: energy policy analysis (I), electric power planning(II), technology choice and project appraisal (III), energy utility operations and management (IV), energy-related environmental policy analysis (V), energy-related environmental control and management (VI), and amiscellaneous category (VII). A short description on each area is given below.

Energy policy analysis (I) is concerned with the evaluation of energy systems with the purpose of guiding thedevelopment and formulation of energy policy. The area covers national or regional energy systemsassessment, public debate on energy policy, energy conservation strategies and energy resource allocationissues.

Electric power planning (II) deals with strategic planning issues during the course of power generation,transmission and distribution, such as power generation expansion planning, electrical transmission networkexpansion planning and power distribution planning.

Technology choice and project appraisal (III) involves the evaluation and selection of energy technologiesand appraisal of energy-related investment project. Where a study specifically deals with the evaluation,appraisal or selection of projects in electricity supply, it shall be classified under electric power planning (II).

Energy utility operations and management (IV) is concerned with the operational issues in energy industrysuch as energy biding and pricing, power plant siting and the management of energy companies. It covers allenergy sources and in the case of power plants, this area also includes the development of DSS aiding themanagement of electricity utility. When there are interactions between this area and Area III, we give a higherpriority to Area III.

Energy-related environmental policy analysis (V) deals with the policy level of energy-related environmentalproblems such as assessment of climate policy, public debate on green-house warming and airpollution control policy. It is closely related to Area I except that energy-related environmental issues arestudied here.

Energy-related environmental control and management (VI) deals with such areas as solid wastemanagement, evaluation of waste storage sites and environmental impact analysis related to majordevelopment projects. To a large extent, its coverage is similar to that of Areas II–IV except that the focus isnow on environmental rather than energy issues.

The miscellaneous category (VII) includes rather unique and specialized areas which could not be includedin any of the above six areas.

We break down ‘‘energy type’’ into six categories: energy in general (EG), coal (C), oil andgas (O/G), nuclear energy (N), renewable energy (RE) and electricity (Elec). The category energy ingeneral (EG) refers to studies that treat energy supply and demand in general terms and do not focuson a specific energy type. The category renewable energy (RE) includes all renewable energy sources,such as hydro, solar, wind and geothermal energy, and biomass. For simplicity, a study is classifiedbased on the primary energy type studied, e.g. a study dealing with the operation of nuclear powerplants would be classified under the category nuclear energy (N) while one that deals with theissues of electricity generation or distribution would be classified under the category electricity (Elec).Studies involving several specific energy types and yet are inappropriate to be classified under EG would bespecified as ‘‘Mix’’.

ARTICLE IN PRESSP. Zhou et al. / Energy 31 (2006) 2604–26222608

4. Main features observed

4.1. Non-temporal features

Fig. 2 shows the breakdown of the 252 studies by source of publication. Operations research, managementscience and decision science journals (Source 4) and energy and natural resource journals (Source 1) togetheraccount for almost two-thirds (64%) of the surveyed studies. The remaining one-third is fairly evenlydistributed among the other four sources. From the breakdown, one may conclude that DA in E&E modelingis a truly multi-disciplinary area.

Fig. 3 shows the breakdown by energy type. Not surprisingly, the largest number of studies deals withelectricity (Elec). Ignoring the category of energy in general (EG), renewable energy has also been widelystudied. Application to renewable energy studies includes exploitation of renewable energy resources such asgeothermal potential [53,79,84,85,95], allocation of renewable energy resources [109,188,240] and evaluationof national renewable energy systems [57,171,184].

Table 1 shows the breakdown of studies by DA method and application area. Since more than one DAmethod may be applied in a study classified under a specific application area, the sum of studies by DAmethod exceeds that by application area. The last column of Table 1 gives the number of studies ineach area. Of the 252 studies, 63% deal with S/P issues and the remaining 37% O/T issues. This demonstratesthe suitability of DA methods to deal with both operational and strategic problems, as has been reportedearlier [2,3]. More specifically, 23% and 21% of studies deal with energy policy analysis and energyutility operations and management, respectively. Energy-related environmental control and managementarea accounts for 18% of the studies. It is followed by the area technology choice and project appraisal(13%), electric power planning (11%) and energy-related environmental policy analysis (10%). Overa quarter of the studies deal with energy-related environmental studies. Examples of such studiesinclude those on environmental impact assessment [24,174,177,180,182,196,204], nuclear waste management[87,110,150,158,178,220] and the analysis of climate change and assessment of greenhouse gas mitigationoptions [80,97,98,137,168,202,203,210,252].

Table 1 shows that MCDM methods are the most commonly used DA methods. Specifically, the last row ofthe table shows that AHP (18%) is the most popular method, followed by MAUT (17%), MODM (14%) andDT (14%). Most DT and ID applications involve technology choice and project appraisal or energy utilityoperations and management, while only a few of DT applications and no ID applications deal with energy orenergy-related environmental policy analysis. The reason may be that the problems in the former two areas aremore technical and the corresponding uncertainties can be more easily modeled by DA representation tools ascompared to those in the latter two areas. In Table 1 it is also found that a majority of DSS applications dealwith two areas, i.e. energy utility operations and management, and energy-related environmental control andmanagement. Many issues in these two areas have some recurring features whereby DSS can be fruitfullyapplied.

Source 610%

Source 57%

Source 439% Source 3

9%

Source 210%

Source 1 25% 1: Journals focusing on energy

/natural resources issues2: Journals focusing on energyengineering issues3: Journals covering the areasof environment, ecology orclimate change4: Operations research,management science anddecision science journals 5: Other journals.

Fig. 2. Breakdown of publications by source of publication.

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Mix, 5%

Elec, 38%

RE, 13%

N, 13%

O/G, 11%

C, 1%

EG, 19%

EG: Energy in generalC: CoalO/G: Oil/Gas N: Nuclear energy RE: Renewable energy Elec: Electricity

Fig. 3. Breakdown of publications by energy type studied.

Table 1

Number of studies classified by application area and DA method

Application

area

SODM MCDM DSS Others Total

numberDT ID MODM MAUT AHP ELECTRE PROMETHEE

I 1 0 18 8 20 3 2 4 11 60

II 2 1 13 4 6 0 0 2 5 27

III 11 4 1 7 7 2 3 1 2 32

IV 19 4 6 13 4 3 2 11 2 54

V 3 0 2 4 9 1 2 3 7 27

VI 4 4 1 11 4 5 1 6 10 43

VII 1 0 0 1 2 0 0 2 3 9

Total

number

41 13 41 48 52 14 10 29 40

I: Energy policy analysis; II: Electric power planning; III: Technology choice and project appraisal; IV: Energy utility operations and

management; V: Energy-related environmental policy analysis; VI: Energy-related environmental control and management; VII:

Miscellaneous.

P. Zhou et al. / Energy 31 (2006) 2604–2622 2609

4.2. Temporal features

We divide the time frame into three 10-year periods, 1975–1984, 1985–1994, and 1995–2004. The totalnumbers of publications are, respectively 34, 73 and 145 in the three periods which indicate a doubling in thenumber every 10 years.

Fig. 4 shows the changes that have taken place by source of publication excluding non-journal publications(Source 6). The breakdown did not change much from 1975–1984 to 1985–1994, with operations research,management science, and decision science journals (Source 4) dominating these two periods. The shares takenup by energy/natural resource journals (Source 1), energy engineering journals (Source 2) and environmental,ecology, and climate change journals (Sources 3), however, increased markedly from a combined share of 27%in 1985–1994 to 62% in 1995–2004. Correspondingly, the share taken up by Source 4 dropped from 67% to31% although in absolute terms, the number of publications remained about the same. This shift might showthe changes in the preferred outlets for researchers that could also be influenced by the launch of several newjournals in the areas represented by Sources 1–3 after 1985. Also, it could be the result of wider penetration ofDA methods to different E&E application problems.

By application level, slightly over 60% of studies deal with S/P issues while the remaining deal with O/Tissues, and these shares have remained virtually unchanged over time. The higher share for studieson S/P issues are likely because these issues are more complex, which makes the application of DA moremeaningful.

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111

2

34 4

4

5 5 5

2233

0%

20%

40%

60%

80%

100%

1975-1984 1985-1994 1995-2004

1: Journals focusing on energy/natural resources issues 2: Journals focusing on energyengineering issues 3: Journals covering the areasof environment, ecology orclimate change4: Operations research,management science anddecision science journals5: Other journals.

Fig. 4. Breakdown of publications by source of publication over time.

II

I

II

IIII

III

IIIIII

IV IV

IV

V V

V

VIVI

VI

VIIVII VII

0%

20%

40%

60%

80%

100%

1975-1984 1985-1994 1995-2004

I: Energy policy analysisII: Electric power planning III: Technology choice and project appraisal IV: Energy utility operationsand management V: Energy-related environmental policy analysisVI: Energy-related environmentalcontrol and management VII: Miscellaneous

Fig. 5. Breakdown of publications by application area over time.

P. Zhou et al. / Energy 31 (2006) 2604–26222610

By application area, energy-related environmental studies (V and VI in Fig. 5) have been steadily increasing,from 15% of total publications in 1975–1984 to 34% in 1995–2004, which is consistent with the growingconcern on environmental issues. Another interesting feature is that the share of studies in electric powerplanning (II) has been increasing, taking up 5.9%, 9.6–12.4% in the three periods, respectively. Most of thesestudies deal with power generation expansion planning [29,62,122–124,159,173,176,192,244]. It might be theresult of the wave of privatization in the electricity sector in recent years but other factors are likely to be atplay as well.

By energy type, DA has most often been applied to electricity (Category ‘‘Elec’’ in Fig. 6). Its share in totalpublications increased from 30% in 1975–1984 to 43% in 1995–2004. Not surprisingly, studies related tonuclear energy (Category ‘‘N’’) has decreased substantially, from 30% in 1975–1984 to 7% in 1995–2004.Although the share taken up by renewable energy studies (Category ‘‘RE’’) remained little change at about6% from 1975–1984 to 1985–1994, it increased to 20% in 1995–2004.

The breakdown by DA method is shown in Fig. 7. The share taken up by DT has decreased from 26% in1975–1984 to 12% in 1995–2004. A declining trend has also been observed for MAUT whose share decreasedfrom 40% in 1975–1984 to 13% in 1995–2004. This may be due to the difficulties in formulating utilityfunctions as have been pointed out by Pohekar and Ramachandran [5]. Conversely, the outranking methodsincluding ELECTRE and PROMETHEE have become more popular. It is also noted that AHP accounts fora significant proportion in each of the periods. Many AHP applications deal with energy policy and energy-related environmental policy issues, such as assessment of domestic solar heating systems [57,184], evaluationand allocation of energy resources [207,208], prioritization of public transportation plans [198,247], andenvironmental cost analysis [107,108]. The popularity of AHP is likely due to its simplicity, ease ofunderstanding and suitability for the evaluation of qualitative criteria. Although there is a small decrease in

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CC

C

RE

RE

RE

Mix

EGEG

EG

O/G

O/GO/G N

NN

ElecElec

Elec

MixMix

0%

20%

40%

60%

80%

100%

1975-1984 1985-1994 1995-2004

EG: Energy in generalC: Coal O/G: Oil/Gas N: Nuclear energy RE: Renewable energy Elec: Electricity

Fig. 6. Breakdown of publications by energy type studied over time.

DTDT

DT

IDID

ID

MODMMODM

MODM

MAUT

MAUT MAUT

AHP

AHPAHP

ELECTRE ELECTRE

DSS DSS

ELECTRE

PROMETHEEPROMETHEE

0%

20%

40%

60%

80%

100%

1975-1984 1985-1994 1995-2004

Fig. 7. Breakdown of publications by DA method used over time.


MODM applications from 1975–1984 to 1985–1994, which is consistent with the findings of Huang et al. [1],the method has become more popular after 1995. A large number of MODM applications deal with energypolicy analysis and electric power planning. To a large extent, the popularity of MODM is due to its flexibilityin creating alternatives and the availability of many user-friendly computational aiding tools [4,5].

4.3. Comparisons with the earlier survey

There are a number of differences between this study and Huang et al. [1] in terms of scope and definitions.First, the study by Huang et al. covered 95 studies from 1960 to 1995 and almost two-thirds of them arejournal and conference papers. We have included these papers in our study but excluded the others which aremainly technical reports. We have included in our study some journal papers published before 1995 whichwere not captured in Huang et al. Second, in the classification of DA methods, Huang et al. divided DAmethods into three groups, i.e. decision making under uncertainty (DMUU), MCDM, and DSS, while wedivide them into SODM, MCDM and DSS because there are some interactions between DMUU and MCDMwhich may lead to confusion. Third, we have refined the classification of application areas in Huang et al.


Despite the above, the differences in findings between the two studies reported below are mainly caused by thenew developments of DA in E&E modeling.

First, after 1995, the share of studies on renewable energy has increased while that on nuclear energy hasdecreased. Second, the share taken up by energy-related environmental studies has also increased significantlyafter 1995. Third, we have found that the MCDM group of methods is the most widely used while in Huang etal.’s study it was found to be the DMUU group of methods. Fourth, although MAUT, AHP and DT havebeen found to be the most commonly used DA methods both in Huang et al.’s study and this study, theirpopularity in the two studies are different. In our study AHP has been found to be the most popular and theDT the least, while in Huang et al.’s study MAUT has been found to be the most popular and the AHP theleast. Fifth, it is observed that MODM has become more popular in our study while few studies deal with it inHuang et al.’s study.

In some respects, the findings in the two studies are similar. For instance, the largest number of studies dealwith electricity (Elec) and energy in general (EG), energy policy analysis is the most common application area,energy-related environmental studies account for a large number of studies, and MAUT, AHP and DT are themost popular DA methods. One possible reason for the popularity of these DA methods is that somespecialized software packages for these methods have been developed, e.g. Logical Decisions (MAUT-based),Expert Choice (AHP-based), HIPRE 3+ (MAUT&AHP-based) and Precision Tree (DT-based).

5. Statistical tests

These findings presented earlier are based primarily on the journal papers in English surveyed. Othersources of publications in English, such as technical reports and theses, and non-English publications are notcovered. It is appropriate to treat the data as a sample of all studies or the research interest in this field. If wemake the assumption that the sample is representative of the population, it is useful to conduct appropriatestatistical testing on some findings.

There have been new developments and trends in the application of DA to E&E modeling after 1995. It istherefore reasonable to use 1995 as a demarcation for hypothesis testing. In our study a total of 107 studiesbefore 1995 and 145 studies after 1995 (including publication in 1995) are sampled. The data for these twoperiods will be used to test the following hypotheses:

H1. There has been a greater emphasis on energy-related environmental issues.

H2. There has been less emphasis on nuclear energy while more on renewables.

H3. There is no significant difference in the share of application level.

H4. The preferred publication outlet for researchers has changed.

H5. The application of SODM has decreased while that of MCDM increased in importance.

The above hypotheses essentially involve the inferences on two proportions, before 1995 and after 1995. Forexample, in the case of H1, let p1 and p2, respectively, be the share of energy-related environmental studiesbefore 1995 and that after 1995. Then the hypothesis might be verified by testing the null hypothesis p1 ¼ p2

versus the alternative hypothesis p24p1. Hence the procedure of statistical inference on two populationproportions [271] could be used to perform this task. The precondition for the testing procedure is that the twosample proportions should have approximate normal distributions. Our tests using normal probability plotsshow that this condition is satisfied. We have conducted the tests for H1–H5 and the results show that all theabove hypotheses could be accepted. Despite its simplicity, the statistical study conducted is helpful inproviding some formal evidence on our findings and the same procedure might be used to test other explicit orimplicit hypotheses.

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Table 3

Comparisons between multiple attribute analysis results and the actual usage revealed by this survey

Application area SODM MCDM DSS

DT ID MODM MAUT AHP Outranking methodsa

I c/C c/C b/A a/B b/A b/B c/B

II c/C c/C a/A b/B b/B c/C b/C

III b/A a/B c/C a/A b/A b/B b/C

IV b/A b/B b/B a/A b/B a/B a/A

V c/B c/C c/B a/B b/A b/B c/B

VI c/B c/B b/C a/A b/B a/B b/B



Miscellaneous.aIncluding ELECTRE and PROMETHEE.

Table 2

Multiple attribute analysis of the application areas

Application area Complexity Uncertainty Multiple criteria Alternative sets Data availability Recurring type

I High High Frequently Selection Difficult Seldom

II Medium Low Frequently Design Easy Periodic

III Medium High Rarely Selection Normal Periodic

IV Low Medium Frequently Selection Easy Common

V High High Frequently Selection Difficult Seldom

VI High Medium Frequently Selection Normal Periodic



Miscellaneous.


6. A multiple attribute analysis

To determine the suitability of different DA methods in each application area, we conducted a multipleattribute analysis similar to that in Huang et al. [1] and compared the results with the actual practices revealedby our survey. The six attributes used in our study are as follows. The first is ‘‘complexity’’ which gives therelative complexity of a problem measured in terms of low, medium and high. The second is ‘‘uncertainty’’which is the level of uncertainty involved in a problem also measured in terms of low, medium and high. Thethird is ‘‘multiple criteria’’ which is how often the problems in an application area involve multiple criteria,either frequently or rarely. The fourth is ‘‘alternative sets’’ which is divided into two categories namely designand selection, reflecting whether the alternatives of one problem are pre-determined or not. The fifth is ‘‘dataavailability’’ which refers to the relative difficulty in obtaining the required data for a DA method and it isgiven by easy, normal or difficult. The last is ‘‘recurring type’’ which is specified by common, periodic orseldom, depending on how often a problem occurs.

Table 2 shows our evaluation for each of the application areas with respect to the above six attributes. Theseevaluations are then used to determine the suitability of different DA methods for each of the applicationareas, which is indicated by ‘‘a’’, ‘‘b’’ and ‘‘c’’ in Table 3. Here ‘‘a’’ indicates that the method is very suitable,‘‘b’’ the method is not so suitable, and ‘‘c’’ the method is not suitable. The actual level of usage of different DAmethods in each application area as revealed in our survey is shown by uppercase letters ‘‘A’’, ‘‘B’’ and ‘‘C’’.The criteria for determining the usage level of a DA method in a particular application area is given by thepercentage of studies using the method in relation to the total number of studies in the area. It is given ‘‘A’’ if


the percentage is more than 20%, ‘‘B’’ if the percentage is between 5% and 20%, and ‘‘C’’ if the percentage isless than 5%.

In general, for problems with high complexity, AHP and ID are preferred. For problems with highuncertainty, DT, ID and MAUT are preferred. But for problems with medium uncertainty and highcomplexity, the outranking methods are preferred. If a problem involves multiple criteria, MCDM should beused. In the case of high uncertainty and multiple criteria, MAUT is preferred. For design problems, MODMshould be used. If data are not easily available, AHP and the outranking methods should be preferred. Finally,DSS is very suitable for recurring problems.

We can obtain a great deal of information from the results shown in Table 3. In the area of energy policyanalysis, the most widely used methods were MODM and AHP. However, our analysis indicates that thecombination of MAUT with AHP may be more suitable because high complexity and uncertainty are generalfeatures in this area. In the area of electric power planning, the popularity of MODM is consistent with ouranalysis. In the area of technology choice and project appraisal, DT, MAUT and AHP have been most widelyused. However, our analysis shows that MAUT in conjunction with ID may be more suitable. In the area ofenergy utility operations and management, the popularity of MAUT and DSS is consistent with our analysis.In the area of energy-related environmental policy analysis, the usage level of MAUT in conjunction withAHP is consistent with our analysis in general. Finally, in the area of energy-related environmental controland management, the popularity of MAUT is consistent with our analysis. In addition, although theoutranking methods have not been so widely used, our analysis shows that the outranking methods includingELECTRE and PROMETHEE might be very suitable in this area, which was not reported in Huang et al. [1].

7. Conclusions

As an update of Huang et al.’s study [1], our study gives the developments of DA in E&E modeling in recentyears. Some of these developments are not in tune with the findings reported in the earlier study. Compared tothe conclusion drawn by Huang et al. that DMUU was the most popular technique, this survey instead showsincreased popularity of MCDMmethods. In the case of application area, energy-related environmental studieshave increased in importance. These two and some other major findings have been tested statistically in ourstudy. In addition, with a much larger number of studies, we have conducted a multiple attribute analysissimilar to that conducted by Huang et al. to determine the suitability of different DA methods in eachapplication area, and then compared the results obtained with the actual practices revealed by our survey. It isobserved that MAUT in conjunction with AHP is suitable for most application areas, which is different fromMAUT in conjunction with ID found in studies by Huang et al. Given the shift of E&E studies from energyissues to energy-related environmental issues, DA methods, particularly MAUT/AHP and the outrankingmethods, are likely to play an important role in E&E modeling in future.

Acknowledgments

We are grateful to Editor-in-Chief Noam Lior and three anonymous referees for their helpful comments.

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