There is a method available to derive the values at this multi-functional process level, viz., the gross sales value method (see below). If this method cannot be applied for some reason, it can be simplified by applying the allocation at the level of the firm, while checking whether this would lead to substantial differences in allocation at the process level. Though combining several processes, allocation at the firm level is much more technology-specific than allocation at the level of the system as a whole. The latter option should generally not be applied, not even as as a simplification. HOW TO MEASURE SALES VALUES? The relevant value to measure the sales of a product is the private value to the decision-maker, as he is influenced by the prices he really gets, or expects to get. Hence, regular subsidies and taxes on activities are part of the price; no correction is needed


Hence, regular subsidies and taxes on activities are part of the price; no correction is needed. This is in contrast to the situation where an economic analysis is being made in terms of alternative social costs, as in Cost-Benefit Analysis. In such an analysis, transfer payments like taxes and subsidies do not reflect real cost and are to be subtracted. If the allocation data are used later, not for allocation purposes but for economic analysis in addition to LCA, a correction for transfer payments may be needed. If subsidies are given for a specific performance, this performance may be defined as a product. An example is that of subsidies for nature conservation measures in agriculture, where, for instance, farmers in the Netherlands may be paid for each successful nest of meadow birds. “Meadow birds” is then a co-product sold to the government, with its own clear share in the total proceeds of the farm

Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002)


“Meadow birds” is then a co-product sold to the government, with its own clear share in the total proceeds of the farm. Some specific problems in calculating shares in (adjusted) total proceeds are worked out in Section 3.9.2. These relate to missing markets, including tax-financed processes, and to market distortions. Below, the operational guidelines for solving the multifunctionality problem are worked out, for the simplified, the detailed and the extended version. Simplified version1 The simplified version has three modeling steps and 4 allocation steps. simplified modeling sm1: Use single-function databases as a basis Cradle-to-gate databases like the ETH database (Frischknecht et al., 1993/1995/1996) on energy-related processes and SAEFL (1998) on packaging have already been allocated by the makers


Without going into the details of how exactly the allocation has been brought about, such general databases are accepted for all but a few main processes in the process tree of the product system. Also, environmentally extended input-output models are becoming available as a general dataset. Since links between the sectors are in terms of one single monetary unit, these models are purely single-function. As improved versions of general databases become available, this option can provide practical solutions to many of the allocation problems. 1 The section on simplified LCA has been prepared together with Erwin Lindeijer, formerly of IVAM BV now of TNO Industry, with adaptations to the format and terminology used in this Guide. See annex C.


Part 2b: Operational annex sm2: Treat open-loop co-production as closed-loop co-production1 In many instances, wastes emanating from the product system are the used versions of primary production in the product system. If it is incorrectly assumed that the recycling takes place within the product system itself, the open loop is transformed into a closed loop. This will usually lead to too high an amount of primary production being subtracted, as the secondary material or product will be a degraded version of the primary material. To increase the validity of the outcome, a quality factor must then be introduced, indicating the lower value of the recycled material. This recycling comes on top of recycling materials as a realistically modeled input. It is to be specified as a subtraction process, to allow for transparent improvements in system specification. In this sense it is a value-corrected substitution method


It is to be specified as a subtraction process, to allow for transparent improvements in system specification. In this sense it is a value-corrected substitution method. A similar problem may occur in situations where the product itself might seem uniform, like electricity. The co-produced electricity in waste incineration has different time-profile characteristics than the electricity obtained from the grid. Hence, its value will be lower (or possibly higher) than that of electricity purchased. In the current liberalised electricity markets, short-term prices may differ by several hundred percent points. In some instances, the quality factor may in fact be near zero, further simplifying the analysis. sm3: Use available physical-causal waste management models (ISO step 2, simplified) Such models have been developed but are not yet available in easily applicable forms2. In practice, such models combine modeling and allocation aspects. This is to be accepted, for the time being


In practice, such models combine modeling and allocation aspects. This is to be accepted, for the time being. simplified allocation sa1: Apply economic allocation where easily possible If data on shares in the proceeds of products from a combined process are easily available, the allocation is quite simple and straightforward. This will often be the case especially for internal studies at firm level. There is then no difference between the simplified and extended versions. If the data for economic allocation are not so easily available at process level, they may be available at systems level. In air transport, for instance, the proceeds from freight and passenger transport are available, avoiding difficult allocation analysis at process level. This step will solve some but usually not all multifunctionality problems. sa2: Use substitution with made-single cradle-to-gate database data This is not real modeling but a simplified solution to the multifunctionality problem


sa2: Use substitution with made-single cradle-to-gate database data This is not real modeling but a simplified solution to the multifunctionality problem. It only makes sense where single-function cradle-to-gate systems can be used in the substitution, e.g., using the databases indicated under sm1 and the gate-to-grave databases mentioned under sm2. This will solve the multifunctionality problem in all common types of flows of materials and products, possibly leaving a few case-specific multi-function processes unallocated. sa3: Apply an easy value-indicating parameter common to the various co-products Depending on the situation, rough parameters for this purpose may be mass, volume or energy content. In a refinery, for instance, these different parameters all roughly co-vary with value. In other cases, like ‘penicillin + fodder production’, such simple parameters hardly make sense. sa4: Use a quick-and-dirty last resort measure This is the bag of tricks of last resort


In other cases, like ‘penicillin + fodder production’, such simple parameters hardly make sense. sa4: Use a quick-and-dirty last resort measure This is the bag of tricks of last resort. In the penicillin example, penicillin prices and prices of a “similar fodder” might be taken from a financial magazine. In recycling, the 50 – 50 % approach of the Nordic Guidelines might be used (Lindfors et al. 1995a,b). For combined waste management, kg share in total waste might be taken. For chemical wastes, this usually will be too coarse a measure, but better than none. A final option is not to split at all, but to allocate all flows to the product investigated. If this product is the main product of a particular process, this yields only a slight overstatement of the environmental interventions. 1 Often called open-loop recycling but covering all non-recycling co-production as well. 2 We expect operational simplified models to become available very soon.


Part 2b: Operational annex Detailed version The detailed version has two modeling steps and one allocation step. detailed modeling dm1: Split up processes that are not really multiple into single ones In this step, multifunctional processes may be split up. Processes are often described empirically as the sum total at a site. In this case, a closer look may reveal that in fact there are different process lines for the different products produced at the same site. This is always a useful check. Only in exceptional cases will this step solve the multifunctionality problem for a process; it usually states the multifunctional problem more precisely. In many instances of background processes, the options discussed above under simplified modeling will have to be used. dm2: Model long-term technical-physical relations in waste processing This step applies to mixed waste processing only. It involves subtracting all emissions caused by each of the wastes processed in a long-term model


It involves subtracting all emissions caused by each of the wastes processed in a long-term model. The (analytically) remaining process will virtually always be a combined one, still requiring some allocation procedure. Conceptually, this situation is very different from dm1, as the processes there were found, upon closer inspection, to be visibly independent. In the present category, processes are in principle combined, but alternatives are modeled as changes against a general background. This modeling method is not yet in common use in LCA, but is being developed. detailed allocation da1: Apply economic allocation based on market value or constructed market value The cost allocation problem in managerial accounting and the allocation problem in LCA for decision support are very similar. In LCA, the same economic principles for solution can be used, which is why this step is named “economic allocation”


In LCA, the same economic principles for solution can be used, which is why this step is named “economic allocation”. The allocation is not applied at a systems level, nor at firms level, but at the level of a detailed in-firm view of the processes involved. Some of these processes are really combined or joint, while others function for one of the co-products only. Allocation is only relevant for the combined or joint processes. In many instances, prices are given at a firm’s interface with the outside world, rather than for processes within the firm. This very common situation will be dealt with in some detail here. The method to be applied is the gross sales value method, adapted here for use in LCA. This preferred method for deriving operational allocation factors has been worked out in more detail in Part 3 of this Guide. Below, an example of economic allocation is worked out in Section 3.9.3.1


Below, an example of economic allocation is worked out in Section 3.9.3.1. Guidelines on other operational aspects of dealing with distorted markets, missing markets and as yet undeveloped markets are worked out in Part 2a of this Guide. Extended options As extended options, two additional modeling options are described, and one additional option for allocation. extended modeling em1: Apply market analysis for the main co-products This is the realistic version of system expansion, in principle taking into account real elasticities of supply and demand. This analysis is quite complicated at the level of detail required for LCA. Therefore, either standardised values for elasticities are to be used, or the analysis is to be restricted to some main flows


Therefore, either standardised values for elasticities are to be used, or the analysis is to be restricted to some main flows. As economic relations are not included in the detailed version of inventory modeling, this extended option may be used as a sensitivity analysis of introducing market effects in the model, giving insight into the way market modeling might influence outcomes. In general, however, the multifunctionality problem will not decrease but increase by introducing market mechanisms. em2: Develop a linear programming model for main processes If main processes are controlled by one organisation, the change in operation can be described using available information on technical relations and the aims of the processes operator. The change induced by having an extra amount of the functional unit can then be indicated involving only environmental interventions, A point of concern, however, is the time horizon of most linear programming models


This tends to be short, while the structural LCA questions treated here require long-term modeling, including investment functions in the models. Operational versions of such models are still lacking.


Part 2b: Operational annex extended allocation ea1: Apply a ‘symmetrical substitution method’ In using a substitution method, the first round of substitution induced would usually lead to substitution by a cradle-to-gate system that itself contains multifunctional processes, thus leading to an endless regress. The symmetrical substitution method avoids this problem of endless regress. How generally applicable this method is, remains an open question. For a fuller description and application in an example, see Section 3.9.3.2 below. 3.9.2 Solving problems of missing and distorted market prices for step da1 In many situations, an estimate can be made of the values of the various co-products, goods and services, including waste processing services. Market prices are the basic data to be used. If market prices are not known, there are several reliable sources for many product prices, including historical prices, current prices and expected prices in terms of futures


If market prices are not known, there are several reliable sources for many product prices, including historical prices, current prices and expected prices in terms of futures. The Worldwide Web is a unique source of price data, increasingly so as web-markets are fast emerging. Hundreds of websites on the most commonly traded products are now available. The relevant price is ‘FOB’, free on board, at the location of the supplier, without insurance or transport1. In several situations, however, market prices are nevertheless not available or, if available, may not reflect the value intended to be measured. For such problematic situations, a number of solutions are available. Problems and solutions are surveyed in Table 3.9.2.1 below. Table 3.9.2.1 : Prices of products with missing or distorted markets. problem 1. market prices not known 2. fluctuating prices 3. inflation 4. trends in real prices 5. different currencies in different processes 6. locally diverging prices 7


problem 1. market prices not known 2. fluctuating prices 3. inflation 4. trends in real prices 5. different currencies in different processes 6. locally diverging prices 7. market prices available only further downstream 8. partially missing prices 9


economically based market distortions (e.g., monopolies) 10.regulations-based market distortions 11 .tax-like financing of activity (e.g., sewer systems) 12.taxes and subsidies on products 13.taxes and subsidies on activities 14.in-firm prices not known 15.missing markets with public provision 16.developing markets for recycling products 17.markets not yet in existence solution look for public sources, preferably FOB prices use three-year averages, or use prices at futures market no problem, as long as the same base year is used in each process no problem, as long as the same base year is used in each process no problem, as long as the same currency is used in each process choose prices at relevant process locations or calculate averages for the relevant region gross sales value method, as worked out under ‘14’ construct prices from costs and known prices use actual market prices, correct in very exceptional cases only accept prices as they are, use value or cost of close alternative


worked out under ‘14’ construct prices from costs and known prices use actual market prices, correct in very exceptional cases only accept prices as they are, use value or cost of close alternative for missing market prices treat as ‘missing market, public provision’ use the price the seller actually receives do not correct for taxes and subsidies on activities


use gross sales value method construct prices based on costs use current prices of similar products to specify the price of future recycled products use expected future market prices If prices are not given in the process data set, providing them is very similar to the methods used for other missing data like some emissions. The only difference is that for most commodities, data on prices are publicly available, in publications and increasingly on the web. ‘Fob’ prices, at the gate, are more 1 The other price type is ‘cif’, stating a specific place of delivery like ‘cif Paris’ for the price of oil delivered in Paris. 1. MARKET PRICES NOT KNOWN


Part 2b: Operational annex relevant than cif prices. If market prices cannot be found, a best estimate is to be made, e.g., using available prices of slightly lower quality and slightly higher quality alternatives as a reference. Solution: Use public sources on prices; if not available, make informed estimate. 2. FLUCTUATING PRICES The problem of unstable prices is very similar to that of unstable emissions. Most processes do not emit regularly over time, in that they show daily, weekly, monthly and seasonal variations and long-term trends. Chemical plants, for instance, usually show a tendency for emissions to become lower in the first years after construction, while emissions go up in the last years of their functioning. For prices, similar variations may occur due to the business cycle. Trend-free irregularities can be approximated by some averaging procedure over the base year. For longer-term variations, one may use longer time series for averaging, as a non-default option


Trend-free irregularities can be approximated by some averaging procedure over the base year. For longer-term variations, one may use longer time series for averaging, as a non-default option. This might be relevant for products with a long-term downward trend in their prices, like abiotic virgin materials and energy resources. Solution: Take three consecutive annual averages, or use prices from futures markets. 3. INFLATION Inflation does not pose a serious problem, as it is not absolute prices but shares in proceeds which are relevant. One should use prices from the same date, especially in times of high inflation rates. It is not necessary to use a specific base year and deflate the prices to this base year, as inflation will influence the prices of all products from the process in the same way; relative prices then remain the same. Base years may differ between processes but should be the same for products from the same process


Base years may differ between processes but should be the same for products from the same process. Solution: For each process use prices at the end of the base year chosen. 4. TRENDS IN REAL PRICES Apart from inflation, real prices may show a trend, like the downward trend for most primary commodities. Prices may then be deflated (or inflated) to a base year. If such trends are the same for the various products of a process, as may be expected, relative prices are not changed by deflating. As long as the same base year is used for expressing the proceeds per product, the choice of base year does not influence the outcomes. Solution: Use the same base year for each process. 5. DIFFERENT CURRENCIES IN DIFFERENT PROCESSES This poses no problem for allocation purposes, as long as the same currency is used within one process


5. DIFFERENT CURRENCIES IN DIFFERENT PROCESSES This poses no problem for allocation purposes, as long as the same currency is used within one process. For economic analysis, as in market analysis or cost-benefit analysis, one common unit is to be established, after which the various currencies are to be converted into a single one. The currency rate valid for the same year for which the process data are chosen is the most relevant one. Values for currencies used in UN/World Bank national account statistics or by the IMF are to be taken for the base year chosen, and used for conversion. Such conversions may be relevant in extended analysis. Solution: Use the same currency for each process. 6. LOCALLY DIVERGING PRICES For some products, especially those with low prices in relation to transport costs, prices may vary steeply with location. The local price of the relevant process is then the most adequate price


The local price of the relevant process is then the most adequate price. In many cases it is not a specific process which is sought, but the processes representative of a region. In this situation, the average for the region is to be used. Transport is to be treated as a separate process. Solution: Use relevant processes or calculate an average value of product prices for processes in the relevant region. 7. MARKET PRICES AVAILABLE ONLY FURTHER DOWNSTREAM In some instances, market prices are only available in useful form after some processing of a material flow into a standard form. An example is that of working up metals from mixed waste to standard quality recycled metal. Standard prices are available for the recycled metal, e.g., aluminium ingots. For the price after the multifunctional process of ‘collection and sorting’, this is often not the case, as these prices may vary with the amounts delivered, transport distances, information on the variability of quality and the like


If the work-up process takes place inside the same firm that does the sorting, the gross sales value method specified under '14' can be used. If this is not the case, market prices can be imputed from the point where prices are available, back to where the multifunctional process produces the flow to be worked up. The method involves using the price per unit (e.g., 1 kg) at the point in the system where it is known and subtracting the costs of the processing that takes place between that point and the multiple process where the price is needed for economic allocation. These costs may be known from experience


Part 2b: Operational annex or may be specified by a specialided cost calculator. They include normal profits. This method is the same as the gross sales value method described under ‘14’. Solution: Use the gross sales value method to construct relevant “fob-like” prices. 8. PARTIALLY MISSING PRICES In some cases, some of the products of a process are priced while others are not. An example is the public processing of waste, co-producing heat and electricity sold on the market. The value of the coproduct ‘waste processing’ can be derived by subtracting the proceeds of heat and electricity from the total costs. Solution: Reconstruct the value of an unknown good or service by subtracting the known market proceeds of other products from the total costs. 9. ECONOMICALLY BASED MARKET DISTORTIONS (E.G., MONOPOLIES) In many situations, there is market failure through normal economic causes


9. ECONOMICALLY BASED MARKET DISTORTIONS (E.G., MONOPOLIES) In many situations, there is market failure through normal economic causes. For some products, economies of scale imply a production volume - where costs are minimum - much larger than the demand can absorb. This results in a natural monopoly, as exemplified by many transport systems with high fixed and low short-term marginal costs. Examples include electricity networks, cable TV networks and computer operating systems. Also, markets imply transparency as regards the quality of all products. This often is a serious problem for secondary materials. Different qualities of secondary materials are often not standardised and the costs of measuring quality may be high, for instance in the case of varying compositions of scrap metals. In such situations, markets may not emerge, as investments for secondary application are too risky


In such situations, markets may not emerge, as investments for secondary application are too risky. Although such circumstances may have effects on prices and hence on allocation shares, the volume of such effects may be limited and will usually affect all products of the oligopolistic or monopolistic firm. Such distortions can therefore be ignored. In cost-benefit analysis, such deviations from "true" market prices are also usually ignored. Solution: Accept prices as they are as a default. For developing markets: see below. 10. REGULATIONS-BASED MARKET DISTORTIONS AND FAILURE This is the most complex type of market failure


Solution: Accept prices as they are as a default. For developing markets: see below. 10. REGULATIONS-BASED MARKET DISTORTIONS AND FAILURE This is the most complex type of market failure. By way of example, let us assume that there is no market for a discarded house, that recycling has been made obligatory, e.g., by means of prohibitive prices for landfill or through outright prohibition of landfill, and that road building is an allowable route for some non-organic, non-metallic mixed granulate fraction resulting from the demolition process, while other useful applications are effectively forbidden. Clearly, such regulatory activities influence prices and may even create markets. There is nothing unusual in this situation, as virtually all markets are created by public regulations. Although such prices are experienced as ‘unnatural’, this is merely a matter of getting used to the idea that markets are hardly ever natural. Hence, the resulting prices are to be used


Although such prices are experienced as ‘unnatural’, this is merely a matter of getting used to the idea that markets are hardly ever natural. Hence, the resulting prices are to be used. For developing markets, see under ‘16’ below. Technological routes which have been cut off through regulations or pricing policy, as in waste processing, are irrelevant for the analysis, although they may, of course, be analysed as an alternative, to see if they have been blocked for sound environmental reasons. Solution: Actual prices are to be used. Take the value or cost of a closely related alternative for missing market prices. 11. TAX-LIKE FINANCING OF ACTIVITIES In many instances a price might appear to be paid for a service, while in fact some sort of tax is being levied for financing purposes. An example is the fee paid for wastewater treatment by households, based on the number of persons or rooms, or some other parameter which only very roughly indicates the volume of sewage water treated


However, if such payments are close to a price per unit of service (e.g., household waste processing paid per binbag or per kg), they can be used as an indication of value. Otherwise, the situation is like that in missing markets with public provision; see under ‘15’ below. Solution: If functioning as a price per unit of service: use as value. If not, treat as missing market with public provision. 12. TAXES AND SUBSIDIES ON MARKETED PRODUCTS The price the producer of a product receives is what counts in the allocation of the process. The effect of taxes and subsidies on prices and volumes is based on elasticities of supply and demand of the products involved. However, this is not a question of the effect of a tax or subsidy. The simpler question here is what the producer actually receives for his products. In the allocation between gasoline and other refinery products, the refinery receives the price excluding the excise on gasoline.


Part 2b: Operational annex Solution: Use the price the seller actually receives, that is, the price for the purchaser minus taxes or plus subsidies. 13. TAXES AND SUBSIDIES ON ACTIVITIES Some taxes and subsidies are levied for environmental reasons. Subsidies to the process may be seen as payment for a (collective) service of that process. If paid per unit of service, as in the example where farmers were paid for each successful meadow bird nest, it is not a subsidy on activities but a price for a product. However, in most instances it is not so clear what the environmental consideration is in taxing activities, nor is it possible to link the taxes and subsidies to any specific product. For such taxes on activities, no correction on proceeds is needed. Solution: Do not correct for taxes and subsidies on activities. 14


For such taxes on activities, no correction on proceeds is needed. Solution: Do not correct for taxes and subsidies on activities. 14. IN-FIRM PRICES NOT KNOWN In many cases, the process definition is at a more detailed level than that of firms, while proceeds from sales are measured at the level of the firm. Some processes within the firm may then not be multiple but function for one of the various products only. An example is the compression and storage of chlorine in the combined production of chlorine, caustic and hydrogen. For the purpose of allocation, we need the prices as they would be inside the firm, at the level of the real multiple process. The allocation procedure then starts with setting derived values for the flows inside the firm or business unit. The method used for this purpose is the gross sales value method1 as used for cost allocation in economic management accounting


The method used for this purpose is the gross sales value method1 as used for cost allocation in economic management accounting. The contribution of a process to the total proceeds is measured in terms of its share in the total costs, as the total costs of all activities is required to produce the total output that is sold. For each of the processes in the firm, the share in the total cost is used to calculate retrospectively the value of the flows originating from the real multiple process. Thus, adjusted proceeds, P’, are constructed for the products as they leave the real multiple process in the firm (process A in Figure 3.9.2.1 below). Computing its share in the total costs is quite straightforward. In disaggregating the operations of the larger business unit, its specific inputs and outputs, that is the flows to be allocated, have to be specified for each process separately. The result again is an allocation factor F


The result again is an allocation factor F. The procedure starts at the process where each product sold originates, and then goes upstream within the firm, until the real multiple process is reached. Solution: Gross sales value method. Total proceeds of the firm are known. For all processes within the firm, compute their share in total costs. This share can be used to compute their shadow contribution to total sales. For a process functioning for one of the firm's products only, subtract the shadow contribution to sales from the total sales of the product. The result is the in-firm shadow proceeds of the in-firm products, as illustrated in Figure 3.9.2.1 for the products of the multifunctional process A. 1 See for an extensive treatment: Bierman, Dyckman and Hilton (1990), Chapter 14 and 15. See for similar methods descriptions: Drury (1992); Horngren and Foster (1991) and Raiborn et al (1993), esp pp 713–23


Part 2b: Operational annex 15. MISSING MARKETS WITH PUBLIC PROVISION Missing markets with public provision are dominant in most of transport infrastructure and in a substantial part of municipal waste processing. (For most specialised waste processing of production wastes, market prices are available.) Ideally, the social value created by the multifunctional facility is to be allocated over the different functions it fulfils, as this value is the driver for the supply of the road or waste incinerator. To reduce the different functions to the same denominator, the value for the user could be used, with the value of the facility being the total of the value for all users. The amount of money people are willing to pay for the service can be estimated, e.g., that for roads can be calculated on the basis of toll road prices. Combined waste processing financed by retributions or taxes presents a similar problem, and similar solutions can be sought


Combined waste processing financed by retributions or taxes presents a similar problem, and similar solutions can be sought. As there is no question of sales, the value can be estimated by the willingness to pay for the various services. This shadow price can then be used to estimate ‘shadow total sales'. Where process parts function for one of the services only, the simplified version of the gross sales value method described under point 14 can be applied to calculate adjusted shadow proceeds. In some situations, the question may be reformulated in terms of the share in the total cost caused by using the transport infrastructure or having one’s household waste processed. In road transport, for example, road damage is to a large extent caused by goods vehicles, especially heavy goods vehicles. A model may be developed specifying the share of the different types of road users in the total costs of maintenance per kilometre


A model may be developed specifying the share of the different types of road users in the total costs of maintenance per kilometre. Such a model might rather be regarded as a part of inventory modeling based on physical relations. For the main body of the road (including viaducts, which are multifunctional themselves, etc) this cost approach does not help to solve the multifunctionality problem. Solution: Compute the share of the service in the total value created by the facility for the different kinds of services rendered. 16. DEVELOPING MARKETS FOR RECYCLED PRODUCTS In many long-term functions, like housing, current recycling processes do not yet provide the kind of volume needed for developing regular markets. (This situation is not only a problem for establishing allocation factors but also for estimating environmental flows.) The first step is the specification of the future recycling process


(This situation is not only a problem for establishing allocation factors but also for estimating environmental flows.) The first step is the specification of the future recycling process. The price for the recycled product is ideally based on a simulation of the future market, in terms of the price level in the base year chosen. This hardly improves on the simpler approach of using the price of an existing, nearly equivalent product, with or without a quality-related correction. In comparative assertions disclosed to the public, only proven or guaranteed techniques and current or guaranteed market prices should be used, adding a sensitivity analysis of the reasonably expected future functioning of the processes. Solution: Use current prices of similar products to specify the price of future recycled products. 17. MARKETS NOT YET IN EXISTENCE The problem is very similar to that of developing markets, with expected future prices as the ideal


17. MARKETS NOT YET IN EXISTENCE The problem is very similar to that of developing markets, with expected future prices as the ideal. The simple solution of using current prices, as in situation 16, may not be applicable here. In comparative assertions there are no current techniques or current market prices to refer to. This implies great demands on the quality of the analysis for estimating the future technical and market functioning of such processes. Solution: Use expected future market prices. 3.9.3 Quantified example of economic allocation and symmetrical substitution An example of a process has been worked out, covering the various multifunctionality situations that are usually distinguished: co-production, combined waste processing and recycling of discarded products


This section first works out the economic allocation (Section 3.9.3.1), and then applies the symmetrical substitution method (Section 3.9.3.2) to the same example, a simplified hypothetical refinery (SHR) process. 3.9.3.1 Economic allocation of simplified hypothetical refinery (SHR) process We take a hypothetical refinery producing a number of product outputs and non-product outputs, and also functioning as a waste processor for a number of waste flows (see Figure 3.9.3.1). These wastes are processed into main products, without it being visible whether and how recycling takes place. All that is available are totals of inputs being processed into outputs in the installation.


Part 2b: Operational annex The proceeds from the six products produced by the installation are summarised in Table 3.9.3.1.1. The share of each product in the total proceeds is the allocation factor share in sales = allocation factor 0.25 0.30 0.05 0.03 0.10 0.27 1.00 = 100% Adjoining processes can be treated in the same way. If process 1 is upgraded with two new products, the total proceeds consist of the sum of the proceeds from processing mixed residues and the sales of the upgraded product, a lubricant. This sum is: The allocation factors are then 0.779 for processing mixed residue and 0.220 for the lubricant as the upgraded product. All other flows, that is, inputs required, wastes with negative value, and emissions, are allocated to each of the products according to their respective allocation factors. INTERPRETATION How can this result be used and interpreted? There are now effectively six analytically constructed “independent” single function processes, one for each product


INTERPRETATION How can this result be used and interpreted? There are now effectively six analytically constructed “independent” single function processes, one for each product. For kerosene, there is an analytically independent process which takes 5% of all flows to be allocated, that is, all the dotted lines. If a product Table 3.9.3.1 .1 : Proceeds from sales and allocation factors for goods and services from an SHR. product 1 : fuel oil (good) 2: naphta (good) 3: kerosene (good) 4: long residue “waste” (good) 5: food waste (service) 6: wood waste (service) Total quantity x price = sales


Part 2b: Operational annex system needs 1 tonne of kerosene, the resulting flows are easily computed, as the process is fully linear. What might seem curious at first is that ‘waste lubricants from garages’ are also part of the allocated process for kerosene production. In the short term, this may be regarded as illogical, since a change in the amount of kerosene for airplanes does not influence the amount of waste lubricant oil from garages. On second thought, however, a more reasonable interpretation is possible. The fixed input and output coefficients of the unallocated process are determined by technical relations and all the elasticities of supply and demand involved. In using such process data, the assumption is that coefficients remain the same, which then also means that technical relations and elasticities of supply and demand remain the same


In using such process data, the assumption is that coefficients remain the same, which then also means that technical relations and elasticities of supply and demand remain the same. Although anybody is of course free to make different assumptions, they may easily fall into the trap of having the inventory model becoming the model of the entire world. Indeed, the actual elasticity of demand and supply for waste lubricant oil from garages makes it probable that methods of processing this flow will change, as well as the cost of waste processing, the volume of waste oil processed, the volume of primary lubricant oil production, car use, etc. Such realism is beyond any operational modeling method. Our models tend to simplify considerably to remain manageable. They do not describe a total reality but give an indication of reasonable expectations about relative consequences of choosing for one or another option


They do not describe a total reality but give an indication of reasonable expectations about relative consequences of choosing for one or another option. Doing this analysis systematically, without invoking ad hoc adaptations, is essential for comparison. If a real-life mechanism is added, it should be added systematically. Similar limitations abound. The consequence of this outcome is that a substantial part of the crude oil input to the refinery is allocated to wood preservation, due to the high volume and highly negative price of its contaminated carbo-hydrates waste flow. This is not realistic, as to some extent the calorific value of the waste replaces crude oil as an energy source (and probably not as a material). So one could reason that the energy part of the oil is replaced by the wood preservation waste


So one could reason that the energy part of the oil is replaced by the wood preservation waste. However, a disproportionate part of energy use may also be due to the greater separation effort required to separate the toxic components of wood preservation from the product outputs. This would mean that an even greater share of the crude oil input would be required. Of course such things can be known and modeled. It involves looking into the “inner workings” of the process. Progress in this area may be expected in the not too distant future, especially if combined with some form of linear programming technique for the optimisation of goal functions. For the time being, such sophistication cannot be part of detailed LCAs, and we will have to stick to the relative clumsiness of limited models. 3.9.3.2 Symmetrical substitution of simplified hypothetical refinery (SHR) process1 © Bo P


3.9.3.2 Symmetrical substitution of simplified hypothetical refinery (SHR) process1 © Bo P. Weidema, 2000, 2.–0 LCA consultants, Borgergade 6,1., 1300 København K, Denmark We take the same hypothetical refinery (see Figure 3.9.3.1) but now apply the symmetrical substitution method. IDENTIFICATION OF THE ECONOMIC INPUTS AND CO-PRODUCTS The refinery has: three independently variable economic inputs: waste lubricants from garages; waste hydrocarbons from wood preservation; waste hydrocarbons from food industry. These inputs are variable in the sense that they are not needed for the process and may therefore be varied depending on supply. one dependent economic input: crude oil. This input is dependent in the sense that its volume depends on the volume of the variable inputs and outputs. three independently variable economic outputs: fuel oil; naphtha; kerosene. Within the technical limits, these outputs are variable, in that they depend on the demand


three independently variable economic outputs: fuel oil; naphtha; kerosene. Within the technical limits, these outputs are variable, in that they depend on the demand. If technical limits are encountered at the specific refinery investigated, the change in demand will lead to changes in the production of a similar refinery without these technical limits. When 1 This part has been supplied to the project by Dr B.P. Weidema. – – –


Part 2b: Operational annex studying changes that are so large that the technical boundaries of the entire refinery industry are encountered, it is necessary to regard these outputs as dependent. We assume here that the change being studied is small. three dependent economic outputs: long residues; mixed residues for upgrading; mixed residues for incineration. These outputs are minimised (not more is produced of them than technically necessary) and thus depend solely on the volume of the inputs and the variable outputs. INDEPENDENTLY VARIABLE ECONOMIC INPUTS For the independently variable economic inputs (waste lubricants and other hydrocarbons) the following conditions apply (irrespective of the economic value of the input). Upstream processes: The volume of these inputs depends on the volume of the upstream processes (processes F, G and H in Figure 3.9.3.2) of which these inputs are wastes, rather than on the volume of the refinery production


The refinery acts as a waste treatment for these inputs. The wastes are assumed to be fully utilised (since they are valuable as inputs to the refinery and the total amount available on the market does not exceed that which can be used by the refinery industry). Thus, the upstream processes are not relevant to the other refinery co-products, i.e., there is a clear cut-off at entry into the refinery. Avoided processes: The production of the crude oil replaced by the variable inputs (process D in Figure 3.9.3.2) is credited to the main products of the upstream processes of which these inputs are wastes (i.e., the products of the garages, wood preservation processes and food industry)


Other effects on the refinery and downstream processes: If the input causes any changes in the environmental exchanges from the refinery or further downstream processes, compared to the use of crude oil as a raw material, these changes are attributed to the main products of the upstream processes of which these inputs are wastes (i.e., the products of the garages, wood preservation processes and food industry). This may for example be due to the nature of the waste (the hydrocarbons from food industry may be vegetable, which may, for example, lead to lower VOC emissions than from comparable fossil raw materials) or due to contamination (e.g., heavy metals from the lubricant use or wood preservation)


To determine the environmental exchanges to be attributed to the products of the garages, wood preservation processes and food industry (products F, G and H in Figure 3.9.3.2) the following information on the system is needed: 1) Changes in the economic outputs resulting from changes in the independently variable inputs (waste lubricants and other hydrocarbons): We assume that there is no change in the independently variable outputs (fuel oil, naphtha, kerosene), since such changes would be undesirable for the refinery, and would thus be avoided by reducing the variable inputs accordingly. We assume that the independently variable inputs (waste lubricants and other hydrocarbons) do not give rise to outputs of long residues and mixed residues for incineration, since these inputs have already been processed once


2) The amount of crude oil replaced by the independently variable inputs (waste lubricants and other hydrocarbons): We assume that the hydrocarbon chains of these inputs are of similar composition as those in crude oil, except, as mentioned under 1), that they do not contain the fractions that give rise to outputs of long residues and mixed residues for incineration. This leads to a slightly lower requirement of crude oil per input of waste hydrocarbons: 1 kton waste hydrocarbons yields 0.91 kton of the outputs fuel oil, naphtha, kerosene and mixed residues for upgrading (12 kton waste – 9.375% process loss), while 1 kton crude oil gives only 0.66 kton of these products (20 kton – 9.375% process loss – 5 kton long residues and mixed residues for incineration). Thus, 1 kton waste hydrocarbons replaces 1.38 kton crude oil. As can be seen from this calculation, we have assumed that the process loss (mainly feedstock used for fuel) does not depend on the type of input


As can be seen from this calculation, we have assumed that the process loss (mainly feedstock used for fuel) does not depend on the type of input. If the waste hydrocarbons do not need as much processing as crude oil (or need more processing), this assumption should be changed accordingly, which would also lead to a change in the amount of crude oil replaced. 3) Changes in the environmental exchanges from the refinery caused by changes in the independently variable inputs (waste lubricants and other hydrocarbons), compared to the use of crude oil as a raw material: The emissions from the refinery can roughly be divided into emissions from combustion – – – –


Part 2b: Operational annex related to the use of process energy, emissions of VOC, and solid and liquid wastes. The combustion emissions depend on the processes required by the different raw materials. We have assumed that there is no change in energy requirement (see point 2), and thus no change in combustion emissions. For VOC emissions from the waste hydrocarbons, it is reasonable to assume that there will be no emissions of methane (compared to 42 kg per kton crude oil) and fewer emissions of the lighter VOCs (we assume a 10% reduction from the 380 kg per kton crude oil), since these inputs have already been processed once. If it is assumed that any contaminants in the waste hydrocarbons are either degraded during processing or left in the product outputs (see point 4), the solid and liquid wastes can also be assumed to be linked to the crude oil only


4f) Changes in the environmental exchanges from downstream processes resulting from changes in the input of waste lubricants from garages, compared to the use of crude oil as a raw material: Heavy metal contaminants (assumed to be 20 kg/kton) will be suspended in proportional amounts in all economic outputs, except the lightest fraction (mixed residues to incinerator). For the fuel fractions (fuel oil and kerosene) this will eventually end up as air pollution from the combustion. Heavy metal contaminants in the naphtha will end up in the products produced from this (plastics) and will be released from waste treatment of these products (we assume combustion). Heavy metal contaminants in the long residues will probably be fixed in the resulting products (asphalts etc.)


Heavy metal contaminants in the long residues will probably be fixed in the resulting products (asphalts etc.). 4g) Changes in the environmental exchanges from downstream processes caused by changes in the input of waste hydrocarbons from wood preservation, compared to the use of crude oil as a raw material: If the waste contains any heavy metals, these will have the same fate as that indicated under 4f). If the contaminants are organic, we assume that they are decomposed during the refinery processing. 4h) Changes in the environmental exchanges from downstream processes caused by changes in the input of waste hydrocarbons from food industry, compared to the use of crude oil as a raw material: We assume that this will not cause any changes in downstream processes. Furthermore, the following information is needed on the environmental exchanges from each of the processes involved: Process F, G, H and : In this context, we do not use real data for these processes


Process D (crude oil production, incl. transport): Standard literature data can be used (Frischknecht et al., 1993/1995/1996). In this example, we limit the calculation to include the following emissions (per kton crude oil): 120 ton Methane: 10 ton NMVOC: 73 ton The calculation to be made (normalised to 1 kton of waste hydrocarbon input to the refinery) is as follows: Environmental exchanges to be attributed to products F, G and H, respectively = (Environmental exchanges from process F, G or H, respectively) – (Environmental exchanges from the production of 1.38 kton crude oil) – (Refinery emissions of methane, lighter VOCs, solid and liquid wastes equivalent to 1.38 kton crude oil input) + (Downstream emissions of heavy metals equivalent to the difference in heavy metal content between the waste hydrocarbon and crude oil)


For waste hydrocarbons from wood preservation, the result is presented in Table 3.9.3.2.1 (not including the environmental exchanges from the wood preservation process itself). Table 3.9.3.2.1: Calculation of selected environmental exchanges to be attributed to waste hydrocarbons from wood preservation (per kton waste hydrocarbon input to the refinery). Emissions to air Cadmium - - Methane NMVOC I: Production of 1.38 kton crude oil 166 ton - - 14 ton 100 ton II: Refinery VOC emissions per 1 .38 kton crude oil 42 kg (i.e., negligible) - 38 kg (i.e., negligible) - III: Downstream emissions of Cd from contamination 20kg To be attributed to waste hydrocarbon per kton input: III –I –II 20kg - 166 ton - 14 ton –100 ton If more information on the different waste hydrocarbons becomes available, the above assumptions and the calculation result can be refined.


Part 2b: Operational annex INDEPENDENTLY VARIABLE ECONOMIC OUTPUTS For the independently variable economic outputs (fuel oil, naphtha, kerosene), the following conditions apply: Upstream processes: The dependent input (crude oil) varies according to overall variations in the outputs. Refinery: The environmental exchanges of the refinery may vary according to the composition of the output, since different processing routes are involved. Residues: The amounts of residue are mainly determined by the raw material composition, but minor variations may be caused by changes in the output composition. The amounts of residue may thus be calculated individually for each of the variable outputs


The amounts of residue may thus be calculated individually for each of the variable outputs. The residues are assumed to be fully utilised, so that any intermediate treatment (upgrading and transport) of the residues before they can replace other products are attributed to the independently variable outputs in proportion to the amount of residues caused by each output (irrespective of the economic value of the residues). Avoided processes: The processes replaced by the residues (processes L, M and N in Figure 3.9.3.2) are credited to the variable outputs in proportion to the amounts of residue caused by each output (irrespective of the economic value of the residues)


To determine the environmental exchanges to be attributed to the independently variable economic outputs (products I, J, and K in Figure 3.9.3.2), the following information on the system is needed: 5) The amount of crude oil corresponding to a change in the independently variable economic outputs: We assume the same amount of crude oil input irrespective of the relative composition of the independently variable economic outputs. 6) The environmental exchanges of the refinery corresponding to a change in the independently variable economic outputs: Combustion emissions will increase if additional processing is needed to produce more of a fraction than the result of one crude distillation and one cracking of the distillation residue. The processing requirement depends on the composition of the raw material input


The processing requirement depends on the composition of the raw material input. The relations given in the ETH data (Frischknecht et al., 1993/1995/1996) can be used to calculate the emissions per type of refinery output, unless more specific data are available. 7) The amount of residues caused by a change in the independently variable economic outputs: The amount of residues (especially the lighter residues) will increase slightly if additional processing is needed to produce more of a fraction than the result of one crude distillation and one cracking of the distillation residue. The processing requirement depends on the composition of the raw material input. Furthermore, the following information is needed on the environmental exchanges from each of the processes involved: Process D: (as above) Process We assume that upgrading of long and mixed residues will lead to emissions of the order of 20% of the total refinery emissions


For mixed residues for incineration, only the pumping to the incinerator is relevant (but assumed to be negligible). Process L: The replaced process is either a dedicated bitumen production, or a change in the composition of the raw material input at a refinery having bitumen as an important product, resulting in a similar change in bitumen output. We assume that 0.17 kton of bitumen is replaced per kton of the independently variable economic outputs. Process M: In parallel to process L, we assume the replacement to be accommodated by a change in the composition of the raw material input at a refinery having the upgraded product as an important product. We assume that 0.09 kton of other refinery products are replaced per kton of the independently variable economic outputs. Process N: The replaced process is the production and supply of fuel oil or natural gas, depending on the local supply situation


Process N: The replaced process is the production and supply of fuel oil or natural gas, depending on the local supply situation. We assume that 0.85 TJ of natural gas is replaced per kton of the independently variable economic outputs. The calculation to be made (normalised to 1 kton of the independently variable economic outputs, i.e., fuel oil, naphtha, kerosene) is as follows: Environmental exchanges to be attributed to fuel oil, naphtha and kerosene, respectively = (Environmental exchanges from production of 32/29 kton crude oil) + (Refinery emissions related to the output in question, cf. Frischknecht et al., 1993/1995/1996) + (Environmental exchanges from the upgrading of the amount of long and mixed residues that can be related to the output in question, cf, Frischknecht et al., 1993/1995/1996) – (Environmental exchanges of the processes replaced by the residues that can be related to the output in question, cf. Frischknecht et al., 1993/1995/1996). – – – –


Part 2b: Operational annex For kerosene, the result is presented in Table 3.9.3.2.2 (not including the environmental exchanges from the wood preservation process itself). Table 3.9.3.2.2: Calculation of selected environmental exchanges to be attributed to kerosene (per kton kerosene output from the refinery). Emissions to air Methane NMVOC I: Production of 32/29 kton crude oil 132 ton 11 ton 81 ton II: Refinery emissions related to kerosene 9 ton 0.04 ton 0.5 ton - 28kg - 470kg III: Upgrading of the amount of long and mixed residues that can be related to kerosene 1.8 ton 0.004 ton 0.05 ton 6kg 94kg IV: Processes replaced by the residues that can be related to kerosene 38 ton 105 ton 2.8 ton 18 ton 25kg 138kg To be attributed to kerosene per kton output: + II + III -IV 8.2 ton 64 ton 9kg 425kg DEPENDENT ECONOMIC OUTPUTS The dependent economic outputs (different residues) are fully utilised in other processes


Thus, a change in demand for these residues will affect the same processes as those replaced by the residues (processes L, M and N). These processes are therefore attributed to the product in which the residues are utilised (irrespective of the economic value of the residues). To determine the environmental exchanges to be attributed to the products in which the residues are used (products O, P and Q in Figure 3.9.3.2), the environmental exchanges from the following processes must be known: Process L, M and N: (as above) Process O, P and Q: In this context, we do not use real data for these processes. The calculation to be made (normalised to 1 kton of the residue) is as follows: Environmental exchanges to be attributed to the product in which the residue is utilised = (Environmental exchanges from process O, P or Q, respectively) + (Environmental exchanges from process L, M or N, respectively).


Part 2b: Operational annex

3.10 Calculation method For matrix calculation rules, see Heijungs et al. (1992) and Heijungs (1994), and/or use the CMLCA software: http://www.leidenuniv.nI/interfac/cml/ssp/cmlca.html Part 2b: Operational annex


Part 2b: Operational annex 4. Impact assessment 4.1 Procedures To be inserted 4.2 Selection of impact categories DEFAULT LIST OF IMPACT CATEGORIES FOR THIS GUIDE Table 4.2.1 : Default list of impact categories impact category A. Baseline impact categories Depletion of abiotic resources Impacts of land use land competition Climate change Stratospheric ozone depletion Human toxicity Ecotoxicity freshwater aquatic ecotoxicity marine aquatic ecotoxicity terrestrial ecotoxicity Photo-oxidant formation Acidification Eutrophication B. Study-specific impact categories Impacts of land use loss of life support function loss of biodiversity Ecotoxicity freshwater sediment ecotoxicity marine sediment ecotoxicity Impacts of ionising radiation Odour malodourous air Noise Waste heat Casualties C


Other impact categories Depletion of biotic resources Desiccation Odour malodourous water single baseline characterisation method available in the Guide? yes yes yes yes yes yes yes yes yes yes yes no no yes yes yes yes yes yes yes no no no other characterisation method(s) available in the Guide? yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no no no no yes no no Main sources: Udo de Haes et al., 1999; Udo de Haes ed., 1996 ... ... ...


Part 2b: Operational annex NEW IMPACT CATEGORIES If a new impact category is added to the “extended WIA–2 list” the following requirements shall be comprehensively and transparently documented and justified. Table 4.2.2: Requirements for documenting and justifying new impact categories. General starting point for the framework of impact categories and indicators: a framework shall be developed which is open to further scientific progress and further detailing of information (WIA–2) General starting points for the total of impact categories: 1 . the categories shall together enable an encompassing assessment of relevant impacts, which are known today (completeness) (ISO/ WIA–2) 2. the categories should have the least overlap as possible, and should avoid double counting unless required by the goal and scope (ISO/ WIA–2) 3. the categories should be internationally accepted , i.e. based on an international agreement or approved by a competent international body (ISO) 4


the categories should be internationally accepted , i.e. based on an international agreement or approved by a competent international body (ISO) 4. the total of the impact categories should amount to a not too high number (WIA–2) Starting points for the selection of categories in a specific LCA study: 1 . the selected impact categories shall be consistent with the goal and scope of the LCA-study (ISO) 2


the selected impact categories shall be consistent with the goal and scope of the LCA-study (ISO) 2. the selected impact categories shall form a comprehensive set of environmental issues related to the goal and scope of the LCA-study (ISO) ECO-lNDICATOR ‘99 Table 4.2.3: Impact categories in the Eco-indicator ’99 approach Impact category sub-categories Damageto human health Damage to ecosystem quality Damage to resources caused by carcinogenic substances caused by respriratory effects caused by climate change caused by ionising radiation caused by ozone layer depletion caused by ecotoxic substances caused by acidification an eutrophication by airborne emissions caused by land use caused by depletion of minerals and fossil fuels Source: Goedkoop & Spriensma, 1999 - - - - - - - - -


Part 2b: Operational annex 4.3 Selection of characterisation methods: category indicators, characterisation models and factors ECO-INDICATOR ’99 FACTORS Table 4.3.1: Eco-indicator ’99 damage factors See the Excel table of impact assessment methods and data on: http://www.leidenuniv.nl/interfac/cml/lca2/index.html Source: Status: Goedkoop & Spriensma, 1999 Authors; weighting part not compatible with ISO 14042 comparative assertion requirements. EPS Table 4.3.2. Environmental Load Units (ELUs) for natural resources


EPS Table 4.3.2. Environmental Load Units (ELUs) for natural resources. Natural resource aluminium (Al) arsenic (As) bismuth (Bi) cadmium (Cd) chromium (Cr) cobalt (Co) copper (Cu) gold(Au) iron (Fe) lead (Pb) manganese (Mn) mercury (Hg) molybdenum (Mo) nickel (Ni) platinum (Pt) rhodium (Rh) silver (Ag) tin (Sn) titanium (Ti) tungsten (W); wolfraam uranium (U) vanadium (V) zinc (Zn) Zirconium (Zr) coal hard coal soft coal total oil crude Code 7429–90–5 7440–38–2 7440–69–9 7440–43–9 7440–47–3 7440–48–4 15158–11–9 7440–57–5 7439–89–6 14280–50–3 16397–91–4 14302–87–5 7439–98–7 7440–02–0 7440–06–4 7440–16–6 7440–22–4 7440–31–5 7440–32–6 7440–33–7 7440–61–1 7440–62–2 23713–49–7 7440–67–7 nvt nvt nvl 8012–95–1 Value (in ELU/kg) 0. 42 4E5 23000 8.5E5 0.68 3.6 40000 6 8E5 3.4E6 45000 0.602 28.3 20.6 0.05 0 05 0 05 0.5 Source: Status: Equation: Steen, 1996 not compatible with ISO 14042, because the characterisation and weighting steps have been combined


The total environmental burden is expressed in Environmental Load Units, is the valuation weighting factor for the EPS method for resource i, while (kg) is the quantity of resource i used.


Part 2b: Operational annex Table 4.3.3: Environmental Load Units (ELUs) for emissions


Substance anthracene arsenic benzo[a]anthracene benzo[a]pyrene benzo[ghi]perylene benzo[k]fluoranthrene cadmium Carbon dioxide Carbon Monoxide CFC–11 chromium (unspecified) chromium III chromium VI chrysene Dinitrogen oxide dust (PM10)1 Ethylene fluoranthrene hydrogen sulfide indeno[1,2,3-cd]pyrene lead mercury Methane Naphtalene nitrogen dioxide nitrogen oxides phenanthrene Polycyclic Aromatic Hydrocarbons (PAH) (unspecified) Polycyclic Aromatic Hydrocarbons Carcinogenic- (carcinogenic-PAH) sulphur dioxide TSP Biological Oxygen Demand (BOD) Chemical oxigen demand (COD) Nitrate Nitrogen Phosphate Phosphorus Biological Oxygen Demand (BOD) Chemical oxigen demand (COD) Nitrate Nitrogen Phosphate Phosphorus Compartment air air air air air air air air air air air air air air air air air air air air air air air air air air air air air air air fresh water fresh water freshwater fresh water freshwater freshwater sea water sea water sea water sea water sea water sea water CAS number 120–12–7


air air air air air air air air air air air air fresh water fresh water freshwater fresh water freshwater freshwater sea water sea water sea water sea water sea water sea water CAS number 120–12–7 7440–38–2 56–55–3 50–32–8 191–24–2 207–08–9 7440–43–9 124–38–9 630–08–0 75–69–4 7440–47–3 7440–47–3 7440–47–3 218–01–9 10024–97–2 74–85–1 206–44–0 7783–06–4 193–39–5 14280–50–3 14302–87–5 74–82–8 91–20–3 10102–44–0 10102–44–0 85–01–8 - - 7446–09–5 ??? - -? 7697–37–2 7727–37–9 7664–38–2 7723–14–0 alg??? alg??? 7697–37–2 7727–37–9 7664–38–2 7723–14–0 Value (in ELU/kg) 21.2 0.0636 0.191 0.8 0.8 0.8 20.3 0.0071 3.4 0.142 1.56 0.395 0.395 0.0545 0.0071 0.0075 0.006 0.00226 0.01 0.0245 0.075 0.0075 0.006 0.00226 0.01 0.0245 0.075 Source: Status: Equation: Steen, 1996 not compatible with ISO 14042, because the characterisation and weighting steps have been combined


The total environmental burden is expressed in Environmental Load Units, is the valuation weighting factor for the EPS method for emitted substance i, while (kg) is the quantity of i emitted. 1 Including dust besides individual chemicals may imply some double-counting according to recent WHO findings.


Part 2b: Operational annex REQUIREMENTS FOR DOCUMENTING AND JUSTIFYING NEW IMPACT CATEGORIES If another category indicator, model or characterisation factor is used than those recommended as best available practice in this Guide, the following requirements shall be comprehensively and transparently documented and justified. Table 4.3.4: Requirements for documenting and justifying new impact categories. 1. 2. 3. 4. 5. 6. 7. 8. 9. ISO starting points the category indicator should (shall for comparative assertions) be modeled in a scientifically and technically valid way in relation to the environmental interventions, i.e., using a distinct identifiable environmental mechanism and/or reproducible empirical observation the category indicators and models shall be environmentally relevant, i.e. shall be sufficiently clearly related to the category endpoints, at least qualitatively the category indicators and models should be internationally accepted, i.e


based on an international agreement or approved by a competent international body value-choices and assumptions should be minimised category indicators can be chosen anywhere in the environmental mechanism of an impact category, from environmental interventions to category endpoints (focal point in environmental mechanism) not included not included not included not included WIA–2 starting points included included not explicitly included included included it should be possible to multiply characterisation factors by mass or other units indicating the magnitude of the environmental interventions the preferred time span for fate and effects is eternity, with 100 years as a second option; all effects of the emission/ extraction occurring now and in the future should be taken into account the category indicators and models should include the modeling of fate, exposure and effects, as relevant the category indicators and models should include effects below thresholds (“less is


account the category indicators and models should include the modeling of fate, exposure and effects, as relevant the category indicators and models should include effects below thresholds (“less is better”approach) selection criteria for baseline method recommended in this Guide included included included included included modified: the baseline category indicators should be linear (linearity) included included included


Part 2b: Operational annex 10. not included 11. 12. ISO starting points not included not included WIA–2 selection criteria for baseline starting points method recommended in this Guide it should be possible to perform Impact assessment without information on time or location (time-and location-independent) the method should be operational for a sufficient number of environmental interventions the uncertainty margins of the indicator result should be as small as possible


Part 2b: Operational annex 4.3.1 Depletion of abiotic resources Table 4.3.1 .1 : ADP factors for characterising abiotic resources based on ultimate reserves and extraction rates


Natural resource actinium (Ac) aluminium (Al) antimony (Sb) argon (Ar) arsenic (As) barium (Ba) beryllium (Be) bismuth (Bi) boron (B) bromine (Br) cadmium (Cd) calcium (Ca) cerium (Ce) cesium (Cs) chlorine (Cl) chromium (Cr) cobalt (Co) copper (Cu) dysprosium (Dy) erbium (Er) europium (Eu) fluorine (F) gadolinium (Gd) gallium (Ga) germanium (Ge) gold (Au) hafnium (Hf) helium (He) holmium (Ho) indium (In) iodine (I) iridium (Ir) iron (Fe) kalium (K;potassium) krypton (Kr) lanthanum (La) lead (Pb) lithium (Li) lutetium (Lu) magnesium (Mg) manganese (Mn) mercury (Hg) molybdenum (Mo) neodymium (Nd) neon (Ne) nickel (Ni) niobium (Nb) osmium (Os) palladium (Pd) phosphorus (P) platinum (Pt) polonium (Po) praseodymium (Pr) protactinium (Pa) Cas-number 7440–34–8 7429–90–5 7440–36–0 7440–37–1 7440–38–2 7440–39–3 7440–41–7 7440–69–9 7440–42–8 7726–95–6 7440–43–9 7440–70–2 7440–45–1 7440–46–2 7782–50–5 7440–47–3 7440–48–4 7440–50–8 7429–91–6 7440–52–0 7440–53–1 7782–41–4 7440–54–2 7440–55–3


7440–38–2 7440–39–3 7440–41–7 7440–69–9 7440–42–8 7726–95–6 7440–43–9 7440–70–2 7440–45–1 7440–46–2 7782–50–5 7440–47–3 7440–48–4 7440–50–8 7429–91–6 7440–52–0 7440–53–1 7782–41–4 7440–54–2 7440–55–3 7440–56–4 7440–57–5 7440–58–6 7440–59–7 7440–60–0 7440–74–6 7553–56–2 7439–88–5 7439–89–6 7440–09–7 7439–90–9 7439–91–0 7439–92–1 7439–93–2 7439–94–3 7439–95–4 7439–96–5 7439–97–6 7439–98–7 7440–00–8 7440–01–9 7440–02–0 7440–03–1 7440–04–2 7440–05–3 7723–14–0 7440–06–4 7440–08–6 7440–10–0 ?????? ADP (in kg antimony eq./ 6.33E13 1E–8 4.71E–7 0.00917 1.06E–10 3.19E–5 0.0731 0.00467 0.00667 0.33 7.08E–10 5.32E–9 1.91E–5 4.86E–8 0.000858 2.62E–5 0.00194 2.13E–6 2.44E–6 1.33E–5 2.96E–6 6.57E–7 1.03E–7 1.47E–6 89.5 8.67E–7 1 .33E–5 0.00903 0.0427 32.3 8.43E–8 3.13E–8 20.9 2.13E–8 0.0135 9.23E–6 7.66E–5 3.73E–9 1 .38E–5 0.495 0.0317 1.94E–17 0.325 0.000108 2.31E–5 14.4 0.323 8.44E–5 1.29 4.79E14 2.85E–7 9.77E6 kg)


Part 2b: Operational annex Natural resource radium (Ra) radon (Rn) rhenium (Re) rhodium (Rh) rubidium (Rb) ruthenium(Ru) samarium (Sm) scandium (Sc) selenium (Se) silicium (Si; silicon) silver (Ag) Sodium (Na) strontium (Sr) sulfur tantalum (Ta) tellurium (Te) terbium (Tb) thallium (Tl) thorium (Th) thulium (Tm) tin (Sn) titanium (Ti) tungsten (W); wolfraam uranium (U) vanadium (V) xenon (Xe) ytterbium (Yb) yttrium (Y) zinc (Zn) Zirconium (Zr) crude oil natural gas* hard coal soft coal fossil energy** * In kg natural gas ! ** In kg antimony/MJ fossil energy ! Cas-number 7440–14–4 ??? 7440–15–5 7440–16–6 7440–17–7 7440–18–8 7440–19–9 7440–20–2 7782–49–2 7440–21–3 7440–22–4 7440–23–5 7440–24–6 7704–34–9 7440–25–7 13494–80–9 7440–27–9 7440–28–0 7440–29–1 7440–30–4 7440–31–5 7440–32–6 7440–33–7 7440–61–1 7440–62–2 7440–63–3 7440–64–4 7440–65–5 7440–66–6 7440–67–7 8012–95–1 nvt nvt nvt nvt ADP (in kg antimony eq./kg) 2.36E7 1.2E20 0.766 32.3 2.36E–9 32.3 5.32E–7 3.96E–8 0.475 2.99E–11 1.84


7440–61–1 7440–62–2 7440–63–3 7440–64–4 7440–65–5 7440–66–6 7440–67–7 8012–95–1 nvt nvt nvt nvt ADP (in kg antimony eq./kg) 2.36E7 1.2E20 0.766 32.3 2.36E–9 32.3 5.32E–7 3.96E–8 0.475 2.99E–11 1.84 8.24E–11 1.12E–6 0.000358 6.77E–5 52.8 2.36E–5 5.05E–5 2.08E–7 8.31E–5 0.033 4.4E–8 0.0117 0.00287 1.16E–6 17500 2.13E–6 3.34E–7 0.000992 1 .86E–5 0.0201 0.0187 0.0134 0.00671 4.81e–4 Source: Status: Equation: Guinée, 1995, with modifications for crude oil, natural gas, hard coal and soft coal


author. The indicator result is expressed in kg of the reference resource antimony. is the Abiotic Depletion Potential of resource i, while (kg, except for natural gas and fossil energy) is the quantity of resource i used. Remark: For fossil energy carriers, new ADPs have been calculated to replace those given in Guinée (1995). The reason is that, contrary to what was stated in Guinée (1995), fossil fuels may be assumed to be full substitutes (both as energy carriers and as materials) which means that the ADPs should not be different, at least in terms of total energy reserve. Hence, an overall fossil energy ADP has first been calculated, using the equation: (S) ADP = Abiotic Depletion Potential Of fossil energy in kg antimony eq./MJ fossil energy;


Part 2b: Operational annex reserve of fossil fuels in MJ; de–accumulation, or fossil energy production, in reserve of antimony, the reference resource, in kg (according to the data in Table 4.3.1.2); de-accumulation of antimony, the reference resource, in (according to the data in Table 4.3.1.2). However, the ultimate reserve of fossil energy has first been derived from the proven reserve (WRI, 1994; Table.9.2, p. 167). A rough estimate of the ultimate reserve of fossil energy can be made on the basis of the fossil carbon content of the earth's crust, which has been estimated by Berner & Lasaga (1989)


167). A rough estimate of the ultimate reserve of fossil energy can be made on the basis of the fossil carbon content of the earth's crust, which has been estimated by Berner & Lasaga (1989). This C-content can be converted to ultimate reserves for fossil energy (natural gas, crude oil and coal) by calculating C (the total C-content in kg of the proven reserves of fossil energy fuels), for which data are available (WRI, 1994), and multiplying the proven reserves of fossil energy by the ratio of C (the total C-content in kg as estimated by Berner & Lasaga) to C This ratio equals Based on the figures for DR and R given in Table 4.3.1.2 for antimony and fossil energy and the ratio given above (fossil fuel DR data based on WRI, 1994; Table 21.1, p


332), the ADP of the use of 1 MJ of fossil energy equals: The ADPs for the individual fossil fuel per kg of fossil fuel extracted (as a result of the inventory analysis) can now be calculated as follows: For consistency reasons the same heating values have been used as in WRI (1994). Note, however, that these values may differ from those provided by other sources (see e.g. Table 4.3.1.5). Note also that the ADPs for most of these four fossil fuel types have changed considerably compared to the values published in Guinée (1995), due to the assumption of full substitutability. 1 For an explanation of these calculations we refer to appendix 2, page 106, of Guinée (1995). Note that in Guinée (1995), the ratio of the total C-content as estimated by Berner & Lasaga to the total C-content of the proven fossil fuel reserves was multiplied by proven reserves in kg. Here it is multiplied by the total proven reserve of fossil fuels in MJ.


Part 2b: Operational annex Table 4.3. 1.2: Values for reserve and de-accumulation Natural resource actinium (Ac) aluminium (Al) antimony (Sb) argon (Ar) arsenic (As) barium (Ba) beryllium (Be) bismuth (Bi) boron (B) bromine (Br) cadmium (Cd) calcium (Ca) cerium (Ce) cesium (Cs) chlorine (Cl) chromium (Cr) cobalt (Co) copper (Cu) dysprosium (Dy) erbium (Er) europium (Eu) fluorine (F) gadolinium (Gd) gallium (Ga) germanium (Ge) gold (Au) hafnium (Hf) helium (He) holmium (Ho) indium (In) iodine (I) iridium (Ir) iron (Fe) kalium (K;potassium) krypton (Kr) lanthanum (La) lead (Pb) lithium (Li) lutetium (Lu) magnesium (Mg) manganese (Mn) mercury (Hg) molybdenum (Mo) neodymium (Nd) neon (Ne) nickel (Ni) niobium (Nb) osmium (Os) palladium (Pd) phosphorus (P) platinum (Pt) polonium (Po) praseodymium (Pr) protactinium (Pa) reserve and de-accumulation rates that can rate based ADPs


Cas-number 7440–34–8 7429–90–5 7440–36–0 7440–37–1 7440–38–2 7440–39–3 7440–41–7 7440–69–9 7440–42–8 7726–95–6 7440–43–9 7440–70–2 7440–45–1 7440–46–2 7782–50–5 7440–47–3 7440–48–4 7440–50–8 7429–91–6 7440–52–0 7440–53–1 7782–41–4 7440–54–2 7440–55–3 7440–56–4 7440–57–5 7440–58–6 7440–59–7 7440–60–0 7440–74–6 7553–56–2 7439–88–5 7439–89–6 7440–09–7 7439–90–9 7439–91–0 7439–92–1 7439–93–2 7439–94–3 7439–95–4 7439–96–5 7439–97–6 7439–98–7 7440–00–8 7440–01–9 7440–02–0 7440–03–1 7440–04–2 7440–05–3 7723–14–0 7440–06–4 7440–08–6 7440–10–0 ??? deaccumulation (kg/yr) 2.90E4 1.05E11 6.06E7 2.90E4 4.50E7 2.90E4 3.79E5 3.20E6 7.45E8 4.00E8 2.00E7 1.85E9 2.90E4 2.90E4 1.13E8 2.48E7 8.90E9 1.30E10 2.90E4 2.90E4 2.90E4 1.75E9 2.90E4 3.50E4 6.50E4 2.17E6 2.90E4 1.49E7 2.90E4 1.40E5 1.64E7 4.90E4 4.05E11 2.08E10 2.90E4 2.90E4 5.60E6 3.20E9 2.90E4 3.09E9 1.88E10 4.80E6 1.08E8 2.30E–5 2.90E4 9.16E8 1.40E7 4.90E4 4.90E4 1.41E11 4.90E4 2.90E4 2.90E4 2.90E4 be used to calculate alternative 1.27E7


4.05E11 2.08E10 2.90E4 2.90E4 5.60E6 3.20E9 2.90E4 3.09E9 1.88E10 4.80E6 1.08E8 2.30E–5 2.90E4 9.16E8 1.40E7 4.90E4 4.90E4 1.41E11 4.90E4 2.90E4 2.90E4 2.90E4 be used to calculate alternative 1.27E7 1.93E21 4.63E15 1.48E17 4.17E16 9.84E18 6.48E16 3.94E15 2.38E17 1.46E17 4.63E15 9.61 E20 1.39E18 2.31E16 2.87E19 5.79E17 1.27E18 2.31E18 6.94E16 6.48E16 2.78E16 1.45E19 1.25E17 3.47E17 1.25E17 9.26E13 1.09E17 1.89E14 2.78E16 2.34E15 1.17E16 2.31E13 1.30E21 4.84E20 2.21E13 6.94E17 4.63E17 2.89E17 1.16E16 5.41 E20 2.20E19 1.85E15 3.47E16 6.48E17 1.78E14 1.74E18 4.63E17 3.47E13 2.31E14 2.43E19 1.16E14 4.63E6 1.90E17 3.24E10 reserveultimate (kg)


Part 2b: Operational annex Natural resource radium (Ra) radon (Rn) rhenium (Re) rhodium (Rh) rubidium (Rb) ruthenium (Ru) samarium (Sm) scandium (Sc) selenium (Se) silicium (Si; silicon) silver (Ag) Sodium (Na) strontium (Sr) sulfur (S) tantalum (Ta) tellurium (Te) terbium (Tb) thallium (Tl) thorium (Th) thulium (Tm) tin (Sn) titanium (Ti) tungsten (W); wolfraam uranium (U) vanadium (V) xenon (Xe) ytterbium (Yb) yttrium (Y) zinc (Zn) Zirconium (Zr) fossil energy * In MJ/yr ** In MJ Cas–number 7440–14–4 ??? 7440–15–5 7440–16–6 7440–17–7 7440–18–8 7440–19–9 7440–20–2 7782–49–2 7440–21–3 7440–22–4 7440–23–5 7440–24–6 7704–34–9 7440–25–7 13494–80–9 7440–27–9 7440–28–0 7440–29–1 7440–30–4 7440–31–5 7440–32–6 7440–33–7 7440–61–1 7440–62–2 7440–63–3 7440–64–4 7440–65–5 7440–66–6 7440–67–7 nvt deaccumulation (kg/yr) 2.90E4 2.90E4 2.90E4 4.90E4 2.90E4 4.90E4 2.90E4 2.90E4 1.80E6 3.60E9 1 .37E7 7.32E7 2.38E8 5.27E10 4.10E5 8.00E4 2.90E4 1.55E4 2.90E4 2.90E4 2.00E8 2.16E9 3.98E7 3.17E7 3.21E7


deaccumulation (kg/yr) 2.90E4 2.90E4 2.90E4 4.90E4 2.90E4 4.90E4 2.90E4 2.90E4 1.80E6 3.60E9 1 .37E7 7.32E7 2.38E8 5.27E10 4.10E5 8.00E4 2.90E4 1.55E4 2.90E4 2.90E4 2.00E8 2.16E9 3.98E7 3.17E7 3.21E7 2.90E4 2.90E4 5.51E5 7.37E9 7.65E8 3.03E14* (kg) 2.08E10 9.26E3 1.16E14 2.31E13 2.08E18 2.31E13 1.39E17 5.09E17 1.16E15 6.53E21 1.62E15 5.60E20 8.69E18 7.21E18 4.63E16 2.31E13 2.08E16 1.04E16 2.22E17 1.11E16 4.63E16 1 .32E20 3.47E16 6.25E16 3.13E18 7.65E11 6.94E16 7.64E17 1.62E18 3.82E18 4.72E20** Source: Status: Equation: Guinée, 1995;WRI, 1994; Berner & Lasaga, 1989 Author or any combination of and The unit of the indicator result depends on that of the ADP


is the Abiotic Depletion Factor of resource i, while is the quantity of resource i used, is the reserve of resource i, and is the de–accumulation of resourcei. is the reserve of antimony, the reference resource, and is the deaccumulation of that reference resource Economic reserves For data on economic reserves, see: http://minerals.usgs.gov/minerals/ For energy data, see: http://www.wri.org/facts/data-tables-enerqy.html and/or http://www.iea.org/ NOTE: If practitioners would like to apply economic reserve data in the ADP approach, they will have to calculate such new ADP factors themselves, including the associated new normalisation factors. reserveultimate


Table 4.3.1 .3: Factors for characterising abiotic resources based 1996b). Material/mineral Iron Core Copper ore, type 1 Copper ore, type 2 Lead ore Zinc ore Nickel ore Gold ore Aluminium ore Chronium ore Platinum ore Phosphorus ore Lime Rock Sand Acantite Adularine Anortite Aragonite Barite Boehmite Chalcopyrite Chromite Covellite Diopside Dolomite Fayalite Fluorite Fluoroapatite Forsterite Galena Goethite Gold Haematite Hydroxyapatite Low albite Magnetite Microcline Millerite Chemical composition - - - - - - - - - - - - - - AIOOH CuS PbS FeOOH Au on exergy content (Finnveden, Factor (MJ/kg ) 0.42 0.63 7.9 0.56 1.9 8.8 8.3 1.1 0.51 0.58 0.28 0.034 0.32 0.032 2.863 0.359 0.983 0.01 0.015 1.607 11.19 0.557 7.221 0.037 0.082 1.159 0.146 0.013 0.532 23.11 0.111 0.078 0.103 0.126 0.402 0.525 0.361 8.404 Source: Status: Equation: Finnveden, 1996b author. The indicator result is expressed in MJ exergy content


The indicator result is expressed in MJ exergy content. is the characterisation factor for abiotic depletion of resource i based on the exergy content, while is the quantity of resource i used. (kg) Part 2b: Operational annex


Part 2b: Operational annex Table 4.3.1.4: 1996)


Alpha elements Ag CI AgF Factors for characterising abiotic resources Name INORGANIC COMPOUNDS Silver chloride Silver fluoride Silver nitrate Silver carbonate Silver oxide Silver dioxide Silver sulfate Silver sulfide Aluminum chloride Analcime Potassium aluminosilicate Sodium aluminate Sodium aluminosilicate (albite) Calcium aluminate Calcium aluminosilicate (anortite) Calcium trialuminate Boermite Kaolinite Gibbsite Magnesium aluminate Aluminum oxide (corundum) Aluminum silicate (andalusite) Aluminum silicate (kyanite) Aluminum silicate (sillimanite) Aluminum sulfate Aluminum sulfide Aluminum carbide Calcium trialuminate Aluminum silicate (mullite) Calcium aluminate Arsenic pentoxide Aurous chloride Auric chloride Auric fluoride Auric trioxide Boron trichloride Boron fluoride Boric acid Diborane Boron oxide Barium carbonate (witherite) Barium chloride Barium fluoride Barium hydroxide Barium oxide Barium peroxide Barium sulfate Barium sulfide Bismuth trioxide Bismuth sulfide Hydrogen


Diborane Boron oxide Barium carbonate (witherite) Barium chloride Barium fluoride Barium hydroxide Barium oxide Barium peroxide Barium sulfate Barium sulfide Bismuth trioxide Bismuth sulfide Hydrogen bromide (aq,400) Potassium bromide (aq, 400) Sodium bromide (aq, 400) Sodium bromate Calcium bromide Tribromomethane based on exergy content (Ayres et al., Mole weight 143.321 126.866 169.873 275.745 231.735 247.735 311.798 247.800 133.341 220.155 278.337 81.970 262.225 158.041 278.210 270.199 119.977 258.161 156.007 142.273 101.961 162.046 162.046 162.046 342.148 150.155 143.959 260.002 426.053 1386.681 229.840 232.420 303.326 253.962 441.932 117.170 67.806 61.833 26.670 69.620 197.349 208.246 175.337 171.355 153.339 169.339 233.402 169.404 465.958 514.152 80.912 119.006 102.894 150.892 199.888 252.731 exergy (in kJ/mol) 22.2 118.5 43.1 115.0 57.6 172.1 139.6 709.5 444.9 104.2 99.9 151.7 105.5 275.4 218.3 500.6 195.3 197.8 209.5 230.3 200.4 45.1 43.9 15.4 529.7 2980.7 4588.2 460.4 618.8


252.731 exergy (in kJ/mol) 22.2 118.5 43.1 115.0 57.6 172.1 139.6 709.5 444.9 104.2 99.9 151.7 105.5 275.4 218.3 500.6 195.3 197.8 209.5 230.3 200.4 45.1 43.9 15.4 529.7 2980.7 4588.2 460.4 618.8 2526.8 216.9 62.2 155.5 437.3 114.7 69.4 26.3 61.3 57.2 132.9 224.6 169.3 3.4 901.9 61.4 2237.3 Au Cl Ba O Ba S Br H Br K Br Na


Part 2b: Operational annex Alpha elements C Si C Ti CV C W Ca O Ca S Name Calcium carbonate Cadmium carbonate Cyanogen chloride Dichlorodifluoromethane Methylene chloride Carbonyl chloride Trichlorofluoromethane Carbon tetrachloride Cobalt carbonate (spherocobaltite) Cupric carbonate Ferrous carbonate (siderite) Cementite Mercurous carbonate Potassium carbonate Lithium carbonate Magnesium carbonate Manganese carbonate Manganese carbide Molybdenum carbide Cyano Sodium cyanide (aq,200) Sodium carbonate Nickel nitrate Nickel carbide Carbon monoxide Carbonyl sulfide Carbon dioxide Lead carbonite (cerussite) Rubidium carbonate Strontium carbonate (strontianite) Zinc carbonate Carbon disulfide Silicon carbide (cubic) Titanium carbide Vanadium carbide Tungsten carbide Calcium carbonide Dolomite 1 -chloro–2,2-difluoroethylene Chlorotrifluoroethylene Tetrachloroethylene Perchloroethylene 1,1,1,2-tetrachoro–2,2-difluoroethane Trichloroacetyl chloride Chromium dicarbide Cyanogen Chromium


1 -chloro–2,2-difluoroethylene Chlorotrifluoroethylene Tetrachloroethylene Perchloroethylene 1,1,1,2-tetrachoro–2,2-difluoroethane Trichloroacetyl chloride Chromium dicarbide Cyanogen Chromium thricarbide Calcium chloride Calcium fluoride Calcium hydroxide Calcium sulfate (gypsum) Calcium nitrate Calcium oxide Calcium silicate (volastonite) Calcium solphate (anhydrite) Calcium sulfide (oldhamite) Tremolite Mole weight 100.089 172.409 61.471 120.914 84.933 98.917 137.369 153.823 118.943 123.555 115.856 179.552 461.189 138.213 73.887 84.321 114.947 176.825 203.891 26.018 49.008 105.989 118.719 188.141 28.011 60.075 44.010 267.199 230.949 147.629 125.379 76.139 40.097 59.911 62.953 195.861 64.102 184.411 98.480 116.471 165.834 165.834 203.831 181.834 180.010 52.036 400.005 110.986 78.077 215.772 74.095 172.172 164.090 56.079 116.164 136.142 72.144 255.852 812.410 exergy (in kJ/mol) 40.6 473.1 45.8 31.5 125.9 1560.2 179.8 85.1 70.1 37.9 81.8 1862.3 1824.6 845.0 41.5 36.4 1142.9 275.0 19.9


74.095 172.172 164.090 56.079 116.164 136.142 72.144 255.852 812.410 exergy (in kJ/mol) 40.6 473.1 45.8 31.5 125.9 1560.2 179.8 85.1 70.1 37.9 81.8 1862.3 1824.6 845.0 41.5 36.4 1142.9 275.0 19.9 23.5 152.4 6.2 23.9 1694.7 1136.7 1032.6 1199.5 1468.3 15.1 2372.0 1118.9 4874.2 87.9 11.4 104.0 53.7 8.6 –18.1 110.2 23.6 8.2 844.6 194.7 81.6 C Cl N C N C N Na C O C O S


Part 2b: Operational annex Alpha elements Cd O Cd S CI Cs CI Cu Cl H CI HO CI K Cl Li Cl Na CI Na CI Rb Name Calcium di-orthosilicate Calcium trisilicate Calcium phosphate Cadmium hydroxide Cadmium sulfate Cadmium oxide Cadmium sulfate Cadmium sulfide Cesium chlorine Cuprous chlorine Perchloryl fluoride Hydrogen chloride Hydrochlorous acid (aq,400) Perchloric acid (aq, 660) Ammonium chloride Ammonium chloride Potassium chloride Potassium perchlorate Lithium chloride Nitrosyl chloride Sodium chloride Sodium chlorate (aq,400) Sodium perchlorate (aq,476) Chlorine dioxide Rubidium chloride Cadmium chloride Cobaltic chloride Chromous chloride Cupric chloride Ferrous chloride (lawrencite) Mercuric chloride Mercurous chloride Magnesium chloride Manganese chlorine Nickel choride Thionyl chloride Sulfuryl chloride Lead chloride (cotunnite) Sulfur monochloride Stannous chloride Strontium chloride Zinc chlorine Chromic chloride Ferric chloride (molysite) Trichlorosilane Phosphorus oxychloride


Sulfuryl chloride Lead chloride (cotunnite) Sulfur monochloride Stannous chloride Strontium chloride Zinc chlorine Chromic chloride Ferric chloride (molysite) Trichlorosilane Phosphorus oxychloride Phosphorus trichloride Uranium trichlorine Silicon tetrachloride Uranium tetrachlorine Phosphorus pentachloride Uranium pentachlorine Cobaltous hydroxide Cobaltous oxide Cobaltic sulfate Cobaltous sulfide (syeporite) Cobaltic oxide Ferrous chromate Chromium oxide Mole weight 172.244 228.323 278.184 146.415 226.477 128.399 208.462 144.464 168.358 98.999 102.450 36.461 52.460 100.459 53.492 53.492 74.555 138.553 42.392 65.459 58.443 106.441 122.440 67.452 120.923 183.306 129.839 122.902 134.452 126.753 271.496 472.086 95.218 125.844 129.616 118.969 134.969 278.096 135.034 189.596 158.526 136.276 158.355 162.206 135.453 153.332 137.333 344.389 169.898 379.842 208.239 415.295 92.948 74.933 154.995 90.997 240.797 223.837 151.990 exergy (in kJ/mol) 95.7 219.8 19.4 59.5 80.6 67.3 88.6 746.9 51.5


158.355 162.206 135.453 153.332 137.333 344.389 169.898 379.842 208.239 415.295 92.948 74.933 154.995 90.997 240.797 223.837 151.990 exergy (in kJ/mol) 95.7 219.8 19.4 59.5 80.6 67.3 88.6 746.9 51.5 76.2 84.6 331.3 331.3 19.6 136.0 70.7 14.3 48.6 73.4 118.8 311.9 82.1 197.6 60.8 144.5 165.9 165.4 97.2 42.3 386.4 72.6 93.4 261.6 230.2 550.1 475.2 513.6 50.7 52.8 99.8 792.2 38.2 129.1 36.5 Co O Co S


Part 2b: Operational annex Alpha elements Cu S F H F K FeO Fe O-a FeS FeSi HI HKO HLiO H Na O Hg O Name Cesium nitrate Cesium oxide Cesium sulfate Cupric hydroxide Cuprix oxide Cupric sulfate Cupric sulfide (covellite) Cuprous oxide Cuprous sulfate Cuprous sulfide Hydrogen fluoride Potassium fluoride Fluorine Silicon tetrafluoride Sulfur hexafluoride Ferric hydroxide Ferrous oxide Ferrous silicate Ferric titanate Ferrous sultate Wustite Ferrous sulfide Pyrites Iron silicide Magnesium ferrate Manganese ferrate Ferric oxide Ferrous silicate (fayalite) Zince ferrate Ferrous oxide (magnetite) Hydrogen iodide (aq,400) Potassium hydroxide Lithium hydroxide Nitric Acid Sodium hydroxide Magnesium hydroxide Talc Manganeous hydroxide Nickel hydroxide Nickel sulfate Water Water Hydrogen peroxide (aq,200) Lead hydroxide Zinc hydroxide Sulfuric acid Hydrogen sulfide Ammonia Phosphoric acid Phosphine Chrysolite Hydrazine Ammonium nitrate Silane Disilane Ammonium sulfate Mercuric oxide (montroydite)


Lead hydroxide Zinc hydroxide Sulfuric acid Hydrogen sulfide Ammonia Phosphoric acid Phosphine Chrysolite Hydrazine Ammonium nitrate Silane Disilane Ammonium sulfate Mercuric oxide (montroydite) Mercuric sulfate Mole weight 194.910 281.809 361.872 239.238 97.561 79.545 159.608 95.610 143.091 223.154 159.156 20.006 58.100 37.997 104.080 146.054 106.869 71.846 131.931 151.745 151.909 68.887 87,911 119.975 83.933 200.004 230.630 159.692 203.778 241.062 231.539 127.912 56.109 23.146 63.013 39.997 58.327 379.289 88.953 92.725 262.864 18.015 18.015 34.015 241.205 99.385 98.078 34.080 17.031 97.995 33.998 277.134 32.045 80.043 32.118 62.220 132.139 216.589 296.652 exergy (in kJ/mol) 18.2 521.8 127.0 36.1 15.3 6.5 89.8 690.3 253.6 791.8 80.0 62.2 466.3 39.6 127.0 161.7 131.4 173.0 113.3 885.6 1157.3 77.9 121.4 236.2 36.4 121.6 107.6 74.1 43.5 74.9 40.9 36.5 107.3 25.5 53.6 9.5 0.9 20.6 25.7 163.4 812.0 337.9 104.0 61.3 294.8 660.6 57.3 146.0 Cu O


Part 2b: Operational annex Alpha elements Hg S I K I Na Mg O Mg S Mn O Mn S NO Ni O Ni S O Pb O Pb O Pb O Sn O Sr O Ti O V O Zn Name Mercuric sulfide Mercurous sulfate Potassium iodide (aq,500) Sodium iodide Potassium nitrate Potassium oxide Potassium sulfite Potassium silicate Potassium sulfate Potassium sulfide Lithium oxide Lithium sulfate Magnesium nitrate Magnesium oxide Magnesium silicate Magnesium sulfate Magnesium sulfide Magnesium orthosilicate Magnesium titanate Magnesium phosphate Manganeseous oxide Manganese oxide Manganeous silicate Manganeous sulfate Manganese sulfide Manganesic oxide Manganese oxide (hausmannite) Molybdenum dioxide Molybdenum trioxide Molybdenum disulfide (molybdenite) Molybdenum sulfide Sodium nitrate Nitric oxide Nitrogen dioxide Nitrous oxide Nitrogen trioxide Nitrogen tetroxide Nitrogen pentoxide Sodium oxide Sodium sulfite Sodium metasilicate Sodium sulfate Sodium silicate Sodium sulfide Sodium orthosilicate Nickel monoxide (bunsenite) Nickel


trioxide Nitrogen tetroxide Nitrogen pentoxide Sodium oxide Sodium sulfite Sodium metasilicate Sodium sulfate Sodium silicate Sodium sulfide Sodium orthosilicate Nickel monoxide (bunsenite) Nickel sulfate Nickel sulfide Nickel sulfide Lead monoxide Yellow lead oxide Litharge Rubidium oxide Stannous oxide Strontium oxide (strontia) Titanium oxide Vanadium oxide Zinc oxide Lead dioxide (plattnerite) Mole weight 232.654 497.242 166.006 149.894 101.107 94.203 158.266 154.288 174.266 110.268 29.877 109.940 148.322 40.311 100.396 120.374 56.376 140.708 160.522 262.879 70.937 86.937 131.022 151.000 87.002 157.874 228.812 127.939 143.938 160.068 288.072 84.995 30.006 46.006 44.013 76.012 92.011 108.010 61.979 126.042 122.064 142.041 182.149 78.044 184.043 74.709 154.772 90.774 240.258 223.189 223.189 223.189 186.939 134.689 103.619 63.899 66.941 81.369 239.189 exergy (in kJ/mol) 674.8 223.4 136.1 –19.4 413.1 302.6 137.9 35.0 943.0 225.7 204.3 57.4 22.0 80.7 901.6 74.9 134.3 130.0 119.4 102.3


223.189 186.939 134.689 103.619 63.899 66.941 81.369 239.189 exergy (in kJ/mol) 674.8 223.4 136.1 –19.4 413.1 302.6 137.9 35.0 943.0 225.7 204.3 57.4 22.0 80.7 901.6 74.9 134.3 130.0 119.4 102.3 142.4 873.5 89.4 171.6 201.2 68.2 1723.1 2891.2 –22.7 88.9 55.6 106.9 106.6 125.7 296.2 287.5 66.1 21.4 67.6 921.4 256.6 23.0 90.4 762.8 1720.2 491.3 289.9 170.2 418.5 318.9 19.4


Part 2b: Operational annex Alpha elements S Sn S Sr S Zn Name Sulfur dioxide Silicon dioxide Silicon dioxide (amorphous) Silicon dioxide (cristobalite) Silicon dioxide (quartz) Stannic oxide Strontium peroxide Titanium dioxide Titanium dioxide (rutile) Uranium dioxide (uraninite) Vanadium dioxide Tungsten dioxide Ozone Lead silicate Sulfur trioxide Antimony trioxide Titanium trioxide Uranium trioxide Vanadium trioxide Tungsten trioxide Lead red oxide (minium) Lead sulfate Lead silicate Strontium sulfate (celestite) Zinc sulfate Antimony tetroxide Zinc silicate Antimony pentoxide Titanium pentoxide Vanadium pentoxide Uranium oxide (pitchblende) Phosphorus decaoxide Lead sulfide Silicon sulfide Stannous sulfide Strontium sulfide Zinc sulfide (sphalerite) Stannic sulfide Titanium sulfide Tungsten sulfide HYDROCARBONS Methylene Methyl Methane Acetylene Ethylene Ethane Methyl acetylene Propadiene Propylene Cyclopropane Propane 1,3-butadiene 1 -butyne 2-butyne Cyclobutane 2-methyl–2-propene


sulfide HYDROCARBONS Methylene Methyl Methane Acetylene Ethylene Ethane Methyl acetylene Propadiene Propylene Cyclopropane Propane 1,3-butadiene 1 -butyne 2-butyne Cyclobutane 2-methyl–2-propene cis–2-butene 1-butylene Mole weight 64.063 60.085 60.085 60.085 60.085 150.689 119.619 79.899 79.899 270.029 82.941 215.849 47.998 283.274 80.062 291.498 143.798 286.028 149.882 231.848 685.568 303.252 506.464 183.682 161.432 307.498 222.824 323.497 223.697 181.881 842.085 283.889 239.254 88.236 150.754 119.684 97.434 182.818 112.028 247.978 14.027 15.035 16.043 26.038 28.054 30.070 40.065 40.065 42.081 42.081 44.097 54.092 54.092 54.092 56.108 56.108 56.108 56.108 exergy (in kJ/mol) 313.5 7.9 2.8 1.9 29.1 140.4 21.4 162.9 61.9 297.5 169.2 31.2 249.1 251.2 385.5 43.9 299.7 69.3 105.2 37.2 75.5 7.1 82.3 83.7 17.8 52.3 413.2 32.5 218.5 825.3 743.7 1866.3 1056.1 891.8 1604.6 1875.9 1796.6 1030.5 900.5 831.7 1265.8 1361.1 1495.8 1899.5 1523.8 2003.9 2043.2 2154.0 2552.3 2516.2 2659.7


Part 2b: Operational annex Alpha elements Name trans–2-butene 1-butene Isobutane n-butane 3-methyl–1-butyne 1-pentyne Pentadiene 2-pentyne cis–2-pentene trans–2-pentene 3-methyl–1-butene Cyclopentane 2-methyl–1-butene 2-methyl–2-butene 1-pentene n-pentane 2,2-dimethyl propane 2-methyl butane Benzol Benzene 1-hexyne 1-hexylene Methyl cyclopentane Cyclohexane Hexahydrobenzene 2-methylpentane 3-methylpentane 2,3-dimethylbutane n-hexane 2,2-demethylbutane Toluene Methylbenzene 1-heptyne Methyl cyclohexane 1-heptene Ethylcyclopentane 2,2-dimethylpentane 2,3-dimethylpentane 2,4-dimethylpentane 3-methlyhexane 3-ethylpentane 2,2,3-trimethylbutane 3,3-diemthylpentane n-heptane 2-methylhexane p-xylene Xylene Ethylbenzene m-xylene o-xylene 1-octyne Ethylcyclohexane 2,2,4-trimethylpentane 4-methylpheptane 3,3-dimethlyhexane 3,4-dimethlyhexane 2,2-dimethlyhexane 3-ethylhexane 2,3,4-trimethylpentane Mole weight 56.108 56.108 58.124 58.124 68.120 68.120 68.120 68.120 70.135 70.135 70.135 70.135


3,3-dimethlyhexane 3,4-dimethlyhexane 2,2-dimethlyhexane 3-ethylhexane 2,3,4-trimethylpentane Mole weight 56.108 56.108 58.124 58.124 68.120 68.120 68.120 68.120 70.135 70.135 70.135 70.135 70.135 70.135 70.135 72.151 72.151 72.151 78.115 78.115 82.147 84.163 84.163 84.163 84.163 86.178 86.178 86.178 86.178 86.178 92.142 92.142 96.174 98.190 98.190 98.190 100.206 100.206 100.206 100.206 100.206 100.206 100.206 100.206 100.206 106.169 106.169 106.169 106.169 106.169 110.201 112.217 114.233 114.233 114.233 114.233 114.233 114.233 114.233 exergy (in kJ/mol) 2805.8 2914.8 3461.8 3303.6 3865.1 3967.9 3910.8 3909.2 4114.5 3931.0 4520.5 4556.9 4604.6 4761 .7 4587.9 4573.1 5170.3 5205.9


Part 2b: Operational annex Alpha elements CHN Name 3-methylpheptane 2,4-dimethlyhexane 3-methyl–3-ethylpentane 2-methylpheptane 2,2,3,3-tetramethylbutane 2,3,3-trimethylpentane 2,2,3-trimethylpentane n-octane 2,5-dimethlyhexane 2,3-dimethlyhexane 2-methyl–3-ethylpentane Cumene 1,3,5-trimethylbenzene n-propylbenzene 1 -methyl–2-ethylbenzene 1,2,3-trimethylbenzene 1-methyl–4-ethylbenzene Isopropylbenzene 1 ,2,4-trimethylbenzene 1 -methyl–3-ethylbenzene 1-nonyne n-propylcyclohexane n-nonane Naphthalene 1 ,2,4,5-tetramethylbenzene n-butylbenzene n-butylcyclohexane n-decane 2-methylnaphtalene Pentamethylbenzene n-undecane Hexamethylbenzene n-dodecane n-tridecane Phenanthrene Anthracene 1,1-diphenylethane n-tetradecane n-pentadecane n-decylbenzene n-hexadecane n-octadecane Triphenylmethane OTHER ORGANIC COMPOUNDS Methyl bromide (bromoethane) Vinyl bromide Ethyl bromide (bromoethane) Monochloromethane Chloroform (trichloromethane) Potassium bicarbonate Hydrogen cyanide Sodium bicarbonate


ORGANIC COMPOUNDS Methyl bromide (bromoethane) Vinyl bromide Ethyl bromide (bromoethane) Monochloromethane Chloroform (trichloromethane) Potassium bicarbonate Hydrogen cyanide Sodium bicarbonate Diiodomethane Cyanamide Formaldehyde Formic acid Formamide Nitromethane Urea Mole weight 114.233 114.233 114.233 114.233 114.233 114.233 114.233 114.233 114.233 114.233 114.233 120.196 120.196 120.196 120.196 120.196 120.196 120.196 120.196 120.196 124.228 126.244 128.260 128.175 134.223 134.223 140.271 142.287 142.202 148.250 156.314 162.277 170.341 184.368 178.236 178.236 182.268 198.395 212.422 218.386 226.449 254.504 244.339 94.93 106.950 108.966 50.488 119.378 100.119 27.026 84.007 267.836 42.040 30.026 46.026 45.041 61.041 60.056 exergy (in kJ/mol) 5413.1 5249.1 5825.1 5857.7 6064.9 5255.0 5880.0 5892.0 6511.5 6716.8 5881.4 6516.0 7376.9 7171.0 8029.4 8682.0 7201.8 7218.1 7665.9 9334.5 9984.8 9700.8 10639.7 11937.4 10109.2 21.6