a fuzzy decision support model for the selection of

Journal of the Eastern Asia Society for Transportation Studies, Vol. 6, pp. 3264 - 3275, 2005

A FUZZY DECISION SUPPORT MODEL FOR THE SELECTION OF ENVIRONMENT-FRIENDLY FUELS FOR ROAD VEHICLES Raymond TAN Associate Professor Chemical Engineering Department De La Salle University-Manila 2401 Taft Avenue 1004 Manila, Philippines Fax: +63-2-524-0563 E-mail: [email protected]

Alvin CULABA Professor Mechanical Engineering Department De La Salle University-Manila 2401 Taft Avenue 1004 Manila, Philippines Fax: +63-2-524-0563 E-mail: [email protected]

Abstract: Road vehicles account for a significant share of air emissions in today’s major urban centers. There are efforts by Philippine government to increase the usage of alternative fuels as substitutes for diesel oil. The most promising fuels include compressed natural gas (CNG) and biodiesel derived from coconut oil. The potential environmental benefits of these alternative fuels can be measured using life cycle assessment (LCA) methodology. This paper presents the application of fuzzy multiple attribute decision making (FMADM) in an LCA model for comparing alternative transportation fuels. The methodology allows quantitative information on the transportation systems’ material and energy flows to be integrated with qualitative information reflecting such aspects as the social acceptability of different types of environmental damage. Fuzzy numbers are used to represent uncertainties in the data so that the model can predict both the magnitude of the environmental impacts of the alternative fuels and the corresponding confidence levels of these estimates. Results of a case study show biodiesel to be superior to both CNG and diesel in terms of overall environmental impact. Key Words: environmental life cycle assessment, alternative fuels, decision support system, fuzzy sets 1. INTRODUCTION Road vehicles account for a significant share of air emissions in today’s major urban centers. Conventional vehicle and engine technology relies on combustion processes that are heavily dependent on petroleum-based fuels and generate greenhouse gases along with other air pollutants. Public policy thrusts to reduce environmental impacts of road transport include modal shifts as well as the utilization of clean energy sources. In the Philippines, there are efforts by government agencies to increase the usage of alternative fuels as substitutes for gasoline or diesel oil. The most promising fuels include compressed natural gas (CNG) and biodiesel derived from coconut oil. The potential environmental benefits of these alternative fuels can be measured using life cycle assessment (LCA) methodology. The modern LCA concept is described in ISO 14040 (1997), which in turn was influenced by the early work of the Society for Environmental Toxicology and Chemistry (1991).The essential features of LCA are: •

Use of a functional unit to provide a fair basis for comparison. The functional unit refers to the final service delivered by a product system; for example, in transportation, a functional unit can be 1 vehicle-km or 1 passenger-km.

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•

•

Cradle-to-grave assessment of impacts. Analysis includes direct and indirect impacts occurring throughout an entire product chain. For example, in transportation systems, the impacts arising from both vehicle emissions and pollutants generated by supporting infrastructure (e.g., fuel production plants, refueling facilities) are taken into account. Multicriterion perspective. Different types of environmental burdens, including ecological (e.g., global warming), health (e.g., toxicity) and natural resource depletion (e.g., petroleum consumption) impacts are considered.

These features enable LCA to give a comprehensive picture of the true impacts arising from a given product system. The components of LCA are: • • • •

Goal and scope definition – identification of parameters, alternatives, system boundaries and assumptions. Inventory analysis – quantification of relevant material and energy streams between a product system and the environment. Impact assessment – classification and characterization of inventory data to corresponding environmental impact categories, followed by valuation. Interpretation – use of results for decision support or problem solving; includes analysis of the effects of uncertainty on outcomes.

LCA provides a framework for quantifying natural resource usage, air emissions release and the resultant environmental impacts per unit of transportation service delivered. Such information provides policy-makers with an adequate assessment of potential environmental benefits of alternative fuels, which will aid in making subsequent cost-benefit analyses and planning. Computer models are used to implement LCA, which is generally highly dataintensive. LCA models simulate the environmental flows and impacts of transportation systems in a virtual environment, thus allowing their behavior to be studied before they are actually built or implemented. The process of development of such models requires reconciling two potentially conflicting considerations. On the one hand, the model must be sufficiently simplified to ensure that the computations are tractable and that the results are easily for policy-makers to interpret; on the other hand the model must not be unduly oversimplified or it may fail to give a faithful simulation of reality. It is recognized in LCA practice that uncertainties must be accounted for in the model’s data and outputs to ensure robustness and to minimize the risk of policy-makers misinterpreting the results. This paper presents the application of fuzzy multiple attribute decision-making (FMADM) in an LCA model for comparing alternative transportation fuels. 2. MODELLING FRAMEWORK A schematic diagram of a generic life cycle system for fuels and vehicles is illustrated in Figure 1. It shows the total energy cycle, which is comprised of the fuel and vehicle cycles. In this study, focus is placed only on the fuel cycle, since prior studies have shown it to be the dominant contributor to total environmental impact (Wang, 1999). Figure 2 shows the decision hierarchy used in the model. The three alternatives considered in this paper are diesel, biodiesel and CNG. These fuels are evaluated based on eight environmental criteria: global warming, acid rain, smog formation, nitrification, human toxicity, oil depletion, coal depletion and NG depletion. Ratings for each attribute are computed using a previously developed fuzzy LCA model (Tan et al., 2004), which in turn is based on GREET 1.5a (Wang, 1999) augmented with impact assessment capability using the EDIP method (Wenzel 3265


et al., 1997). Details of the computational methods used in LCA can also be found in Heijungs and Suh (2002).

Vehicle Cycle

Feedstock Recovery

Fuel Production

Fuel Cycle

Vehicle Production

Vehicle Operation

Vehicle Disposal

Total Energy Cycle

Figure 1. Total Energy, Vehicle and Fuel Cycles (Wang, 1999)

Global Warming Acid Rain Diesel Smog Formation Nutrification Aggregate Environmental Performance

Biodiesel Human Toxicity Oil Depletion

CNG

Coal Depletion NG Depletion

ALTERNATIVES

CRITERIA

OVERALL OBJECTIVE

Figure 2. Decision Hierarchy Used in the Model Probability theory is the most commonly used model of data uncertainty. Classical probability deals with quantifying tendencies of random events based on the frequency of occurrence of different outcomes. However, as explained by Tan et al. (2002), this theory is

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inappropriate for describing uncertainty arising from vagueness, subjectivity, or incomplete information. This form of uncertainty is fuzzy or possibilistic nature and is best dealt with using fuzzy mathematics (Dubois and Prade, 1988). Imprecise quantities can be represented using fuzzy numbers (Kaufmann and Gupta, 1991). The membership function, µ(x), of a fuzzy number is also referred to as its possibility distribution. Generally distributions represent subjective degree of belief, or plausibility, of a range of values, and thus unlike probability distributions do not necessarily result from distinct mathematical rules. However, stylized triangular or trapezoidal distributions are often employed for simplicity (Mauris et al., 2001). As an example a fuzzy number with a triangular possibility distribution is shown in Figure 3. The value 1 is called the kernel of the fuzzy number, and represents the most plausible range of values. This range is assigned a possibility level or membership value of 1. The interval [0, 3] represents the range of all marginally plausible values with non-zero possibility, and is called the support of the fuzzy number. For simplicity, triangular fuzzy numbers are denoted here by the extremes of the kernels and support, so that the number shown is (0, 1, 3). A fuzzy number can be denoted by an interval for any given membership value; for example, for µ(x) = 0.5, the corresponding interval for the fuzzy number (0, 1, 3) is [0.5, 2].

1

µ(x)

0.8 0.6 0.4 0.2 0 0

1

2

3

4

x

Figure 3. The Triangular Fuzzy Number (0, 1, 3) Fuzzy multiple attribute decision making (FMADM) is a decision methodology used for selecting an optimal alternative from a predefined set of candidates based on multiple (and potentially conflicting) criteria under conditions of uncertainty. It consists of two separate problems: • •

Computation of an aggregate score for each alternative, usually through some form of weighted average. Fuzzy arithmetic is often used (Kaufmann and Gupta, 1991; Moore and Lodwick). Comparison and ranking of the alternatives. This procedure is usually done by reducing each fuzzy score into a corresponding crisp or defuzzified value to facilitate comparison.

Different FMADM methods have been demonstrated for comparison of technological alternatives (Chen, 1997; Cheng, 1999; Cheng and Lin, 2003). A comprehensive review is given by Chen and Hwang (1992). Simple additive weighting (SAW) is a FMADM procedure that uses the weighted arithmetic average of normalized scores in the different decision criteria to rank specified alternatives. 3267


It is widely used in different decision domains because of its simplicity. Here the basic SAW procedure is modified by using triangular fuzzy numbers to represent imprecise scores and weights. Calculations are carried out using fuzzy arithmetic; computations with triangular fuzzy numbers yield results with approximately triangular distributions (Kaufmann and Gupta, 1991; Moore and Lodwick, 2003). The procedure involves computing the total environmental impact rating of each alternative with Equation 1: (x1, x2, x3)j

=

Σi (w1, w2, w3)i × {(y1, y2, y3)ij × [maxj(y3)ij]−1}

(x1, x2, x3)j

=

fuzzy total environmental impact of alternative (j)

(w1, w2, w3)i

=

fuzzy weight of environmental impact category (i)

(y1, y2, y3)ij

=

fuzzy environmental impact of option (j) for category (i)

maxj(y3)ij

=

negative ideal (worst) solution for impact category (i)

(1)

where:

Weights describe the relative importance of each criterion. Fuzzy decision theory facilitates the translation of linguistic ratings into numerical values. This study uses the fuzzy weights shown in Figure 4, which are based on Chen (1997) and Cheng (1999). For example, a criterion of “low” importance is given a fuzzy weight of (0, 0.3, 0.5).

1 Very Low Low

0.75

µ(x)

Moderate 0.5

High Very High

.

0.25 0 0

0.1 0.2 0.3 0.4

0.5 0.6 0.7 0.8 0.9

1

x

Figure 4. Membership Function of Linguistic Weights Once aggregate scores have been computed, the alternatives can be given preliminary ranks based on a defuzzified “average” value. In the case of triangular fuzzy numbers, the simplest approach is to use the kernels, or the values with µ(x) = 1. The degrees of dominance can then be computed based on the extent of overlap between the fuzzy distributions, in a manner analogous to statistical tests of hypothesis (Tan et al., 2004). A large area of overlap between any two fuzzy numbers signifies weak dominance. Adamo (1980) defined dominance at any given membership value, µ(x) = α, as the absence of overlap between the α-cut intervals. Figure 5 illustrates these concepts with the triangular fuzzy numbers (0, 1, 3) and (2, 2, 3). Note that, based on the kernels, the latter is the larger value, since 2 > 1. However, the distributions overlap somewhat; for example, at α = 0, the corresponding intervals are [0, 3] and [2, 3]. The overlap ceases at α = 0.5, where the α-cuts are [0.5, 2] and [2, 2.5]. The 3268


value of α at which the overlap disappears can be considered as the degree of indifference or similarity between the two numbers, since its value rises with increased overlap between the fuzzy distributions. Its logical complement, (1 – α), can be considered as the degree of dominance. Note that there will be a general trend of greater indifference or weaker dominance as the spread (or uncertainty levels) of the fuzzy distributions are increased. This relationship is perfectly logical since increased data uncertainty is bound to decrease the confidence level of the final decision or ranking.

1

µ(x)

0.8 0.6 0.4 0.2 0 0

1

2

3

4

x

Figure 5. Degrees of Indifference and Dominance Between Fuzzy Numbers 3. RESULTS AND DISCUSSION Results of the use of fuzzy SAW for the comparative LCA of three different fuels are discussed. In the results it should be noted that lower impact scores are considered as more desirable. The fuzzy environmental impact scores of diesel, biodiesel and CNG, prior to aggregation, are shown in Figures 6 and 7. These are results for the use of these fuels in light vehicles as described in Wang (1999) and Tan et al. (2004). Diesel and biodiesel are assumed to be used in compression-ignition engines while CNG is assumed to be used in spark-ignition engines. The impact scores for global warming, acid rain, smog formation, nitrification and human toxicity are normalized and expressed in microperson-year equivalents per vehicle-km, while the impact scores for depletion of oil, coal and NG are expressed in microperson equivalents per vehicle-km. A person-year equivalent is the quantity of impact generated by the average person in the world in 1990; for resource depletion, a person-equivalent is the average quantity of resource available in the world per person in 1990. Normalization to these units is common practice in LCA; details can be found in Wenzel et al. (1997) and Tan et al. (2004). The results are fuzzy, with the length of the bars signifying the spread or uncertainty of the fuzzy distributions. These uncertainties arise from the cumulative effect of variabilities in each part of the fuel cycle (Wenzel et al., 1997); in this case they result from factors such as: vehicle age, type and fuel economy; driving conditions; and energy consumption during fuel production, distribution and storage. It is notable that the variability for CNG tends to be much greater than that of the other two fuels. Two contributing factors include the uncertainty with regard to engine type (e.g., purpose-built CNG or converted from gasoline) and uncertainties about the CNG production and distribution infrastructure, as discussed below.

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5

40 30 20

10

7

4 3 2

1

3 2

Diesel

CNG

Biodiesel

Diesel

2.5

3.5

HTP (microperson-year eq. per km)

3.0 NP (microperson-year eq. per km)

4

0

CNG

Biodiesel

Diesel

5

1

0

0

6

CNG

50

8

Biodiesel

6

POFP (microperson-year eq. per km)

60

AP (microperson-year eq. per km)

GWP (microperson-year eq. per km)


2.5 2.0 1.5 1.0

2.0

1.5

1.0

0.5

0.5

CNG

Biodiesel

CNG

Biodiesel

Diesel

Diesel

0.0

0.0

Figure 6. Environmental Impact Scores of Alternatives for Global Warming (GWP), Acid Rain (AP), Smog Formation (POFP), Nutrification (NP) and Human Toxicity (HTP) It is notable that biodiesel has very low impact with respect to global warming. This property is inherent in all biofuels, which are largely CO2-neutral; on a life-cycle basis the CO2 uptake from photosynthesis during fuel raw material production offsets the CO2 emissions from the downstream combustion of the biofuel. Since CO2 emissions are the dominant cause of global warming, the almost total elimination of this gas from the net exhaust of the system drastically reduces the impact. Fossil fuel depletion impacts of biodiesel are also very low due to the assumption of a high level of utilization of agricultural residue as process fuel in the biodiesel system. Low sulfur content of biodiesel reduces acid rain potential compared to diesel. Diesel and biodiesel both have much lower smog formation impact (caused mainly by

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emissions of volatile organic compounds or VOCs) and greater nutrification potential (due to higher NOx levels generated in compression-ignition engines) than CNG. Human toxicity effects are about the same for all three fuels. As expected, diesel and CNG exhibit high oil and NG depletion potentials, respectively. The use of a life cycle framework in assessing environmental impacts gives some surprising results. These impacts represent those generated not just by the vehicles themselves, but also those resulting from the upstream fuel distribution infrastructure, as shown in Figure 1. For example, CNG also exhibits high coal depletion and acid rain formation rates through the use of coal-derived electricity for NG compression. The environmental impacts generated from coal combustion in the plants that supply power to the CNG production and dispensing facilities are included in the total or life cycle-based impact scores. For similar reasons, the uncertainty margins for the CNG system tend to be much greater than those of diesel and biodiesel. Part of the variability arises from uncertainty about the specific source of electricity to be used for CNG production, which will depend on the location of the site within the Philippine power grid.

0.4

4.5

0.4

1.0 0.5

0.2 0.2 0.1

1.5

CNG

Biodiesel

0.0 Diesel

CNG

Biodiesel

2.0

0.5

0.0 Diesel

2.5

1.0

0.1

0.0

3.0

CNG

1.5

0.3

3.5

Biodiesel

2.0

4.0

0.3

NG RDP (microperson eq. per km)

Coal RDP (microperson eq. per km)

2.5 Oil RDP (microperson eq. per km)

5.0

Diesel

3.0

Figure 7. Environmental Impact Scores of Alternatives for Oil, Coal and NG Depletion Table 1 shows the weights assigned to the eight environmental criteria for two scenarios. The linguistic or subjective ratings are converted to fuzzy weights using Figure 4. Table 1. Linguistic Weights Used in Scenarios I and II Environmental Criterion Scenario I Scenario II Global Warming moderate very high Acid Rain moderate high Smog Formation moderate high Nutrification moderate moderate Human Toxicity moderate very high Oil Depletion moderate high Coal Depletion moderate low NG Depletion moderate moderate

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Figure 8 shows the fuzzy aggregate environmental impacts of the three alternatives when all environmental criteria are equally considered to be moderately important (Scenario I). Biodiesel has the lowest overall environmental impact, followed by diesel and then by CNG. Some overlap is evident in the scores. The degrees of indifference are 0.67 between biodiesel and diesel, 0.6 between biodiesel and CNG, and 0.92 between diesel and CNG. The corresponding degrees of dominance for the pairwise comparisons are thus 0.33, 0.4 and 0.08, respectively. Figure 9 shows the results for Scenario 2. Although the ranking of the alternatives remains the same, the degrees of indifference are 0.4 between biodiesel and diesel, 0.38 between biodiesel and CNG, and 0.92 between diesel and CNG. The corresponding degrees of dominance for the pairwise comparisons are thus 0.6, 0.62 and 0.08, respectively. Thus, the degree to which biodiesel is superior to the other two fuels, taking into account uncertainty in the data, is increased. The change can be attributed mainly by the increased weight given to global warming, a criterion in which biodiesel outperforms diesel and CNG significantly.

1

Diesel

µ(x)

0.8

Biodiesel CNG

0.6 0.4 0.2 0 0.00

1.00

2.00

3.00

4.00

x

Figure 8. Fuzzy Aggregate Environmental Impacts for Scenario I

1

Diesel

µ(x)

0.8

Biodiesel CNG

0.6 0.4 0.2 0 0.00

1.00

2.00

3.00

4.00

x

Figure 9. Fuzzy Aggregate Environmental Impacts for Scenario II The importance of having fuzzy model outputs can be illustrated as follows. Suppose the human decision-maker using the model defines a minimum degree of dominance of 0.2; that

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is, the degree of dominance must exceed 0.2 for the difference in environmental impact between two alternatives to be considered significant. In both scenarios, the degree to which biodiesel dominates the other two fuels will be judged to be significant (> 0.2). On the other hand, although the aggregate impact of diesel is lower than that of CNG, the degree of dominance does not exceed the predefined threshold (0.08 < 0.2); hence, the difference in performance is insignificant. The results of the case can be interpreted as: biodiesel is environmentally superior to diesel and CNG, while the latter two fuels do not differ significantly from each other. The minimum degree of dominance is analogous to the level of significance used in standard statistical tests. It provides a threshold value to measure the degree of dissimilarity between two alternatives in light of the fuzzy uncertainty of the data. The choice of 0.2 as the threshold value in this case is purely illustrative, and depending on the problem context another value may be used. In the literature of fuzzy decision theory, degrees of dominance have been interpreted in terms of subjective or linguistic equivalents (Adamo, 1980; Chen, 1997; Cheng, 1999; Cheng and Lin, 2002; Tan et al., 2004). Table 2 shows typical interpretations. Thus it becomes possible to translate numerical results from the model into linguistic statements which can be understood more easily for purposes of policy development and decision support. For instance, in Scenario II where the numerical degree to which biodiesel dominates diesel is 0.6, the resulting interpretation may be roughly stated as, “biodiesel is moderately superior to diesel.” Table 2. Interpretation of Degree of Dominance Numerical Subjective or Linguistic Value Interpretation 0 none 0.25 weak 0.5 moderate 0.75 strong 1 very strong The main advantage of using fuzzy SAW in selection problems of this type is that the decision process takes into account uncertainties in the model data. Note that the results of conventional analysis is embedded as a subset of the fuzzy distributions. The vertex of the triangular fuzzy distribution represents the average aggregate environmental impact of each fuel, which can be calculated without suing fuzzy numbers. In Scenario II, the crisp (nonfuzzy) impact scores of biodiesel, diesel and CNG are 1.4, 2.3 and 2.5, respectively. From these results alone it is not immediately apparent that diesel is only weakly superior to CNG. Once the fuzzy distributions are displayed as in Figure 9, the extent to which they overlap gives an indication of the degree of the superiority; the difference in the environmental scores of diesel and CNG in this case proves to be negligible. It is also evident from the geometry of the fuzzy numbers that degree of dominance is determined not just by the distance between the vertices of the triangles, but also by the “spread” or the uncertainty of the fuzzy distributions. It becomes more difficult to establish definitive superiority when there is a high degree of uncertainty in the data – as will often happen when new or unproven technologies are being assessed. The method described here thus gives an effective framework for comparative assessment of alternative fuels for transportation, including those (such as hydrogen) which are not yet technologically mature.

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4. CONCLUSIONS A decision support model for comparing alternative motor vehicle has been developed based on fuzzy weighted averaging and life cycle assessment principles. The model predicts environmental impacts measured on a life cycle basis and incorporates data uncertainty using fuzzy decision theory. The fuzziness of the environmental impact estimates allows alternatives to be ranked in order of preference without losing information regarding the degrees of superiority or dominance among the competing technologies. These two levels of information are found to be essential to effective decision support. This feature is essential in effective decision support systems to prevent misinterpretation of the model outputs by human users. The methodology also allows quantitative information on the transportation systems’ material and energy flows to be integrated with qualitative information (in the form of linguistic ratings) reflecting such aspects as the social acceptability of different types of environmental damage. The model is demonstrated through a comparison of diesel, biodiesel and CNG. For the specified weights used in the decision scenarios, biodiesel was predicted to be superior to the other two fuels in terms of environmental considerations. Currently the model is being expanded to provide comprehensive decision support for a wider range of fuels (e.g., hydrogen, ethanol and methanol), vehicle types (e.g., fuel cell and electric vehicles) and transport modes (e.g., buses, trains and motor cycles). The work includes refining of the database to improve accuracy of predictions, and development of an upgraded version capable of processing non-triangular (e.g., trapezoidal or Gaussian) fuzzy distributions. Future versions also will be made more user-friendly by including graphical user interface. Ultimately, non-environmental considerations, including economic and social aspects, should also be integrated into this fuzzy decision framework. REFERENCES Adamo, J. M. (1980) Fuzzy decision trees. Fuzzy Sets and Systems, Vol. 8, No. 3, 207220. Chen, S. J. and Hwang, C. L. (1992) Fuzzy Multiple Attribute Decision Making: Methods and Applications. Springer, New York. Chen, S. M. (1997) A new method for tool steel materials selection under fuzzy environments. Fuzzy Sets and Systems, Vol. 92, No. 2, 265 – 274. Cheng, C. H. (1999) Evaluating weapon systems using ranking fuzzy numbers. Fuzzy Sets and Systems, Vol. 107, No. 1, 25-35. Cheng, C. H. and Lin, Y. (2002) Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research, Vol. 142, No. 1, 174-186. Dubois, D. and Prade, H. (1988) Possibility Theory: An Approach to the Computerized Processing of Uncertainty. Plenum Press, New York. Heijungs, R. K. and Suh, S. (2002) The Computational Structure of Life Cycle Assessment. Kluwer, Dordrecht.

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ISO 14040 (1997) Environmental management: Life cycle assessment – principles and framework. International Organisation for Standardisation, Geneva. Kaufmann, A. and Gupta, M. M. (1991) Introduction to Fuzzy Arithmetic: Theory and Applications. International Thomson Computer Press, London. Mauris, G., Lasserre, V. and Foulloy, L. (2001) A fuzzy approach for the expression of uncertainty in measurement. Measurement, Vol. 29, No.2, 165-177. Moore, R. and Lodwick, W. (2003) Interval analysis and fuzzy set theory. Fuzzy Sets and Systems, Vol. 135, No. 1, 5-9. SETAC (1991) A technical framework for life cycle assessments. Society for Environmental Toxicology and Chemistry, Pensacola. Tan, R. R., Culaba, A. B., Purvis, M. R. I. (2002) Application of possibility theory in the life cycle inventory assessment of biofuels. International Journal of Energy Research, Vol. 26, No. 8, 737-745. Tan, R. R., Culaba, A. B. and Purvis, M. R. I. (2004) Possibilistic uncertainty propagation and compromise programming in the life cycle analysis of alternative motor vehicle fuels. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 8, No. 1, 23-28. Wang, M. (1999) GREET 1.5 – transportation fuel cycle model. Report ANL/ESD-39, Argonne National Laboratory, Chicago. Wenzel, H., Hauschild, M. and Alting, L. (1997) Environmental Assessment of Products. Vol. 1: Methodology, Tools and Case Studies in Product Development. Chapman and Hall, London.

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