ÐÏࡱá > þÿ ä æ þÿÿÿ à á â ã î o ð € ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿì¥Á 5@ ø¿ ; bjbjÏ2Ï2 ýþ X X 2 á " ÿÿ ÿÿ ÿÿ ˆ È È È È < < < d dª dª dª ° « ô ¬ ï v (² Ü ¶ ( ,¶ ,¶ ,¶ _¹ ’ ñº | m» @ ”î –î –î –î –î –î –î $ ‹ð R Ýò ~ ºî < ÀÀ ¹ @ _¹ ÀÀ ÀÀ ºî È È ,¶ ,¶ ó Ïî PÅ PÅ PÅ ÀÀ ¼ È R ,¶ < ,¶ èé ¬ PÅ ÀÀ ”î PÅ PÅ hÅ " < hÅ ,¶ ² œƒeÒÇ dª |à š hÅ É ä åî 0 ï hÅ [ó Ä Ö [ó hÅ È È È È hÅ \ [ó < ÄÅ @ » Ê w¼ PÅ ½ t {½ E » » » ºî ºî Ä¥ dª ìÄ d dª Estimation of Supply Chain Cadmium, Lead, Nickel, and Zinc Intensity with the Mixed-Unit Input-Output Life Cycle Assessment (MUIO-LCA) Model
Troy Hawkinsa, Chris Hendricksonb, H. Scott Matthewsc
Green Design Institute
Carnegie Mellon University
5000 Forbes Avenue, Pittsburgh, PA 15213 USA
atrh@andrew.cmu.edu, bcth@andrew.cmu.edu, chsm@andrew.cmu.edu
Abstract
The risks associated with releases of heavy metals are of great concern for companies, regulators, and society at large. Understanding how and why we use these toxic chemicals can help us use them more efficiently. Here LCA and MFA are combined in the formulation of a mixed-unit, input-output life-cycle assessment (MUIO-LCA) model to help improve environmental decision-making with respect to heavy metals. The 1997 U.S. Benchmark IO Accounts including nearly 500 sectors of the US economy were augmented with additional sectors for explicitly handling physical flows of Cd, Pb, Ni and Zn as described by the US Geological Survey. The model allows for material usage, environmental releases, and other flows of interest to be estimated for the complete supply chains of goods and services.
Benefits of using the MUIO-LCA model for evaluating the life-cycle impacts and material flows associated with products include greater detail, explicit tracking of material flows, and the ability to model production of select commodities based on mass units rather than dollars. The inclusion of process sectors based on physical quantity reduces the burden on the model user to calculate the cost associated with these commodities and allows for better estimation of the impacts associated with imported goods by removing the dependence of physical flows on price.
We use the MUIO-LCA model to estimate consumption of cadmium, lead, nickel and zinc throughout the entire supply chain of each sector of the economy providing insight into the material intensity of products and processes. By coupling material and economic transaction data the MUIO-LCA model presented here provides a more complete picture of the movement of metals through the economy than either MFA or economic IO techniques alone could provide.
Introduction
The use of materials and the resultant environmental impacts are important problems. Tracking material flows through industrial processes is vital to understanding of the effects of changing consumption patterns and production technologies. A tool for providing information about the flows of environmentally relevant materials would provide valuable guidance for improving efficiency and reducing anthropogenic burdens on natural systems. Approaches that deal with the flows in and out of a specific process are only useful to a limited extent due to the interconnectedness of processes within our economy.
National scale input-output models generally rely on national input-output accounts consisting of monetary transactions between sectors of the economy. In this work, national models for the U.S. economy are augmented with sectors representing the physical flow of cadmium, lead, nickel and zinc to create a mixed-unit input-output life-cycle assessment (MUIO-LCA) model. Results from the MUIO-LCA model are provided in dollars for 500 sectors of the economy and mass units for the additional cadmium, lead, nickel, and zinc sectors.
Cadmium, lead, nickel, and zinc were chosen as the focus of the Mixed-Unit Input-Output model for a combination of reasons including toxicity, wide-spread use, policy-relevance, and interactions among their material cycles ADDIN EN.CITE ADDIN EN.CITE.DATA (ATSDR '99, '04a, b, c, Audry '04, EC '83, EPA '90, '93, '97, '05a, b, NRC '80, '93, OSHA '92, Smith '95). Cadmium and lead were chosen primarily due to concerns about their toxicity ADDIN EN.CITE EPA200568068068012EPAPriority Chemicals and Fact Sheets Website1/3/052005Washington, D.C.U.S. Environmental Protection Agencyhttp://www.epa.gov/epaoswer/hazwaste/minimize/chemlist.htmUi19921321321326Ui, J.Industrial Pollution in Japan1992Tokyo, Japan.United Nations University Press(EPA '05a, Ui '92). Zinc was added because of its occurrence with cadmium and lead in ore and its prevalence in the economy ADDIN EN.CITE ADDIN EN.CITE.DATA (Gordon '03, Gordon '04, Graedel '05). All cadmium and much lead is produced as a co-product (or by-product) of zinc production ADDIN EN.CITE USGS199814514514512USGSMinerals Yearbook 1997: Vol. I Metals and MineralsJune 20061998Reston, VAU.S. Geological Survey, U.S. Department of Interiorhttp://minerals.usgs.gov/minerals/pubs/commodity/myb/(USGS '98). Nickel was selected because of its relationship to the flows of cadmium and lead. Nickel is used in both nickel-cadmium and nickel-metal hydride batteries. Nickel-cadmium batteries represent the largest cadmium flow, accounting for 80% of cadmium use while nickel-metal hydride batteries are the most common battery technology used in hybrid vehicles ADDIN EN.CITE Higgins200769669669617Higgins, CJMatthews, HSHendrickson, CTSmall, MSLead Demand of Future Vehicle TechnologiesTransportation Research Part DTransportation Research Part D103-114122007Stempel199872872872817Stempel, RCOvshinsky, SRGifford, PRCorrigan, DANickel-Metal Hydride: Ready to ServeIEEE SpectrumIeee Spectrum29-3435111998(Higgins '07, Stempel '98).
The groundwork for the MUIO accounts presented here was laid by many earlier studies. A mixed-unit IO account based on the most appropriate units for measuring the output of each sector was recently suggested by Duchin ADDIN EN.CITE Duchin200461761761727Duchin, FInput-Output Economics and Material FlowsRensselaer Working Papers in Economics04242004December 2004Rensselaer Polytechnic Institutewww.rpi.edu/dept/economics/www/workingpapers/('04a). Earlier work by Ayres and Kneese ADDIN EN.CITE Ayres1969474717Ayres, Robert U.Kneese, A.V.Production, Consumption and ExternalitiesAmerican Economic Review282-297591969('69) and Kneese et al. ADDIN EN.CITE Kneese197048486Kneese, A.V.Ayres, Robert U.d'Arge, R.C.Economics and the Environment: A Material Balance Approach1970Washington D.C.Resources for the Future('70) applied the mass-balance principle to input-output analysis forming a basic framework for modeling physical flows. During the energy crises of the 1970s mixed-unit input-output techniques were used in a number of energy analyses ADDIN EN.CITE Bullard1975787817Bullard, CWHerendeen, RAEnergy impact of consumption decisionsProceedings of the IEEEProceedings of the IEEE484- 4936331975Bullard1978777717Bullard, CWPenner, PSPilati, DANet Energy Analysis: Handbook for Combining Process and Input-Output AnalysisResources and EnergyResources and Energy267-313131978Casler1984757517Casler, S.Wilbur, S.Energy Input Output Analysis - a Simple GuideResources and EnergyResources and Energy187-201621984ISI:A1984TK31700005<Go to ISI>://A1984TK31700005 Hannon1978747417Hannon, B.Stein, R. G.Segal, B. Z.Serber, D.Energy and Labor in Construction SectorScienceScience837-84720243701978ISI:A1978FW52400010<Go to ISI>://A1978FW52400010 Herendeen1978767617Herendeen, Robert A.Input-output techniques and energy cost of commoditiesEnergy PolicyEnergy Policy162-165621978http://www.sciencedirect.com/science/article/B6V2W-48XK6JW-48/2/1ba08c686165176f5aafe7c9c48d0aeb (Bullard '75, Bullard '78, Casler '84, Hannon '78, Herendeen '78). Leontief ADDIN EN.CITE Leontief197014914914917Wassily LeontiefEnvironmental Repercussions and the Economic Structure: An Input-Output ApproachThe Review of Economics and StatisticsThe Review of Economics and Statistics262-2715231970August 1970file:///C:/Documents%20and%20Settings/trh/My%20Documents/Files/CMU/trh_References/Full_Text/Leontief_EnvironmentalRepercussionsandtheEconomicStructure_AnInputOutputApproach.pdf('70) introduced a pollution sector with mass unit emission flows into a national model. Duchin ADDIN EN.CITE Duchin2004222212Faye DuchinInput-Output Economics and Material FlowsRensselaer Working Papers in EconomicsApril 20062004December 2004http://www.economics.rpi.edu/www/workingpapers/rpi0424.pdffile:///C:/Documents%20and%20Settings/trh/My%20Documents/Files/CMU/trh_References/Full_Text/Duchin_IOEconomicsandMaterialFlows.pdfWeisz2004232312Weisz, HDuchin, FPhysical and Monetary Input-Output Analysis: What Makes the Difference?Rensselaer Working Papers in EconomicsApril 20062004December 2004.http://www.economics.rpi.edu/www/workingpapers/rpi0422.pdffile:///C:/Documents%20and%20Settings/trh/My%20Documents/Files/CMU/trh_References/Full_Text/Weisz_PhysicalandMonetaryIOAnalysis_WhatMakestheDifference.pdf(Duchin '04b, Weisz '04) presented an extended input-output model based upon physical quantities and prices. Giljum ADDIN EN.CITE Giljum20056316316315Giljum, SHinterberger, FLutz, CMeyer, BModeling Global Resource Use: Material Flows, Land Use, and Input-Output ModelsHandbook of Input-Output Economics in Industrial Ecology2005Giljum200463063063017Giljum, SHubacek, KAlternative Approaches of Physical Input-Output Analysis to Estimate Primary Material Inputs of Production and Consumption ActivitiesEconomic Systems ResearchEconomic Systems Research301-3101632004('04, '05b) provides additional guidance on the development of mixed-unit IO models. Suh ADDIN EN.CITE Suh200414314314317Sangwon SuhFunctions, Commodities and Environmental Impacts in an Ecological-Economic ModelEcological EconomicsEcological Economics451-4674842004('04a) demonstrated how to integrate process-specific physical flow data with monetary input-output models and noted the advantages of the input-output models in accounting for circularity of flows in environmental life-cycle assessment. Konijn et al. ADDIN EN.CITE Konijn1997505017Konijn, P.de Bohr, S.van Dalen, J.Input-Output Analysis of Material Flows with Applications to Iron, Steel and ZincStructural Change and Economic Dynamics129-15381997('97) and Hoekstra ADDIN EN.CITE Hoekstra2003262632Hoekstra, R.Structural Change of the Physical Economy: Decomposition Analysis of the Physical and Hybrid Input-Output TablesPhD2003AmsterdamFree University('03) have utilized both physical and monetary units in an input-output table in tracing the resources flows in a national economy introducing the mixed-unit input-output model. Hawkins ADDIN EN.CITE Hawkins200663563563517Hawkins, TroyHendrickson, Chris T.Higgins, CortneyMatthews, H. ScottA Mixed-Unit Input-Output Model for Environmental Life-Cycle Assessment and Material Flow AnalysisEnvironmental Science & TechnologyEnvironmental Science & Technology1024-10314132006('06b) presented a model based on the summary-level US IO tables with added sectors to track flows of cadmium and lead. Lin ADDIN EN.CITE Lin199816216217Lin, X. and K.R. PolenskeInput-Output Modeling of Production Processes for Business ManagementStructural Change and Economic DynamicsStructural Change and Economic Dynamics205-22691998('98) provides an example of coke making for an enterprise specific input-output model. Thus the usefulness of input-output models for materials flow analyses, tracking the movements of particular materials or energy through industrial processes, product use and natural reservoirs has been shown to be useful ADDIN EN.CITE ADDIN EN.CITE.DATA (Ayres '01, Baccini '91, Bailey '04a, '04b, Duchin '91, '92, Giljum '05a, NRC '04, Suh '04b, Suh '04c, Takase '05).
Method for Creating a Mixed-Unit Model
The MUIO make and use accounts are created by adding rows and columns to the 1997 U.S. Benchmark make and use tables. Existing economic sectors are modified to reflect the movement of activity to these new sectors. Cadmium, lead, nickel, and zinc commodities measured in physical units are represented by an additional column in the make (supply) table and an additional row in the use table. Similarly, industries which produce commodities measured in physical units are represented by an additional row in the make table and an additional column in the use table.
Flows of cadmium, lead, nickel, and zinc used to create MUIO make and use tables are generally based on data published by the U.S. Geological Survey in the annual Minerals Yearbook ADDIN EN.CITE USGS199814514514512USGSMinerals Yearbook 1997: Vol. I Metals and MineralsJune 20061998Reston, VAU.S. Geological Survey, U.S. Department of Interiorhttp://minerals.usgs.gov/minerals/pubs/commodity/myb/(USGS '98). The Minerals Yearbook chapter for each mineral generally includes data about the extraction, production, imports, exports and stocks. Data for each mineral are compiled by USGS Commodity Specialists from voluntary surveys, company reports, trade associations publications, journals, international exchanges (such as the New York Mercantile Exchange or the London Metal Exchange), personal communications, and the U.S. Census Bureau. The level of detail of physical flow data published in the Minerals Yearbook differs for each material. In general, the most detailed and most reliable information is available at the early stages of material production. For example, survey data are available for production of zinc ore concentrates and refined slab zinc. In certain cases, such as for lead and zinc, the end use of the material is also well understood. However, the flow of material from refining operations through manufacturing facilities to end use in products is difficult to track. End uses of cadmium and nickel are based on industry association estimates and are considered not as well characterized as those of lead and zinc.
In addition to the Minerals Yearbooks, the USGS also publishes a number of materials flow analyses, recycling assessments, and other special reports. All of this data provides an excellent base from which to build models of the flows of individual metals. These data were used to create the metal specific make, use, and final demand tables for cadmium, lead, nickel, and zinc. In many cases missing flows could be imputed from other values provided by the USGS. Additional data gaps were filled with values obtained from peer-reviewed articles ADDIN EN.CITE ADDIN EN.CITE.DATA (Gordon, et al. '03, Gordon, et al. '04, Graedel, et al. '05, Hawkins '06a), U.S. Census Bureau Industry Reports ADDIN EN.CITE USCB200269269269227USCB1997 Economic Census, Mining Industry Reports Series4/9/072002U.S. Census Bureauhttp://www.census.gov/prod/www/abs/97ecmini.htmlUSCB200269369369327USCB1997 Economic Census, Manufacturing Industry Reports Series4/9/072002U.S. Census Bureauhttp://www.census.gov/prod/www/abs/97ecmini.html(USCB '02a, b), and the U.S. Foreign Trade Database ADDIN EN.CITE US DoC199969169169145US DoC,U.S. Exports History and U.S. Imports History on CD-ROM1999U.S. Department of Commerce, Economic and Statistics Administration, U.S. Census Bureau. Washington, D.C.(US DoC '99).
REF _Ref168295950 \h Figure 1 provides an overview of the normalized MUIO matrices. The make table is made up of the monetary transactions sectors (D’), together with make tables for each of the physical commodities cadmium, lead, nickel, and zinc. Within each of the normalized physical transactions make tables, transactions are measured in tonnes metal produced in a commodity per unit output of metal by the industry. Elements outside of the partitions made by the boundaries of the individual metal / monetary transaction industries and commodities are zero.
The use matrix is made up of a series of use matrices for each of the metals and one for the dollar transaction sectors. For example, use of nickel commodities by nickel transactions industries is tracked in PNi. Usage of metal commodities by monetary transaction industries is found in the downstream requirements partitions labeled CD. The direct supply chains of metal transaction industries are found in the upstream requirements (or supply chains) partitions labeled CU.
Final demand vectors and value added are represented in yellow. Final demand for the monetary transactions sectors is measured in dollars (lower rows) while final demand for the physical transactions sectors is measured in tonnes of metal. Row sums of the use table (before normalization) together with the row sums of the final demand table yields total commodity output (q).
Value added is represented in below the use table. In monetary IO tables, value added is generally used to balance the use table. That is the column sums of the use table together with the column sums of value added is equal to total industry output or the row sums of the make table. However, because units in the MUIO model are not consistent across commodities, total industry output cannot be calculated from the use table. Monetary value added includes labor payments, taxes, and other value added. Additional rows representing material flows external to the economy under consideration were used to balance the physical tables. These included metal extraction from the environment, scrap, and other unaccounted for material (generally assumed to be wastes and environmental releases).
The monetary transactions sectors B’ and D’ are imputed from the values provided in the 1997 U.S. Benchmark IO tables by removing the dollar values of flows that have been replaced by physical flows in the additional metal sectors. Monetary values of physical flows are calculated as the product of the mass of the physical flow and the average 1997 price. The value of metal in compound commodities is assumed to be equal to that of the metal itself. In most cases the physical transaction commodities represented in the model are early in the supply chain of their end use products and so the differences in price should not have a large effect.
[Figure 1]
Calculation of the total requirements matrix from the make and use tables ADDIN EN.CITE BEA200213613613612BEA,U.S. Department of Commerce, Bureau of Economic Analysis1997 Benchmark Input-Output Accounts of the United States2006March 212002U.S. Department of Commerce, Bureau of Economic Analysishttp://www.bea.gov/bea/dn2/home/benchmark.htm(BEA '02) was performed following the procedure used by the U.S. Bureau of Economic Analysis for its 1997 Benchmark Model. A special adjustment is made to correct for the production of scrap. Scrap is separated from the make table in order to prevent the use of scrap from stimulating additional activity by the industry in which it was produced. This is accomplished by creating an industry by one vector of scrap output (h) and setting all production of the scrap commodity in the make table equal to zero. After this adjustment the total industry output can be calculated as the sum of the rows of the make table together with the scrap output of each sector.
g = Vi + h1An industry by one column vector of the ratio of the value of scrap produced by each industry by the total output of the industry is defined.
EMBED Equation.3 2 The normalized make and use matrices are calculated as before.
EMBED Equation.3 3 EMBED Equation.3 4 The normalized make matrix is modified to account for the proportion of the total output of the commodity that is produced by each industry adjusted for the value of scrap.
EMBED Equation.3 5 Here the adjusted make matrix (W) is used to calculate the industry by commodity total requirements.
Industry by Commodity Total Requirements = W(I – BW)-16
The MUIO Industry-by-Commodity Total Requirements matrix provides an opportunity to calculate the economy-wide material intensity of material use. In this analysis the MUIO-LCA total requirements matrix is used to provide guidance on supply chain consumption of cadmium, lead, nickel, and zinc per dollar of output for each of the 483 monetary transaction commodities included in the MUIO-LCA model.
Results from the MUIO-LCA Model
Entries in the rows of the Industry-by-Commodity Total Requirements Matrix corresponding to the output of the physical industries indicate the use of cadmium, lead, nickel, and zinc throughout the supply chain of the 483 commodities of the 1997 Benchmark Model. In REF _Ref168306030 \h Table 1 through REF _Ref168220532 Table 4 results are presented for the top ten sectors in terms of material intensity per dollar of additional final demand.
[Table 1]
[Table 2]
[Table 3]
[Table 4]
Sectors with high material use per dollar of commodity output are a good place to focus efforts to reduce metals use. These sectors offer an opportunity to conserve resources and reduce environmental impacts with the least amount of adverse economic impact. Consider for example lead use per dollar commodity output. A large ratio of lead use per dollar commodity output indicates that the contribution of lead to the total value of the commodity is small. Therefore an investment toward reducing the amount of lead used throughout the supply chain of the commodity should not have a large relative impact on the price of the commodity. The application of the material use to dollar value ratio for environmental policy prioritization is most appropriate for resolving problems related to supply availability or the environmental burdens associated with material production. Environmental burdens for primary material are caused by material extraction and processing. Collection, transportation, and remanufacturing are the biggest causes of concern in the case of secondary material.
In Table 1, we find that power-driven handtool manufacturing (333991) consistently demonstrates the most intense supply chain use of lead per dollar final demand. Roughly 4.0 grams of lead in ores and base bullion are consumed to produce one dollar of output of power-driven handtools. Of this material, 3.2 grams per dollar enters the supply chain as refined soft lead and lead in alloys produced by primary lead smelters (column 2). An additional 9.3 grams of secondary lead enters the power-driven handtool supply chain per dollar spent. Most of this material, 12 g/$, is contained in lead-acid storage batteries used throughout the supply chain. Presumably the remaining 0.5 grams of lead per dollar is included in other parts of the power-driven handtools, consumed by the processes used to produce them, or disposed of as waste.
Upon inspection of REF _Ref168306030 \h \* MERGEFORMAT Table 1 we find that use of lead in lead-acid batteries dominates lead-use in many products. In fact the US Geological Survey estimates that 88% of lead produced in 1997 was used in the manufacture of lead-acid batteries ADDIN EN.CITE USGS199814514514512USGSMinerals Yearbook 1997: Vol. I Metals and MineralsJune 20061998Reston, VAU.S. Geological Survey, U.S. Department of Interiorhttp://minerals.usgs.gov/minerals/pubs/commodity/myb/(USGS '98). Lead in batteries dominates the use of lead in elevator and moving stairway manufacturing (333921), lawn and garden equipment manufacturing (333112), boat building (336612), hand and edge tool manufacturing (332212), motor home manufacturing (336212), and rolling mill and other metalworking machinery (33351A). In addition animal production, except cattle, poultry, and eggs (112A00), sugarcane and sugar beet farming (1119A0), and cattle ranching and farming (112100) appear in the top 10 sectors for use of lead in lead-acid storage batteries per dollar.
Certain sectors use lead in other forms. Jewelry and silverware manufacturing (339910) likely consumes lead in metal alloys and in solder. Dental laboratories (339116) and dental equipment and supplies manufacturing (339114) both have complicated supply chains in which lead is consumed in the production of equipment and other supplies. Some sectors appear in the use of multiple materials. Jewelry and silverware (339910) appears near the top of the list for all four of the metals described here. Dental laboratories (339116) and dental equipment and supplies manufacturing (339114) are in the top 10 lists for lead, nickel, and zinc. High cadmium and nickel intensity is calculated for the storage battery manufacturing (335911).
The highest level of consumption occurred for jewelry and silverware manufacturing which reported an overall zinc consumption rate of 39 grams per dollar. In general smaller values were reported for top cadmium consuming sectors than for lead, nickel, or zinc. Low values are caused by small metal flows or large dollar values of products. Because the IO 1997 commodities include a number of sub-commodities it is likely that low values for cadmium reflect the small fraction of the total flows in each sector made up of products containing cadmium. For example, the flow of cadmium per dollar spent on NiCd batteries should be high. However the supply chain flow of cadmium per dollar spent on storage batteries is small. This is because NiCd batteries only account for a small portion of the total dollar value of storage batteries. This highlights the effect of commodity definitions on MUIO-LCA results.
So far we have presented the intensity of material use per dollar spent for the monetary transactions sectors in the MUIO-LCA model. However, many of these sectors include products with which the typical consumer is unfamiliar because they occur early on in the supply chains of more familiar products. We would like to provide guidance on products which consumers choose to purchase directly. This can be done by filtering for products with a high percentage of personal consumption expenditures (PCE). Personal consumption expenditure in each sector is tracked in the final demand portion of the 1997 U.S. Benchmark Account. Total domestic consumption (DC) can be calculated as:
DC = q + eimports – eexports
where DC is Domestic Consumption, q is Total Commodity Output eimports is imports, and eexports is exports. A summary of total material use (primary and secondary) for the top 10 sector in terms of material intensity together with the personal consumption expenditures, domestic consumption, and PCE/DC ratio for each metal can be found in REF _Ref168373329 \h \* MERGEFORMAT Table 5 through REF _Ref168373332 \h \* MERGEFORMAT Table 8. We can see that many of the sectors appearing in the top 10 have very small percentages of personal consumption expenditure.
[Table 5]
[Table 6]
[Table 7]
[Table 8]
In REF _Ref168373556 \h Figure 2 we present the distribution of sectors by their PCE/DC ratio. We find that roughly 65% of the sectors have PCE/DC ratios less than 0.2. The percentage of commodity sectors increases roughly linearly for sectors with PCE/DC ratios between 0.2 and 1.0. Nine sectors have a PCE/DC ratio slightly greater than one. Although this is unexpected, it is likely due to special exceptions in the way monetary transactions are accounted for in these sectors. These 9 sectors are other amusement, gambling, and recreation industries (713A00); other ambulatory health care services (621B00); museums, historical sites, zoos, and parks (712000); funds, trusts, and other financial vehicles (525000); elementary and secondary schools (611100); home health care services (621600)
Hospitals (622000); colleges, universities, and junior colleges (611A00); and other accommodations (721A00).
[Figure 2]
The top 10 total material use (primary and secondary) per dollar sectors with PCE/DC ratios greater than 0.2 for cadmium, lead, nickel, and zinc can be found in REF _Ref168374772 \h \* MERGEFORMAT Table 9. We can see that power-driven handtools (333991) still appear at the top of the list for lead and jewelry and silverware (339910) are still in the top two across the four metals. However, certain sectors appear in these lists which did not appear in the overall top 10. Kitchen utensil, pot, and pan manufacturing (332214) and household cooking appliance manufacturing (335221) appear in the list for zinc. Automobile and light truck manufacturing (336110) appears in the list for lead. Watch, clock, & other measuring & controlling device mfg. (33451A) appears in the lists for zinc, nickel, and cadmium.
In REF _Ref168375328 \h Figure 3 we present the distribution of supply chain material use per dollar for cadmium, lead, nickel, and zinc overall and for sectors with PCE/DC ratios greater than 0.2. As expected the distributions for the filtered results decrease more steeply with rank than the overall results. However, the top sectors for each metal have material intensities of roughly the same magnitude as the overall results. Overall the material intensity of zinc use is slightly higher for the highest ranked sectors, however near the 50th percentile overall lead and zinc intensity are within the same order of magnitude. The material intensities for lead and zinc consuming sectors with PCE/DC ratios greater than 0.2 are generally within the same order of magnitude across ranks. Material intensities for cadmium and nickel sectors with PCE/DC ratios greater than 0.2 decrease significantly more quickly than the overall results.
[Figure 3]
The inverse prices of refined cadmium, lead, nickel, and zinc are provided in REF _Ref164185606 \h \* MERGEFORMAT Table 10 for comparison with the results of our study. We would expect to observe a supply chain consumption of metal in ore per dollar value greater than or equal to the inverse price for the supply chain of the refined metal commodity itself. This is not observed because inputs for the metal commodities themselves in the MUIO-LCA model are measured in tonnes. The inverse price represents an effective limit for the use of refined metal in products. Nickel use in storage battery manufacturing is valued at 7 g/$, roughly 17% of the inverse price of nickel. This indicates that the value of nickel is a large fraction of the value of storage battery output. Recall that a specific physical flow sector has been created in the MUIO-LCA model to account for the flow of lead-acid batteries. Thus the storage battery manufacturing sector represents only flows of storage batteries other than lead-acid.
[Table 10]
Limits and Uncertainty in the MUIO-LCA Model
Some aspects of the model itself should be discussed. Results of the model are affected by certain assumptions and simplifications made in its development. Two items are important to the results discussed here, aggregation of IO 1997 sectors and the assumptions made in creating the downstream use of physical commodities.
The sectors used in the 1997 U.S. Benchmark Accounts are created by aggregating together a number of similar products. In some cases however these sectors include a wider variety of products. For example, iron and steel mills (331111) includes 19 commodities such as coke oven products ; pig iron; slag; iron and steel powders, paste, and flakes; steel ingots; hot rolled steel sheet and strip; steel bars, steel pipes and tubes; and steel rails. In other cases the specific products are difficult to define. For example, 99.5% of primary nonferrous metal, except copper and aluminum (331419 ) is classified in the 1997 U.S. Benchmark detailed item output as primary nonferrous metals, not elsewhere classified (331419T). This aggregation and uncertainty muddies the connections between material consumption and specific products.
Another aspect of the model that introduces error to our results is the assumption we have made in assigning the downstream consumption of physical commodities. In the development of the MUIO account, rows of the 1997 U.S. Benchmark use table were used to estimate downstream consumption of physical commodities. Each physical flow commodity was mapped to the most closely related 1997 U.S. Benchmark commodity. The row was then inspected and modified by zeroing out entries for which no flow of the physical commodity was expected. The dollar transactions remaining in the row (including the final demand sectors) were summed and each was divided by the total remaining transactions to provide a percentage. Physical flows to the monetary sectors of the economy were then distributed according to these percentages.
This method relies on our judgements about the flow of materials into the monetary sectors. The strengths of this method are that it captures the complexity of flows in the economy and that it matches closely the form of the original 1997 U.S. Benchmark Model. The primary weakness is that when certain flows are not zeroed out for a given metal the distribution of its use becomes more like the average use of the 1997 U.S. Benchmark commodity leading to results which overestimate the use of certain metals in applications which may in fact involve higher consumption of another metal. Thus use of this second group of metals would be underestimated.
Another important clarification to make is that the dollar flows associated with the added physical flow sectors have been removed from the monetary transactions portion of the MUIO make and use tables. In the case of lead, the value of lead flows into storage battery manufacturing (335911) has been removed and replaced with a physical flows of lead to a lead-acid battery manufacturing sector whose output is measured in tonnes of lead contained in lead-acid batteries. Thus the remaining value in the storage battery manufacturing sector (335911) pertains primarily to sales of nickel metal hydride, nickel cadmium, and lithium ion battery chemistries.
Discussion
We have demonstrated how a mixed-unit input-output model could be used to determine the material intensity in terms of material use per unit economic value of a product. Material intensity was calculated for each of the 483 commodities of the 1997 U.S. Benchmark Model. Material intensity can be used as a scoping tool for prioritization of environmental policy. High material use per dollar indicates that a large portion of the overall value of a product is associated with the value of the material from which it is made. In the case of materials for which we would like to decrease use, these products likely offer opportunities to substitute the material currently used with another material. For example, we find that the material intensity of cadmium, lead, nickel, and zinc is high in jewelry and silverware manufacturing. These products are long-lived and purchased relatively infrequently. Policy measures encouraging the substitution of these metals in this sector could reduce material consumption with minimal disruption to the economy. However, it is important to maintain perspective on the magnitude of material flows. Although this reduction might be made with little disruption, it would have only a small impact on the total flows of these materials.
In other cases policy-makers may decide that sectors with high material intensity in fact represent important uses of the material. In these cases action could be taken to encourage proper use and disposal of the material. Knowing the material intensity of certain sectors could also help predict and mitigate the possible consequences of policies which increase the costs associated with the use of a material. For example, we have shown that lead, specifically lead-acid batteries, is important to various agricultural activities. However, policy-actions which increase the cost of storage batteries could have a significant impact on agriculture. Because a large fraction of lead is sourced from recycled material and because the recycle rate is high (~80%), continuing the use of lead-acid batteries in agriculture while it is discouraged elsewhere could reduce the negative impacts of the transition.
Calculating material intensities of materials of interest is important to understanding the impacts of policies intended to reduce their use. The simple calculation of material intensity can also be used to predict the sectors which would be impacted most by decreases in the availability or increases in the prices of select materials. For example, we would expect a policy which imposes more stringent regulations on secondary lead smelters to have the greatest effect on the price of power-driven handtools (333991), elevators and moving stairways (333921), jewelry and silverware (339910), and lawn and garden equipment (333112). Similarly a shortage of zinc would have the greatest impact on personal consumption of jewelry and silverware (339910); motorcycles, bicycles, & parts (336991); watch, clock, and other measuring and controlling devices (33451A); electric lamp bulbs and parts (335110); heating equipment, except warm air furnaces (333414); and household laundry equipment (335224).
By explicitly representing metal commodities at early stages in the product supply chain the MUIO model provides a clearer picture of the material requirements and intensity for sectors of the economy. Care should be taken in interpreting the material intensities calculated using the MUIO model for several reasons. Most of these we have already mentioned. First several metal commodities are often lumped into a single commodity sector, such copper, nickel, lead, and zinc mining (212230) or primary nonferrous metal, except copper and aluminum (331419). Although a list of detailed item output by commodity is available as a supplement to the 1997 U.S. Benchmark Input Output Account, even the detailed information is not specific enough to determine specific materials. For example, 99.5% of primary nonferrous metal, except copper and aluminum (331419) is classified in the 1997 U.S. Benchmark detailed item output as primary nonferrous metals, not elsewhere classified (331419T).
Second, the method we have used to create the downstream use of metals assumes that the use of each metal commodity is similar to the dollar transactions for the most closely related 1997 Benchmark sector. In cases where we have improved information flows are manually set to zero. Material is then allocated across the remaining 1997 Benchmark industry and final demand sectors according to the percentages of the total remaining dollar transactions. Allocating in this way causes the model to yield certain metal flows which reflect the average for the associated sector rather than the actual flows which might be slightly higher or lower.
Third, the material intensities calculated with the MUIO model do not correspond to the size or hazard associated with each sector. A high material intensity indicates that the amount of material consumed throughout the supply chain of a product is large compared with the producer price of the product. This metric is useful for minimizing the impact of measures to reduce material use. It is also useful for identifying products whose supply chains consume large amounts of material per unit value in the final product. However, in certain cases reducing the overall flow of a material will require reducing flows in low material intensity applications. For example, reducing the use of zinc significantly would necessarily involve reductions in zinc used for galvanizing or protecting steel against corrosion. Galvanizing is included in iron and steel mills (331111) which do not appear in the top 10 sectors for zinc intensity. Nonetheless, this sector is important to significantly reducing zinc use. This example also demonstrates the impact of sectoral aggregation. In fact, the material intensity of zinc use in the galvanizing process is likely high. However, because galvanizing accounts for only a small fraction of the total receipts by iron and steel mills the zinc intensity is low. In other cases the hazard associated with a low material intensity is sufficiently high to warrant action. For example, the use of lead as a gasoline additive would not be indicated by a high material intensity, however the risk associated with airborne lead resulting from combustion of leaded fuel is high.
Despite these complications, the use of mixed-unit input-output accounts to determine the material intensity of products is an important tool for performing analyses related to resource economics and industrial ecology. By combining monetary input-output accounts with material flow data the MUIO-LCA model is capable of providing a more complete picture of the supply chains of products and processes than either monetary input-output analysis or material flow analysis alone. Reducing material consumption in the supply chains of products targeted based on a high material intensity allows for efficient steps toward dematerialization of an economy. Reduced material use is accompanied by reduced environmental degradation, releases of toxic material, and energy consumption. Identifying and examining material intense sectors allows us to focus our efforts on sectors which consume a large amount of material per unit product value. Focusing on these sectors could help improve the economic efficiency of dematerialization efforts.
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Tables and Figures
CommoditiesIndustriesCommoditiesPNi
(t Ni/t Ni)CD
(t Ni/$)$ Final Demand (e’$)P Final Demand (e’P)Total Commodity Output (q)PZn
(t Zn/t Zn)CD
(t Zn/$)PPb
(t Pb/t Pb)CD
(t Pb/$)PCd
(t Cd/t Cd)CD
(t Cd/$)CU
($/t Ni)CU
($/t Zn)CU
($/t Pb)CU
($/t Cd)B'
($/$)IndustriesDPNi
(t Ni/t Ni)Total Industry Output (g)DPZn
(t Zn/t Zn)DPPb
(t Pb/t Pb)DPCd
(t Cd/t Cd)D'
($/$)Value AddedTotal Commodity Output (q)Figure SEQ Figure \* ARABIC 1. Structure of make and use accounting framework used for the MUIO-LCA model.
Table SEQ Table \* ARABIC 1. Top 10 lead consuming sectors, all values provided in grams per dollar.
Ores & base bullion (mine production) Primary Lead Secondary Lead New Lead-Acid Storage Batteries Power-driven handtool mfg (333991)4Power-driven handtool mfg (333991)3.2Power-driven handtool mfg (333991)9.3Power-driven handtool mfg (333991)12Elevator & moving stairway mfg (333921)3.2Elevator & moving stairway mfg (333921)2.5Elevator & moving stairway mfg (333921)7.5Elevator & moving stairway mfg (333921)9.8Lawn & garden equip. mfg (333112)2.0Lawn & garden equip. mfg (333112)1.6Jewelry & silverware mfg (339910)6.6Lawn & garden equip. mfg (333112)6.1Jewelry & silverware mfg (339910)1.3Jewelry & silverware mfg (339910)1.0Lawn & garden equip. mfg (333112)4.7Boat bldg (336612)2.8Boat bldg (336612)0.92Boat bldg (336612)0.72Secondary processing of other nonferrous (331492)4.3Motor home mfg (336213)2.7Motor home mfg (336213)0.89Motor home mfg (336213)0.7Nonferrous metal, exc. Cu & Al, shaping (331491)3.9Hand & edge tool mfg (332212)2.0Secondary processing of other nonferrous (331492)0.84Secondary processing of other nonferrous (331492)0.66Dental laboratories (339116)2.7Rolling mill & other metalworking machinery (33351A)1.9Nonferrous metal, exc. Cu & Al, shaping (331491)0.76Nonferrous metal, exc. Cu & Al, shaping (331491)0.6Dental equip. & supplies mfg (339114)2.2Animal production, exc. cattle & poultry & eggs (112A00)1.5Hand & edge tool mfg (332212)0.66Hand & edge tool mfg (332212)0.52Boat bldg (336612)2.1Sugarcane & sugar beet farming (1119A0)1.4Rolling mill & other metalworking machinery (33351A)0.64Rolling mill & other metalworking machinery (33351A)0.5Motor home mfg (336213)2.1Cattle ranching & farming (112100)1.4
Table SEQ Table \* ARABIC 2. Top 10 zinc consuming sectors, all values provided in grams per dollar.
Zn MiningPrimary ZnSecondary ZnJewelry & silverware mfg (339910)17Jewelry & silverware mfg (339910)17Jewelry & silverware mfg (339910)22Secondary processing of other nonferrous (331492)11Secondary processing of other nonferrous (331492)11Secondary processing of other nonferrous (331492)14Nonferrous metal, exc. Cu & Al, shaping (331491)11Nonferrous metal, exc. Cu & Al, shaping (331491)11Nonferrous metal, exc. Cu & Al, shaping (331491)13Primary nonferrous metal, exc. Cu & Al (331419)8.8Primary nonferrous metal, exc. Cu & Al (331419)8.8Primary nonferrous metal, exc. Cu & Al (331419)11Dental laboratories (339116)6.9Dental laboratories (339116)6.9Dental laboratories (339116)8.7Ferroalloy & related product mfg (331112)6.1Ferroalloy & related product mfg (331112)6.1Dental equip. & supplies mfg (339114)7.2Dental equip. & supplies mfg (339114)5.8Dental equip. & supplies mfg (339114)5.8Ferroalloy & related product mfg (331112)6.3Cu rolling, drawing, & extruding (331421)3.5Cu rolling, drawing, & extruding (331421)3.5Cu rolling, drawing, & extruding (331421)4.7Primary smelting & refining of Cu (331411)3.1Primary smelting & refining of Cu (331411)3.1Primary smelting & refining of Cu (331411)4.2Other Al rolling & drawing (331319)2.5Other Al rolling & drawing (331319)2.5Other Al rolling & drawing (331319)2.7
Table SEQ Table \* ARABIC 3. Top 10 nickel consuming sectors, all values provided in grams per dollar.
Nickel Mining Primary Nickel Secondary Nickel Storage battery mfg (not incl. lead-acid) (335911)6.8Storage battery mfg (not incl. lead-acid) (335911)6.2Jewelry & silverware mfg (339910)2.2Jewelry & silverware mfg (339910)3.2Jewelry & silverware mfg (339910)2.9Secondary processing of other nonferrous (331492)1.4Secondary processing of other nonferrous (331492)2.1Secondary processing of other nonferrous (331492)1.9Nonferrous metal, exc. Cu & Al, shaping (331491)1.1Nonferrous metal, exc. Cu & Al, shaping (331491)1.9Nonferrous metal, exc. Cu & Al, shaping (331491)1.8Primary nonferrous metal, exc. Cu & Al (331419)1.0Primary nonferrous metal, exc. Cu & Al (331419)1.6Primary nonferrous metal, exc. Cu & Al (331419)1.5Dental laboratories (339116)0.87Ferroalloy & related product mfg (331112)1.4Ferroalloy & related product mfg (331112)1.2Ferroalloy & related product mfg (331112)0.84Dental laboratories (339116)1.3Dental laboratories (339116)1.2Dental equip. & supplies mfg (339114)0.72Dental equip. & supplies mfg (339114)1.1Dental equip. & supplies mfg (339114)0.96Ferrous metal foundaries (331510)0.26Cu rolling, drawing, & extruding (331421)0.78Cu rolling, drawing, & extruding (331421)0.71Iron & steel mills (331111)0.24Primary smelting & refining of Cu (331411)0.70Primary smelting & refining of Cu (331411)0.64Al foundries (33152A)0.24
Table SEQ Table \* ARABIC 4. Top 10 cadmium consuming sectors, all values provided in grams per dollar.
Cd Mining (together with Pb & Zn)Cd Recovery (from primary Zn smelting)Secondary Cd, INMETCOStorage battery mfg (not incl. lead-acid) (335911)0.20Storage battery mfg (not incl. lead-acid) (335911)0.18Cutting tool & machine tool accessory mfg (333515)0.015Jewelry & silverware mfg (339910)0.078Jewelry & silverware mfg (339910)7.2E-02Secondary processing of other nonferrous (331492)0.014Secondary processing of other nonferrous (331492)0.051Secondary processing of other nonferrous (331492)4.8E-02Primary battery mfg (335912)5.7E-03Nonferrous metal, exc. Cu & Al, shaping (331491)0.046Nonferrous metal, exc. Cu & Al, shaping (331491)4.3E-02All other forging & stamping (33211A)2.9E-03Ferroalloy & related product mfg (331112)0.034Ferroalloy & related product mfg (331112)3.1E-02Metal heat treating (332811)2.3E-03Dental laboratories (339116)0.031Dental laboratories (339116)2.9E-02Hardware mfg (332500)1.3E-03Dental equip. & supplies mfg (339114)0.026Dental equip. & supplies mfg (339114)2.4E-02Special tool, die, jig, & fixture mfg (333514)1.3E-03Ferrous metal foundaries (331510)0.01Ferrous metal foundaries (331510)9.7E-03Al foundries (33152A)1.2E-03Iron & steel mills (331111)9.9E-03Iron & steel mills (331111)9.2E-03Oil & gas field machinery & equip. (333132)1.2E-03Primary battery mfg (335912)9.4E-03Primary battery mfg (335912)8.8E-03Lawn & garden equip. mfg (333112)1.1E-03
Table SEQ Table \* ARABIC 5. Total primary and secondary zinc use summary.
DescriptionMaterial Use, g/$Personal Cons. Exp.* (PCE), million $Domestic Consumption (DC), million $PCE / DCJewelry and silverware manufacturing (339910)39 19,000 21,000 87%Secondary processing of other nonferrous (331492)25 - 1,200 0%Nonferrous metal, except copper and aluminum, shaping (331491)24 57 7,300 1%Primary nonferrous metal, except copper and aluminum (331419)20 - 10,000 0%Dental laboratories (339116)16 - 3,000 0%Dental equipment and supplies manufacturing (339114)13 - 2,300 0%Ferroalloy and related product manufacturing (331112)12 - 2,300 0%Copper rolling, drawing, and extruding (331421)8.2 - 8,800 0%Primary smelting and refining of copper (331411)7.3 - 8,100 0%Other aluminum rolling and drawing (331319)5.2 - 750 0%
Table SEQ Table \* ARABIC 6. Total primary and secondary lead use summary.
DescriptionMaterial Use, g/$Personal Cons. Exp.* (PCE), million $Domestic Consumption (DC), million $PCE / DCPower-driven handtool manufacturing (333991)13 1,000 4,100 25%Elevator and moving stairway manufacturing (333921)10 - 1,600 0%Jewelry and silverware manufacturing (339910)7.6 19,000 21,000 87%Lawn and garden equipment manufacturing (333112)6.2 620 6,100 10%Secondary processing of other nonferrous (331492)5 - 1,200 0%Nonferrous metal, except copper and aluminum, shaping (331491)4.5 57 7,300 1%Dental laboratories (339116)3.1 - 3,000 0%Boat building (336612)2.9 4,200 5,400 78%Motor home manufacturing (336213)2.8 3,200 3,500 90%Dental equipment and supplies manufacturing (339114)2.5 - 2,300 0%
Table SEQ Table \* ARABIC 7. Total primary and secondary nickel use summary.
DescriptionMaterial Use, g/$Personal Cons. Exp.* (PCE), million $Domestic Consumption (DC), million $PCE / DCStorage battery manufacturing (335911)6.4 2,100 5,100 41%Jewelry and silverware manufacturing (339910)5.1 19,000 21,000 87%Secondary processing of other nonferrous (331492)3.2 - 1,200 0%Nonferrous metal, except copper and aluminum, shaping (331491)2.9 57 7,300 1%Primary nonferrous metal, except copper and aluminum (331419)2.5 - 10,000 0%Ferroalloy and related product manufacturing (331112)2.1 - 2,300 0%Dental laboratories (339116)2 - 3,000 0%Dental equipment and supplies manufacturing (339114)1.7 - 2,300 0%Copper rolling, drawing, and extruding (331421)0.88 - 8,800 0%Primary smelting and refining of copper (331411)0.77 - 8,100 0%
Table SEQ Table \* ARABIC 8. Total primary and secondary cadmium use summary.
DescriptionMaterial Use, g/$Personal Cons. Exp.* (PCE), million $Domestic Consumption (DC), million $PCE / DCStorage battery manufacturing (335911)0.18 2,100 5,100 41%Jewelry and silverware manufacturing (339910)0.073 19,000 21,000 87%Secondary processing of other nonferrous (331492)0.062 - 1,200 0%Nonferrous metal, except copper and aluminum, shaping (331491)0.044 57 7,300 1%Ferroalloy and related product manufacturing (331112)0.032 - 2,300 0%Dental laboratories (339116)0.029 - 3,000 0%Dental equipment and supplies manufacturing (339114)0.024 - 2,300 0%Cutting tool and machine tool accessory manufacturing (333515)0.019 - 5,300 0%Primary battery manufacturing (335912)0.015 2,000 2,200 91%Ferrous metal foundaries (331510)0.01 - 17,000 0%
Figure SEQ Figure \* ARABIC 2. Distribution of commodities by personal consumption expenditure to domestic consumption ratio.
Table SEQ Table \* ARABIC 9. Top 10 material consuming sectors by total use of zinc, lead, nickel, and cadmium for sectors with Personal Consumption Expenditures to Domestic Consumption ratio greater than 20%.
ZincLeadNickelCadmium1Jewelry & silverware mfg. (339910)39Power-driven handtool mfg. (333991)13Storage battery mfg. (335911)6.4Storage battery mfg. (335911)0.182Motorcycle, bicycle, & parts mfg. (336991)3.2Jewelry & silverware mfg. (339910)7.6Jewelry & silverware mfg. (339910)5.1Jewelry & silverware mfg. (339910)0.0733Watch, clock, & other measuring & controlling device mfg. (33451A)1.8Boat bldg. (336612)2.9Motorcycle, bicycle, & parts mfg. (336991)0.43Primary battery mfg. (335912)0.0154Electric lamp bulb & part mfg. (335110)1.5Motor home mfg. (336213)2.8Primary battery mfg. (335912)0.39Motorcycle, bicycle, & parts mfg. (336991)0.00645Heating equip, exc. warm air furnaces (333414)1.4Hand & edge tool mfg. (332212)2.1Watch, clock, & other measuring & controlling device mfg. (33451A)0.24Power-driven handtool mfg. (333991)0.00556Household laundry equip. mfg. (335224)1.4Animal, except poultry, slaughtering (311611)1.1Power-driven handtool mfg. (333991)0.24Household goods repair & maintenance (811400)0.00377Primary battery mfg. (335912)1.3Storage battery mfg. (335911)1.0Household laundry equip. mfg. (335224)0.20Watch, clock, & other measuring & controlling device mfg. (33451A)0.00368Household cooking appliance mfg. (335221)1.1Motorcycle, bicycle, & parts mfg. (336991)0.94Heating equip, exc. warm air furnaces (333414)0.19Household laundry equip. mfg. (335224)0.00299Kitchen utensil, pot, & pan mfg (332214)1.1Automobile & lt. truck mfg. (336110)0.76Electric lamp bulb & part mfg. (335110)0.18Heating equip, exc. warm air furnaces (333414)0.002910Power-driven handtool mfg. (333991)0.99Cheese mfg. (311513)0.76Household cooking appliance mfg. (335221)0.15Electric lamp bulb & part mfg. (335110)0.0026
Figure SEQ Figure \* ARABIC 3. Supply chain material use by sector, g / $.
* Overall
** PCE / DC > 0.2
Table SEQ Table \* ARABIC 10. Cadmium, Lead, Nickel, Zinc Prices and Their Inverses.
Average Price 2006
$/kg1 / Price
g / $Cadmium2.081,4481Lead1.692, 1.263593, 794Nickel23.9341.9Zinc3.202,5, 3.093,6313, 3241Average New York dealer price
2North American Producer price
3London Metals Exchange
499.95% purity in 5 short ton lots
5Special high grade zinc
6Cash price
Estimation of Supply Chain Cadmium, Lead, Nickel, 16th International Input-Output Conference
and Zinc Intensity with the MUIO-LCA Model July 2007
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