Explicit Accounting Methods for Recycling in LCI Samuel A. Newell and Frank R. Field III Materials Systems Laboratory, MIT Room E40-209, 77 Massachusetts Ave., Cambridge, MA, 02139, USA tel: (617) 253-2146. fax: (617) 258-7471. email:
[email protected],
[email protected]
ABSTRACT The life cycle inventories (LCIs) of recyclable and recycled materials can be strongly affected by how the savings from recycling are accounted. Unfortunately, most LCI methodologies do not explicitly address the important and subjective question of how to allocate inputs and outputs between primary and secondary material. This paper describes an LCI methodology that enables the analyst to explore and define the accounting rules more explicitly. Examples are developed to demonstrate the workings of the methodology and its implications for materials selection. key words: life cycle analysis, inventory analysis, recycling INTRODUCTION Life cycle inventory (LCI) is frequently viewed as the least controversial stage of life cycle analysis. Generating an inventory seems to be a straightforward, if tedious, application of basic notions of engineering process analysis -- define a control volume, then identify and measure all flows across the control volume. While it is generally agreed that the impact and improvement stages of life cycle analysis tend to be value-laden, the inventory itself is largely assumed to be value-neutral, if only because it is so easy to state precisely what an inventory is. In practice, however, the potential infinitude of processes both upstream and downstream of the product or process of interest, as well as the inaccessibility of proprietary data, require analytical and conceptual compromises in the inventory stage. To facilitate the assembly of inventories in finite time with finite budgets, institutions have worked to develop guidelines that identify where an analysis can "cut corners" and still develop an acceptable life cycle inventory. The SETAC manual of practice is one such instrument, although many are in existence or under development [1]. However, there are areas where judgment enters into the practice of life cycle inventory that are of a different scope and nature from the standard engineering shortcuts that are required for practicality. These are areas where the purely technical description of life cycle fails to capture the real issues which underlie the application of the methodology. Examples of this difficulty arise in various guises. One example is the question of the energy content of a product that itself derives from a resource that can be used as a source of energy. Reasonable people can differ as to whether the energy content of the material used in the product has been consumed, and thus should be included in the energy inventory, or merely transformed, and thus should not be included, since it can be recovered later. 1
One area where this problem has been particularly difficult to resolve has been product recycling. As product recyclability has received greater emphasis, material suppliers have increasingly chosen to emphasize the extent to which material recycling can benefit the environment, and life cycle arguments have been used to reinforce this position. Reductions in inputs or outputs should be included in life cycle inventories. Including recycling is straightforward for simple closed-loop systems in steady state, as shown in Fig. 1. Figure 1. A closed loop recycling system. X Materials Mining & Refining
M Product 40% of intputs Manufacture
60% of inputs
U Product Use
D Disposal 40% of output
60% of output
Recycling R
Assume X, M, U, R, and D are the quantities of inputs and outputs per kilogram of material associated with mining/refining, manufacturing, using, recycling, and disposing of a product, respectively. Then the total load per kilogram of product is simply 0.4X + M + U + 0.6R + D. (From here on, all categories of inputs and inputs will be referred to generally as “loads” or “burdens.”) However, such simple systems are uncommon, and a more typical system might look like that shown in Fig. 2. Figure 2. An open loop recycling system. X1
M1
U1
D1
Materials Mining & Refining
Product 1 Manufacture
Product 1 Use
Disposal
Recycling
R
Materials Mining & Refining
Product 2 Manufacture
Product 2 Use
Disposal
X2
M2
U2
D2
Now including recycling is not so straightforward. In fact, life cycle arguments can lead to different design rankings, depending upon accounting assumptions about how scrap is processed, what it is used for, and who gets credit for recycling savings.
2
A simple numerical example can demonstrate the problem. Table 1 shows a hypothetical life cycle inventory for a single pollutant in each stage of a product life cycle for two completely substitutable primary materials, A and B. Table 1. Emissions for materials A and B. Process Material A Primary Extraction 300 Use 350 Disposal/Recovery 15 Total
665
Material B 100 500 50 650
Essentially, A is a material that is "dirty" to produce, but leads to a less-polluting product and is less difficult to dispose of and recover. When pollutant emissions are summed over the entire life-cycle of the product, the high pollutant emissions during production of material A offset the lower emissions elsewhere in the life cycle. Thus, one might argue that material B is preferable to material A, all other things held equal. However, an alternative argument can also be made that enables the analyst to argue away the potentially pernicious effects of primary extraction of A because material A is so much easier to recycle than material B. In the event that material A is recycled, less "virgin" A will be produced, thus reducing total releases of pollutant. In effect, the fact that material A is recycled suggests that the "correct" way to account for emissions is to "credit" the present use of A with the fact that its ultimate recyclability will lead to lower pollution downstream from the current use. Table 2 summarizes the argument, assuming that 25% of the material is recovered in both cases: Table 2. Emissions for materials A and B with recycling. Process Material A Primary Extraction 300 Use 350 Disposal/Recycling 15 Credit for 25% Recovery -75 Total
590
Material B 100 500 50 -25 625
Now, it is material A whose life cycle is more attractive, provided, of course, that the material is actually recycled. This simple example reveals a particularly complex set of issues that cannot be simply resolved by resorting to technical arguments. In the first case, the life cycle inventory gives no credit for the displacement of virgin material by recyclate, while the second case seems to undervalue the fact that extraction of material A leads to emissions that have to be dealt with now, irrespective of the future uses to which the material may be put. In either case, a simple accounting rule (the 3
inclusion or exclusion of the net effects of recycling upon the entire material A/B inventory) embeds a strong assumption that cannot be easily identified or explored within the context of the analysis. From the perspective of actually trying to use LCA for product and process decisions, the inability to consider key issues explicitly can lead to choices that are inconsistent with one’s policy objectives. It may seem as if confusions in accounting assumptions might be best remedied by standardization. SETAC could adopt one of these standard accounting rules into its Guidelines, which, at present, demand only that accounting be "reasonable" and "explicit" [1]. However, even if the second method seems reasonable in the example given above, there are plenty of instances where it might seem unreasonable. For example, suppose that it is possible to produce a material that is 100% recyclable using a process that consumes no resources and produces no emissions. Unfortunately, each kilogram of material extracted leads to the release of a kilogram of cadmium to the water supply. If recycling credits are assumed, then the LCI would have no cadmium, despite the release of cadmium during the manufacturing process. This is not to argue that recycling should not be taken into consideration in LCIs. Rather a closer look suggests that two problems need to be addressed. First, the simple accounting schemes suggested above, although both widely practiced, can mislead by excluding important information. Second, accounting assumptions must not be left buried in the methodology without explanation. What is needed is the construction of inventory methods which explicitly capture the subjective elements of accounting. Then implications of these judgments can be demonstrated, and the rationale for these judgments can be made explicit. This paper outlines a methodology for addressing the judgmental elements of LCI accounting for the specific case of recycling, and shows how such elements can be isolated from the technical elements of the analysis. The proposed approach helps users apply their own judgment, so as to produce the most "reasonable" results, and it does so in an intuitively structured and explicit fashion. This approach illustrates the kinds of developments that are needed to ensure that LCI retains its transparency in the face of increasing analytical complexity.
PRODUCT STREAM LIFE CYCLE INVENTORY (PS-LCI) AND THE ADJUSTABLE K-FACTOR The present work arose out of the observation that the two problematic accounting rules presented above are both widely practiced, usually without any explanation or even a statement of what assumptions are made. The first method follows the principle of "you make it, you're responsible" and allocates all primary emissions to the primary product, which means that the LCI implicitly places zero value on recyclability. All credit is given to the user of the recycled material. Consequently, two primary materials that are the same in all respects except that one is more easily recyclable receive the same LCA rating. On the other hand, the second method values recyclability so highly that it allocates all mining and refining emissions to secondary material, and none to primary. 4
Alternative accounting rules have been proposed by the EPA and other organizations [2]. The most sophisticated of these alternatives has been proposed by Franklin Associates. The Franklin methodology provides credit to recyclables by asking the analyst to assume recovery rates and total number of times that recycling will occur. Total "emissions avoided" are estimated based on the total amount of primary material production that will be displaced in the future by recyclate. Emissions avoided translate into credits, which are distributed evenly on a per pound basis among all generations, primary and recycled alike. Finally, those credits are subtracted from the baseline LCI of each product, where the baseline assumes the use of virgin materials [3]. The shortcomings of this model are twofold. One, the model is limited to product systems where recycling will displace virgin material production, and where the avoided emissions associated with that forgone production can be estimated. Recyclate can often be used in such a variety of applications that identifying the future material that will be displaced may be very difficult. Two, the principle of sharing emissions evenly on a per pound basis makes sense, but it is not the only approach. The discussion in the next section should illustrate why this method may not be consistent with certain policy objectives. The proposed methodology here is similar in that it is based on projecting a whole materials stream, but it does not attempt to estimate "emissions avoided" in the future, and it offers a continuum of possible accounting schemes to suit any set of policy objectives. Outline of PS-LCI Mechanics In this paper, a new life cycle accounting methodology is proposed called "Product Stream Life Cycle Inventory" (PS-LCI). The name derives from the wide system boundaries that have to be drawn in order to account for the recyclability of a material. The system includes not only the primary material, but the entire stream of recycled or partially recycled products which flow from it. PS-LCI requires the analyst to estimate what kind of product stream will flow from a particular primary material application. The analyst assumes recycling rates, the total number of times recycling will occur, and inventory data for an assumed set of processing technologies. The analyst also has to specify a value for a subjective allocation parameter, which determines how to allocate burdens among primary and secondary materials. Allocation involves two main steps: 1) Defining "total stream load” as the sum of all projected loads (within each inventory category) from mining, refining, recycling and disposing of all species over the multi-generation product stream. 2) Reallocating loads by dividing total load over the product stream on a per-kilogram basis. The user's input for the subjective allocation parameter (as explained in the following section) determines whether to allocate the same burden per kilogram to primary and secondary or to allocate more to one than the other. Finally, the output is the assigned inventory for the recyclable primary material. This inventory must be added to a product's manufacturing and use inventories to yield the total LCI for a product. Each step in the model is described in detail in the following sections. 5
Characterizing the Materials Stream When primary material is recycled, some fraction of the original material is reincarnated into a new product. If that second material is itself recycled, then some smaller fraction of the original material becomes a third generation of product. The "materials stream" in the PS-LCI model can be described by the total number of times the original material is recycled and by the net recycling rate in each generation. "Net recycling rate" incorporates both collection efficiencies and processing efficiencies. The model described here assumes the same net rate at each generation, but it can be adapted to handle problems with variable recycling rates. The two quantities required for allocating emissions over the product stream are total mass and total load. "Total mass" refers to the sum of original mass plus the fraction that gets recycled into a second product generation plus the fraction of that mass that becomes a third product, and so on. Total mass is a fictitious construct, as it double and triple counts the atoms that become reused instead of landfilled. For n generations at a constant recycling rate of r, the total mass sum becomes MT, as shown in Fig. 3. Figure 3. The concept of “total mass.” Mass
TOTAL
m m*r
m*r^2 m*r^3 Time
r = net recycling rate n = total no. generations m = primary mass
MT= total stream mass
"Total load" is the sum of the mining, refining, recycling, and disposal burdens in every stage of the original material's life. “Total load” is defined to include all of the burdens associated with materials processing, but none of the burdens from the manufacture and use of any products in the materials stream because those burdens should be dedicated. Total load is computed by adding burdens as they will occur over time. First, raw material is extracted and refined, releasing the lion's share of total stream load. At the end of the first product's life, some fraction, r, will be recycled, while the remaining fraction, 1-r, will be disposed of. Recycling and disposal further contribute to total environmental load. Over time, the burdens might look something the series of flows shown in Fig. 4.
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Figure 4. The concept of “total load.” Environmental Burdens TOTAL
Drop-off over time due to secondary burden < primary decreasing mass/generation
Time
Each process step in the materials stream contributes (mass processed)*(burden per kg processed) to total load, because each process's emissions data is input on a per-kilogram basis. For an entire product stream with n generations each of which is recycled at a rate r, and with burden data given by X, R, and D units per kg processed for mining/refining, recycling, and disposal, respectively, the total load reduces to the expression for Etot in Equation 1, below. mX + m(1 − r)D
Burden in Generation 1
+ mrR + mr(1 − r)D
Burden in Generation 2
+ mr n−1 R + mr n−1 D
Burden in Generation n
−−−−−−−−−−−−−−−−−−−−−−−−−− E tot =
n m X+D+R r−r 1−r
(Equation 1)
Explicit Subjective Allocation of Streamwide Emissions over Product Stream Allocation of total load, as defined above to include all of the burdens associated with materials processing, may be desirable for the following reason: Insofar as the producers of recyclable material are responsible for the existence of the entire materials stream, they can be given some credit for environmental load reductions due to recycling. By the same argument, the primary producers must also take responsibility for some of the burdens that occur downstream. Allocation is the trickiest component of accounting, so a simplified case is presented before the general case. One simple approach to emissions allocation is to divide total load by total mass, yielding an average load per unit of mass. Average load per kilogram is given by:
e=
(X+D )(1−r )+R(r−r n ) 1−r n
(Equation 2)
where e = average streamwide load per kg = Etot/Mtot.
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This method yields results similar to Franklin’s, except that it is based on environmental burden created rather than burden avoided. Burden created is relatively easy to predict because it is usually dominated by the initial mining and refining stages. Burden avoided, on the other hand, can be a much larger number with a much larger variance because it depends on what materials are displaced by the recyclate in the future, and what those materials' requisite mining and refining burdens would have to have been. However, this method and the Franklin method are both as inflexible as the two simplistic ones; any user who disagrees with the special case where average loads are assigned equally between primary and secondary material would have to discard the model. Fortunately, a simple modification generalizes the PS-LCI model to fit any policy perspective. To do so, a dimensionless parameter, k, can be defined as the ratio of load allocated per kilogram of the primary material, e1, to load allocated per kilogram of all generations of recycled material, e2. The decision maker's choice of k, therefore, explicitly defines the accounting rules. Mass balance requires that the load assigned to the primary plus the total load assigned to all secondary generations should equal the total stream load. That is, Etot=e1m1+e2m2, where m1 is the primary mass, and m2 is the total mass of all later generations (i.e. m2=Mtot-m1). Plugging m1 and m2 into the equation for Etot and solving simultaneously with k=e1/e2 for e1 and e2 gives the new formulae for per-kilogram loads of primary and secondary:
e1 =
(X+D )(1−r )+R(r−r n ) (1−r )+k −1 (r−r n )
(Equation 3)
e2 =
(X+D )(1−r )+R(r−r n ) k(1−r )+(r−r n )
(Equation 4)
where k=e1/e2. If k=1 then the expressions for both e1 and e2 reduce to e, the average load per kg for the entire product system. The above calculations can be expanded from one inventory category to the entire inventory space. X, R, D, e1 and e2 then become vectors whose individual components each represent an emissions or resource category. The vector e1 gives the complete LCI for a recyclable primary material under the assumptions of PS-LCI. PS-LCI is an LCI methodology whose accounting assumptions are clear and explicit, through the definition of k. Still, the usefulness of the methodology depends on a few practical issues: 1) How does the user choose a k that represents his policy objectives? 2) How demanding are the data requirements, and how robust are the results under typical uncertainties in the data? In the next section, a demonstration case and sensitivity analysis will be presented, and the issues surrounding choosing k will be discussed.
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DEMONSTRATION ANALYSES Example 1 PS-LCI is especially applicable to metals, characterized typically by high recycling rates and by much lower emissions in recycling processes than in primary extraction. Consider aluminum automotive body panels. Body panels require wrought alloys which, at present, are only made from virgin aluminum. At the end of the automobile’s life, however, most of the valuable scrap is likely to be recycled. Recyclate will not be used in body panels, but it might be used in automotive castings or any other cast aluminum product, which in turn might again be recycled. Consider, energy use for one kilogram of primary aluminum, and assume: ; n=3, corresponding to recycling the material two times before disposal. ; r=80% for both recycling rates. ; k=1, so that emissions are divided evenly per pound between primary and secondary material. ; X,R, and D are given by the inventory data in Table 3.
Table 3. Energy data for aluminum. Life Cycle Stage for Aluminum MJ/kg Source Mining & Refining (to ingot) X=225 [4] Recycling (to ingot) R=9 [4] Disposal D=0.42 [5] Additional assumptions used to derive inventory data from the sources indicated: ; Electric energy is converted to MJ of primary energy based on 33% conversion efficiency. ; Disposal energy reflects transport of scrap by truck over an average distance of 500 km to landfill. Under these base case assumptions, the energy burden allocated to one kilogram of primary aluminum is e1=98 MJ/kg. This result falls in between the results from the two simplistic accounting rules, with 225 MJ/kg when recycling is not accounted for, and 45 MJ/kg when the primary material is charged only with the energy use associated with production of the non-recycled fraction of the material. What’s more interesting than the result itself, however, is what it demonstrates about the method. By varying the input parameters n,r, and k, it is possible to explore sensitivity of the results to different assumptions and to uncertainties in what future recycling rates might be. Sensitivity to the Number of Product Generations, n Sensitivity to n depends on the recycling rate, r, and on the allocation factor, k. At low r, sensitivity to n is small. The larger the r value and the smaller the k value, the higher the sensitivity to n. In the automotive body panel example, the sensitivity of wrought aluminum's energy burden to n is shown in Fig. 5. Notice that as n changes from 0 to 1, the inventory changes markedly. As n changes from 3 to 4, the inventory changes very little. Fortunately, when n is small, n can be predicted with greater accuracy than when n is large. For example, 9
steel automotive body panels are likely to be recycled into rebar while rebar is quite unlikely to be recycled again, thus n=2 with little uncertainty. On the other hand, aluminum parts are likely to be recycled at least once, but it is difficult to say whether they will be recycled three, four, or five times. This example demonstrates that if n is small -- and this is the only case in which e1’s sensitivity to n is high, as shown in Fig. 3 -- n can often be predicted with negligible error. Sensitivity to the Recycling Rate, r Any inventory methodology that accounts for recyclability will be highly sensitive to recycling rate. Fig. 5 shows the sensitivity of wrought aluminum's energy burden to r, with k=1 and n=3. If r were impossible to predict, the PS-LCI inventory would have little meaning. However, r is known to within a few percentage points in many major applications, and within those few points, the sensitivity is not so high, especially at higher r values. Later product generations may actually have a different r, but with diminishing mass in each subsequent generation, assuming the same r as in the first generation tends to cause little distortion in the overall allocation to primary material. Sensitivity to the Subjective Allocation Factor, k Figure 5 shows the sensitivity of wrought aluminum's energy burden to k, with r=80% and n=3. Clearly, the choice of k affects the inventory considerably, so it is important for PS-LCI practitioners to choose k carefully and to be able to justify the choice.
200
150
100
50
0
1
2 3 4 5 10 Allocation Factor, k
Energy Allocated to Primary Al (MJ/kg)
Energy Allocated to Primary Al (MJ/kg)
Energy Allocated to Primary Al (MJ/kg)
Figure 5. Sensitivity of aluminum’s e1 to PS-LCI parameters. 200
150
100
50
200
150
100
50
0
0
1 2 3 4 5 10 100 Number of Product Generations, n
0% 20% 40% 60% 80% 100% Recycling Rate, r
Example 2 Choice of k makes a substantial difference for materials that are dirty to extract and clean to recycle, such as aluminum; it makes a much smaller difference for materials that may not be recyclable at all, such as polymer composites. Consider the comparison of aluminum and noryl for an automotive fender. In addition to the assumptions for aluminum from Example 1, see Table 4 for data on noryl’s energy requirements. 10
Table 4. Energy data for noryl. Life Stage for Noryl Fender Pair MJ/kg Source Mining/Refining, incl. embedded energy (X) 176 [6, 7] Manufacturing 22 [6] Use 276 see below Disposal (D) 0.42 [5] Further, assume that ; Noryl fenders weigh 2.98 kg, the aluminum 2.86, and the rest of the car weighs 1409 kg. ; Aluminum and noryl are each allocated 276 MJ/kg for use, based on 12,000 mi/yr over 12 years, with 25 mpg fuel economy, 135 MJ/gal and allocation of total energy to the fender based on marginal application of the 10-5 rule (so that the fraction of energy allocated to the fender is equal to half of its weight fraction). ; The noryl fenders are landfilled, rather than recycled or incinerated. ; Aluminum manufacturing burdens are 6.2 MJ/kg associated with rolling [7] and 35 MJ/kg associated with forming the fender [6].
Implications of PS-LCI for Materials Selection Without accounting for recycling, aluminum has a higher lifecycle energy burden (1468MJ / fender pair) than noryl (1360 MJ / fender pair). However, if aluminum is recycled twice (n=3), with r=80%, then aluminum’s energy burden falls below noryl’s for k < =3. If aluminum is recycled only once, then aluminum is lower only for k=1. If noryl is fully credited for 100% incineration at the end of its life, then no k>=1 makes aluminum lower, given n=2 and r=80%. In sum, arguments can be made in favor of aluminum’s energy benefits due to its light weight and recyclability, but only if very low values for k are chosen. Conclusions would be different for different designs, and given different inventory data sets. This example only illustrates that materials rankings may depend strongly on the value chosen for k, so it is important that k is chosen carefully.
DISCUSSION Choosing k The parameter k embeds the user’s strategy. For example, if the user believes that prices do not fully capture the value of recyclability, and if the policy objective is to minimize all future environmental loads, then the user should choose k small enough to provide significant credit for recyclability. Choosing a discrete value for k may require careful consideration. The parameter k can be thought of as a representation of the decision maker’s relative valuation of primary and secondary material. A basic concept from managerial accounting is that the higher value co-products in a multi-product system are assigned more of the system wide costs. Extending this concept to environmental accounting, the higher value materials in a materials stream should be assigned a 11
larger share of streamwide load. For example, if primary material is more valuable than secondary because it is higher purity, then k should be greater than one. An exact k can be chosen by taking the value ratio between primary and secondary, where the notion of "value" depends on the decision maker’s objectives. Value could be represented by price, purity, or any measure that the decision maker is prepared to defend. Alternatively, the relevant measure of value might be not the value of the material itself, but the value added by each user to the materials stream by investment in recycling. If the ability to recycle is driven more by primary user investment in design for disassembly, then k would be small. If recycling is driven more by the secondary user’s compromise in product performance or investment in technologies for processing otherwise useless scrap, then k would be larger. The LCI practitioner is forced to address these subjective issues that underlie any accounting method in order to choose a value for k. Strategic Adjustments to k The parameter k could be set lower if the user's policy objectives are to stimulate increased investment in recyclables. Low k corresponds to a lower emissions score for recyclable primary and might tip decisions in favor of producing more of that material. On the other hand, too low a k may stimulate overproduction of primary material just because it is recyclable. Another user who cares more about emissions today than emissions in the future might want to upgrade k. k and the Time Value of Emissions Like most LCA tools, this model does not discount pollutants over time. The implicit assumption is that future environmental burdens matter as much as current burdens. This is a controversial assumption that is embedded in this model. Most LCA tools make the same assumption, aggregating burdens over time without discounting. However, zero discounting is controversial because marginal damage of burdens might change over time, and because there is likely to be more capital available in the future for mitigating/abating environmental impacts. If the user wanted to assume otherwise, it would not be easy to incorporate into the PS-LCI model. The simplest approach would be for the user to determine, "at what rate would I trade emissions today for emissions at year t in the future?" If n=2, and recycling is expected to occur in 10 years, then t=10. If the exchange rate between burdens today and burdens in 10 years is determined to be 2 to 1, then k would simply be increased by a factor of 2. That would weigh burdens today more heavily in any LCI-based decision. For n>2, choosing a single t is trickier, but some sort of average t could be used. Treatment of Secondary Material In generating an LCI for secondary material, an apparent conflict arises. There is a need to obey mass and energy balance by using the same n, r, and k that was used to evaluate the parent material and following the equations given in this paper. On the other hand, allocating all of the burdens to secondary that have been deallocated from primary may seem to lead to perverse incentives. Consider the comparison of materials A and B for a particular product. Material A is scrap that would otherwise be landfilled. Processing A into the product would generate no new burdens, although A is assigned 100 load units as its share of the primary production burden. 12
Alternatively, the product could be made out of virgin, non recyclable material B, generating 50 units of new burden, and the type A scrap could be landfilled. The life cycle inventory would indicate that alternative B is better, although this would clearly be a mistake, since choosing B would in fact make the world 50 units dirtier. This apparent conflict will actually not arise if k is chosen carefully and carried through the materials system. Consider the following cases: 1) Secondary material is indistinguishable from and perfectly substitutable for primary, so their values are the same and k=1. In this case, consuming secondary leads to just as much of an increase in mining and refining as primary does, so allocating equal emissions to secondary is reasonable. 2) Secondary material is so low in quality/value that it will be landfilled. In this case, k should be very high and virtually no emissions will be allocated to secondary. There are no perverse incentives. 3) Scrap is moderately valuable relative to primary, such that k takes on an intermediate value, say four. Now there is no precise interpretation or physical meaning of e2, and it is difficult to say whether there will be perverse incentives. At least the moderate allocation to secondary makes more sense than full allocation to primary or equal treatment between primary and secondary. The greatest limitation for PS-LCI’s applicability to secondary material is the difficulty in looking up the material stream to determine which values for n, r, and k are applicable. Fortunately, the most important information is whether n is greater than 1; beyond that, the outcome is less sensitive to uncertainties in n, r, and k, but sensitivity analysis should be explored. CONCLUSIONS If there is a "right" way to do LCIs, it is the answer to the question, "what will be the marginal change to the world resulting from each alternative at each decision point?" Unfortunately, the answer to that question is so complex and uncertain that it will not be a part of most decision tools. People need to simplify problems by limiting system boundaries, and by using rules of thumb both for describing complex systems and for aggregating different types of information over time and space. Biased by different sets of values, people will tend to come up with rules of thumb. The formulation proposed here makes sense technically, and it helps to clarify the LCI recycling problem, but most importantly, it provides a simple language for decision makers to develop and describe their rules of thumb. The PS-LCI model applies decision principles to LCA methodology. The model focuses the recyclability accounting problem by structuring the model around one value parameter, k, and three technological factors, the number of generations, the recycling rate, and the emissions data. This structure helps the user to describe the purely technical aspects of the problem description, and, separately, to decide upon a k that is most consistent with his policy objectives. The model embeds the user strategy, and it does so explicitly. Finally, the approach taken here suggests a general principle for LCA methodology: by structuring problems around technological 13
parameters and separate "policy" parameters, it becomes possible to improve both the flexibility and the transparency of LCA tools. One of the key advantages of flexibility and transparency is that they facilitate debate. The model presented here frames the recycling accounting problem in such a way that any position can be represented by a different value of k, so all points of view can be expressed in a common PS-LCI language. Debate can focus on accuracy in the technological factors, and the policy judgments behind k. Recyclability accounting significantly improves the outlook of materials that are dirty to extract and clean to recycle, while disfavoring materials that are clean to extract and relatively dirty to recycle. Consequently, the debate may be highly charged. In a debate that is only semi-scientific, a common language helps.
ACKNOWLEDGMENTS The authors thank Randy Kirchain, J. Neely, and Brian Zuckerman for their thoughtful comments.
REFERENCES 1. Consoli, F. et. al., 1993. Guidelines for Life-cycle Assessment: A Code of Practice. Society of Environmental Toxicologists and Chemists, Pensacola, FL. 2. Vigon, B.W., et. al., 1993. Life-Cycle Assessment: Inventory Guidelines and Principles, EPA/600/R-92/245, U.S. EPA, Washington, D.C. 3. Boguski, Terri K., Robert Hunt and William Franklin, 1994. “General Mathematical Models for LCI Recycling,” Resources, Conservation, and Recycling. 12:147-163. 4. Haberstatter, K. and F. Widmer, 1991. Ecobalance of Packaging Materials State of 1990, Swiss Federal Office of Environment, Forests, and Landscape (BUWAL), Berne, Switzerland. pp. A22-27. 5. European Aluminum Association (EAA), 1994. Aluminum and Ecology, LCI - Report from WG 2, EAA, Brussels, Belgium. 6. IKP, Stuttgart, 1996. Emissions Inventory Data for Steel, Aluminum and Noryl, Personal Communication. 7. Tillman, A., H. Baumann, E. Eriksson, and T. Rydberg, 1991. Packaging and the Environment, Chalmers Industriteknik, Goteborg, Sweden. pp.19-81.
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