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POLCAGE 1.0—a possibilistic life-cycle assessment model for evaluating alternative transportation fuels
Environmental Modelling & Software 19 (2004) 907–918 www.elsevier.com/locate/envsoft
POLCAGE 1.0—a possibilistic life-cycle assessment model for evaluating alternative transportation fuels Raymond R. Tan a,, Alvin B. Culaba b, Michael R.I. Purvis c a b
Chemical Engineering Department, De La Salle University—Manila, 2401 Taft Avenue, 1004 Manila, Philippines Mechanical Engineering Department, De La Salle University—Manila, 2401 Taft Avenue, 1004 Manila, Philippines c Department of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK Received 12 November 2002; received in revised form 27 March 2003; accepted 4 October 2003
Abstract A composite software model for the comparative life-cycle assessment (LCA) of 10 different fuel options for the Philippine automotive transport sector was developed. It is based on the GREET fuel-cycle inventory model developed by the Argonne National Laboratory for the United States Department of Energy. GREET 1.5a is linked to an impact assessment submodel using the Danish environmental design of industrial products (EDIP) method. This combined inventory–impact assessment model is enhanced further with possibilistic uncertainty propagation (PUP) and possibilistic compromise programming (PCP) features that allow the 10 alternatives to be ranked in the presence of multiple criteria and uncertain data. Sensitivity and scenario analysis can also be performed within the composite model. Some current and anticipated Philippine conditions, including electricity generation mix, are incorporated in the prototype’s built-in database. The software model, designated as POLCAGE 1.0 (possibilistic LCA using GREET and EDIP), is coded in Microsoft Excel and Visual Basic. The model’s capabilities and features are demonstrated using a case study based on its default scenario. # 2003 Elsevier Ltd. All rights reserved. Keywords: Life-cycle assessment (LCA); Decision support system (DSS); Alternative fuels
1. Introduction Automotive transport is a major contributor to local and global air pollution as well as fossil fuel resource depletion. In the Philippines, for example, road vehicles accounted for 13% of the country’s primary energy consumption in the late 1990s, as well as a proportionate share of the estimated 63 106 ton per annum national CO2 emission inventory (World Resources Institute, 2000). Urban air pollution has been recently cited as a major obstacle to the Philippines’ development (The World Bank, 1999). A number of measures have been taken by the government, particularly the ratification of the Clean Air Act in the late 1990s. (Philippine Department of Environment and Natural Resources, 2000).
Corresponding author. Fax: +63-2-524-0563. E-mail address: [email protected] (R.R. Tan).
1364-8152/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2003.10.004
Alternative propulsion systems are considered to be the most promising long-term solution to the environmental impacts resulting from road vehicle use (Poulton, 1994). In the Philippines, there is considerable interest in developing commercial petroleum alternatives such as natural gas or biodiesel (Philippine Department of Energy, 2000). However, efforts have largely been disorganized, in part due to the lack of an effective means of screening alternative technologies. Since the social and economic impacts of a large-scale technological transition in the automotive transport sector are considerable, it is essential that the alternative fuels or vehicle systems selected will deliver the environmental benefits anticipated. A decision support system (DSS) can help make the assessment process more rational, consistent and reliable (Huang et al., 1995). In this study, a software-based DSS using lifecycle modeling concepts was developed to aid in determining the best environmental option (BEO) from a list of alternative technologies.
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2. Life-cycle assessment Life-cycle assessment (LCA) is a holistic procedure for estimating environmental impacts of a technological system on a cradle-to-grave basis. In the past decade, LCA has become accepted as an effective tool for environmental management, particularly in the context of decision support. International standardization efforts were started by the Society of Environmental Toxicology and Chemistry (SETAC, 1991); more recently, the International Organization for Standardization developed the ISO 14040 series LCA standards (ISO, 1997, 1998, 2000a,b). The ISO 14040 standards identify four components or stages in a ‘‘full’’ LCA: . Goal and scope definition—definition of the purpose of the LCA, the intended audience or user, and the scale and boundaries of the technological system under scrutiny . Inventory analysis—the quantification of material and energy flows of the defined system . Impact assessment—classification, characterization and valuation of environmental impacts resulting from the material and energy flows of the defined system . Interpretation—use of all previous information to meet the designated objectives. LCA as described in the ISO standards is comprehensive and time-consuming. Krozer and Vis (1998) argued that this discourages many potential LCA users, and suggested that methodological simplification will promote more active use of the life-cycle concept. Thus, the development of ‘‘streamlined’’ LCA (SLCA) procedures has been a priority area in recent LCA research (SETAC, 1999). The SLCA approach used in this study stems directly from the method used in the GREET (Greenhouse Gases, Regulated Emissions and Energy use in Transportation) fuel-cycle model (Wang, 1996, 1999, 2001). The model developed combines two streamlining procedures: . Limiting analysis to priority pollutants and inventory parameters. Focus is placed on quantifying the flows of energy (total, fossil and petroleum-derived), greenhouse gases (primarily CO2, N2O and CH4) and miscellaneous air pollutants associated with motor vehicle use (VOCs, CO, NOx, PM10 and SOx) which are the principal pollutants associated with fuel combustion (Poulton, 1994; Wang, 1996, 1999, 2001). Emission levels are generally proportionate to energy usage; the latter can thus be used as a rough index of over-all environmental performance. This is particularly true for comparison of fuels derived from the same feedstock (Kreith et al., 2002).
. Limiting the size of the system analyzed by moving system boundaries. Life-cycle analysis of the energy carrier used for vehicle propulsion is called fuelcycle assessment. The vehicle itself has its own lifecycle. Assessment of the combined fuel and vehicle life-cycle systems is referred to as total energy cycle assessment (TECA). Fig. 1 illustrates the distinction between these forms of LCA. Fuel-cycle impacts tend to outweigh those of the vehicle cycle because the useful lives of motor vehicles, especially in the Philippines, typically lasts several decades. Thus, it often becomes possible to arrive at a reliable decision based purely on analysis of the fuel cycle. LCA-based modeling is further complicated by the presence of multiple evaluation criteria (Azapagic and Clift, 1999) and data uncertainty. The former is addressed in POLCAGE 1.0 (possibilistic LCA using GREET and EDIP) using a simple multiple-attribute decisionmaking (MADM) procedure. The latter has traditionally been treated using a probabilistic approach (Wenzel et al., 1997; General Motors Corporation, 2001; Wang, 2001); in POLCAGE 1.0, an alternative representation of data uncertainty based on fuzziness is employed. The use of fuzzy sets instead of classical probability to represent numerical uncertainty is known as possibility theory (Dubois and Prade, 1988). However, the terms remain somewhat interchangeable in the literature, so that fuzzy (imprecise) numbers are said to have possibility (as opposed to probability) distributions. The possibilistic approach has proven to be comparable to probabilistic ones but offers the advantage of computational efficiency (Kaufmann and Gupta, 1991; Mauris et al., 2001; Tan et al., 2002). Validation of the methods used in the model was accomplished through comparative testing with conventional techniques, as suggested by Borenstein (1998) and Qureshi et al. (1999).
Fig. 1.
Fuel, vehicle and total energy life cycles (Wang, 1999).
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3. Alternative fuel cycles simulated in POLCAGE 1.0 POLCAGE 1.0 focuses on the life cycles of eight alternative fuels and energy carriers: . Electricity for use in electric vehicles (EVs). . Liquid (LH2) and gaseous or compressed hydrogen (GH2) produced by water electrolysis, for use in fuel cell vehicles (FCVs). . Bioethanol (BioEtOH) derived from cellulosic agricultural residue, for use as a neat (pure) fuel in vehicles with spark-ignition (SI) engines. . Biodiesel (BD) derived from coconut oil, for use in vehicles with compression-ignition (CI) engines. . Liquid (LNG) and compressed natural gas (CNG) for use in vehicles with SI engines. . Methanol (MeOH) derived from natural gas, for use as a neat fuel in vehicles with SI engines. Conventional diesel and gasoline are also modeled in POLCAGE 1.0 to provide a baseline for comparison.
4. Components of POLCAGE 1.0 4.1. The GREET fuel-cycle inventory submodel The GREET model was developed by the Argonne National Laboratory in the mid-1990s for the United States Department of Energy (Wang, 1996). This public-domain model can be downloaded from the Argonne website (http://www.transportation.anl.gov). GREET version 1.5a (Wang, 1999) was used as the inventory submodel of POLCAGE. It is coded in Microsoft Excel and Visual Basic, and its modular structure allows users to create new fuel pathways or modify existing ones. The most recent version of this model is GREET 1.6, which is enhanced with graphic user interfaces (GUIs) and Monte Carlo simulation capability (Wang, 2001). For its use in the composite model, GREET 1.5a was recalibrated to reflect Philippine conditions, such as domestic energy mix and fuel standards (Philippine Department of Energy, 2000). 4.2. The EDIP impact assessment submodel The environmental design of industrial products (EDIP) method was developed in the mid-1990s by a consortium that included the Technological University of Denmark, the Confederation of Danish Industries, the Danish Environmental Protection Agency and private-sector partners. The impact assessment procedure specified under the EDIP framework relies on classification under predefined environmental impact cate-
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gories, characterization using equivalency factors, and normalization with weighting using the concept of the person-equivalent (Hauschild and Wenzel, 1997; Wenzel et al., 1997). The eight air pollutants and three energy resources modeled in the GREET submodel contribute to only eight EDIP impact categories. In POLCAGE 1.0, the EDIP impact assessment method is coded onto the GREET 1.5a spreadsheet to allow comprehensive analysis based on environmental impact themes (rather than just inventory flows) to be performed. Although in principle, it is possible to perform multicriterion analysis using just the inventory results from GREET, in practical decision-making scenarios human users may also want to be able to view impact assessment results, thus necessitating the use of the EDIP method. Numerical impact coefficients of EDIP were modified as needed to reflect current Philippine air quality standards (Philippine Department of Environment and Natural Resources, 2000). In this submodel, the normalization and weighting factors for global warming and resource depletion are based on world statistics, since these are global environmental concerns. For the local and regional environmental impact categories, the original EDIP weighting and normalization factors are used. The Philippine pollutant inventory statistics necessary for recalibration of these factors were unavailable at the time of model development; future versions of the software will incorporate these modifications as soon as the data become available. However, this is not a particularly serious shortcoming since, as discussed in the next section, the environmental impact scores are subsequently reduced to dimensionless form. Further mathematical operations effectively remove the effect of the EDIP weighting and normalization factors from the model’s final output. 4.3. The possibilistic uncertainty propagation module In POLCAGE 1.0, imprecise model parameters are represented as fuzzy numbers with triangular possibility distributions. Possibilistic uncertainty propagation (PUP) is accomplished using a Visual Basic module which performs iterative calculations through the spreadsheet and subsequently stores output values in a designated worksheet. The mechanics of PUP are based on fuzzy arithmetic (Kaufmann and Gupta, 1991). Details of the methodology, including comparative validation tests, can be found in Tan and Culaba (2001a) and Tan et al. (2002). Data uncertainty is traditionally modeled using classical probability theory. By definition, such uncertainty is assumed to be the consequence of system randomness, although the Bayesian framework employs probability as a measure of strength of belief. In comparison, fuzzy set theory describes imprecision
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associated with vagueness or ambiguity. This form of uncertainty arises, for example, in linguistic expressions or subjective beliefs. Possibility theory is an interpretQ ation of fuzziness that assigns a possibility value, , within the interval [0, 1] that is proportionate to the degree of plausibility of a statement or argument. Quantitative possibilistic uncertainty can be expressed through fuzzy numbers. These numbers are ‘‘spread out’’ over a range of values, in contrast to crisp (ordinary) numbers which have unique values. The possibility at any given value marks the ‘‘degree of truth’’ that the fuzzy number assumes that particular value. In a normal fuzzy number, at least one value has a possibility of 1. Possibility distributions, unlike their probabilistic counterparts, do not result from any specific mathematical rules but are generally reflections of subjective belief. However, stylized triangular or trapezoidal distributions are often employed for simplicity (Kaufmann and Gupta, 1991; Mauris et al., 2001). If triangular possibility distributions are assumed, a fuzzy quantity can be expressed as a triad of the distribution mode and bounds, as in Eq. (1). If the distribution is symmetrical, the mode lies exactly halfway between the outer limits of the number. ~e ¼ ðeL ; eM ; eR Þ
ð1Þ
The crisp values eL, eM, and eR are the lower bound, mode, and upper bound, respectively, of triangular possibility distribution. To illustrate this notation, Fig. 2 shows the triangular possibility distribution of the fuzzy number (0.5, 1.0, 2.5). 4.4. The possibilistic compromise programming submodel Although POLCAGE 1.0 allows the user to access intermediate data, such as inventory values from the GREET submodel and normalized environmental impacts from the EDIP component, the principal output comes in the form of aggregate impact scores which allow straightforward ranking of the alternatives
being evaluated. Aggregation, weighting and pairwise comparison is accomplished in the possibilistic compromise programming (PCP) submodel. In order to account for valuation uncertainty, this submodel contains user-defined weights, dominance thresholds and pessimism levels (this concept is discussed in more detail below). PCP is based on compromise programming (CP) (Zeleny, 1973) which is modified to allow handling of imprecise data in the form of fuzzy numbers. Comprehensive descriptions of the PCP method, including comparative validation tests, are given by Tan and Culaba (2001b). The CP is an MADM procedure that ranks alternatives based on a modified weighted averaging procedure (Zeleny, 1973). The method is characterized by conceptual simplicity, computational efficiency, and solution robustness, characteristics which make it easily understood by human users and thus attractive for use in LCA. Eq. (2) shows the normalization procedure that converts scores in each environmental impacts category into dimensionless form. This procedure allows aggregation of impacts originally measured on different performance scales (Yoon and Hwang, 1995). The procedure expresses each environmental impact score as a fraction of the worst score found for a given impact category among all the enumerated alternatives. As a result of the normalization, weighting factors previously applied by using the EDIP impact assessment method cancel out to prevent double valuation of impacts. ~yij ¼ ~eij ½ej 1 ð2Þ Variable y˜ij is the normalized possibilistic index of alternative (i) for criterion (j), e˜ij is the possibilistic index of alternative (i) for environmental criterion (j), and ej is the worst impact score found among the specified alternatives for category (j). The latter quantities are generated from the inventory results using the EDIP method described in the previous section. Note that ej is a crisp (non-fuzzy) value. It can be seen that this mathematical transformation effectively removes the effect of the EDIP weighting and normalization factors from the model’s final output. The CP involves simply aggregating the normalized scores of each alternative into a single composite index, as in Eq. (3): " #1=P X P ~ui ¼ wj ~yij ð3Þ j
Fig. 2.
Triangular possibility distribution of (0.5, 1.0, 2.5).
Variable u˜i is the possibilistic aggregate index of alternative (i), wj is the crisp weight of criterion (j), and P is the scaling parameter. Each criterion is given an appropriate weight factor depending on its relative impor-
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tance in determining the over-all score. In addition, the scaling parameter is also specified. If P ¼ 1, the result is the simple additive weighting (SAW) method. As P is increased, the aggregation becomes progressively more pessimistic; the worst scores become more significant in determining the composite impact indices regardless of the weights used. This is particularly significant in environmental contexts where decisions tend to be determined by the worst rather than best features of a given technological alternative. As argued by Geldermann et al. (2000), perfect compensation among attribute scores is not realistic in environmental decision-making. For example, a fuel which is potentially carcinogenic is unlikely to be considered as a viable alternative, even if it outperforms its competitors in all other environmental categories. The weights wj are normalized such that X 1¼ wj ð4Þ j
All computations with possibilistic data are carried out using established rules of fuzzy arithmetic, details of which can be found in Kaufmann and Gupta (1991) and Mauris et al. (2001). The results of the computations on the original symmetric triangular fuzzy numbers also have approximately triangular distributions, but are no longer symmetric. After the aggregate indices have been calculated, pairwise comparison of the alternatives is performed by taking the fuzzy differences, ~ ua ~ ub , for a 6¼ b. The possibility distribution of each pairwise difference can be used to gauge the degree of superiority since the ranking of fuzzy or possibilistic quantities is not as straightforward as the comparison of crisp numbers (Perny and Roy, 1992). A minimum significant degree of dominance, H, is assigned a priori by the user to establish a threshold level for asserting superiority among the alternatives. The degree of dominance, Gab, can then be calculated using Eq. (5) as the logical complement of the expQ ression ð~ ua ~ ub ¼ 0Þ; the latter is the possibility that the difference between the two environmental scores is zero. This concept is also illustrated in Fig. 3. Gab ¼ 1
Y
ð~ ub ¼ 0Þ ua ~
ð5Þ
Significant dominance is indicated when Gab H. Establishing significance of differences relative to predetermined thresholds in this manner is superficially analogous to the use of levels of significance in conventional statistical hypothesis tests, although the latter are of course based on probability rather than possibility theory. This concept is illustrated in a later section using specific model outputs.
Fig. 3.
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Possibilistic degree of dominance.
5. Operational features of POLCAGE 1.0 The POLCAGE 1.0 prototype is coded in Microsoft Excel and Visual Basic. It consists of the GREET 1.5a spreadsheet model with additional sheets and modules, as well as new fuel cycles. Fig. 4 shows the user’s view of the POLCAGE screen. Only eight sheets are immediately visible when the model is opened: the copyright sheet of the original GREET model, the inventory plots sheet containing crisp (non-fuzzy) inventory displays, and six sheets that comprise the new computational and display features. All other sheets containing inventory calculations are hidden from view. PUP, which cannot be implemented in the spreadsheet environment, is coded as a Visual Basic procedure. The operational features of the POLCAGE 1.0 model are as follows: . Control sheet. This sheet contains the 20 imprecise model inputs modeled as fuzzy numbers with triangular possibility distributions. The first 10 imprecise parameters are the vehicle fuel economy figures used in the simulations, which are based on the default assumptions of GREET with 10% error margins. The remaining 10 imprecise inputs pertain to efficiency parameters of the upstream fuel production processes. Most of the values used are based on a recent fuel-cycle study by General Motors Corporation (2001). This sheet also contains scenarios for power generation mix and coproduct allocation method in electrolytic hydrogen production. . PUP module. This Visual Basic procedure executes fuzzy computations in POLCAGE using the imprecise parameters specified in the control sheet. It generates the triangular possibility distributions of the model outputs and stores the parameters in the fuzzy output sheet. . Crisp output sheet. Conversion of the inventory data from the GREET submodel into the corresponding impact parameters takes place in this sheet. Matrix
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Fig. 4. View of POLCAGE 1.0 screen.
calculations are employed to implement the EDIP impact assessment procedure. The values of the EDIP impact factors are also stored in this sheet. Only eight impact categories are applicable to the inventory outputs of the GREET submodel: global warming (GWP), acidification (AP), photochemical ozone formation (POFP), nutrification (NP), human toxicity via inhalation (HTP), and resource depletion (RDP) of oil, coal and natural gas. These impact categories arise as a consequence of the choice of inventory parameters in the GREET submodel as explained in previous sections. . Fuzzy output sheet. When triangular possibility distributions are assumed, each fuzzy model output is expressed as a triad consisting of a lower bound, a mode (or average), and an upper bound. This sheet stores the triads, which are generated from the crisp model outputs by the PUP module. . Displays sheet. This sheet contains a three-dimensional histogram summarizing the crisp environmental impacts of the alternative fuels, as well as a series of box plots showing the possibilistic model outputs for each inventory parameter and impact category. Hence, the user has access to graphical displays of intermediate or disaggregated model data, which may be necessary for in-depth analysis. . PCP sheet. The PCP sheet controls the aggregation of environmental impacts and the subsequent comparison of the alternatives. Aggregation is controlled by specifying the scaling parameter, P, and the weights of each of the eight impact categories used. The choice of the value of P controls the degree of pessimism of the aggregation. For example, for
P ¼ 1, PCP becomes a simple weighted-averaging procedure. As the value of P is increased, high impact category scores become more significant in determining the aggregate environmental score of an alternative. Further control is possible through the weight vector. Equal weights are assigned to the impact categories in the default case, but the user may input a new weight vector manually. Furthermore, a weight-sensitivity analysis procedure based on that used by Geldermann et al. (2000) can be implemented as follows. The user selects an impact category whose weight is to be varied. The weight assigned to this category can then be varied from 0 (no importance), to 0.125 (equal in importance to all other categories), and finally to 1 (overriding importance, implying 0 weight for all other categories). Once the scaling parameter and weight vector are specified, the aggregated possibilistic environmental scores are calculated. Comparison of alternatives is then possible using two display options. First, the user may specify two alternatives to be compared. The difference between the aggregate scores of the selected alternatives is then displayed as a possibilistic number with a triangular distribution. The degree of dominance can then be deduced from the mode and spread of the possibility distribution (Tan and Culaba, 2001b). Alternatively, the user can specify the minimum significant degree of dominance, H, in which case the model output is displayed in the form of a trivalent dominance matrix. In the latter, the values 1, 0 and +1 indicate inferiority, indifference (or equivalence) and superiority, respectively.
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. PCP display sheet. This sheet displays the possibilistic aggregate environmental scores of the alternatives, in the form of box plots. The display clearly shows both the average values and the uncertainty margins, thus allowing users to make rapid, approximate estimates of dominance relationships among the fuels being evaluated, without having to rely on the features of the PCP sheet.
6. Case study: comparison of alternative fuels for Philippine automotive transport Since the model is intended to provide decision support for policy makers trying to identify clean transportation fuels, the following case study is provided to illustrate how exactly the model assists the human user. Simulation assumptions used are based on the best available Philippine data. 6.1. Simulation parameters The case study presented here used the default parameters of the POLCAGE 1.0 prototype. The following assumptions, being the most important, bear some discussion: . Assessment is normalized to 1 km traveled by the end-user vehicle. . The power generation mix was based on the 10-year projections of the Philippine Department of Energy (2000) for the year 2009: 45% coal, 10% oil, and 16% natural gas, with renewables making up the balance. Many of the fuel options and vehicle technologies included in the model are unlikely to be in any commercial use until the end of the decade. The year 2009 was used because the currently available projections of the Philippine Government do not extend beyond that year. . Eight kilogram of oxygen gas are liberated for every kilogram of hydrogen produced by electrolysis of water. In principle, coproduct allocation rules or displacement can be employed to divide environmental impacts between the oxygen and hydrogen life cycles. However, if hydrogen is produced commercially to meet projected Philippine transportation fuel requirements, it is likely that the large quantities of oxygen produced will be in great excess of any economic demand for this gas as an industrial commodity. It is more probable that the bulk of the oxygen will simply be vented into the atmosphere as a process waste. In the simulations, hydrogen is thus treated as the sole economically valuable product of the electrolysis process. . For simplicity, equal weights were assigned to the eight EDIP environmental impact categories.
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. The PCP parameters employed were P ¼ 2 and H ¼ 0:5. The choice of scaling factor value reflects some degree of pessimism which is implicit in environmental decision-making; large environmental impacts tend to be more decisive factors than small ones. The specified minimum degree of dominance corresponds to the linguistic equivalent, ‘‘moderately superior or inferior,’’ implying that slight differences in over-all environmental impacts (degree of dominance < 0:5) can be dismissed as being insignificant. Such an application of possibility as the numerical equivalent of a linguistic or subjective expression is consistent with conventional applications of fuzzy sets (Mauris et al., 2001; Tan et al., 2002). 6.2. Results and discussion LCA models like GREET are limited to the accounting of material and energy flows. In some cases, inventory data alone may suffice to meet a user’s needs. However, for decision-making and public policy formulation a comprehensive LCA including impact assessment and error sensitivity analysis is preferable. The prototype model POLCAGE 1.0 is capable of environmental impact classification, characterization and valuation, as well as data uncertainty processing under a possibilistic computational framework. Multilevel data access also allows users to view intermediate or disaggregated information throughout the model, thereby allowing the model’s chain of reasoning to be scrutinized. This accessibility was identified by Mazzarachio et al. (1996) as a vital feature of an effective decision support model. Four output display options of POLCAGE 1.0 are shown here. Fig. 5 shows the box plot display of aggregate possibilistic environmental scores of the 10 alternatives. The impact scores are dimensionless and normalized within the interval [0, 1], with 0 being the best possible score. Both magnitude and uncertainty are visible in the box plot format, thus allowing approximate dominance relationships to be estimated. For example, the average impact score of methanol (MeOH) is about 0.47, while that of gasoline is roughly 0.52. However, considerable overlap is evident when the possibilistic uncertainty margins are taken into account, making it difficult to assert the superiority of methanol to gasoline without further in-depth analysis. Visual inspection of the box plot display allows approximate dominance relationships to be estimated. When the minimum significant degree of dominance, H, is specified, a crisp or defuzzified dominance matrix can be used to display outranking relationships among the alternatives. The dominance matrix for the case study, evaluated at H ¼ 0:5, is shown in Fig. 6. The cell elements denote row-to-column comparisons. For example, the ‘‘0’’ in row 41, column 21 of the
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Fig. 5.
Possibilistic aggregate environmental scores of alternative fuels.
spreadsheet indicates that gasoline is not significantly different from methanol; the degree of dominance does not exceed the threshold value of 0.5. Cell entries of ‘‘0’’ in the dominance matrix indicate indifference between the alternatives under comparison, which occurs when aggregate impact scores are equivalent (as in the case of a diagonal cell that denotes the comparison of an alternative with itself), or when the difference between scores does not exceed the possibilistic threshold of significance, H. Entries of ‘‘1’’ indicate superiority
Fig. 6.
while ‘‘1’’ indicates inferiority. Only half of the matrix needs to be shown since complementary pairwise relationships are readily deduced from the given display. The possibility distribution of the difference in the impact scores of a pair of alternatives can also be displayed. This allows the exact degree of dominance to be determined without having to adjust the threshold parameter H. Fig. 7, for example, shows the difference between the total environmental impacts of gasoline and bioethanol. The most possible (or average) value is
Defuzzified dominance matrix.
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Fig. 7. Possibilistic difference of aggregate impact scores of gasoline and methanol.
0.05, indicating that methanol on the average is superior to gasoline. However, possibility distribution encloses some negative values as well, which indicates the possible superiority of gasoline to bioethanol. The possibility that the difference is zero—or that the two fuels have equivalent impacts—is about 0.65. This value implies a degree of dominance of 0.35 as its logical complement, which fails to exceed the preset value of H. The result can be interpreted linguistically as ‘‘methanol is not significantly superior to gasoline.’’
Fig. 8.
In some situations, the user may not be satisfied with just the aggregated impact results. For such cases, POLCAGE 1.0 allows access to intermediate model data such as inventory flows and disaggregated environmental impacts. For example, it may be of interest to explain the relatively poor evaluation of methanol (MeOH), which at first sight seems unexpected. Fig. 8 shows crisp environmental impacts resulting from the GREET–EDIP submodels combination. These impacts are based on the possibilistic modes or averages of the input data, and are normalized and
Crisp environmental impacts of alternative fuels.
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weighted according to the EDIP guidelines (Wenzel et al., 1997). A disaggregated environmental impact display of this form allows the user to investigate the strong and weak points of the alternative fuel under scrutiny. In this case, methanol exhibits relatively high impact scores in the global warming (GWP), photochemical ozone formation (POFP) and natural gas depletion (RDP NG) categories, thus explaining its low over-all rating. Effective decision support entails two conflicting considerations. On one hand, results must be sufficiently concise so as not to overwhelm the user with large quantities of data; on the other hand, detailed information might also be required further into the decision process (Mazzarachio et al., 1996). Thus, a decision support tool should be able to satisfy both criteria depending on the user’s demand. A principal feature of the POLCAGE 1.0 is that partially or fully processed results can be viewed by the user. This feature allows the model’s chain of reasoning to be traced, in the same manner that a human consultant might be asked to provide not just final recommendations, but explanations of how such conclusions came about. The availability of different forms of output displays in POLCAGE 1.0 allows effective decision support. The user, for instance, may rely on the box plot display to gain a quick overview of the relative merits of the alternatives. Generation of a series of dominance matrix at different values of H can then be used to gain more insight into the degrees of superiority (or inferiority) of the fuel options. If these are insufficient, the user then has the option to view disaggregated environmental impacts or inventory flows to answer questions about specific environmental aspects of each fuel. Comprehensive sensitivity analysis with respect to aggregation parameters and other model inputs can then be used to model different viewpoints and scenarios that are relevant to the decision process. Modeling of data uncertainty is another fundamental feature of POLCAGE 1.0. However, a possibilistic framework is used instead of the conventional probabilistic one. Uncertainty in the input data propagates through the model and the error margins are reflected in the model output. The model thus provides the user with two levels of information: first, the magnitude of environmental effects, and second, the associated confidence levels of these estimates. Without the latter, there is significant potential for confusion; a user, for instance, might interpret one energy source as being superior to another, when in fact the difference is marginal. This point has been illustrated in the preceding comparison of gasoline and methanol. By retaining both types of information throughout its calculations, the model facilitates proper interpretation of its outputs.
POLCAGE 1.0 is envisioned as a support tool for clarifying environmental issues and priorities in the context of automotive fuels evaluation. The model provides users with information in a concise, rational and consistent form. Significantly, the information is also available at various levels of aggregation, which facilitates detailed analysis of the decision-making process. For the conditions and assumptions considered the model outcomes suggest that electricity (for EVs) and biodiesel are the BEOs. However, at this stage the model does not include cost elements, and the influence of human judgment in providing experiential inputs should not be ignored.
7. Conclusion The composite software tool POLCAGE 1.0 was developed to provide users with a decision support model capable ranking alternative motor vehicle fuels using comprehensive LCA. In contrast, the GREET model, which is used as a subcomponent of POLCAGE, by itself is capable only of inventory analysis and calculation of total greenhouse gases. The enhanced model incorporates impact classification and aggregation using the EDIP method. Methodological weaknesses of conventional LCA models were addressed by using PUP for processing input data uncertainty and PCP for multiple-criterion analysis. Other features of the model include sensitivity and scenario analysis with respect to power generation mix, coproduct allocation schemes and impact valuation. For effective decision support, POLCAGE 1.0 provides information at different levels of aggregation. Users can immediately view summary results, but also have the option to scrutinize each step of the model’s computations by accessing partially processed data, such as emissions inventories or disaggregated environmental impacts. Significantly, both point estimates of environmental effects and the corresponding uncertainty margins are available for viewing. By providing two levels of information to the user, the risks of misinterpretation are reduced. The extensive use possibility theory allows POLCAGE 1.0 to be used as a decision-support tool for selecting the BEO from the specified fuel alternatives without the rigidity of previous methods. The principle underlying POLCAGE 1.0 is thus compatible with the ‘‘soft’’ decision framework envisioned by Krozer and Vis (1998) and Geldermann et al. (2000). This view assumes that a decision support model serves to elucidate different scenarios and viewpoints to aid the user, instead of mechanically providing a ‘‘correct’’ answer that overrules human judgment. To date the model features and results have been shown to the Philippine
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Department of Science and Technology (DOST), which has expressed some interest in using LCA for screening new technologies. Current efforts are underway to have the model used by the Department of Energy (DOE) for policy development. Future work on the model itself will focus on the following areas of development: . New fuel and energy pathways within the GREET submodel. . Possibilistic weighting of environmental impact categories. . User-defined possibilistic parameters, possibility distributions and fuel options. . Graphic user interface. . Development of a generic code for the analysis of manufacturing processes by combining the algorithms used in POLCAGE with an existing model (Pineda-Henson et al., 2002). . Development of hierarchical or multi-level aggregation through composite programming (Stansbury et al., 1992; Mazzarachio et al., 1996). This modification will allow, for example, partial aggregation of ecological and resource depletion impacts under separate categories. Economic considerations can also be introduced in this expanded framework to allow cost–benefit analysis to be performed. Acknowledgements The authors wish to acknowledge the financial support of De La Salle University—Manila, through the Faculty Development Program, and The British Council. We are also grateful to Dr. Stanley Santos for his assistance in miscellaneous aspects of our work.
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ISO 14041, 1998. Environmental management—life cycle assessment—goal and scope definition and inventory analysis. International Organisation for Standardisation, Geneva. ISO 14042, 2000a. Environmental management—life cycle assessment—life cycle impact assessment. International Organisation for Standardisation, Geneva. ISO 14043, 2000b. Environmental management—life cycle assessment—life cycle interpretation. International Organisation for Standardisation, Geneva. Kaufmann, A., Gupta, M.M., 1991. Introduction to Fuzzy Arithmetic: Theory and Applications. International Thomson Computer Press, London. Kreith, F., West, R., Isler, B., 2002. Efficiency of advanced ground transportation technologies. Journal of Energy Resources Technology 124 (3), 173–179. Krozer, J., Vis, J.C., 1998. How to get LCA in the right direction? Journal of Cleaner Production 6 (1), 53–61. Mauris, G., Lasserre, V., Foulloy, L., 2001. A fuzzy approach for the expression of uncertainty in measurement. Measurement 29 (3), 165–177. Mazzarachio, P., Raggi, A., Barbiroli, G., 1996. New method for assessing the global quality of energy systems. Applied Energy 53 (4), 315–324. Perny, P., Roy, B., 1992. The use of fuzzy outranking relations in preference modeling. Fuzzy Sets and Systems 49 (1), 33–53. Philippine Department of Energy, 2000. Philippine Energy Plan 2000–2009. Manila. Philippine Department of Environment and Natural Resources, 2000. Implementing rules and regulations of RA 8749—Clean Air Act of 1999. Manila. Pineda-Henson, R., Culaba, A.B., Mendoza, G.A., 2002. Evaluating environmental performance of pulp and paper manufacturing using the analytic hierarchy process and life-cycle assessment. Journal of Industrial Ecology 6 (1), 15–28. Poulton, M.L., 1994. Alternative Fuels for Road Vehicles. Computational Mechanics Publications, Southampton. Qureshi, M.E., Harrison, S.R., Wegener, M.K., 1999. Validation of multicriteria analysis methods. Agricultural Systems 62 (2), 105–116. SETAC, 1991. A Technical Framework for Life Cycle Assessments. Society of Environmental Toxicology and Chemistry, Washington, DC. SETAC, 1999. Streamlined Life-Cycle Assessment: a Final Report from the SETAC North America Streamlined LCA Workgroup. Society of Environmental Toxicology and Chemistry, Washington, DC. Stansbury, J., Bogardi, I., Lee, Y.W., Woldt, W., 1992. Multiobjective decision-making under uncertainty. In: Goicochea, A., Duckstein, L., Zionts, S. (Eds.), Multiple Criteria Decision-Making. In: Proceedings of the ninth International Conference— Theory and Applications in Business, Industry, and Government. Springer-Verlag, Berlin. Tan, R.R., Culaba, A.B., 2001a. Life cycle assessment of compressed and liquefied natural gas with possibilistic uncertainty analysis of greenhouse gas emissions. In: Proceedings, 49th National Convention of the Philippine Society of Mechanical Engineers, Manila. Tan, R.R., Culaba, A.B., 2001b. Life cycle impact assessment using possiblilistic compromise programming. In: Proceedings, 50th Annual Convention of the Philippine Association for the Advancement of Science, Manila. 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 26 (8), 737–745. Wang, M., 1996. GREET 1.0—transportation fuel cycles model: methodology and use. Final Report ANL/ESD-33. Argonne National Laboratory, USA. Wang, M., 1999. GREET 1.5—transportation fuel cycle model. Final Report ANL/ESD-39. Argonne National Laboratory, USA.
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Wang, M., 2001. Development of GREET 1.6 fuel cycle model for transportation fuels and vehicle technologies. Final Report ANL/ ESD/TM-163. Argonne National Laboratory, USA. Wenzel, H., Hauschild, M., Alting, L., 1997. Environmental assessment of products. Methodology, Tools and Case Studies in Product Development vol. 1. Chapman & Hall, London. The World Bank, 1999. Philippines: the challenge of economic recovery. Washington, DC.
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POLCAGE 1.0—a possibilistic life-cycle assessment model for evaluating alternative transportation fuels Raymond R. Tan a,, Alvin B. Culaba b, Michael R.I. Purvis c a b
Chemical Engineering Department, De La Salle University—Manila, 2401 Taft Avenue, 1004 Manila, Philippines Mechanical Engineering Department, De La Salle University—Manila, 2401 Taft Avenue, 1004 Manila, Philippines c Department of Mechanical and Design Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK Received 12 November 2002; received in revised form 27 March 2003; accepted 4 October 2003
Abstract A composite software model for the comparative life-cycle assessment (LCA) of 10 different fuel options for the Philippine automotive transport sector was developed. It is based on the GREET fuel-cycle inventory model developed by the Argonne National Laboratory for the United States Department of Energy. GREET 1.5a is linked to an impact assessment submodel using the Danish environmental design of industrial products (EDIP) method. This combined inventory–impact assessment model is enhanced further with possibilistic uncertainty propagation (PUP) and possibilistic compromise programming (PCP) features that allow the 10 alternatives to be ranked in the presence of multiple criteria and uncertain data. Sensitivity and scenario analysis can also be performed within the composite model. Some current and anticipated Philippine conditions, including electricity generation mix, are incorporated in the prototype’s built-in database. The software model, designated as POLCAGE 1.0 (possibilistic LCA using GREET and EDIP), is coded in Microsoft Excel and Visual Basic. The model’s capabilities and features are demonstrated using a case study based on its default scenario. # 2003 Elsevier Ltd. All rights reserved. Keywords: Life-cycle assessment (LCA); Decision support system (DSS); Alternative fuels
1. Introduction Automotive transport is a major contributor to local and global air pollution as well as fossil fuel resource depletion. In the Philippines, for example, road vehicles accounted for 13% of the country’s primary energy consumption in the late 1990s, as well as a proportionate share of the estimated 63 106 ton per annum national CO2 emission inventory (World Resources Institute, 2000). Urban air pollution has been recently cited as a major obstacle to the Philippines’ development (The World Bank, 1999). A number of measures have been taken by the government, particularly the ratification of the Clean Air Act in the late 1990s. (Philippine Department of Environment and Natural Resources, 2000).
Corresponding author. Fax: +63-2-524-0563. E-mail address: [email protected] (R.R. Tan).
1364-8152/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2003.10.004
Alternative propulsion systems are considered to be the most promising long-term solution to the environmental impacts resulting from road vehicle use (Poulton, 1994). In the Philippines, there is considerable interest in developing commercial petroleum alternatives such as natural gas or biodiesel (Philippine Department of Energy, 2000). However, efforts have largely been disorganized, in part due to the lack of an effective means of screening alternative technologies. Since the social and economic impacts of a large-scale technological transition in the automotive transport sector are considerable, it is essential that the alternative fuels or vehicle systems selected will deliver the environmental benefits anticipated. A decision support system (DSS) can help make the assessment process more rational, consistent and reliable (Huang et al., 1995). In this study, a software-based DSS using lifecycle modeling concepts was developed to aid in determining the best environmental option (BEO) from a list of alternative technologies.
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2. Life-cycle assessment Life-cycle assessment (LCA) is a holistic procedure for estimating environmental impacts of a technological system on a cradle-to-grave basis. In the past decade, LCA has become accepted as an effective tool for environmental management, particularly in the context of decision support. International standardization efforts were started by the Society of Environmental Toxicology and Chemistry (SETAC, 1991); more recently, the International Organization for Standardization developed the ISO 14040 series LCA standards (ISO, 1997, 1998, 2000a,b). The ISO 14040 standards identify four components or stages in a ‘‘full’’ LCA: . Goal and scope definition—definition of the purpose of the LCA, the intended audience or user, and the scale and boundaries of the technological system under scrutiny . Inventory analysis—the quantification of material and energy flows of the defined system . Impact assessment—classification, characterization and valuation of environmental impacts resulting from the material and energy flows of the defined system . Interpretation—use of all previous information to meet the designated objectives. LCA as described in the ISO standards is comprehensive and time-consuming. Krozer and Vis (1998) argued that this discourages many potential LCA users, and suggested that methodological simplification will promote more active use of the life-cycle concept. Thus, the development of ‘‘streamlined’’ LCA (SLCA) procedures has been a priority area in recent LCA research (SETAC, 1999). The SLCA approach used in this study stems directly from the method used in the GREET (Greenhouse Gases, Regulated Emissions and Energy use in Transportation) fuel-cycle model (Wang, 1996, 1999, 2001). The model developed combines two streamlining procedures: . Limiting analysis to priority pollutants and inventory parameters. Focus is placed on quantifying the flows of energy (total, fossil and petroleum-derived), greenhouse gases (primarily CO2, N2O and CH4) and miscellaneous air pollutants associated with motor vehicle use (VOCs, CO, NOx, PM10 and SOx) which are the principal pollutants associated with fuel combustion (Poulton, 1994; Wang, 1996, 1999, 2001). Emission levels are generally proportionate to energy usage; the latter can thus be used as a rough index of over-all environmental performance. This is particularly true for comparison of fuels derived from the same feedstock (Kreith et al., 2002).
. Limiting the size of the system analyzed by moving system boundaries. Life-cycle analysis of the energy carrier used for vehicle propulsion is called fuelcycle assessment. The vehicle itself has its own lifecycle. Assessment of the combined fuel and vehicle life-cycle systems is referred to as total energy cycle assessment (TECA). Fig. 1 illustrates the distinction between these forms of LCA. Fuel-cycle impacts tend to outweigh those of the vehicle cycle because the useful lives of motor vehicles, especially in the Philippines, typically lasts several decades. Thus, it often becomes possible to arrive at a reliable decision based purely on analysis of the fuel cycle. LCA-based modeling is further complicated by the presence of multiple evaluation criteria (Azapagic and Clift, 1999) and data uncertainty. The former is addressed in POLCAGE 1.0 (possibilistic LCA using GREET and EDIP) using a simple multiple-attribute decisionmaking (MADM) procedure. The latter has traditionally been treated using a probabilistic approach (Wenzel et al., 1997; General Motors Corporation, 2001; Wang, 2001); in POLCAGE 1.0, an alternative representation of data uncertainty based on fuzziness is employed. The use of fuzzy sets instead of classical probability to represent numerical uncertainty is known as possibility theory (Dubois and Prade, 1988). However, the terms remain somewhat interchangeable in the literature, so that fuzzy (imprecise) numbers are said to have possibility (as opposed to probability) distributions. The possibilistic approach has proven to be comparable to probabilistic ones but offers the advantage of computational efficiency (Kaufmann and Gupta, 1991; Mauris et al., 2001; Tan et al., 2002). Validation of the methods used in the model was accomplished through comparative testing with conventional techniques, as suggested by Borenstein (1998) and Qureshi et al. (1999).
Fig. 1.
Fuel, vehicle and total energy life cycles (Wang, 1999).
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3. Alternative fuel cycles simulated in POLCAGE 1.0 POLCAGE 1.0 focuses on the life cycles of eight alternative fuels and energy carriers: . Electricity for use in electric vehicles (EVs). . Liquid (LH2) and gaseous or compressed hydrogen (GH2) produced by water electrolysis, for use in fuel cell vehicles (FCVs). . Bioethanol (BioEtOH) derived from cellulosic agricultural residue, for use as a neat (pure) fuel in vehicles with spark-ignition (SI) engines. . Biodiesel (BD) derived from coconut oil, for use in vehicles with compression-ignition (CI) engines. . Liquid (LNG) and compressed natural gas (CNG) for use in vehicles with SI engines. . Methanol (MeOH) derived from natural gas, for use as a neat fuel in vehicles with SI engines. Conventional diesel and gasoline are also modeled in POLCAGE 1.0 to provide a baseline for comparison.
4. Components of POLCAGE 1.0 4.1. The GREET fuel-cycle inventory submodel The GREET model was developed by the Argonne National Laboratory in the mid-1990s for the United States Department of Energy (Wang, 1996). This public-domain model can be downloaded from the Argonne website (http://www.transportation.anl.gov). GREET version 1.5a (Wang, 1999) was used as the inventory submodel of POLCAGE. It is coded in Microsoft Excel and Visual Basic, and its modular structure allows users to create new fuel pathways or modify existing ones. The most recent version of this model is GREET 1.6, which is enhanced with graphic user interfaces (GUIs) and Monte Carlo simulation capability (Wang, 2001). For its use in the composite model, GREET 1.5a was recalibrated to reflect Philippine conditions, such as domestic energy mix and fuel standards (Philippine Department of Energy, 2000). 4.2. The EDIP impact assessment submodel The environmental design of industrial products (EDIP) method was developed in the mid-1990s by a consortium that included the Technological University of Denmark, the Confederation of Danish Industries, the Danish Environmental Protection Agency and private-sector partners. The impact assessment procedure specified under the EDIP framework relies on classification under predefined environmental impact cate-
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gories, characterization using equivalency factors, and normalization with weighting using the concept of the person-equivalent (Hauschild and Wenzel, 1997; Wenzel et al., 1997). The eight air pollutants and three energy resources modeled in the GREET submodel contribute to only eight EDIP impact categories. In POLCAGE 1.0, the EDIP impact assessment method is coded onto the GREET 1.5a spreadsheet to allow comprehensive analysis based on environmental impact themes (rather than just inventory flows) to be performed. Although in principle, it is possible to perform multicriterion analysis using just the inventory results from GREET, in practical decision-making scenarios human users may also want to be able to view impact assessment results, thus necessitating the use of the EDIP method. Numerical impact coefficients of EDIP were modified as needed to reflect current Philippine air quality standards (Philippine Department of Environment and Natural Resources, 2000). In this submodel, the normalization and weighting factors for global warming and resource depletion are based on world statistics, since these are global environmental concerns. For the local and regional environmental impact categories, the original EDIP weighting and normalization factors are used. The Philippine pollutant inventory statistics necessary for recalibration of these factors were unavailable at the time of model development; future versions of the software will incorporate these modifications as soon as the data become available. However, this is not a particularly serious shortcoming since, as discussed in the next section, the environmental impact scores are subsequently reduced to dimensionless form. Further mathematical operations effectively remove the effect of the EDIP weighting and normalization factors from the model’s final output. 4.3. The possibilistic uncertainty propagation module In POLCAGE 1.0, imprecise model parameters are represented as fuzzy numbers with triangular possibility distributions. Possibilistic uncertainty propagation (PUP) is accomplished using a Visual Basic module which performs iterative calculations through the spreadsheet and subsequently stores output values in a designated worksheet. The mechanics of PUP are based on fuzzy arithmetic (Kaufmann and Gupta, 1991). Details of the methodology, including comparative validation tests, can be found in Tan and Culaba (2001a) and Tan et al. (2002). Data uncertainty is traditionally modeled using classical probability theory. By definition, such uncertainty is assumed to be the consequence of system randomness, although the Bayesian framework employs probability as a measure of strength of belief. In comparison, fuzzy set theory describes imprecision
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associated with vagueness or ambiguity. This form of uncertainty arises, for example, in linguistic expressions or subjective beliefs. Possibility theory is an interpretQ ation of fuzziness that assigns a possibility value, , within the interval [0, 1] that is proportionate to the degree of plausibility of a statement or argument. Quantitative possibilistic uncertainty can be expressed through fuzzy numbers. These numbers are ‘‘spread out’’ over a range of values, in contrast to crisp (ordinary) numbers which have unique values. The possibility at any given value marks the ‘‘degree of truth’’ that the fuzzy number assumes that particular value. In a normal fuzzy number, at least one value has a possibility of 1. Possibility distributions, unlike their probabilistic counterparts, do not result from any specific mathematical rules but are generally reflections of subjective belief. However, stylized triangular or trapezoidal distributions are often employed for simplicity (Kaufmann and Gupta, 1991; Mauris et al., 2001). If triangular possibility distributions are assumed, a fuzzy quantity can be expressed as a triad of the distribution mode and bounds, as in Eq. (1). If the distribution is symmetrical, the mode lies exactly halfway between the outer limits of the number. ~e ¼ ðeL ; eM ; eR Þ
ð1Þ
The crisp values eL, eM, and eR are the lower bound, mode, and upper bound, respectively, of triangular possibility distribution. To illustrate this notation, Fig. 2 shows the triangular possibility distribution of the fuzzy number (0.5, 1.0, 2.5). 4.4. The possibilistic compromise programming submodel Although POLCAGE 1.0 allows the user to access intermediate data, such as inventory values from the GREET submodel and normalized environmental impacts from the EDIP component, the principal output comes in the form of aggregate impact scores which allow straightforward ranking of the alternatives
being evaluated. Aggregation, weighting and pairwise comparison is accomplished in the possibilistic compromise programming (PCP) submodel. In order to account for valuation uncertainty, this submodel contains user-defined weights, dominance thresholds and pessimism levels (this concept is discussed in more detail below). PCP is based on compromise programming (CP) (Zeleny, 1973) which is modified to allow handling of imprecise data in the form of fuzzy numbers. Comprehensive descriptions of the PCP method, including comparative validation tests, are given by Tan and Culaba (2001b). The CP is an MADM procedure that ranks alternatives based on a modified weighted averaging procedure (Zeleny, 1973). The method is characterized by conceptual simplicity, computational efficiency, and solution robustness, characteristics which make it easily understood by human users and thus attractive for use in LCA. Eq. (2) shows the normalization procedure that converts scores in each environmental impacts category into dimensionless form. This procedure allows aggregation of impacts originally measured on different performance scales (Yoon and Hwang, 1995). The procedure expresses each environmental impact score as a fraction of the worst score found for a given impact category among all the enumerated alternatives. As a result of the normalization, weighting factors previously applied by using the EDIP impact assessment method cancel out to prevent double valuation of impacts. ~yij ¼ ~eij ½ej 1 ð2Þ Variable y˜ij is the normalized possibilistic index of alternative (i) for criterion (j), e˜ij is the possibilistic index of alternative (i) for environmental criterion (j), and ej is the worst impact score found among the specified alternatives for category (j). The latter quantities are generated from the inventory results using the EDIP method described in the previous section. Note that ej is a crisp (non-fuzzy) value. It can be seen that this mathematical transformation effectively removes the effect of the EDIP weighting and normalization factors from the model’s final output. The CP involves simply aggregating the normalized scores of each alternative into a single composite index, as in Eq. (3): " #1=P X P ~ui ¼ wj ~yij ð3Þ j
Fig. 2.
Triangular possibility distribution of (0.5, 1.0, 2.5).
Variable u˜i is the possibilistic aggregate index of alternative (i), wj is the crisp weight of criterion (j), and P is the scaling parameter. Each criterion is given an appropriate weight factor depending on its relative impor-
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tance in determining the over-all score. In addition, the scaling parameter is also specified. If P ¼ 1, the result is the simple additive weighting (SAW) method. As P is increased, the aggregation becomes progressively more pessimistic; the worst scores become more significant in determining the composite impact indices regardless of the weights used. This is particularly significant in environmental contexts where decisions tend to be determined by the worst rather than best features of a given technological alternative. As argued by Geldermann et al. (2000), perfect compensation among attribute scores is not realistic in environmental decision-making. For example, a fuel which is potentially carcinogenic is unlikely to be considered as a viable alternative, even if it outperforms its competitors in all other environmental categories. The weights wj are normalized such that X 1¼ wj ð4Þ j
All computations with possibilistic data are carried out using established rules of fuzzy arithmetic, details of which can be found in Kaufmann and Gupta (1991) and Mauris et al. (2001). The results of the computations on the original symmetric triangular fuzzy numbers also have approximately triangular distributions, but are no longer symmetric. After the aggregate indices have been calculated, pairwise comparison of the alternatives is performed by taking the fuzzy differences, ~ ua ~ ub , for a 6¼ b. The possibility distribution of each pairwise difference can be used to gauge the degree of superiority since the ranking of fuzzy or possibilistic quantities is not as straightforward as the comparison of crisp numbers (Perny and Roy, 1992). A minimum significant degree of dominance, H, is assigned a priori by the user to establish a threshold level for asserting superiority among the alternatives. The degree of dominance, Gab, can then be calculated using Eq. (5) as the logical complement of the expQ ression ð~ ua ~ ub ¼ 0Þ; the latter is the possibility that the difference between the two environmental scores is zero. This concept is also illustrated in Fig. 3. Gab ¼ 1
Y
ð~ ub ¼ 0Þ ua ~
ð5Þ
Significant dominance is indicated when Gab H. Establishing significance of differences relative to predetermined thresholds in this manner is superficially analogous to the use of levels of significance in conventional statistical hypothesis tests, although the latter are of course based on probability rather than possibility theory. This concept is illustrated in a later section using specific model outputs.
Fig. 3.
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Possibilistic degree of dominance.
5. Operational features of POLCAGE 1.0 The POLCAGE 1.0 prototype is coded in Microsoft Excel and Visual Basic. It consists of the GREET 1.5a spreadsheet model with additional sheets and modules, as well as new fuel cycles. Fig. 4 shows the user’s view of the POLCAGE screen. Only eight sheets are immediately visible when the model is opened: the copyright sheet of the original GREET model, the inventory plots sheet containing crisp (non-fuzzy) inventory displays, and six sheets that comprise the new computational and display features. All other sheets containing inventory calculations are hidden from view. PUP, which cannot be implemented in the spreadsheet environment, is coded as a Visual Basic procedure. The operational features of the POLCAGE 1.0 model are as follows: . Control sheet. This sheet contains the 20 imprecise model inputs modeled as fuzzy numbers with triangular possibility distributions. The first 10 imprecise parameters are the vehicle fuel economy figures used in the simulations, which are based on the default assumptions of GREET with 10% error margins. The remaining 10 imprecise inputs pertain to efficiency parameters of the upstream fuel production processes. Most of the values used are based on a recent fuel-cycle study by General Motors Corporation (2001). This sheet also contains scenarios for power generation mix and coproduct allocation method in electrolytic hydrogen production. . PUP module. This Visual Basic procedure executes fuzzy computations in POLCAGE using the imprecise parameters specified in the control sheet. It generates the triangular possibility distributions of the model outputs and stores the parameters in the fuzzy output sheet. . Crisp output sheet. Conversion of the inventory data from the GREET submodel into the corresponding impact parameters takes place in this sheet. Matrix
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Fig. 4. View of POLCAGE 1.0 screen.
calculations are employed to implement the EDIP impact assessment procedure. The values of the EDIP impact factors are also stored in this sheet. Only eight impact categories are applicable to the inventory outputs of the GREET submodel: global warming (GWP), acidification (AP), photochemical ozone formation (POFP), nutrification (NP), human toxicity via inhalation (HTP), and resource depletion (RDP) of oil, coal and natural gas. These impact categories arise as a consequence of the choice of inventory parameters in the GREET submodel as explained in previous sections. . Fuzzy output sheet. When triangular possibility distributions are assumed, each fuzzy model output is expressed as a triad consisting of a lower bound, a mode (or average), and an upper bound. This sheet stores the triads, which are generated from the crisp model outputs by the PUP module. . Displays sheet. This sheet contains a three-dimensional histogram summarizing the crisp environmental impacts of the alternative fuels, as well as a series of box plots showing the possibilistic model outputs for each inventory parameter and impact category. Hence, the user has access to graphical displays of intermediate or disaggregated model data, which may be necessary for in-depth analysis. . PCP sheet. The PCP sheet controls the aggregation of environmental impacts and the subsequent comparison of the alternatives. Aggregation is controlled by specifying the scaling parameter, P, and the weights of each of the eight impact categories used. The choice of the value of P controls the degree of pessimism of the aggregation. For example, for
P ¼ 1, PCP becomes a simple weighted-averaging procedure. As the value of P is increased, high impact category scores become more significant in determining the aggregate environmental score of an alternative. Further control is possible through the weight vector. Equal weights are assigned to the impact categories in the default case, but the user may input a new weight vector manually. Furthermore, a weight-sensitivity analysis procedure based on that used by Geldermann et al. (2000) can be implemented as follows. The user selects an impact category whose weight is to be varied. The weight assigned to this category can then be varied from 0 (no importance), to 0.125 (equal in importance to all other categories), and finally to 1 (overriding importance, implying 0 weight for all other categories). Once the scaling parameter and weight vector are specified, the aggregated possibilistic environmental scores are calculated. Comparison of alternatives is then possible using two display options. First, the user may specify two alternatives to be compared. The difference between the aggregate scores of the selected alternatives is then displayed as a possibilistic number with a triangular distribution. The degree of dominance can then be deduced from the mode and spread of the possibility distribution (Tan and Culaba, 2001b). Alternatively, the user can specify the minimum significant degree of dominance, H, in which case the model output is displayed in the form of a trivalent dominance matrix. In the latter, the values 1, 0 and +1 indicate inferiority, indifference (or equivalence) and superiority, respectively.
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. PCP display sheet. This sheet displays the possibilistic aggregate environmental scores of the alternatives, in the form of box plots. The display clearly shows both the average values and the uncertainty margins, thus allowing users to make rapid, approximate estimates of dominance relationships among the fuels being evaluated, without having to rely on the features of the PCP sheet.
6. Case study: comparison of alternative fuels for Philippine automotive transport Since the model is intended to provide decision support for policy makers trying to identify clean transportation fuels, the following case study is provided to illustrate how exactly the model assists the human user. Simulation assumptions used are based on the best available Philippine data. 6.1. Simulation parameters The case study presented here used the default parameters of the POLCAGE 1.0 prototype. The following assumptions, being the most important, bear some discussion: . Assessment is normalized to 1 km traveled by the end-user vehicle. . The power generation mix was based on the 10-year projections of the Philippine Department of Energy (2000) for the year 2009: 45% coal, 10% oil, and 16% natural gas, with renewables making up the balance. Many of the fuel options and vehicle technologies included in the model are unlikely to be in any commercial use until the end of the decade. The year 2009 was used because the currently available projections of the Philippine Government do not extend beyond that year. . Eight kilogram of oxygen gas are liberated for every kilogram of hydrogen produced by electrolysis of water. In principle, coproduct allocation rules or displacement can be employed to divide environmental impacts between the oxygen and hydrogen life cycles. However, if hydrogen is produced commercially to meet projected Philippine transportation fuel requirements, it is likely that the large quantities of oxygen produced will be in great excess of any economic demand for this gas as an industrial commodity. It is more probable that the bulk of the oxygen will simply be vented into the atmosphere as a process waste. In the simulations, hydrogen is thus treated as the sole economically valuable product of the electrolysis process. . For simplicity, equal weights were assigned to the eight EDIP environmental impact categories.
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. The PCP parameters employed were P ¼ 2 and H ¼ 0:5. The choice of scaling factor value reflects some degree of pessimism which is implicit in environmental decision-making; large environmental impacts tend to be more decisive factors than small ones. The specified minimum degree of dominance corresponds to the linguistic equivalent, ‘‘moderately superior or inferior,’’ implying that slight differences in over-all environmental impacts (degree of dominance < 0:5) can be dismissed as being insignificant. Such an application of possibility as the numerical equivalent of a linguistic or subjective expression is consistent with conventional applications of fuzzy sets (Mauris et al., 2001; Tan et al., 2002). 6.2. Results and discussion LCA models like GREET are limited to the accounting of material and energy flows. In some cases, inventory data alone may suffice to meet a user’s needs. However, for decision-making and public policy formulation a comprehensive LCA including impact assessment and error sensitivity analysis is preferable. The prototype model POLCAGE 1.0 is capable of environmental impact classification, characterization and valuation, as well as data uncertainty processing under a possibilistic computational framework. Multilevel data access also allows users to view intermediate or disaggregated information throughout the model, thereby allowing the model’s chain of reasoning to be scrutinized. This accessibility was identified by Mazzarachio et al. (1996) as a vital feature of an effective decision support model. Four output display options of POLCAGE 1.0 are shown here. Fig. 5 shows the box plot display of aggregate possibilistic environmental scores of the 10 alternatives. The impact scores are dimensionless and normalized within the interval [0, 1], with 0 being the best possible score. Both magnitude and uncertainty are visible in the box plot format, thus allowing approximate dominance relationships to be estimated. For example, the average impact score of methanol (MeOH) is about 0.47, while that of gasoline is roughly 0.52. However, considerable overlap is evident when the possibilistic uncertainty margins are taken into account, making it difficult to assert the superiority of methanol to gasoline without further in-depth analysis. Visual inspection of the box plot display allows approximate dominance relationships to be estimated. When the minimum significant degree of dominance, H, is specified, a crisp or defuzzified dominance matrix can be used to display outranking relationships among the alternatives. The dominance matrix for the case study, evaluated at H ¼ 0:5, is shown in Fig. 6. The cell elements denote row-to-column comparisons. For example, the ‘‘0’’ in row 41, column 21 of the
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Fig. 5.
Possibilistic aggregate environmental scores of alternative fuels.
spreadsheet indicates that gasoline is not significantly different from methanol; the degree of dominance does not exceed the threshold value of 0.5. Cell entries of ‘‘0’’ in the dominance matrix indicate indifference between the alternatives under comparison, which occurs when aggregate impact scores are equivalent (as in the case of a diagonal cell that denotes the comparison of an alternative with itself), or when the difference between scores does not exceed the possibilistic threshold of significance, H. Entries of ‘‘1’’ indicate superiority
Fig. 6.
while ‘‘1’’ indicates inferiority. Only half of the matrix needs to be shown since complementary pairwise relationships are readily deduced from the given display. The possibility distribution of the difference in the impact scores of a pair of alternatives can also be displayed. This allows the exact degree of dominance to be determined without having to adjust the threshold parameter H. Fig. 7, for example, shows the difference between the total environmental impacts of gasoline and bioethanol. The most possible (or average) value is
Defuzzified dominance matrix.
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Fig. 7. Possibilistic difference of aggregate impact scores of gasoline and methanol.
0.05, indicating that methanol on the average is superior to gasoline. However, possibility distribution encloses some negative values as well, which indicates the possible superiority of gasoline to bioethanol. The possibility that the difference is zero—or that the two fuels have equivalent impacts—is about 0.65. This value implies a degree of dominance of 0.35 as its logical complement, which fails to exceed the preset value of H. The result can be interpreted linguistically as ‘‘methanol is not significantly superior to gasoline.’’
Fig. 8.
In some situations, the user may not be satisfied with just the aggregated impact results. For such cases, POLCAGE 1.0 allows access to intermediate model data such as inventory flows and disaggregated environmental impacts. For example, it may be of interest to explain the relatively poor evaluation of methanol (MeOH), which at first sight seems unexpected. Fig. 8 shows crisp environmental impacts resulting from the GREET–EDIP submodels combination. These impacts are based on the possibilistic modes or averages of the input data, and are normalized and
Crisp environmental impacts of alternative fuels.
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weighted according to the EDIP guidelines (Wenzel et al., 1997). A disaggregated environmental impact display of this form allows the user to investigate the strong and weak points of the alternative fuel under scrutiny. In this case, methanol exhibits relatively high impact scores in the global warming (GWP), photochemical ozone formation (POFP) and natural gas depletion (RDP NG) categories, thus explaining its low over-all rating. Effective decision support entails two conflicting considerations. On one hand, results must be sufficiently concise so as not to overwhelm the user with large quantities of data; on the other hand, detailed information might also be required further into the decision process (Mazzarachio et al., 1996). Thus, a decision support tool should be able to satisfy both criteria depending on the user’s demand. A principal feature of the POLCAGE 1.0 is that partially or fully processed results can be viewed by the user. This feature allows the model’s chain of reasoning to be traced, in the same manner that a human consultant might be asked to provide not just final recommendations, but explanations of how such conclusions came about. The availability of different forms of output displays in POLCAGE 1.0 allows effective decision support. The user, for instance, may rely on the box plot display to gain a quick overview of the relative merits of the alternatives. Generation of a series of dominance matrix at different values of H can then be used to gain more insight into the degrees of superiority (or inferiority) of the fuel options. If these are insufficient, the user then has the option to view disaggregated environmental impacts or inventory flows to answer questions about specific environmental aspects of each fuel. Comprehensive sensitivity analysis with respect to aggregation parameters and other model inputs can then be used to model different viewpoints and scenarios that are relevant to the decision process. Modeling of data uncertainty is another fundamental feature of POLCAGE 1.0. However, a possibilistic framework is used instead of the conventional probabilistic one. Uncertainty in the input data propagates through the model and the error margins are reflected in the model output. The model thus provides the user with two levels of information: first, the magnitude of environmental effects, and second, the associated confidence levels of these estimates. Without the latter, there is significant potential for confusion; a user, for instance, might interpret one energy source as being superior to another, when in fact the difference is marginal. This point has been illustrated in the preceding comparison of gasoline and methanol. By retaining both types of information throughout its calculations, the model facilitates proper interpretation of its outputs.
POLCAGE 1.0 is envisioned as a support tool for clarifying environmental issues and priorities in the context of automotive fuels evaluation. The model provides users with information in a concise, rational and consistent form. Significantly, the information is also available at various levels of aggregation, which facilitates detailed analysis of the decision-making process. For the conditions and assumptions considered the model outcomes suggest that electricity (for EVs) and biodiesel are the BEOs. However, at this stage the model does not include cost elements, and the influence of human judgment in providing experiential inputs should not be ignored.
7. Conclusion The composite software tool POLCAGE 1.0 was developed to provide users with a decision support model capable ranking alternative motor vehicle fuels using comprehensive LCA. In contrast, the GREET model, which is used as a subcomponent of POLCAGE, by itself is capable only of inventory analysis and calculation of total greenhouse gases. The enhanced model incorporates impact classification and aggregation using the EDIP method. Methodological weaknesses of conventional LCA models were addressed by using PUP for processing input data uncertainty and PCP for multiple-criterion analysis. Other features of the model include sensitivity and scenario analysis with respect to power generation mix, coproduct allocation schemes and impact valuation. For effective decision support, POLCAGE 1.0 provides information at different levels of aggregation. Users can immediately view summary results, but also have the option to scrutinize each step of the model’s computations by accessing partially processed data, such as emissions inventories or disaggregated environmental impacts. Significantly, both point estimates of environmental effects and the corresponding uncertainty margins are available for viewing. By providing two levels of information to the user, the risks of misinterpretation are reduced. The extensive use possibility theory allows POLCAGE 1.0 to be used as a decision-support tool for selecting the BEO from the specified fuel alternatives without the rigidity of previous methods. The principle underlying POLCAGE 1.0 is thus compatible with the ‘‘soft’’ decision framework envisioned by Krozer and Vis (1998) and Geldermann et al. (2000). This view assumes that a decision support model serves to elucidate different scenarios and viewpoints to aid the user, instead of mechanically providing a ‘‘correct’’ answer that overrules human judgment. To date the model features and results have been shown to the Philippine
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Department of Science and Technology (DOST), which has expressed some interest in using LCA for screening new technologies. Current efforts are underway to have the model used by the Department of Energy (DOE) for policy development. Future work on the model itself will focus on the following areas of development: . New fuel and energy pathways within the GREET submodel. . Possibilistic weighting of environmental impact categories. . User-defined possibilistic parameters, possibility distributions and fuel options. . Graphic user interface. . Development of a generic code for the analysis of manufacturing processes by combining the algorithms used in POLCAGE with an existing model (Pineda-Henson et al., 2002). . Development of hierarchical or multi-level aggregation through composite programming (Stansbury et al., 1992; Mazzarachio et al., 1996). This modification will allow, for example, partial aggregation of ecological and resource depletion impacts under separate categories. Economic considerations can also be introduced in this expanded framework to allow cost–benefit analysis to be performed. Acknowledgements The authors wish to acknowledge the financial support of De La Salle University—Manila, through the Faculty Development Program, and The British Council. We are also grateful to Dr. Stanley Santos for his assistance in miscellaneous aspects of our work.
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