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c Assessing Waste-Reduction Efficiency INTRODUCTION SARA Section 313~) charged NAS To assess the value of obtaining mass balance information, or portions thereof [e.g., production rate], to determine the waste re- duction efficiency of different facilities, or categories of facilities, including the effec- tiveness of toxic chemical regulations pro- mulgated under laws other than this title." In analyzing the use of mass balance data for this purpose, the committee defined ef- ficiency as progress made by a facility in re- ducing its waste (see below). The waste un- der consideration could be contained on site, released to the environment from the facili- ty, or contained and shipped off site to another facility. This chapter evaluates both EMB and MA practices for use in a program to track waste-reduction progress according to their potential for providing indicators of (a) the amount of waste generated and the association between the level of manu- facturing activity, (b) the amount of reduction at the source of generation (versus reduction before treatment, such as by incineration), and (c) the comparability of the collected data among a wide variety of manufacturing facilities. Regarding the "effectiveness of toxic 39 chemical regulations promulgated under laws other than this title," the committee assessed mass balance data for its usefulness as a generic indicator of the effectiveness of any relevant regulations while accounting for changes in production rate at a reporting facility. This approach is consistent with discussions of this part of the charge by Congress (U.S. Congress, House, 1986~. Waste Reduction In the absence of a widely accepted defi- nition for waste reduction, the committee assigned to waste reduction the same defini- tion given for waste minimization in Form R (see Appendix C) for TRI reporting. In this report, therefore, waste reduction includes any of the following activities performed on wastes generated within a facility: re- cycling or reuse on site; recycling or reuse off site; equipment or technology modifica- tions, production procedure modifications, and redesign (modification) or reformation of a product; substitution of raw materials and improved housekeeping, training, and inventory control, as well as any other technique that results in reduction or

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40 elimination of waste released to any environ- mental medium. This definition is consistent with that presented in a previous NRC report (NRC, 1985~. Waste reduction was defined in that report to include "both changes in the pro- duction processes and recycling and reuse of hazardous materials either at or away from the site of generation." Recycling, particu- larly recycling within manufacturing facili- ties, is included in this report's definition of waste reduction, because it is a technique that usually cannot be divorced from the manufacturing activity. Furthermore, re- cycling is clearly different from the treat- ment of waste before environmental release. The committee acknowledges that this definition of waste reduction is broader than those others have proposed (e.g., Sarokin et al., 1985; OTA, 1986, 1987; see Appendix I). However, the committee chose a broad definition of waste reduction to allow analy- sis of mass balance information to be applied to any facility reporting waste information, regardless of the way the facility deals with the waste it generates. In discussing the OTA's findings and conclusions on waste re- duction, the committee acknowledges that the OTA's definition of waste reduction differs from that used in this report. Waste-Reduction Efficiency Some measure of waste-reduction efficiency assists in comparing the waste- reduction efforts of different facilities and in determining whether waste reduction is limited by the type and rate of operation. Congress defined waste-reduction efficiency by example: "For example, can this [mass balance] information reasonably be used to compare different facilities in the same business to determine whether one is apply- ing more rigorous environmental control than another, or delineate whether reduced releases of chemicals reflect improved con- trol or limited operation" (U.S. Congress, House, 1986~. For this report, the committee defined waste-reduction efficiency as a quantitative measure of progress in waste reduction. A waste-reduction-efficiency measure is most useful when it is generally applicable among facilities of many types. A consistent definition of the waste that is ~455 BALANCE INFORMS TION subject to reduction is essential to es- tablishing any waste-reduction-efficiency measure, especially for different regulations that affect similar facilities. Waste defini- tions in regulations developed under SARA and the Resource Conservation and Recovery Act (RCRA) differ, although they affect similar manufacturing facilities; therefore, waste-reduction measures developed from these facilities might vary as a result. Total waste is the object of RCRA, but individual chemicals contained in waste are the object of SARA. Reductions in total waste could occur and be documented under RCRA without being documented under SARA if specific waste components were not produced by the manufacturer. Further- more, no information is collected under either regulation to indicate whether a reduction in total waste quantities generally correlates with reductions in the listed chemicals within the wastes (see Appendix I). DATA FOR ASSESSING WASTE-REDUCTION EFFICIENCY Amounts of waste generated as the result of specific activities from specific sources within facilities can be reported in three ways: For each source, such as a single mixing tank. For each prodfuctio'' I, such as phosgene production within a chemical pro- duction facility. For an entire facility, such as each waste stream exiting the facility into air, water, or land. Many different activities (and there- fore, many different sources) make up a single production unit, and one or more pro- duction units are present at any facility. Collecting data from each individual source theoretically would achieve the most accurate reporting. Such source-specific in- formation often is required for EMB. However, collecting, reporting, producing, and evaluating such extensive and detailed information for all regulated facilities in the United States would be an enormous task.

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ASSESSING WASTE-REDUCTION EFFICIENCY Collecting production-unit operation and flow data is a less detailed way to assess waste reduction. Such data include describ- ing each step of an activity associated with each production unit and tracing the flow of material through each step. The data also include the amount of material handled in the entire production unit and the total amount lost at each step. Production-unit data can disclose minute changes in waste- stream composition and flow rate. Although less detailed than source-specific data, pro- duction-unit data are much more detailed than data typically collected through pollu- tion-control programs, which often focus on limiting overall releases from facilities (e.g., discharges from an on-site waste-water treatment facility handling waste streams from multiple production units). Despite accurate monitoring of waste-reduction practices, these data are difficult to use for comparative analysis for several reasons. No standard nomenclature exists for production units. Furthermore, production units do not have fixed boundaries; some facilities consider transfer activities as part of a pro- duction-unit process and other facilities consider these as activities outside of the production unit. Also, production units with identical names can vary extensively among different facilities. Facility-level data, the least detailed type of data considered, are collected through TRI reporting and many pollution-control programs. For specific chemical reporting (as for the TRI), facility-level dicta are suf- ficient to allow measurement of the facili- ty's waste-reduction efficiency, because all sources and production units generating waste within the facility are represented. If progress were made in reducing the amount of waste generated from a specific produc- tion unit, then there would likely be less waste contributed to streams exiting the facility. The committee evaluated only MA in- formation for practical applications to assess waste-reduction efficiency. EMB was not evaluated because it requires a detailed analysis of all material streams within and across facility boundaries in order to attain closure for all mass flows into and out of a facility. Closure, however, is not a criterion for assessment of waste-reduction efficiency. As discussed later in this chapter, individual components (e.g., production rate) of mass 41 balance data are evaluated for assessing waste-reduction efficiency, and these data can be obtained through the MA approach. Instead of assessing waste-reduction effi- ciency, EMB can be useful in the identifica- tion and characterization of sources of waste within a facility. For example, E.I. du Pont de Nemours & Co. at Beaumont, Texas, has used EMB for a mass balance of total car- bon in its acrylonitrile manufacturing facility to trace the steps in the process in which byproducts with high boiling temperatures are formed (Jordan, 1988~; Oman and Ham (1988) describe how the foundry industry uses EMB to locate sources of waste within the foundry production. However, OTA concluded (1986) that for an entire facility EMB typically would have a level of error such that it would give little information on where substances appear as waste in the facility. REPORTING REQUIREMENTS A facility-level MA approach was used as part of the New Jersey Industrial Survey. Because the survey was not conceived with waste-reduction efficiency as an explicit component, the state did not assess the rele- vance of the MA data for this purpose. Several organizations, however, have used these MA data for waste-reduction investigations. Based on data collected through the New Jersey Industrial Survey, the Natural Re- sources Defense Council (NRDC) has pro- posed a single waste-reduction performance standard that would require facilities to manufacture, produce, or otherwise use specified chemicals with a minimum level of eff iciency (Clarence-Smith, l 98Sa; see Appendix if. In this case, efficiency is de- fined as the ratio of the total amount of a chemical released to the environment from a facility to the total amount of that chemical routed through the facility in one year. In addition, INFORM, Inc. (a nonprofit re- search organization that identifies and reports on practical actions to protect and conserve natural resources), used MA data collected under the New Jersey Industrial Survey for a preliminary characterization of waste generation in organic chemical manu- facturing facilities located in New Jersey (Sarokin et al., 1985~.

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42 INFORM, Inc., and OTA concluded that the government files containing information are too fragmented and incomplete to assess waste-reduction progress adequately (Sarokin et al., 1985; OTA, 1986~. Under the New Jersey Worker and Community Right-to- Know Act of 1983, the state now requires the reporting of waste-minimization infor- mation, but reporting this same information on TRI's Form R is optional. As mentioned in Chapter 3, New Jersey plans to use this MA information in developing waste-re- ductioh efforts that consider levels of ef- ficiency within facilities or across industries. MA data will be used to set priorities among industries and facilities for waste-reduction attention. The optional "Waste Minimization Sec- tion" on TRI's Form R requests the following information: Type of modification (waste-minimiza- tion activity). Quantity of the chemical in the waste stream before treatment or disposal. Index (ratio of reporting year produc- tion to base year production). ~ Reason for minimization action. However, Form R instructions suggest that the database will contain an aggregation of different types of information that could be used to assess waste-reduction efficiency. The instructions indicate that the amounts of waste from cleanups of areas associated with abandoned operations should not be reported separately from waste generated from on- going operations. Therefore, reported amounts of cleanup wastes and other one- time wastes will mask the amounts of, and trends in, wastes generated from ongoing operations. In addition, facilities are allowed to present data based on estimates, and any one facility could use different estimation methods for reporting in succeeding years. Trends in wastes reported from one year to the next' therefore, could be the result of changes in estimates rather than changes in operations. Under RCRA, generators of hazardous wastes certify on a manifest associated with each shipment that they have a program to minimize their wastes. In 1984, Congress passed the Hazardous and Solid Waste Amendments (HSWA) (P.L. 98-616; 42 USC 6901), which served to amend RCRA. M4SS BALANCE INFORM4TION Under HSWA, generators have been required to describe their-hazardous-waste-minimiza- tion program as part of a biennial report on their hazardous-waste generation. Until re- cently, there was little guidance or structure for this waste-minimization report. Now, however, EPA has released a 1987 Hazardous Waste Report, the Waste-Minimization Pack- age [EPA Form 8700-13A(5-80) (Rev. 11- 85) (Rev. 12-87~. The quantities of RCRA waste in 1986 and 1987 are reported with a production ratio for the two years. Qualita- tive information is also requested on any re- sulting change in the toxicity of the waste and any change in the impact on air emis- sions and water discharges. The focus of this biennial reporting is on RCRA waste codes, not on specific chemical constituents and therefore would not add any information for assessing the efficiency of reducing the amount of specific chemicals in waste streams from facilities reporting to the TRI. NORMALIZATION OF WASTE-RELATED DATA Normalization is a procedure for adjust- ing the reported amount of waste by divid- ing it with such MA components as amount of input (e.g., raw material) or output (e.g., product). This procedure can help account for changes in the amount of waste generated that are due to changes in the rate of production, i.e., a decrease in the amount of waste generated at a facility from one year to the next could be due to manu- facturing less product, instead of real pro- gress in waste reduction. Typically, some measure of product OlltpUt is used as the normalization factor (Eberhardt, 1987; Delcambre, 1987; Hollod and McCartney, 1988~. Waste-generation data not correlated to production can mask waste-reduction suc- cesses as well as failures (OTA, 1987~. Other types of normalization factors are discussed later in this chapter. Figure 5.1 presents idealized"black-box" mass balance diagrams to illustrate how waste-reduction reporting might proceed if either total waste or waste-component (toxic chemical) options were pursued and the MA data were sufficiently accurate to make quantitative distinctions. This hypothetical example illustrates that data interpretation is not straightforward, and interpretation can

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ASSESSING WASTE-RED UCTION EFFICIENCY At a Time (To) Before Waste Reduction Ra`` Mate~ ials b D = A = 100 100 100 43 Products PRODUCTION UNIT . E = ~ 4B, C = ~ ~ h = . 30 ~ Wastes b = 10 D = 10 = 5 f = 5 = 10 b = 60 D= 5 E = 10 h= 5 At a Ti~ne (T`,~ ~ Aftet Braste Reduction Ra`` llate~ ials A = 100 b = 100 D = 100 PRODUCTIOI~ UNIT ~ lYastes b = D = 5 E = 5 f - 5 - C = 15 5 b = 50 D= 0 E= 0 1l= 20 NOTE: Capital letters refor to TPl-listed chemicals. and lo~ercasc letEc~s refer to unlisted chemicals. P~ oduc1-s E = 159, I f = 35 FIGURE 5.1 Id~lized mass balance diagrams for production units or facilities before and after waste reduction.

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44 become difficult as the number of com- pounds and processes increases. Each box represents a production unit or an entire facility that may use and produce a number of chemicals. In this example, the processes are presumed to be nonconservative, which means that chemicals in raw materials react to form an entirely different set of chem- ical products. In contrast, conservative pro- cesses result in no transformations of the in- put chemical berg., use of a solvent for cleaning). The upper box in Figure 5.1 shows three raw material streams going in, and the out- flow of two product streams and two waste streams. The specific chemicals involved are referred to by letters. Capital letters refer to chemicals that are TRI-listed; lowercase let- ters refer to unlisted chemicals. Quantities have been assigned to each chemical to show how waste-reduction statistics might be developed. Since separation processes are not perfect, some byproducts found in wastes are also present in the product, and some of the product chemicals are also found in the waste. TRI-listed chemicals not fed into the facility may be produced in the pro- cess, appearing in the waste or product streams. The lower box depicts the effects on the same production unit or facility of waste reduction through production changes to improve product yield. The number of input and output streams remains the same. Table 5.1 presents the results of three dif- ferent approaches to calculating waste- reduction efficiency for the two situations presented in Figure 5.1. The approaches represent reduction-efficiency calculations based on the total amount of waste gen- erated, an aggregation of all TRI-listed chemicals contained in the waste, and each TRI-listed chemical. For each approach the amount of waste generated is normalized for production changes by dividing the amount of waste by the amount of product made or, alternatively, by the amount of TRI-listed chemical in the product. Percent changes in these waste/product ratios are then compared between the initial (To) and waste-reduced (TWR) cases. Substantially different figures for percentage of improvement in reducing the normalized waste amounts were obtained, ranging from -30% to +70%, de- pending on the basis of the calculation. The minus percentage indicates that waste generation had increased for chemical C as a M4SS BALANCE INrFORMATION result of waste-reduction efforts. Also, although large-percent reductions for the majority of TRI-listed chemicals might be achieved, overall waste reduction might be less. After a common point of comparison, such as production, is chosen, the decision must be made whether the toxic release is to be compared with the entire stream or with the quantity of the same toxic chemical in that stream. Calculating waste-reduction efficiency based on some chemical-specific normalization ratio would lead to significant nonuniformity in reporting. In some cases, no interpretation is possible. For example, chemical D appears in the waste, but not in the product; no ratio of the compound in waste to production is possible, although a ratio based on input is. On the other hand, chemical C, not present in the input material, appears in the waste and, in this case, only a product-based ratio is possible. Thus, there is a higher likelihood of obtain- ing consistent and useful ratios if the de- nominator is based on the total stream rather than a chemical component therein. For cases when waste-reduction efforts include a fundamental production change to use new input chemicals, evaluation by examining changes in reduction efficiency would be meaningless because the composi- tion and therefore the potential toxicity of the waste would likely change. Normalized data by themselves might show a reduction in waste, but they would not indicate whether any change in potential toxicity had occurred. MA Data Selection for Normalization In assessing the utility of any normalizing factor, several questions must be asked. Are the relevant data on chemical com- ponents currently collected; if not, can they be collected without an extensive effort? How would the burden of additional measuring and monitoring requirements af- fect efforts to make progress in waste reduc- tion? Among production units, facilities, or in- dustries, is there some commonality among the chemicals that are to be measured? The greater the diversity of chemicals, the less meaning such comparisons may have. Are the points of measurement suf

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ASSESSING WASTE-REDUCTION EFFICIENCY TABLE S.1 Vanations in Wast~Reduction Efficiency Calculation Normalized Waste Amounts 45 Waste Amounts TRI-Listed Chemical in Waste (lb)/ Generated (lb) Waste (Ib)/Product (lb) TRI-Listed Chemical in Product (lb) Waste Description To TWR To TWR A% To TWR is% Whole waste 120 105 .67 .54 19 All TRI-listed chemicals 40 25 .22 .13 41 Chemical C 10 is .06 .os -3010.0 HP Chemical D 15 5 .08 .03 60NP NP Chemical E 15 5 .08 .03 60.10 .03 70 Note: Calculations performed on data presented in Figure 5.1. Kerr. To = Initial process with a production of 180 lb. TWR = Wast~reduced process with a production of 195 lb. A% = Percent improvement in reducing the Normalized Waste Amounts. NP = Chemical "not present" in product system. ficiently similar among production units, fa- cilities, or industries to make valid comparisons? As the diversity of processes used to manufacture a product increases, the likelihood diminishes for obtaining com- parable data among the different processes. Are the measurements used for nor- malization performed on the same chemical? In other words, is the chemical measured in a waste stream also present in the stream (e.g., product stream) where measurement is made for the normalization factor? This question illustrates the problem of con- structing a normalization ratio for noncon- servative processes in which a given chemi- cal is not necessarily present at all points in the process. An evaluation of these practical consid- erations is presented in Table 5.2. The rela- tive merit of using different MA compo- nents (input, mass flow rate within the facility, and production) for normalization is given for each consideration. Input would encompass all data for which inputs are normally measured; mass flow rate within the facility would encompass data collected at some point between the flow of chemicals into and out of the facility; production would encompass all data for which outputs are normally measured. It is assumed that the amount of waste chemical generated is known and would be the numerator for a normalization ratio based on one of the MA components, which would be the de- nominator. Although data collected through MA practice do not require high levels of accuracy, if the accuracy of the data is poor, the utility of the waste-reduction assessment is diminished. The merit of each MA component for normalization shown in Table 5.2 is pre- sented in two different ways. In one (~Total~), the denominator is the total MA component (input, production, etc.), and the second (~Compound") is one chemical of interest of that component. A qualitative assessment of the usefulness of each MA component is presented to indicate that dif

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46 AL455 BALANCE INFORMATION TABLE 5.2 The Relative Ment of MA Components for Normalization Ranking by MA Component Input Flow Rate within the Facility Production Chemical Chemical Chemical Consideration Total Compound Total Compound Total Compound Currently measured? 4 2 2 1 4 1 Measurable? 4 3 2 2 4 2 Commonality of compounds Between production units? 3 2 3 3 4 3 Between industnes? 1 1 1 1 1 1 Commonality of measuring point? 4 4 1 1 4 4 Required reporting? 1 1 1 1 4 1 Consistent ratios at measuring point? 2 1 3 2 3 2 Key: 1 = Seldom. 2 = Sometimes. 3 = Often. 4 = Usually. ferences in the way data are collected must be understood before any attempt is made to standardize waste-reduction accounting pro- cedures. . Comparability of Normalized Data A primary goal in comparative waste- reduction assessments among facilities and industries is that reduction progress be com- pared, through the normalization of chemi- cal-specific waste data, to determine whether one facility or industry is less effi- cient than another in the handling and use of a chemical. Comparison of normalized waste-related -data between facilities must be done with caution. INFORM, Inc., found that facilities using similar quantities of hazardous chemicals generated greatly dif- ferent quantities of waste. However, some of the observed differences in quantities of waste generated were due in part to dif- ferent processes and uses of chemicals at each facility (Sarokin et al.' 1985~. Normalization with any of the types of MA component data listed in Table 5.2 poses problems. Manufacturing industries vary considerably in the types of information typically obtained and obtainable. Although most manufacturers usually obtain some measure of raw material input and product output, the units can be in pounds or in pro- ducts such as refrigerators, automobiles, spools of tape, or square feet of film. This difference in units imposes a high degree of nonuniformity in available data. Even for facilities that manufacture the same product, raw material inputs can be di- verse, depending on the exact process used. Different manufacturing routes also might

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ASSESSING WASTE-REDUCTION EFFICIENT result in significant differences in product contaminants and waste-stream compositions even in those cases in which a standard pro- duct, such as a commodity chemical, is pro- duced. For example, aniline can be manu- factured through the reduction of nitroben- zene by using iron and hydrochloric acid; through ammonolysis with chlorobenzene, ammonia, and cuprous oxide; or through va- por-phase hydrogenation of nitrobenzene. Each method uses a different group of TRI- listed chemicals. For any industry that produces manu- factured goods, more often than not there are significant differences in the chemical content of the product (e.g., the use of vari- ous alcohols and dyes or the use of mercury in glass thermometers). When measuring a chemical flowing through some point within a facility that produces manufactured goods, one finds that common chemicals (among facilities) become even less likely because of the number and degree of material transformations within the facility as a function of the manufacturing process. If meaningful comparisons in waste- reduction progress are to be made, even be- tween similar facilities that manufacture the same product, the point at which normaliza- tion data are measured must be the same or very similar. Because all manufacturing facilities have material inputs and product outputs, these would be the obvious points to consider for obtaining data relevant to waste-reduction progress. As indicated in Table 5.2, product output is the only point required to be reported under current environmental regulations. As discussed previously, total production can be reported to the TRI as an index and in the RCRA biennial report as a production ratio so that confidential business information is not disclosed. OTA concluded that to pro- vide a reliable measure of waste reduction, data must be correlated with production (OTA, 1986~. An alternative to using points of input or output is to measure some point of material flow within a facility. However, this alter- native could lead to nonuniformity because the diversity of manufacturing processes would result in the inability to establish a common appropriate point of reference within the facility. Some meaningful measuring point might be found in a linear production line in which all the chemicals of 47 concern could be measured; however, the problem becomes increasingly intractable when multiple conversions take place in complex interconnected manufacturing sys- tems. For example, Bodwitch (1988) states that "in an automotive assembly facility there may be a single chemical component which is contained in over 2,000 separate chemical mixtures used as part of the hundreds of specific assembly operations required to manufacture motor vehicles." A similar problem arises in a complex chemical man- ufacturing operation, in which chemical transfers and transformations take place throughout. When highly controlled studies of the internal workings of a process have been undertaken, difficulties in obtaining chemical-specific data forced the use of sur- rogates, such as carbon (Iordan; 1988; Nickolaus, 1988), equivalent pounds (Redington, 1988), or methyl group (Supple, 1988~. Although Table 5.2 presents qualitative figures, it indicates that a normalizing factor based on a total stream MA component would be more practicable than one based on individual chemicals. The concentration of individual chemicals can vary, even among facilities manufacturing the same product, because of the diversity of processes used. Furthermore, the concentration of individual chemicals in raw materials or products rare- ly is monitored, except when quality control for that component is important, and the mass streams are relatively homogeneous. In petroleum refining, for example, the content of trace metals in the crude petroleum input or in the products typically would not be measured because the heterogeneous nature of the material handled makes such measurement highly impractical. The foun- dry industry provides another example of variable feedstock composition: it is virtual- ly impossible to measure reliably the lead concentration of scrap metal input (Oman and Ham, 1988~. Likewise, measurement of the constituents in the product becomes less practical when nonhomogeneous products, such as automobiles, are involved (e.g., the amount of heavy metals in the paint or chrome on the bumpers and accessories exit- ing in each different automobile). No one method of normalization is generally applicable. Comparisons among production units, facilities, and industries become meaningless if the normalization

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48 factor is based on the specific chemicals. In addition, no particular advantage is apparent in using the input component or an internal measure of mass flow rate compared with using the production component to normalize reduction reporting. AGGREGATION OF WASTE REDUCTION-EFFICIENCY DATA A normalizing factor could be used in many ways to measure progress. The most obvious way is to compare waste generation between different periods by comparing the waste-generation ratios (e.g., waste genera- tion/production). A decrease in this ratio indicates a reduction in waste generation. This method of assessment provides unam- biguous results for specific waste streams generated within a single production unit. Such ratios become less meaningful as waste data are aggregated from various production units within one facility. Table 5.3 shows how the waste-genera- tion ratio could be calculated for the wastes generated in different production units (designated 1, 2, and 3) and for the entire facility when the data are combined. This hypothetical example illustrates the limitations of using aggregated data even though it is normalized. The generation ratio for each production unit is shown in Table 5.3 for a base (starting) year and for a comparison (later) year. Even though the generation ratio decreased for each produc- tion unit, there was no change in the over- all generation ratio, which could lead to the erroneous conclusion that the facility as a whole did not improve its waste-reduction efficiency. The same phenomenon may occur when multiple wastes are aggregated for all the waste generated from a produc- tion unit where multiple products are pro- duced (Benforado, 1988) and, on a larger scale, when facility statistics are combined to form industry-wide and national statistics. If such accounting procedures are to be of benefit in managing wastes, they should be maintained at the smallest practical produc- tion-unit level. A more general nationwide statistic developed by weighting individual facility information would probably cause progress to appear to be slower than it truly was. OTA (1986) also concluded that waste- reduction data should be process-specific or ~455 BALANCE INFO~TION production- unit-specific, because facility- level reporting would be too complex to ob- tain meaningful data. Waste-Reduction Efficiency-Weighting Statistics The problem of masking waste-reduction progress through data aggregation can be mitigated by using either of two calculation procedures that weights annual changes in generation ratios (e.g., waste generation/ production) for each production unit accord- ing to either (1) relative amounts of waste generated or (2) amount of waste generation expected, based on changes in annual pro- duction. These options are demonstrated in Figure 5.2, using the hypothetical numerical information from Table 5.3. The first option described by OTA (1986) allows no credit if, for example, a produc- tion unit is changed to produce no waste. As shown in Figure 5.2, if Reporting Unit 1 waste generation drops to zero, the weighted statistic (R) remains approximately the same ( 1 1.6% compared with 1 1.3%~. The second option compares the actual waste generation in the comparison year with the amount of waste generation expected if no change in waste reduction had occurred for the comparison year. This option has the advantage that credit is given for eliminat- ing a waste stream. In this case if Unit 1 waste dropped to zero, the percentage of overall waste reduction would improve to 57o/o. Both approaches require that data sum- mations be calculated from the original indi- vidual production-unit information. Conse- quently, the number of calculations that must be performed to compile meaningful facility, industry, and national statistics can escalate enormously. The experience with collecting waste data under RCRA shows that it would be very difficult for the government to collect and analyze accurate and timely data from a very large number of facilities and for an even larger number of processes and waste streams (OTA, 1986~. In the context of TRI reporting, each chemical requires a specific normalizing factor (be- cause of different production quantities as- sociated with each chemical at each facility). For example, EPA initially estimated about 320,000 TRI reports from 32,000 facilities (Federal Register, 1988a), which, if

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50 OTA Method R - N [(Unit) Waste Quantity)c(Uniti Generation Ratio % Change)c] ~-1 N ~ tUniti Waste Quantity)c i=1 R (75)(11) + (65)(11) ~ 5(20) 75 + 65 + 5 1.3% If Reporting Unit 1 waste generation in comparison year approaches zero, (65)( 1 1 ) ~ 5(20) R65 + 5 Alternate Method "4SS BAlANCE INFORMATION = 1 1.6% N N ~ [(Unit) Production~c(Uniti Generation Ratiob] - ~ (Unit) Waste Quantity)c i=1 i=1 N ~ [(Unit) Waste Quantity)c(Uniti Generation Ratio)b] i=1 R= (310)(.27) + (105)(.70) + (122)(.05) - (75 + 65 + 5) (310)(.27) ~ (105)(.70) + (122)(.05) - x 100 = 11% If Reporting Unit 1 waste generation in comparison year approaches zero' R= (310)(.27) + (105)(.70) + (122)(.05) - (65 + 5) (310)(.27~) + (105)(.70) + (122)(.05) Where: R = Weighted waste reduction statistic (%) c = Comparison year b = Base year 100 = 57% FIGURE 5~ Wast~reduction weighting methods using data from Table 53. (Source: OTA, 1986; alternate method adapted from N~cic, 1987)

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ASSESSING WASTE-REDUCTION EFFICIENCY developed on a production-unit basis, could require annual accounting for millions of production units, or for hundreds of millions of wastes if reported on a chemical-specific basis. For the first cycle of TRI reporting, approximately 1S,000 facilities reported. This lower-than-expected response is at least partially due to noncompliance. Indexing Normalization factors account for the fact that the amount of chemicals released is often a function of the amount of product made. An increase in waste could reflect an increase in production. On the TRI Form R. normalization factors called indexes can be calculated from virtually any production in- formation that "closely reflects activities involving the chemical" (Appendix C). The instructions to Form R list several acceptable examples of indexes, including the following: the amount of chemical or paint produced in 1987/amount produced in 1986; appliances coated in 1987/appliances coated in 1986; and value of sales in 1987/value of sales in 1986. If the index is based on mass for all manufacturers, then there can be a com- parison of information between, for ex- ample, facilities or industries (if the pre- viously described problems associated with aggregated data are recognized). The use of an index based on the number of product units simplifies reporting for those industries that traditionally report production in non- mass units, but it prevents the compilation of summary statistics of disparate units, such as automobiles and refrigerators. The use of dollar sales in indexing produces a statistic that can be aggregated, but it is much less dependable as a quantitative measure be- cause prices for various commodities change from year to year. Several issues related to industrial diver- sity merit consideration when indexing is used to compile a national waste-reduction database. One option would be to require all facilities to report in the same units, which would result in data that could be compared and aggregated easily. The data would, however, lose substantial accuracy. There would be cases in which forcing the use of the same units would distort the data, al- though taken as a whole, the data would be in a useful form for policymakers and for 51 revealing general waste reduction trends. A variant of this option would be to specify the measurement units to be used industry by industry, thereby ensuring comparison within an industrial sector at the expense of some cross-industrial comparisons. Another option would be to allow facili- ties to choose their own units. This option would most likely result in the most accurate facility-level data. Aggregating these data, however, would be impossible. This ap- proach possibly would decrease the useful- ness of the database for policymakers. It is also possible to assess improvements in waste-reduction efficiency by multiplying the amounts of waste generated by produc- tion after both have been indexed to the same base year, for example, waste-reduc- tion efficiency = (production index>(waste index). Such a calculation, based on mass or other physical-unit production, could be made for progress reporting at the appro- priate level (e.g., production unit or facility) without divulging production information. It must be kept in mind that the larger the reporting unit, the greater the problem as- sociated with aggregation of data. If such a system were used for reporting, it could be used as an indication of progress by that re- porting unit, but it could not be further ag- gregated to show progress of a group of re- porting units, for example, on a national basis, because of the nearly insurmountable problems of properly compiling an accurate, meaningful national statistic for waste-re- duction efficiency. A national statistic that could be compiled from such data would be of three tallies: (a) those facilities or production un- its showing improvement in waste-reduction efficiency, (b) those showing no change, and (c) those showing a decrease. Such a statistic would be meaningful from year to year only for a nominal measurement of progress (i.e., low level of information). Evaluation of Waste- Reduction- Efficiency Data The previously described concepts for evaluating waste-reduction efficiency are variations of a single approach that might best be described as mass (of waste)-based systems. The two major measurement para

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52 meters are (a) the mass of waste per unit of time (e.g., pounds of waste per year) and (b) the mass of waste per unit of manufactured product (e.g., pounds of waste per ton of ethylene produced). Both of these measure- ments are made on absolute scales because the data can be expressed on a scale that has zero as the lowest value. One limitation of this type of scale is that the focus can be drawn to an endpoint, such as zero, without differentiating the degrees of difficulty in achieving waste reduction progress, as shown in Figure 5.3. Several significant anomalies must be dealt with when one uses mass-based ab- solute scales for assessing waste-reduction progress. The first anomaly is that no credit accrues for past implementation of produc- tion modification or recycling and reuse to reduce waste. Facilities with past substantial waste reduction successes, therefore, could be perceived as making insufficient progress in subsequent years, if less opportunity were to exist for waste reduction. A second anomaly derives from the di- versity among private-sector manufacturing technologies. The relationship between a particular manufacturing technology and its specific opportunities for waste reduction is an important factor that leads to very dif- ferent possibilities for waste reduction among industries (Royston, 1979~. The dif- ferences among processes might substantially affect the waste-generation rate, even when the same product is manufactured. When comparing waste-reduction progress between two facilities making different products, technical expertise is sometimes needed to distinguish actual from apparent progress. For example, one of the facilities could ap- pear to make greater waste-reduction prog- ress because it makes a product that can contain relatively large amounts of waste without affecting quality or performance. The other facility produces a product that cannot tolerate any additional impurities and thus less opportunity exists for waste reduction. The third anomaly is that the rate at which new modifications are implemented can be related to a series of larger manu- facturing and related financial decisions. Those improvements that demonstrate the greatest cost-effectiveness compared with the present technology are instituted first. Waste reduction, therefore, competes on a M45S BALANrCE INFOR~4TION substantially different basis for capital in- vestment funds from one facility to another and will result in different constraints on the timing of introducing waste-reduction prac- tices and different amounts of waste re- duction achieved between facilities. A fourth anomaly of the mass-based as- sessment techniques is that they depend heavily on the actual amount of production. The amount of production is controlled by numerous market factors, and therefore variations in production lead to increases or decreases in the amount of waste generation per year. Normalizing waste generation by production does not necessarily remove this anomaly, because there might be a nonlinear relationship between waste and production. Waste-reduction progress can be more realistically evaluated if the data are coupled with a description of each new reduction technology introduced at a facility. Many factors other than the mass of waste are im- portant in making progress in waste reduc- tion, and a mass-based system does not re- flect these other factors. For example, re- ducing the amount of a trace toxic con- stituent in a waste stream would indicate lit- tle progress in mass waste reduction but might be more beneficial than reducing large amounts of less toxic constituents. Conse- quently, even though a properly constructed, mass-based waste-reduction-efficiency statistic might allow for some comparisons within and among the manufacturing in- dustries, it would not be proper to require uniform standards of waste-reduction efficiency, irrespective of waste toxicity differences and manufacturing diversity. OTA also concluded that if government were to require waste reduction, it would face major difficulties in determining what is technically and economically feasible or practical for a specific industrial operation (OTA, 1986~. Alternative MA Practice Systems The approaches to waste-reduction re- porting discussed thus far focused on specific mass balance components, such as production, to provide a better under- standing of the amounts of waste generated at reporting facilities. This section contains a discussion of three alternatives to the use of specific MA components. The commit

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ASSESSING WASTE-REDUCTION EFFICIENCY Mass of waste generated o \ \ \ \ B \A \ T. Ime FIGURE 53 Patterns of waste generated over tune at different manufacturing facilities. Each cume (A, B. and C) represents a hypothetical facility. tee did not perform any comparative analy- ses for these alternatives to collecting MA data; in-depth analyses of these alternatives were considered to be beyond the committee's charge. Throughput Systems A throughput measure, which would have no one point of measurement, was suggested by Clarence-Smith (1988a) as an attempt to address the problem that no one measuring point within a facility is perfectly suitable for normalization of data waste. The throughput accounting procedure is an ap- proach for mandating a waste-reduction standard, as described in Appendix ]. In this context, throughput is defined as the sum of all mass quantities flowing out of a facility allowing for chemical conversions and including inventory changes. Obtain- ing these data in a meaningful way multi- plies the problems associated with the in- dividual MA components described earlier in the chapter. This conceptual approach does little to resolve the problem of nonuniformity of re- porting or the analysis of such data when 53 analyses are done on a chemical-specific basis in a nonconservative production. Generally, the throughput model is seriously flawed in its attempt to treat all industries and all facilities uniformly. A discussion of several additional problems witl7 this pro- posal is given in Appendix J. Toxicity-Based System Fatkin (1988) describes an approach to track the reduction or replacement of highly toxic chemicals while reducing the presence of other chemicals in the waste. The amount of chemicals designated as highly toxic that are input to a facility (rather than contained in the waste) is used as the numerator, and the denominator is the quantity of the pro- duct derived from the use of those chemi- cals. For chemicals considered less toxic, the amount of waste generated would constitute the numerator. With this approach, incon- sistencies in reporting could occur if a high- ly toxic chemical were totally replaced with a less toxic one. In this case, the numerator of the statistic would change from the input amount to the amount in the waste. Conse- quently, progress, or the lack of it, shown in

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54 each statistic would have to be explained and documented as these transfers take place from year to year. In addition, such a method requires the construction of a system to classify materials by their relative toxici- ties. This can be a time-consuming, con- troversial, and complex task. It would, how- ever, have the advantage of focusing on the most important toxicants and limiting the number of chemicals that must be accounted for if consensus can be reached on the toxicity criteria. OTA concluded that the best way to measure waste reduction is to determine the changes in the absolute amounts of hazardous components. How- ever, government might have to assist waste- generating facilities in selecting the most hazardous components of waste for reduc- tion (OTA, 1986~. Successful Project Reporting A third approach does not emphasize establishing ~ baseline or calculating changes from year to year. Rather, this approach would have facilities report their successful projects and estimates of the amount of waste prevented from being generated by those projects (Benforado, 1988~. A national collection of this information would help to establish a database on successfully applied methods that reduce toxic chemical releases. Such a database would provide the informa- tion needed for a successful technical as- sistance program. It would be extremely useful to individuals in charge of assistance programs to know methods that work and those that are failures before they recom- mend them to facility managers. The disadvantage to the reduction database is that, when used alone, it may not supply enough information on the actual changes in waste generation from year to year. CONCLUSIONS The necessary components of a program to track waste-reduction progress are knowledge of the waste generated and its re- lation to the level of manufacturing activity, the extent of waste reduction at the source (versus reduction after treatment), and the comparability of the collected data among a wide variety of facilities. ~4SS BALANCE INFORM TION Mass balance information obtained through EMB or MA practices is not generally useful for strict determination of waste-reduction efficiency or progress in waste reduction. Both practices fail to recognize technological and economic limita- tions in achieving waste reduction, within and between industries, or the progress that was made before an accounting program was instituted. Consequently, uniform absolute goals such as a fixed rate of reduction of waste per year or a fixed efficiency require- ment over a period of a year are inap- propriate. Any measure of waste-reduction ef- ficiency must be based upon an unam- biguous definition of the "waste" subject to reduction. Reporting of waste-reduction progress on a chemical-specific basis is most feasible if a normalizing factor is used. Because no single normalizing scheme is generally applicable to all manufacturing facilities, attempts to calculate a ratio of the amount of a chemical in the waste to the amount of the same chemical in one or more mass balance components would lead to a lack of uniformity in reporting and inter- pretation. Actual waste-reduction progress is best defined and monitored by the generator at the production-unit level. Progress in reduction is masked when the mass balance data from a smaller reporting unit, such as a production unit, are used to develop an industry-wide or national statistic. This masking of progress associated with a specific product also takes place when a pro- duction unit produces many products and generates multiple wastes that are accounted for in a combined fashion. A production-normalized weighting sta- tistic could be used to measure waste-reduc- tion progress at the facility or industry level. However, the diversity of chemical products and manufactured goods that involve the use of toxic chemicals often makes it difficult to normalize waste data on a consistent and comparable basis. Specific data on produc- tion units must be used if meaningful waste- reduction statistics are desired at a national level; it would be difficult to assemble the data at this level. The use of chemical-spe- cific data for normalization further increases calculational problems significantly and raises serious concerns about confidentiality because of the detailed data that must be

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ASSESSING WASTE-REDUCTION EFFICIENCY supplied. Even such a normalized statistic must be interpreted with caution, because the relationship between waste generation and production might not be linearly corre- lated. MA data, such as waste reports, production reports, and descriptions of waste-handling practices, coupled with descriptions of the results of implemented waste-reduction techniques, could in some cases contribute to forming a useful picture of waste-reduction progress and help provide information on reduction techniques. Such reporting could not, however, be meaningfully aggregated at the industry or national levels because too much information on indiviclual production processes is lost, and waste-reduction progress would likely be obscured. There is no advantage in providing the raw material input informa- tion in reporting waste-reduction progress. The greater the number of listed and un- listed chemicals involved with this approach, 55 the more intractable the problem of inter- pretation and comparison of progress within between, or among industries. Basing evaluation of waste-reduction progress data on the reduction of specific chemicals is not necessarily more effective than basing it on the reduction in total quantity of waste-chemical constituents or waste toxicity. The reduction of a listed chemical might not reduce the total waste stream and could increase the quantity through greater use of a nonreguiated material of unknown hazard or toxicity. Expert analytic assistance as an adjunct to an MA data collection program would be helpful in addressing whether waste reduc- tion has been accomplished by replacing a chemical of known health effects with a dif- ferent chemical of unknown health effects. Normalized data by themselves might show a reduction in waste, but they would not indi- cate whether any change in potential health effects had occurred.

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