a new perspective for assessing the sustainability of countries
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A New Perspective for Assessing theSustainability of CountriesÖzgür Kabak a & Füsun Ülengin ba Industrial Engineering Department , Istanbul Technical University ,34367 Maşka, Ýstanbul, Turkeyb Dogus University, Industrial Engineering Department , Zeamet Sk.No:21, Acibadem 34722, Istanbul, TurkeyPublished online: 22 Sep 2008.
To cite this article: Özgür Kabak & Füsun Ülengin (2008) A New Perspective for Assessing theSustainability of Countries, Journal of Transnational Management, 12:4, 3-32, DOI: 10.1300/J482v12n04_02
To link to this article: http://dx.doi.org/10.1300/J482v12n04_02
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A New Perspective for Assessingthe Sustainability of Countries
Özgür KabakFüsun Ülengin
ABSTRACT. Sustainable development is the concept that plays an im-portant role for the countries in macro as well as micro perspective. Inthis paper, a fuzzy logic based assessment framework is proposed tomeasure the sustainable development level of the countries. The pro-posed model is constructed by analyzing the shortages and favorableparts of existing sustainability measurement approaches. Contrary to ex-isting approaches that divide sustainable development (SD) into hierar-chical parts, it evaluates SD at indicator level. The model uses fuzzylogic due to its capacity to handling vague situations. The proposedframework consists of four steps. The initial step determines the indica-tors to measure the sustainability. Subsequently, a factor analysis is con-ducted to reduce the number of indicators and obtain mutually exclusivegroups of indicator. In the second step, the weights of the previouslyformed indicator groups are determined in accordance with the opinionsof the experts. In the third step, performance scores of the countries aremeasured from the indicator values. In the fourth step, the countries arecompared according to their performance scores and to the weights ofindicators. The proposed model is applied to evaluate the relative posi-tion of Turkey in SD perspective. The effects of improvements in some
Özgür Kabak is Research Assistant, Industrial Engineering Department, IstanbulTechnical University, 34367 Maçka, Ýstanbul, Turkey (E-mail: [email protected]).
Füsun Ülengin is Professor of Decision Analysis, Dogus University, Industrial En-gineering Department, Zeamet Sk. No:21, Acibadem 34722, Istanbul, Turkey (E-mail:[email protected]).
The authors wish to thank Professor Burç Ülengin, Professor Lerzan Özkale andProfessor Seval Sözen for their invaluable contribution to the specification of indicatorweights.
Journal of Transnational Management, Vol. 12(4) 2007Available online at http://jtran.haworthpress.com
� 2007 by The Haworth Press. All rights reserved.doi:10.1300/J482v12n04_02 3
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SD dimensions on the position of three countries selected are also inves-tigated. Developed as such, the model will guide the policy makers inidentifying and prioritizing the most urgent problems that need to besolved in order to obtain improvement in the relative positions of thecountries. doi:10.1300/J482v12n04_02 [Article copies available for a fee fromThe Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address:<[email protected]> Website: <http://www.HaworthPress.com>� 2007 by The Haworth Press. All rights reserved.]
KEYWORDS. Sustainable development, countries, ranking
Every natural system, especially the earth, has limits. The continu-ously growing global economy has driven many natural systems to anear collapse. Examples abound: ozone depletion, global warming, de-pletion of aquifers, collapse of fisheries, forest destruction, soil erosionand so on (Phillis & Ansdriantiatsaholiniaina, 2001).
The close relationships and dependencies between economics andecology are well known and best described by the concept of sus-tainability. As we enter the new millennium, one of the most challeng-ing problems is how to assess, build and maintain a sustainable economythat will allow the human society to enjoy a sufficiently high standard ofliving without destroying its natural and biological support.
Sustainable development (SD) has become an essential question ofinternational environment policy, at least since the summit of the UnitedNations in Rio 1992. Probably the best definition of SD is set forth in thereport of the World Commission on Environment and Development in1987–the Brundtland Commission: “Sustainable Development is thedevelopment that meets the needs of the present without compromisingthe ability of future generations to meet their own needs (WCED,1987).”
The concept of sustainability, thus, combines the needs of present andfuture generations, and takes the interdependencies of economic activi-ties and ecological status into account. Sustainability is difficult to defineor measure because it is an inherently vague and complex concept. Fuzzylogic is well suited to handle such a vague and uncertain concept.
The purpose of this paper is to propose a fuzzy multi-attribute modelwhich develops a mechanism for measuring sustainability. The secondsection underlines the importance of SD and indicates the reason to useindicators. The third section gives an overview of currently existing in-dicators of sustainability. The fourth section underlines the necessity of
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using a fuzzy multi-attribute model. The fifth section introduces theframework of the proposed model. The sixth section compares 36 coun-tries based on the proposed model and the sensitivity analysis results.Finally, conclusions and suggestions are provided.
WHY TO USE SUSTAINABILITY INDICATORS?
In order to initiate and monitor SD, a standardized framework will beof great practical utility. Such a framework requires a four-phasedmethodology to: (1) describe the problem in a defined context; (2) de-termine the context-dependent economic, ecological and societal is-sues; (3) translate these issues into measurable sustainability indicators;and (4) assess the contribution of sustainability indicators to the overallsustainability development (Cornelissen et al., 2001).
Operational definitions and indicators are a prerequisite for imple-menting sustainability in practical policy decisions. The 1992 EarthSummit recognized the important role that indicators can play in help-ing countries to make informed decisions about SD. This recognition isarticulated in Chapter 40 of Agenda 21 which calls on countries at thenational level, as well as international, governmental and non-govern-mental organizations to develop and identify indicators of SD that canprovide a solid basis for decision making at all levels. Agenda 21 specif-ically calls for the harmonization of efforts to develop SD indicators atthe national, regional and global levels, including the incorporation of asuitable set of these indicators in common, regularly updated andwidely accessible reports and databases (United Nations, 2001).
Indicators can provide crucial guidance for decision making in a vari-ety of ways. They can translate physical and social knowledge intomanageable units of information that can facilitate the decision mak-ing process. This can help to measure and calibrate progress towardsSD goals. The basic aim of indicators is to provide adequate informa-tion that is tailored to quantitative sustainability objectives. Brink(1992) states that such information should: (a) give a clear indication asto whether objectives of sustainability are met; (b) concern the systemas a whole; (c) have a quantitative character; (d) be understandable tonon-scientists; and (e) contain parameters which can be used for periodsof one or more decade.
Chapter 40 of Agenda 21 (UNSDC, 1997) urges the development ofindicators for SD. In particular, it asks countries to formulate the Sus-tainable Indicator concept, in order to identify such indicators. In the
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course of evaluating the progress in implementing Agenda 21, the Com-mission on Sustainable Development began developing a set of indica-tors of SD (Spangenberg et al., 2002). The first version was finalized in1996 with the suggestion of 134 indicators (United Nations, 1996) andput to a field test, resulting in a final version published in 2001 (UnitedNations, 2001). In both versions, the indicators are divided into fourareas: economic, environmental, social and institutional.
STATE OF THE ART
Even though there is no consensus about the way of measuringsustainability, a frequently adopted approach consists of characterizingthe sustainability in terms of a set of indicators (Diaz-Balteiro &Romero, 2004). Attempts have been made to measure sustainabilityusing different sustainability indicators. There are two differentperspectives on the measurement of sustainability; namely weakand strong sustainability. Weak sustainability is based on neoclassicaleconomics and embodies a basic assumption that different indicators ofsustainability can be substituted for each other. Strong sustainability,however, does not accept the substitutability among the indicators.Strong sustainability is supported by ecological economics.
One example of weak sustainability measure that does attempt to com-press various components of development into one measure is the re-cently developed Environmental Sustainability Index (ESI). The ESI isan average of twenty two equally weighted indicators that cover fivecomponents important for environmental sustainability, but this has beencriticized because the selection of components was claimed to be biasedtowards the richer countries at the expense of the poorer (Morse, 2003).
Another example of weak sustainability is the Human DevelopmentIndex (HDI) which was created specifically to replace the GDP/GNPfamily and hence lessen a perceived overemphasis on economic per-formance as the central measure of development. HDI is published an-nually in the Human Development Report from the United NationsDevelopment Program (UNDP) in New York (Pearce, 2003). There arethree basic components in the HDI: (1) an adjusted measure of gross na-tional product (GNP); (2) a measure of life expectancy (a surrogate forhealth); and (3) measures of educational attainment. One of the basiccritiques about the HDI is that the aggregation procedure across thethree main components appears to be ad hoc (Pearce, 2003). The UNDPhave made promises to include an environmental dimension to the HDI;
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the ultimate goal being “environmentally sensitive HDI.” However, de-spite its existence for more than a decade, the HDI has not even beenmodified to take on board wider issues of sustainability (Morse, 2003).On the other hand, the three factors originally considered by the HDI areassumed to have equal importance. The supporting argument for the se-lection of these three components is that they are essential. If they areessential, they cannot be substitutable for each other. Another flaw ofthe HDI is that it accepts that weaknesses in some dimensions can becompensated by strengths in some other dimensions. This means thatcosts of environmental deterioration (e.g., forest damage) can be com-pensated by benefits from manufactured capital (e.g., income). How-ever, extreme weaknesses should not be compensated, even by verygood performances on other dimensions. In fact if any weakness can becompensated by a strength, a decrease in life expectancy by one yearcan be compensated by some increase in adjusted real GNP, which isnonsense. Finally, while it is designed to compliment economic indica-tors by encouraging a broader vision of human development the HDIcould still be seen in terms of a goal with rankings of inferior/superiorand worse/better that has been criticized by the critics of economic de-velopment.
Other examples of weak sustainability indicators are damage costcalculations. Damage cost calculations try to quantify the external ef-fects of environmental pollution. The methodology is based on welfaretheory and cost-benefit analysis. One example of damage cost indicatoris that proposed by Pearce and Atkinson (1993), which uses nationalsavings, national income, depreciation of man-made capital and dam-age to natural resources as indicators of national sustainability andprovides an economic evaluation through damage cost calculations.This method also uses “weak sustainability,” meaning a situationwhere both natural and human-made capital have a high degree of sub-stitution are interchangeable; e.g., the rate of savings, minus amortiza-tion of human made capital, minus rate of depletion and degradation ofnatural resources, all taken as a percentage of the GDP. For example,according to these estimates, developed countries like Germany or theUnited States are sustainable because of high savings ratios (Rennings& Wiggering, 1997).
Physical indicators quantifying thresholds of critical ecological func-tions can be characterized as indicators of strong sustainability. There-fore, the existence of reference values and sustainability thresholds canbe viewed as the most important requirements that will avoid completesubstitutability among them. For example, AMOEBA (Dutch, standing
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for General Method for Ecosystem Description and Assessment) indi-cators use sixty environmental quality components to evaluate specifi-cally the ecosystem through the aggregation and addition of indicators’values. Therefore, the approach is based on an ecological perspectiveand the economic side of the problem is not considered. In the AMOEBAapproach, quantitative and verifiable objectives are developed to al-low a quantitative description and assessment of ecosystem. It is basedon choosing a representative number of target variables, measuringthe current stock or concentration, and comparing it to a reference thatimplies sustainability. From the methodological point of view, it can becriticized for the aggregation process, simple addition of indicators andthe reference to historical situations. It is a very crude, preliminary solu-tion for measuring sustainability (Reennings & Wiggering, 1997; Weferinget al., 2000).
Pressure-state-response indicators of the OECD (Organization forEconomic Co-operation and Development) (OECD, 2001) are based onpressure indicators on environment quality, environmental state indi-cators and human response indicators to environmental damage. Indi-cators are simply placed in three categories: pressure indicators try toanswer questions about the cause of problems. Indicators in this cate-gory include emissions and waste amounts. State indicators answerquestions about the state of the environment. Indicators in this cate-gory include urban air quality, ground water quality, temperaturechanges and concentrations of toxic substances. Response indicators,on the other hand, try to answer questions about what is done to solvethe problem. Indicators placed in this category include internationalcommitments or recycling rates. It evaluates the sustainability of OECDcountries from the ecological and economical perspective through in-ternational comparison of environmental indicators. The aim of theOECD indicators is to make international comparisons of environmen-tal indicators. That is why the indicators are selected based on the avail-ability of data in all members. Therefore, the indicator set is not wellfounded. Indicators are not related to sustainability goals and give littleinformation about the essential functions. It can be used as a first step toimplement more advanced indicator sets in the future (Rennings andWiggering, 1997).
Another example for non-substitutability is the Well-Being assess-ment that gives equal weight to people and the ecosystem for the evalua-tion of a region (Prescott-Allen, 1995; Prescott-Allen, 2001). In thisassessment, the concept is expressed in the metaphor of the Egg ofWell-Being. Each subsystem is divided into five element groups. The
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well-being assessment is undertaken by a technical team in consultationwith participants from governments, communities and interest groupsin the area being assessed. It uses the Barometer of Sustainability de-signed to measure human and ecosystem well-being together withoutsubmerging one in the other. In order to measure the sustainability, thesystem is broken down into its parts down the hierarchy until the levelwhere measurable indicators are reached. The basic advantage of thismethod is that the sustainability of a system is determined according tothe worst component. As a result, people and ecosystem subsystemscannot compensate each other. However this is not valid for the othercomponents in the hierarchy. For example, the components that con-stitute the ‘people’ dimension of the system can substitute each other.One of the basic drawbacks of this method is that it is hard to define theranges of the bands for the Barometer of Sustainability for every indica-tor because the contribution of indicator values to the overall sustain-ability level is not clear. On the other hand, the indicators at the bottomof the hierarchy can be dependent on each other, so that an effect or acause can be involved in the process many times by means of indicatorsrelated to each other. Additionally, the impact of each indicator onsustainability may not be of equal importance. That is why the use ofweights may be more appropriate. However, the method does not sug-gest a systematic way for assessing the weights.
As can be seen from the literature survey, for the sake of analysis, re-searchers generally break down sustainability into a large number of in-dividual components. However, there is not a common unit of measure-ment for the overall assessment of sustainability. In order to operate thesustainability, an integrated assessment of the ecological, social andeconomic features is required. A systemic approach combining thosemultidimensional components is needed.
WHY FUZZY MULTIPLE ATTRIBUTE DECISION MAKING?
There are four theoretical trends in multidimensional analysis (Ulenginet al., 2001). The first trend is the Value/Utility System Approach whichaims to construct a value system by aggregating the DM’s preferences.Expert Choice using AHP (Saaty, 1988), SMART (Von Winterfeld &Edwards, 1986) and VISA (Belton et al., 1997) are well-known soft-ware applications based on this approach. However, the use of such acompensatory method to evaluate the SD level of countries will result inthe problem of “weak sustainability” mentioned previously. Another
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trend is the Multi-Objective Optimization Approach, which aims tosolve multi-criteria problems where there are no discrete alternativesusing the extension of Mathematical Programming (Siskos & Spyridakos,1999). However, due to the discrete nature of the sustainability indica-tors, Multi-Objective Optimization Techniques are not suitable forevaluating the SD levels of countries. The last trend, the French School,proposes the use of a non-compensatory approach and, thus, is suitablefor the SD evaluation. This school uses an outranking relation that al-lows incomparability among decision actions. According to Roy (1977),given two alternatives a and b, a outranks b means that the analyst hasenough reasons to admit that in the eyes of the DM, a is at least as goodas b. In outranking methods, little attention is devoted to the construc-tion of a value or utility function and avoids the trade-off analysis. As aresult, in this study, outranking methods are found suitable for measur-ing SD.
On the other hand, in proposing a framework to measure and com-pare the SD level of countries, one important problem is the uncer-tainty contained in the information about sustainability. Additionally,it is very hard to find exact quantitative values for some of the indica-tors. For example, it is very difficult to arrive at straightforward valuesto measure ecological sustainability. Generally, environmental and socio-economical information is fragmentary and often qualitative. In reality,the border between the sustainable and the unsustainable is not sharpbut rather fuzzy. This means that it is not possible to determine the exactreference values for sustainability, and a scientific evaluation of uncer-tainty must always be considered in the procedure of sustainability as-sessment. In general, we set a number of criteria for the sustainabilityof a system and we call this system sustainable if its dynamics neverdrive it outside the boundaries of acceptable values for these criteria.Therefore, in addition to the knowledge about the current situation, itis important to formulate targets for assessing progress towardgoals. The attainment of these targets not only gives assurance ofSD, it also allows for sustained use by humans. However, spa-tial-environmental systems are complex systems characterized bysubjectivity, incompleteness and imprecision. What appears unsus-tainable for an environmentalist may be sustainable for an economistand the ingredients signifying sustainability may differ for these spe-cialists. Therefore, the specification of clearly defined targets is ratherunrealistic. Fuzzy logic has the ability to deal with complex conceptswhich are not amenable to a straightforward quantification and containambiguities.
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The starting point of decision models in a fuzzy environment was theBellman and Zadeh (1970) model. Traditional approaches to discretemulti-criteria evaluation in a fuzzy environment are provided by Yager(1978), van Laarhoven and Perdrycz (1983), Baas and Kwakernaak(1977), Baldwin and Guild (1979), Bonissone (1982) and Tong andBonissone (1984). For a detailed overview of these methods see Ribeiro(1996).
Phillis and Andriantiatsaholiniaina (2001) developed a model calledSustainability Assessment by Fuzzy Evaluation (SAFE). The model as-sumes the system as a combination of ecological and human subsystemsthat are evaluated by means of four components. These components areevaluated by means of three types of indicator: pressure, status and re-sponse indicator; individual indicators are used to measure these indica-tors. During the passage from indicator stage to the overall assessmentstages, at each level fuzzy if-then rules are used. However, one basic as-sumption of the model is that the indicators are independent of eachother. Additionally, equality among indicators is assumed; thus, theweights are not used and it is necessary to define many if-then rules. Onthe other hand, at all levels, fuzzification and defuzzification are ap-plied. This may result in a loss of information and give an incorrectsolution. These drawbacks reduce the efficiency of the method.
However, all these methods are utility based and aimed at finding asingle ‘optimal’ solution through the transitive comparability axiom.Most of them are based on the existence of a rigid frontier between ad-missible and inadmissible actions, which are often fuzzy (Aouam et al.,2003). Due to its non-compensatory nature and its ability to take into ac-count the uncertainties, a fuzzy outranking approach is used to measureand compare the sustainability of countries. Roy (1977) and Siskos etal. (1984) used outranking relations effectively by introducing fuzzyconcordance relations and fuzzy discordance relation. A fuzzy con-cordance relation is an aggregation of fuzzy partial relations; each isconsidered a model for unique criterion. The fuzzy discordance rela-tion takes into account the importance of the difference between theperformances of alternatives for each criterion. The revised versionmethodology proposed by Aouam et al. (2003) is used due to its abilityto combine both the concept of fuzzy outranking and fuzzy attributes toprovide a more flexible way of comparing the alternatives.
This paper, thus, attempts to propose an efficient framework for inte-grating the environmental and socio-economic aspects of the evaluationand enable a clear and simple presentation of the different dimensionsof sustainability. It shows the relations between different indicators, and
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also takes into account the fuzzy threshold values, as well as theimprecisely determined indicators.
THE FRAMEWORK OF THE PROPOSED MODEL
The proposed model to evaluate the sustainability of the countries iscomposed of four steps. Figure 1 gives the flow chart of the model. Thedetails of the flowchart are set forth below.
Determination of the Independent Indicator Groups
In the first step, appropriate indicators are selected, with the purposeto take into account both the ecological and economical indicators.
The set of indicators proposed by the United Nations (2001) is foundsuitable for this study. However, experience has shown that long lists ofindicators are impractical. Once the list of all potential environmental,economic and social indicators has been compiled, it is important tomake a final selection in order to generate a set of strategic indicatorsthat can be measured. A methodological framework that can assist inthe selection of appropriate indicators is needed. An indicator is fullyrepresentative if it covers the most important parts of the component
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FIGURE 1. The Flow Chart of the Proposed Model
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concerned, if it is accurate, is measured in a standardized way and it iswell founded, i.e., the indicator’s relationship to the component it repre-sents is well established. An indicator should directly reflect the objec-tive of the element concerned if it measures its actual achievementrather than factors that could advance or impede its achievement. Itshould be feasible, i.e., it should depend on data that is readily available.This final set of indicators should be robust but not necessarily exhaus-tive; in other words, the list shall include only those indicators that re-veal critical features. Accordingly factor analysis is applied in the studyin order to form mutually exclusive indicator groups. The purpose offactor analysis is to define the underlying structure in a data matrix. Itaddresses the problem of analyzing the structure of the interrelationshipamong a large number of variables and defines a set of common under-lying dimensions. As a result, a summary of data is obtained and datareduction, in which large numbers of variables are explained by fewfactors, is achieved (Hair et al., 1995).
Determination of Weights
In the second step the weights of Indicator Groups are determined inaccordance with the opinions of the experts based on a 1-6 scale. Theexperts are requested to evaluate the contribution of each indicatorgroup to SD of a country. They have opportunity to indicate their prefer-ences as very high, high, fairly high, fairly low, low and very low. Thenthese preferences are represented as triangular fuzzy numbers (TFNs)according to Chen and Hwang’s (1992) scale 5. Subsequently, bymeans of fuzzy arithmetic operations, logic of which used by Raj andKumar (1999), firstly average weight of each indicator is calculated andnormalization is then applied for normalized weights.
Measurement of Performance Scores
In the third step, performance scores are measured. Instead of usingthe data for each indicator group directly, normalization is made to ob-tain a common scale to allow aggregation and to facilitate fuzzy compu-tation. For this purpose, the revised version of the fuzzy normalizationproposed by Phillis and Andriantiatsaholiniaina (2001) is used. The ba-sic difference of the revised approach is that not only the target valuesbut also the sustainability thresholds of each indicator group are takeninto account. This permits the possibility of avoiding the trade-offamong the indicators. Additionally, due to the vague and uncertain
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characteristics of these two values, they are represented by TFNs. Thenormalization formula for fuzzy targets and sustainability thresholds isconstructed based on the normalization formula for crisp targets andthresholds according to the definitions in Chen and Hwang (1992).
Fuzzy Outranking Approach for the Comparison of Countries
As mentioned before, in this study the revised version of the method-ology proposed by Aouam et al. (2003) is proposed for the comparisonof countries. Herein details of methodology will not be given butchanges made with respect to Aouam et al. (2003) will be emphasized.The original paper can be analyzed for details.
The method proposed by Aouam et al. (2003) can take both crisp andfuzzy inputs. An outranking intensity is introduced to determine the de-gree of overall outranking between competing alternatives, which arerepresented by fuzzy numbers. The comparison of these degrees ismade through the concept of overall existence ranking index (I(a)). Inthe methodology first of all fuzzy concordance (dC(a,b)) and discor-dance functions (dD(a,b)) are calculated. Then outranking intensity(df(a,b)) is measured that is used to get the overall outranking intensity(I(a)). In order to get fuzzy concordance function, the partial outrankingnumber (dj(a,b)) and the fuzzy concordance number (C(a,b)) are calcu-lated respectively. At this level maximum non-significant threshold (sj)and criteria weights (pj) are inserted to the model. Additionally, in orderto form the fuzzy discordance function, fuzzy discordance numbers(Dj(a,b)) are used in which fuzzy veto thresholds (vj) are included.
The main drawbacks encountered in Aouam et al. (2003)’s method-ology are related to fuzzy operations. Although it is claimed that themodel can take fuzzy inputs, there are some practical problems in its ap-plication. In Aouam et al. (2003), the subtraction operations are not de-fined in accordance with the fuzzy subtraction and the comparison offuzzy numbers are not provided in some definitions.
In the original Aouam et al. (2003), if the inputs are TFNs, there is apossibility of getting TFNs such that a > b or b > c or a > c (TFN is con-sidered as (a; b; c)), which is, in fact, in conflict with the logic behindTFNs (because for a TFN a � b � c relation should be satisfied). An-other drawback faced in Aouam et al. (2003) is that the comparisons offuzzy inputs are not considered in the method. In the proposed approachboth of the mentioned problems are eliminated. The possibility of get-ting illogical TFNs is removed by applying appropriate calculations and
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the comparison of fuzzy attributes is handled with Baldwin and Guild’s(1979) fuzzy ranking approach.
SUSTAINABILITY ASSESSMENT RESULTSFOR SELECTED COUNTRIES
The proposed multi-attribute fuzzy model for sustainability assess-ment is applied to measure the development sustainability of thirty sixselected countries. Before the model is applied, the countries that willbe involved in evaluation should be selected. Two main factors are con-sidered during the selection process: (1) a large spectrum of countries istried to be taken into account in order to give insight not only to the de-veloped countries but to countries at different stages of the SD level ac-cording to the Wellbeing of Nations report (Prescott-Allen, 2002), and(2) countries which have sufficient data to analyze the sustainability in-dicators are selected. As a result, twelve countries from the top, twelvecountries from the middle and twelve from the bottom of the list are se-lected. Their rank order according to the Wellbeing of Nations report isgiven in Table 1.
Determination of the Selected Independent Indicator Groups
In the first step, the indicator set is determined from the indicator setof the United Nations’ Commission on SD indicators (United Nations,2001), since it includes all dimensions of SD and it is developed for na-
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TABLE 1. Rank Order of the Selected Countries According to the Wellbeing ofNations Index
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tional level evaluations. However, when the indicator value was notavailable for 90% of the selected countries, this indicator was elimi-nated from consideration. As a result, a matrix that consists of thirty sixcountry values for forty seven indicators is gathered (see Table 2).
Subsequently, the data set of selected countries and indicator set areobtained from a SD software, ‘Dashboard of Sustainability,’ that con-tains sixty indicator data set for more than two hundred countries(O’Connor, 2003). The data correspond to the year 2000’s values.
Finally, the factor analysis is applied to reduce the large number ofindicators into independent indicator groups. For this purpose the factorloadings higher than 0.4 are taken into account. As a result, twelve fac-tor groups and fourteen representative indicators are determined (seeTable 3 and also the factor analysis results in the Appendix).
The details about the indicators as well as their interpretations can befound in United Nations, 2001. On the other hand, during the selectionphase of the “representing indicator” the following process is conducted.If there is a meaningful relation between the indicators constituting thefactor, the indicator having the highest factor loading is selected as therepresenting indicator of the related factor. For example the first factor iscomposed of eleven indicators; namely GNP, Internet, Energy Use,Telephones, SD Membership, Energy Efficiency, Recycling, SecondarySchools, Fertilizer, Municipal Waste and Urbanization. Due to the factthat all of those indicators are related to economical growth, the factor islabeled “economic well-being” and the representing indicator is selectedto be “GNP” because it has the highest factor loading.
However if no meaningful relation is revealed, two indicators havinghighest factor loadings are accepted to represent the factor. For in-stance, for the factor which consists of “income distribution” and“wood harvesting intensity,” no meaningful relation is detected be-tween related indicators. As a result, all of these two indicators are usedduring the evaluation phase.
Determination of the Weights
In the second stage of the proposed model, the weights of indicatorgroups are determined according to three experts’ preferences. One ofthe experts is a professor in the Civil Engineering Faculty, Department ofEnvironmental Engineering, Istanbul Technical University, and the re-maining two are Professors of Economics at the Management Faculty ofthe same university. Finally, these preferences are aggregated by meansof fuzzy arithmetic operations. Table 4 gives the corresponding results.
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�zg�r Kabak and F�sun Ülengin 17
TABLE 2. Initial Set of Selected Indicators (O’Connor, 2003; United Nations,2001)
Data compiled from sources and worked to create table.
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TABLE 2 (continued)
Data compiled from sources and worked to create table.
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TA
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Measurement of Performance Scores
The measurement of performance scores is realized by the fuzzy nor-malization method that is introduced in section 5.3. In order to apply themethodology, the target and threshold values for each indicator arespecified according to the international standards and agreements. Afterthe specification of the threshold values, the performance scores of eachselected country from each indicator are measured. To give an ideaabout the specification of the threshold values two examples are given.
In this study, the economic welfare factor is represented by the percapita gross national income, which is a utility indicator. For the specifi-cation of the target values and lower threshold value, the World Bankclassification of the countries according to gross national income istaken as the basis (see Table 5) (Heildheus, 2003). As provided in Table5, the upper bound of the low income is $755 and lower bound of thehigh income is $9266. Since low income is an undesirable situation andhigher income is a target for sustainability, in this study $750 is selectedas the threshold value and $9250 as the target value of the economicwelfare factor. These values are represented with triangular numbers asT1 = (8750, 9250, 9750) and LB1 = (650, 750, 850).
The environmental threats factor is represented by CFCs. CFCs areforbidden in many countries according to several international agree-ments (United Nations, 2001). That is why its target value is taken as 0.However no specific information about its upper bound could be ob-tained. Hence, this threshold level is found by investigating the valuesthat the countries received from that indicator and its value is specified
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TABLE 4. Fuzzy Weights of the Indicator Groups
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as 45%. As a result, the target and threshold values in triangular num-bers are T4 = (0, 0, 0) and UB4 = (40,45,50), respectively.
Table 6 provides the target and threshold values of all the indicatorsused in the normalization process of this study.
After the specification of the targets and threshold values, the perfor-mance score of selected countries from each representing indicator is cal-culated by means of the fuzzy normalization method. For example Turkey’sGNP is 3022.4$. Due to the fact that GNP is a utility indicator, the equationno [5] is used and the performance score of Turkey for GNP is found as(0.239, 0.267, 0.300). Similarly Turkey’s CFCs level is 58,93 grams percapita. Since this indicator is a cost indicator, the equation no [6] is used incalculations and the performance score is found as (0, 0, 0).
�zg�r Kabak and F�sun Ülengin 21
TABLE 5. World Bank Country Classification According to Gross National In-come (Heildheus, 2003)
Data compiled from source and worked to create table.
TABLE 6. Target and Threshold Values for the IndicatorsDow
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Results of the Fuzzy Outranking Model
Using the data of year 2000, the relative position of selected coun-tries is specified according to overall outranking intensities (I(Ai)’s)(see Figure 2). The I(Ai) values and the corresponding ranks of thecountries are given in Table 7.
Based on these results, eight groups of countries can be detected (seeTable 8). The first three groups (1, 2 and 3) that can be accepted to rep-resent the most sustainable countries consist of European countries andUS. Countries that are in groups 4, 5 and 6 correspond to the developingcountries and finally groups 7 and 8 represent undeveloped countries.Turkey is in the fourth group and this provides that it is necessary tomake significant improvements in order to reach the sustainability levelof the first three groups of countries.
ALTERNATIVE SCENARIOS FOR THREE COUNTRIES
A what-if analysis is conducted for three countries, namely Turkey,France and Ecuador, in order to reveal the most important indicatorsthat will have an impact on the improvement of sustainability. Turkey isselected in order to see the impact of the policy suggestions in our home
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FIGURE 2. The Relative Position of Selected Countries According to I(Ai) Values
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country. France is selected since it is found to be a member of Group 2in terms of its sustainability, although it enjoys a high economic power.It also appears at the lower part of this group. Finally Ecuador is se-lected because it represents one of the lowest groups.
Turkey
Turkey has a weak position in terms of the renewable energy indica-tor. Therefore this indicator is selected as one of the indicators forwhat-if analysis. Additionally, GNP and GDFI indicators are also sub-ject to the what-if analysis because of their relative high weights. Threescenarios, which are introduced in Table 9, are used for this purpose.
As set forth in the scenario results (Table 9), in scenario 1 Turkey’srenewable energy level is increased from 0.126% to the level of Den-mark (6.719%), Turkey’s position improves significantly (from 14th to11th). Our reason of selecting Denmark is due to its top place in Europein terms of this indicator.
�zg�r Kabak and F�sun Ülengin 23
TABLE 7. The Rank of the Selected Countries According to Their I(Ai) Values
TABLE 8. The Groupings of the Countries
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In scenario 2, GDFI is increased from 23.19% to 33%, which is thetarget sustainability level of GDFI. Turkey again moves to 11th positionin this scenario.
Finally, in scenario 3 Turkey’s GNP is increased from 3022.4$ to3644$ (optimistic forecast for 2004). It can be seen that Turkey’s posi-tion does not change based on this improvement.
Figure 3 provides the changes of Turkey’s position according to thescenarios (Scenario numbers are indicated in parenthesis).
These results expose that in order to improve its relative position,Turkey shall give its primary focus on the indicators with respect towhich it is currently below the sustainability threshold.
The improvement mentioned for an indicator group value shall not besolely taken as an increase in value. It is necessary to remember thateach indicator group is composed of different indicators. For example,the GNP indicator represents the economic welfare factor. It may not be
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TABLE 9. Scenarios for Turkey
FIGURE 3. Changes in Turkey’s Position in Different Scenarios
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possible to realize improvement on the sustainability level only by in-creasing GNP. In parallel with an increase in GNP, it may be necessaryto make an improvement in all the indicators that GNP represents, i.e.,internet, energy use, telephones, SD membership, energy efficiency,recycling, secondary schools, fertilizer, municipal waste and urbaniza-tion.
France
Two scenarios are used to analyze the possibility of improvement inthe rank of France. In scenario 1, the increase in cropland, for whichFrance has a weak value, is increased to a level higher than the target.This resulted with an improvement of the position (from 7th to 5th).However as can be seen in scenario 2, the improvement in the level ofrenewable energy in a way to reach the Denmark level provides thehighest level of improvement in the position of France (from the 7thrank to the 3rd rank) (see Table 10). Figure 4 shows the changes in the
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TABLE 10. Scenarios for France
FIGURE 4. Changes in France’s Position in Different Scenarios
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position of France with respect to two scenarios (scenario numbers areindicated in parenthesis).
Ecuador
In order to analyze the possibility of improving the relative positionof Ecuador in terms of sustainability, two indicators for which it hasthe weakest position, namely GNP and Deforestation, are selected.In scenario 1, GNP level is increased to the level of countries thathave high-medium income ($2996). In scenario 2, on the other hand,the deforestation is set to a level lower than the target. For in both ofthe scenarios Ecuador’s position improves from 31st to 29th. Table 11provides the corresponding improvements and Figure 5 sets forth thechange of the position of Ecuador based on these scenarios (scenarionumbers are indicated in parenthesis).
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TABLE 11. Scenarios for Ecuador
FIGURE 5. Changes in Ecuador’s Position in Different Scenarios
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The results demonstrate that the improvement in the indicators forwhich a country has a weak position result with a higher level of impacton the sustainability level of the country. This is the result of the charac-teristic of the model which does not permit trade-off up to a specifiedlevel for each indicator.
CONCLUSIONS AND FURTHER SUGGESTIONS
This millennium will be marked by the efforts to manage sustainabilityefficiently if humankind is to avoid the collapse of life-supporting sys-tems, unstable economies and social unrest. Although sustainability is agoal for international and national policy makers, there is no measur-ing yardstick against which to assess practical policy (Phillis andAndrantiatsaholiniaina, 2001). Most existing methods of sustainabilitymeasurement use either pure economic or ecological criteria. However,it is widely accepted that sustainability should consider both concepts.In other words, ecological and economic sustainability indicators arenot exclusive but complementary. The proposed methodology providesan efficient framework for the relative sustainability position of thecountries by encompassing ecological, social, political and economicalconcepts.
The proposed methodology also denies the degree of substitution thatweak sustainability assumes and, hence accepts that these indicatorscannot compensate each other at least up to a specified threshold value.In fact, threshold values should build the core of indicator sets for SD.
On the other hand, sustainability is difficult to define or measure be-cause it is an inherently vague and complex concept. Fuzzy logicoperations allow us to handle the uncertainty which is important insustainability problems. By also taking into account the fuzziness of theweights of the indicators as well as those of the threshold values in theproposed methodology, a fuzzy outranking multi-attribute decisionmaking approach is used to derive the overall sustainability of the coun-tries. In fact, it is important for a multi-attribute method to providesound advice/recommendations and insights for seemingly conflictingproblems in various situations. The proposed model introduces variousfuzzy numbers for determining the degree of outranking between se-lected countries in terms of their sustainability development level. Thecomparison is accomplished by using the concept of overall existence,which in turn generates the fuzzy outranking relations. Finally, an over-
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all outranking intensity index is calculated in order to provide the over-all ranking of all the countries.
The proposed method improves on Aouam et al. (2003) by usingmore accurate fuzzy attributes as input.
Developed as such, the proposed methodology is expected to providea systematic tool to deal with sustainability, which is crucial for policymakers if they are to secure future developments. As a further sugges-tion, the methodology proposed in this study can be applied to both cur-rent and potential members of NATO or the EU. Such an evaluationwould indicate whether the resulting organizations would become bipo-lar or not in terms of sustainability.
The expert group can be enlarged in an international base in a way totake into account perspectives for the relative importance of differentindicators. In the methodology, the averaging process which is used forthe aggregation of experts’ opinions is suitable only in case of a smallnumber of experts (i.e., at most 5). However, whenever the number ofexperts is increased, a more accurate group decision making approach isnecessary in order to reflect the real preference of the group (Tsao,2004).
Finally, Data Envelopment Analysis (DEA) can also be included inthe methodology in order to provide more precise policy changes foreach country under investigation.
REFERENCES
Aouam, T., Chang S.I. and Lee E.S., 2003. Fuzzy MADM: An outranking method. Eu-ropean Journal of Operational Research, 145:317-328.
Baas, S.M. and Kwakernaak, H., 1977. Rating and ranking of multiple aspect alterna-tive using fuzzy sets, Automatica, 13:47-58.
Baldwin, J.F. and Guild, N.C., 1979. Comparison of fuzzy sets on the same decisionspace, Fuzzy Sets and Systems, 2:213-231.
Bellman, R. and Zadeh L.A., 1970. Decision making in fuzzy environment, Manage-ment Science, 17:141-164.
Belton, V., Ackermann, F., Shepherd, I., 1997. Integrated support from problem struc-turing through to alternative evaluation using COPE and VISA, Journal of Multi-criteria Decision Analysis, 6:115-130.
Bonissone, P.P., 1982. A fuzzy set based linguistic approach: Theory and applications.In: M.M. Gupta and E. Sanches (editors), Approximate Reasoning in DecisionAnalysis, 329-339.
Brink, B.J.E.T., 1991. The AMOEBA approach as a useful tool for establishing sus-tainable development. In: Kuik, O., Verbruggen, H. (Editors), In Search of Indica-tors of Sustainable Development, Kluwer Academic Publishers, The Netherlands.
28 JOURNAL OF TRANSNATIONAL MANAGEMENT
Dow
nloa
ded
by [
Nor
thea
ster
n U
nive
rsity
] at
02:
43 1
6 N
ovem
ber
2014
Chen, S. and Hwang, C., 1992. Fuzzy multiple attribute decision making: Methods andapplications. Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, Germany.
Cornelissen, A.M.G., Berg, J., Koops W.J., Grossman M., and Udo, H.M.J., 2001. As-sessment of the contribution of sustainability indicators to sustainable develop-ment: A novel approach using fuzzy set theory, Agriculture Ecosystems andEnvironment, 86:173-185.
Diaz- Balteiro, L. and Romero, C., 2004. In search of a natural systems sustainabilityindex, Ecological Economics, 49:401-405
Hair, J.F., Anderson R.E., Tatham R.L., and Black W.C., 1995. Multivariate data anal-ysis with readings, Prentice-Hall International Inc., New Jersey
Heildheus, F., 2003. Poverty and development strategies, http://www.uni-hohenheim.de/~i490a/lectures/M5101/m5101_1.pdf
Laarhoven P.J.M. and Perdrycz W., 1983. A fuzzy extension of Saaty’s priority theory,Fuzzy Sets and Systems, 11:229-241.
Morse S. 2003. Greening the United Nations’ Human Development Index?, Sustain-able Development, 11:183-198
O’Connor, J., 2003. Dashboard of sustainability, http://esl.jrc.it/envind/dashbrds.htm.OECD (Organisation for Economic Co-operation and Develepment), 2001. OECD En-
vironmental Indicators towards Sustainable Development, Paris, FrancePearce D.W., and Atkinson, G.D., 1993. Capital theory and the measurement of sus-
tainable development: An indicator of ‘weak’ sustainability, Ecological Econom-ics, 8:103-108.
Pearce, D., 2003. Environment And Economic Development, (in print)Phillis, Y.A. and Andriantiantsaholiniaina, L.A., 2001. Sustainability: An ill-defined
concept and its assesment using fuzzy logic, Ecological Economies, 37:435-456Prescott-Allen, R., 1995. Barometer of sustainability: a method of assessing progress
towards sustainable societies, PADATA, Victoria, Canada; IUCN, Gland, Switzer-land.
Prescott-Allen, R., 2001. The Wellbeing of nations, Island Press, USAPrescott-Allen, R., 2002. Wellbeing of nations index, http://www.iucn.org/info_and_
news/press/wbon.htmlRaj P.A., Kumar D.N., 1999. Ranking alternatives with fuzzy weights using maximiz-
ing set and minimizing set, Fuzzy Sets and Systems, 105:365-375.Rennings, K. and Wiggering, H., 1997. Steps towards indicators of sustainable devel-
opment: Linking economic and ecological concepts, Ecological Economics, 20:25-36.
Ribeiro, R.A., 1996. Fuzzy multiple attribute decision making: A review and new pref-erence elicitation techniques, Fuzzy Sets and Systems, 78:155-181.
Roy, B., 1977. Partial preference analysis and decision-aids: The fuzzy outranking re-lation concept. In: D. E. Bell, R. L. Keeney and H. Raiffa (editors), Conflicting Ob-jectives in Decisions, New York, 40-75.
Saaty T.L., 1988. Multicriteria Decision Making: The analytical hierarchy process,RWS, Pittsburg.
Siskos, J.L., Lochard, J., and Lombard, J., 1984. A Multicriteria decision making meth-odology under fuzziness: Application to the evaluation of radiological protection in
�zg�r Kabak and F�sun Ülengin 29
Dow
nloa
ded
by [
Nor
thea
ster
n U
nive
rsity
] at
02:
43 1
6 N
ovem
ber
2014
nuclear power plants. In: H.J. Zimmerman (editor), TIMS/Studies in ManagementScience, 20, Elsevier Science Publishers, B.V., North-Holland, 261-283
Siskos, Y. and Spyridakos, A., 1999. Intelligent multi-criteria decision support: Over-view and perspectives, European Journal of Operational Research, 113:236-246.
Spangenberg, J.H., Pfahl, S., Deller, K., 2002. Towards indicators for institutionalsustainability: lessons from an analysis of Agenda 21, Ecological Indicators, 2:61-77
Tong, R.M., and Bonissone, P.P., 1984. Linguistic solutions to fuzzy decision prob-lems. In: H.J. Zimmerman (Editor), TIMS/Studies in Management Science, 20,Elsevier Science Publishers, B.V., North-Holland, 323-334
Tsao, C.T., 2004. An impact-adjusted fuzzy multi-criteria decision making model,MCDM 2004, Whistler, B.C. Canada, August 6-11
Ulengin, F., Topçu, Y.Ý., and Þahin, Þ.Ö., 2001. An integrated decision aid system forBosphorus water-crossing problem, European Journal of Operational Research,134:179-192
United Nations, 1996. Indicators of sustainable development: Framework and method-ologies, United Nations, New York.
United Nations, 2001. Indicators of sustainable development: Guidelines and method-ologies, United Nations, New York.
UNSDC (United Nations Sustainable Development Commission), 1997. Proceedingsof the First Session on Agenda 21, April - June 1997, Newyork, USA
Von Winterfeld, D. and Edwards, W., 1986. Decision analysis and behavioral research,Cambridge University Press, Cambridge
WCED (World Commission on Environment and Development), 1987. Our commonfuture, Oxford University Press, Oxford
Wefering, F.M., Danielson, L.E. and White N.M., 2000. Using the AMOEBA ap-proach to measure progress toward ecosystem sustainability within a shellfish res-toration project in North Carolina, Ecological Modeling, 130:157-166.
Yager, R.R., 1978. Fuzzy decision making including unequal objectives, Fuzzy Setsand Systems, 1:87-95.
SUBMITTED: October 2006REVISED: December 2006
ACCEPTED: May 2007
doi:10.1300/J482v12n04_02
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�zg�r Kabak and F�sun Ülengin 31
APPENDIX
FIGURE A.1. Results of Factor Analysis, Total Variance Explained
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APPENDIX (continued)
FIGURE A.2. Results of Factor Analysis, Rotated Component Matrix
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