development of a consumer environmental index and results for washington state consumers

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Development of a ConsumerEnvironmental Index andResults for Washington StateConsumersJeffrey Morris and H. Scott Matthews

Keywords:

consumptionenvironmental impact assessmentenvironmental indicatorindustrial ecologyinput−output analysis (IOA)life cycle assessment (LCA)

Supplementary material is availableon the JIE Web site

Address correspondence to:Jeffrey MorrisSound Resource Management2217 60th Lane NWOlympia, WA [email protected]

c© 2010 by Yale UniversityDOI: 10.1111/j.1530-9290.2010.00246.x

Volume 14, Number 3

Summary

Consumer choices affect sustainability of societal systems, andstate governments increasingly are interested in environmen-tal impacts of consumption. This article describes a ConsumerEnvironmental Index (CEI) to track the impacts of productpurchase, use, and disposal and applies this initial CEI toWashington State in the United States. CEI has modules forproduct and service use, upstream resource extraction andmanufacturing, and downstream disposal. CEI uses hybrid lifecycle assessment (LCA) methods, combined with purchas-ing data from the Bureau of Labor Statistics (BLS) ConsumerExpenditure Survey.

For Washington State, when human health and ecosystemtoxicity impact was assessed with the TRACI/CalTOX meth-ods, weighted aggregate and per consumer impacts in all cat-egories increased during the 6 years from 2000 to 2005. Forimpacts per real dollar spent, only the CEI’s climate changecomponent declined, falling nearly 7% between 2000 and2005.

Purchasing details in the BLS expenditure surveys enablethe CEI to track environmental impact details on 700 individualcategories of products and services. For example, sugar, motoroil, and wood heat appear to have serious environmentalimpacts, whereas recycling of paper, cardboard, and food andbeverage container discards can be as effective at reducinggreenhouse gas emissions as cutting vehicle fuel usage nearlyin half. Such results may serve to increase understanding ofenvironmentally effective actions to reduce climate, humanhealth, and ecosystem impacts of consumption.

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Introduction

Most life cycle research on environmental im-pacts and sustainability has focused on specificpollutants, such as greenhouse gases (GHGs; e.g.,Van de Weghe and Kennedy 2007); on compar-isons of specific products, such as biofuels versuspetroleum fuels (e.g., Farrell et al. 2006); or oncomparisons of methods for managing consumerdiscards (e.g., Morris 2005). Even the most am-bitious attempts at portraying societywide envi-ronmental impacts provide results for only a sin-gle time period (Brower and Leon 1999). Yet, toset goals and holistically measure environmentalprogress in the 21st century, policy makers needcomprehensive, robust time series statistics forthe environment, similar to the measurementson the economy created by economists in the20th century—such as the consumer price index(CPI), producer price index (PPI), unemploy-ment rate, labor productivity rate, and nationalincome and product accounts.

This article discusses a first step toward thatset of environmental time series statistics—thedevelopment of a consumer environmental in-dex (CEI). The CEI was created at the requestof the Washington State Department of Ecol-ogy (WADOE),1 which wanted to develop a“basket of goods” indicator like the CPI, exceptfocused on environmental impacts rather thanprice changes. The CEI tool monitors environ-mental impacts of consumer choices and trackstheir changes over time. Just as the CPI portraystrends in the prices consumers pay for productsand services, the CEI tracks trends in environ-mental emissions and their impacts caused bythe production, use, and disposal of items pur-chased each year by Washington’s consumers.The CEI declines when consumers decrease thetoxic substances, pollution, and wastes causedby consumption of goods and services or whenproducers make consumer products with lowerimpacts.2

The initial version of the CEI presented herefocuses on the potential for consumer choices tocause

• climate change;• harm to public health from particulates,

toxics, and carcinogens; and• harm to ecosystems from toxics.

The CEI model’s framework is flexible to al-low future extensions of the CEI to track otherenvironmental impacts.3

This article describes the rationale for devel-oping the CEI, the data used in its calculation,and how those data are organized and aggre-gated to calculate time series that track potentialthreats to the environment from consumer pur-chases and use and disposal of goods and services.The CEI is a general method, but in this article weillustrate its use by showing results as applied toWashington and focusing on assumptions neededfor that case study. This study devotes particu-lar attention to aggregation methodology. Ac-curately weighting food, energy, transportation,and other price changes is critical for calculatinga representative CPI. Similarly, scientifically de-veloped factors for aggregating emissions of pol-lutants provide the basis for calculating the po-tential of these pollutant releases to cause eachof the five environmental impacts assessed by theinitial version of the CEI.

CEI Basics

At a high level, the CEI tracks trends in con-sumers’ environmental impacts caused by

• what consumers buy and how their purchas-ing patterns are changing;

• growth in average consumer spending;• growth in the number of consumers;• changes in the efficiency with which man-

ufacturers convert energy and material re-sources into products and services to reducethe pollution output for any given productor service;4

• changes in the efficiency with which con-sumers use products and services, such asmotor vehicles and electricity; and

• changes in how consumers manage productdiscards.

Figure 1, the basic CEI modules flow chart,shows the ten current modules that together com-pute the CEI for 2000–2005. The annual consumerexpenditure modules incorporate U.S. Bureau ofLabor Statistics (BLS) Consumer ExpendituresSurvey (CES) data. The upstream module in-corporates impacts from resource extraction and

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Figure 1 Basic consumer environmental index modules flow chart.

refining, manufacturing, and transportation andhandling of products through their supply chainto the point of retail sale. The product use moduletracks impacts during consumers’ use of productsand services. The product disposal module calcu-lates environmental impacts from managementmethods for product and packaging discards. Thegraphs module computes and graphs the annualindexes.

The CEI model is updatable annually whenBLS releases new CES data and every 5 yearswhen the U.S. Department of Commerce Bu-reau of Economic Analysis (BEA) releases neweconomic input−output (EIO) matrices from theeconomic censuses. The CEI also is expandablewhen more detailed data on product life cycleemissions become available.

CEI Estimated ExpenditureDetails

To measure the composition of total expen-ditures by consumers, the CEI relies on the BLS

Consumer Expenditure Survey.5 This annual sur-vey consists of a rolling sample of approximately16,000 households in the United States, 8,000 ofwhich are utilized for a quarterly interview sur-vey on monthly expenditures, with the rest usedfor a 2 week diary survey of smaller purchases,including food, clothing, household furnishing,entertainment and recreational equipment, andhousekeeping supply items. The CES data at anational level have been used in other studies toconnect expenditure categories with economicsectors and perform consumer-level assessments(Weber and Matthews 2008b).

Expenditures data from these BLS surveys aredone at the national level and broken down intofour geographical regions, as well as disaggregatedfor about the top 25 metropolitan statistical areas(MSAs; e.g., Seattle and Portland). The SeattleMSA encompasses two thirds of Washington’spopulation; the Portland MSA includes the 6%of Washington’s population living in and aroundVancouver, Washington. To derive Washing-ton State−level consumption estimates, the CEI

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then uses the western United States region to ap-proximate expenditures by the remaining 28% ofWashington consumers.

CEI Estimated Life CycleEmissions Details

Three phases—upstream (including extrac-tion and refining of raw materials, product man-ufacturing, and handling and transportation offinished products to the point of retail sale), use,and end of life—encompass the assumed life cy-cle of products and services in the CEI model.Reuse and recycling avoid major portions of theupstream phase, thereby conserving energy andreducing releases of waste and pollutants.6 Thefollowing discussion describes the modules withinthe CEI model that represent these three compo-nents of the product life cycle.

Modeling of Impacts Upstream FromConsumer

The CEI’s upstream pollution estimates arecalculated from the benchmark input−outputtables of the U.S. economy, as provided bythe BEA (www.bea.gov) for 1997. In particu-lar, the 1997 United States Industry by Indus-try Benchmark economic input−output life cycleassessment (EIO-LCA) model (www.eiolca.net),maintained by the Green Design Institute atCarnegie Mellon University (CMU), was used forthe analysis (Lave et al. 1995; Hendrickson et al.1998).7 This particular EIO-LCA model versionis built on data from the 1997 economic censusand is augmented by energy and environmentalemissions intensities for each of 491 industry sec-tors in the United States.

As discussed by Cicas and colleagues (2006),emissions data in the 1997 U.S. EIO-LCAmodel include the U.S. Environmental Protec-tion Agency’s (EPA’s) Toxics Release Inventory(TRI) emissions data for 2000 and criteria air pol-lutant emissions8 from the U.S. EPA’s AIRDataReport for 1999. The EIO-LCA model also esti-mates greenhouse gas emissions on the basis of theIntergovernmental Panel on Climate Change’s(IPCC’s) revised 1996 guidelines for nationalgreenhouse gas inventories (IPCC 1996), theU.S. Department of Energy’s transportation data

book for 1999, and U.S. EPA’s (2003) inventoryof greenhouse gas emissions and sinks. These dataprovide the pollutant emissions information usedto measure upstream environmental impacts.

Future refinements of the CEI model will addinformation from BEA’s recently released 2002input−output table as well as updated time serieson energy use and pollution releases. As demon-strated below, this will allow the CEI to tracktrends in energy and pollution intensity that re-sult from technological change and producer ef-ficiency improvements as well as the trends fromchanges in consumer behavior reflected in theinitial version of the CEI model.

Modeling Impacts From ConsumerProduct Use

Consumers typically have little control overmanufacturing practices for any given productand only limited choices among competing prod-ucts with substantially different manufacturingimpacts. They influence upstream environmen-tal impacts mainly by choosing what and howmuch to buy.

Nonetheless, consumers make choices duringproduct use that directly affect pollutant emis-sions levels. For example, consumers decide howwarm or cool to keep their home and how muchhot water to use. Consumer spending on energyresources is detailed in the BLS expenditure sur-vey. As EIO-LCA models consider the upstreamsupply chain by default, they fully capture en-vironmental releases for electricity consumptionthrough measurement of releases associated withelectricity production and delivery of that powerto consumers.

For other home air and water heating, homeair conditioning, and vehicle energy sources,input−output LCA models only capture emis-sions from extraction of the raw materials, suchas petroleum used to manufacture fuels, refining ofthese energy resources, and distribution of energyproducts to retailers. Pollutant emissions fromcombustion of fuels at home or in the consumer’svehicle are not covered by such upstream-focusedmodels. For emissions from home and vehicle fueluse for the Washington case study, the CEI re-lies on data from the WADOE Air Quality Pro-gram. WADOE particulate emissions profiles for


http://www.bea.gov

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home combustion of natural gas and consumervehicle gasoline combustion are presented belowin the discussion of examples of how the modelcombines and aggregates upstream and use phasedata to yield the environmental impact indexes.WADOE’s Air Quality Program also providedthe data the CEI model uses to calculate annualemissions of particulates from tire tread and brakepad wear on passenger vehicles.

For use impacts other than from fuel combus-tion or from tire tread and brake pad wear, theCEI relies on estimates of pollutant releases on aproduct-by-product basis. For this initial versionof the CEI, the goal was to concentrate on thethree categories of consumer spending that, ac-cording to previous life cycle studies and a specialissue of the Journal of Industrial Ecology,9 cause thegreatest environmental impacts—transportationproducts and services, food, and household op-erations (including utilities, appliances, andlighting).10

TransportationMotor vehicle fuel consumption accounts for

many of the environmental impacts of driving.Impacts from motor oil consumption are alsolikely to be important. To calculate the impactsof motor oil use, the CEI uses data from pub-lished studies on the constituents of used motoroil and the emissions likely to result from mo-tor oil leakage, dumping, and combustion (Wongand Wang 2001; Boughton and Horvath 2004).These emissions profiles are combined with es-timates regarding the rate of engine motor oilleakage and combustion in cylinders, the rateand types of illegal disposal by do-it-yourself oilchangers, and the amount of used oil recyclingin Washington State. On the basis of these in-puts, the CEI model calculates that the annual re-leases of waterborne pollutants from leakage anddumping of used motor oil, combined with theatmospheric emissions from motor oil combustedin vehicle engines, are between three and fourtimes more toxic to ecosystems than annual at-mospheric emissions from combustion of vehiclefuels.11

FoodThe 1997 U.S. EIO-LCA model captures

emissions for food consumed away from home as

purchases from restaurants. Use phase emissionsfrom food consumption at home mainly have todo with energy used for cooking and dish wash-ing. The CEI measures those as part of overallemissions from energy used for home utilities.

An important aspect of food purchases andconsumption is not captured in the current CEImodel, however. Washington State consumersmay purchase a greater percentage of their foodfrom local and organic growers than the averageU.S. consumer. For future updates to the CEI,it would be useful to find reliable data on the ef-fects on upstream pollutant emissions of local andorganic food consumption in Washington versusthe U.S. average, to determine whether adjust-ments are needed to the EIO-LCA model’s av-erage upstream emissions estimates for food pro-duction to reflect local and organic food choicesby consumers.12

Household OperationsThe use phase for household utilities and ap-

pliances mainly involves energy consumption.The CEI captures emissions from energy con-sumption through the EIO-LCA model for elec-tricity and through emissions data on fuels fromWADOE’s Air Quality Program. The CEI in-cludes emissions from wood burning for residen-tial heating based on WADOE Air Program woodstove emissions estimates and U.S. Energy Infor-mation Administration estimates of householdwood consumption in Washington.13

The CEI model also includes estimates of theuse phase impacts from household use of pesti-cides and paints. These estimates are based onvarious studies on pollutant releases from theuse of these products and on local and nationaldata on the types of pesticides and paints usedby households (Census Bureau 2000–2005; EPA2001; Dickey 2005; James and Yang 2005; Morrisand Bagby 2008).

Product End-of-Life Impacts

When products or packaging materials reachthe end of their useful life, consumers makechoices about what to do with these discards—reuse, recycle, or compost them or throw them inthe garbage. These decisions directly affect pol-lutant emissions.


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State and local governments also play a rolein the end-of-life phase of a product’s life cycleand the life cycle of its packaging. Decisions andservices offered by governments regarding pro-grams, regulations, and infrastructure can make iteasier for consumers to make better choices aboutdiscards management. For example, governmentscan promote choices, such as reuse or recycling,that reduce pollutant emissions compared withdisposal of discarded products or packaging.

The CEI uses the database from the U.S.EPA/North Carolina State University/ResearchTriangle Institute Decision Support Tool (DST)for Municipal Solid Waste (MSW) Managementto calculate emissions from landfilling, incinera-tion, recycling, and composting of product andpackaging discards (Research Triangle Institute2002).14 The CEI model uses these emissions es-timates to calculate environmental impacts fromdisposal of products and packaging. Because theseemissions estimates are not material specific, theCEI model supplements them with GHG emis-sions estimates for specific waste materials, suchas newspaper, cardboard, food scraps, aluminumcans, and wood from U.S. EPA’s Waste Reduc-tion Model (WARM) software.15

WADOE maintains records as to which land-fills used by Washington communities have land-fill gas (LFG) collection systems and which of thelandfills collecting landfill gases use them for en-ergy generation versus flaring. In computing cli-mate change impacts from waste management fa-cilities, the WARM model subtracts greenhousegas offsets for energy generated by landfills andwaste-to-energy incinerators. These offsets arebased on the fact that energy from waste reducesthe supply required from the electrical energygrid.

WARM calculates greenhouse gas offsets onthe basis of the U.S. profile of average fossil energysources used to generate electricity. This U.S. av-erage profile includes a high proportion of coal.The CEI model adjusts the WARM estimates toaccount for the fact that natural gas is the in-cremental electrical energy source in Washing-ton. That is, WARM uses GHG emissions fromcombustion of the average mix of fossil fuels—84 kilograms (kg) carbon dioxide (CO2) equiv-alent (eCO2) per million British thermal units(BTUs)16 of input fossil fuel used for electricity

generation in 2004. Alternatively, the CEI modeluses GHG emissions from combustion of just nat-ural gas—53 kg per million BTUs.

Recycling OffsetsThe CEI model uses the DST database to

calculate emissions offsets (or increments) whenWashington State recycling rates are higher (orlower) than U.S. average recycling rates.17 Manu-facturing recycled-content products dramaticallyreduces energy use and pollutant emissions versusvirgin-content manufacturing.

Pollutant releases included in the upstreammodule represent the upstream phase of a prod-uct’s life cycle at the U.S. average mix of vir-gin and recycled content. When MSW recy-cling rates in Washington are higher (or lower)than U.S. recycling rates, the CEI model givesWashington consumers a credit offset (or debitincrement) for the reduced (or increased) emis-sions.18 These adjustments reflect the upstreamemissions differential if the proportion of virgin-content and recycled-content manufacturing inthe United States reflected Washington State re-cycling rates rather than U.S. average recyclingrates.19 The credit or debit is calculated as a revi-sion of 1997 U.S. EIO-LCA model results withinthe upstream module, as is done in hybrid lifecycle analysis methods.

The recycling credit or debit is implementedacross all EIO-LCA sectors for paper (includingall types of recyclable paper and cardboard asa group), plastic bottles, plastic film and bags,and glass containers. The credit for aluminumcans is implemented only for sectors likely touse aluminum can packaging—that is, sectors in-volved in producing food; beverages; housekeep-ing supplies; pharmaceuticals; film and photo-graphic supplies; pet food, supplies, and services;hair care, oral hygiene, shaving, cosmetic, anddeodorant products; and tobacco products.

The CEI model does not presently include arecycling credit for steel cans. This is because suf-ficiently disaggregated data did not exist at thetime this study was conducted to separate out theuses of ferrous metals for cans versus other prod-ucts, such as machinery and cars. At this time,the CEI model also does not attempt to estimaterecycling credits for other materials diverted fromdisposal. In addition to a lack of consensus about


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methodological issues, there are several specificreasons for this:

• The lack of closed loop recycling for a par-ticular material. Emissions offsets for mostnonclosed loop recycling options have notbeen well documented. Nor are data read-ily available on the distribution of recycledquantities among the nonclosed loop op-tions for any given recycled material.

• The lack of consensus as to what consti-tutes recycling versus diversion from dis-posal (sometimes called beneficial use todistinguish it from recycling) for materialssuch as used motor oil, wood from construc-tion and demolition activities, and usedtires.

• The lack of significant recycling levels formaterials such as used carpet.

• The absence of a mechanism for tracking re-cycling material credits for individual com-ponents of complex products, such as com-puters.

• The existence of situations in which somehousehold discards, such as used vehicles,do not flow through the municipal solidwaste management system. The initial CEIfocuses on household choices regarding dis-posal versus recycling of household discards.At this point, households do not have muchsay in the extent to which discards such asused vehicles are recycled.

CEI Pollutant AggregationDetails

For each of the 700 BLS CES products andservices, the CEI modules yield a life cycle in-ventory of emissions quantities for hundreds ofpollutants. To calculate a readily understandablemeasure of the environmental effects of consumerspending, we need to aggregate these emissionsdata. We evaluated a number of multicriteriaanalysis and pollutant weighting methods for ag-gregating emissions into environmental impactcategories. To be responsive to WADOE’s visionfor the “basket of consumer goods” environmen-tal indicator, the CEI model needed to aggregatepollutant emissions into five impact categoriesmeasuring climate change, human health, and

ecosystem health. This made several very well-known methodologies unworkable as the basisfor indexing pollutant emissions.

For example, ecological footprint analysishas become one of the best known methodsfor aggregating the environmental impacts ofeconomic activity.20 It produces a memorable,quotable statistic: the land area necessary to sus-tainably produce the resources used by a giveneconomic activity and to sustainably sequester ordispose of its waste emissions.

In practice, the central component of a foot-print in most applications is the estimated forestarea needed to sequester carbon dioxide emis-sions. The footprint approach is designed to mea-sure impacts of resource use and land-intensivedisposal but has no natural extension to othertypes of environmental impact, such as humanhealth and ecosystem impacts from toxic and car-cinogenic pollutants. There is no obvious mean-ing to the land area needed to offset a given num-ber of environmentally caused human cancers,for example. Thus, this method had little to of-fer for aggregating pollutant emissions into thethree human health categories included in theCEI.

The exhaustive review by Toffel andMarshall (2004) compares 13 methods for mea-suring human toxicity potentials (HTPs) on thebasis of their “complexity and realism” in con-sidering various pollutants’ toxicity, persistence,concentration, and actual human intake. Twodimensions of their analysis are of particular im-portance for the CEI: the completeness of thecoverage of pollutants (i.e., how many pollutantsare evaluated by each model?), and the fate andtransport modeling.

Toffel and Marshall (2004) find that mostof the available methods for evaluating humanhealth impacts from the release of pollutants intothe environment have serious defects, at leastfrom the perspective of the CEI. For example,some only consider effects on workers, not thegeneral public; some fail to consider the fate andtransport of pollutants from the point at whichthey are released into the environment. Twomethods are based only on information aboutgovernment regulations.21

These defects are avoided by five analyticalmethods for measuring HTPs:


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• Eco-indicator99, which is based on the Eu-ropean Union System for the Evaluation ofSubstances;

• Environmental Design of Industrial Prod-ucts (EDIP), which is a creation of the Dan-ish Environmental Protection Agency;

• U.S. EPA’s Tool for the Reduction and As-sessment of Chemical and Other Environ-mental Impacts (TRACI; Bare 2002; Bareet al. 2003);

• the system of human toxicity potentialweights reported by Hertwich and col-leagues (2001); and

• EPA’s Risk Screening Environmental Indi-cators (RSEIs).

Three of the methods—Eco-indicator99,EDIP, and TRACI—incorporate data on humanhealth and on climate change, ecotoxicity, andother environmental impacts. Of these, TRACIhas the advantage that the number of pollutantsit includes is much greater than Eco-indicator99or EDIP (Toffel and Marshall 2004). In ad-dition, TRACI separately indexes the humanhealth respiratory impacts of three criteria airpollutants—particulates and the particulate pre-cursors sulfur oxides (SOx) and nitrogen oxides(NOx).22

TRACI’s human health impact scores for pol-lutants that cause cancers and noncancers wereoriginally based on HTPs developed by Hertwichand colleagues (2001). For the currently in-process update of TRACI, EPA is waiting on fi-nalization of human health and ecosystem im-pact potential scores from the harmonizationmodel USEtox, developed under the auspicesof the United Nations Environment Program(UNEP) and the Society of Environmental Tox-icologists and Chemists (SETAC). For this rea-son, the TRACI model update was not finalizedat the time this initial CEI model was developed.As a result, the current version of the CEI re-lies on CalTOX 4.5 for human toxicity, humancarcinogenicity, and ecosystem toxicity poten-tial scores.23 These characterization factors werejudged to be the best available on the basis oftheir incorporation of Hertwich and colleagues’(2003) changes and other updates to the originalTRACI characterization factors for aggregatingpollutants into impact categories.

In addition, for indexing a pollutant’s globalwarming potential (GWP), the CEI adjustsGWPs in the initial TRACI model to reflect 100-year GWPs published in the IPCC’s Fourth As-sessment Report.24 The CEI uses weights fromthe initial version of TRACI to index humanhealth respiratory impacts caused by criteria airpollutants, as detailed by Bare (2002) and Bareand colleagues (2003).25

Examples of CEI Model Dataand Calculations

This section provides examples of the data andcalculations used by the CEI model to computethe indexes of environmental impact from con-sumption. To detail how the CEI combines dataon spending, emissions, and the relative environ-mental impacts of different pollutants, the narra-tive below follows the figure 1 flow chart for thespecific examples of 2005 natural gas and gasolinepurchases by Washington consumers. Purchasesof natural gas and gasoline are just two of the ap-proximately 700 line-item products and servicesexpenditures covered by the BLS CES.

In 2005, Washington consumers spent $984million on natural gas and $5,305 million ongasoline, respectively. In 1997 dollars, these pur-chase amounts are $523.5 million and $2,883million, respectively, on the basis of CPI-specificitem indexes for utility gas service and gasolinein 2005 compared with 1997.

The 1997 industry benchmark EIO-LCAmodel used in the CEI is based on purchasesfrom producers. Hence, consumers’ natural gasand gasoline purchase amounts in 2005 notonly need to be deflated to 1997 price levels,they must also be adjusted to reflect producerprices instead of consumer prices. BEA providesconsumer price breakdowns for natural gas andgasoline. These breakdowns indicate that theproducer price for the natural gas distributionsector is the same as the consumer’s retail price.Conversely, the producer price for the petroleumrefineries sector accounts for only 45.1% ofthe retail price the consumer pays for gasoline.Thus, the EIO-LCA model inputs for naturalgas and gasoline purchases by Washington con-sumers in 2005 amounted to $523.5 million and


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Table 1 Particulate pollutants from natural gasdistribution and petroleum refineries (metric tonsemitted per $100 million purchases)

Sulfur NitrogenEIO-LCA sector dioxide oxides PM10

Natural gas distribution 234 250 23.3Petroleum refineries 422 246 43.6

$1,300.2 million, respectively, in 1997 producer-price dollars.

Table 1 shows emissions of particulates(PM10) and particulate precursors (SO2 andNOx) from $100 million of purchases from thenatural gas distribution and petroleum refineriessectors in 1997, as reported by the CMU EIO-LCA model at www.eiolca.net. This model’s pro-duction sector emissions of GHGs, criteria airpollutants, and EPA TRI pollutants for all 491sectors included in that EIO-LCA model are usedby the CEI model to calculate environmental im-pacts from production of the goods and servicespurchased by consumers.

The three pollutants shown in table 1 are asso-ciated with respiratory impacts on human healthbut are not associated with any of the other fourenvironmental impacts covered by this initialCEI model. The indicator substance for humanhealth respiratory impact potential is particulatematter no larger than 2.5 microns in diameter(PM2.5). We convert point source emissions ofmetric tons of sulfur dioxide, nitrogen oxides, andPM10 to equivalent metric tons of PM2.5 emis-sions, denoted by ePM2.5,26 by multiplying theseemissions by their characterization factors for hu-man health respiratory disease potential of 0.241,0.042, and 0.600, respectively.27 For example, theproduction and distribution of the $523.5 mil-lion consumer purchases of natural gas causes apotential for human respiratory impacts equal tothe potential human respiratory impacts from 423metric tons of PM2.5 emitted to the atmosphere,as indicated in table 2.

Table 2 lists the potential human health respi-ratory disease indicator (PM2.5 equivalents) forall three life cycle stages for consumption of nat-ural gas and gasoline. The upstream stage impactsshown in the table are computed from the pro-duction emissions data in table 1 on the basis of

Table 2 PM2.5 indicator for potential human healthrespiratory impacts from 2005 Washingtonconsumer purchases of natural gas and gasoline(metric tons ePM2.5)

Life cycle phase Natural gas Gasoline

Upstream 422.6 2,553.6Use 613.0 4,284.5End of life 0.0 0.9

Total life cycle 1,035.6 6,839.0

2005 expenditures for these two consumer goods.In addition, the upstream for petroleum refineriesalso includes the potential respiratory health im-pacts from transportation, wholesale, and retailactivities that are involved in moving and han-dling gasoline from the point of production at re-fineries to the point of purchase by consumers atgasoline stations. These postproduction handlingphases for gasoline account for 30% of overall up-stream phase impacts.28

Table 2 also shows use and end-of-life life cy-cle phase impacts from these consumer purchases.The CEI model allocates end-of-life waste man-agement emissions to consumer products on thebasis of each production sector’s relative spendingon the paper and packaging items that accountfor most of the municipal solid waste materialshandled by recycling and disposal activities. Theassumption is that spending on these materialsis, to a large extent, related to packaging andpromotional items used in marketing products toconsumers.

Table 3 shows 2005 emissions of particulatesand particulate precursors as estimated by WA-DOE from combustion of natural gas and gaso-line by consumers. Applying characterization fac-tors for point and mobile source particulates and

Table 3 Emissions of particulates and particulateprecursors in 2005 from use phase combustion ofnatural gas and gasoline (metric tons)

Pollutant Natural gas Gasoline

PM2.5 287.5 280.6PM10 287.5 303.5Sulfur dioxide 22.7 1,345.8Nitrogen oxides 3,555.9 69,673.8


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particulate precursors yields the use phaseePM2.5 emissions shown in table 2 for poten-tial human health respiratory impacts as a resultof combustion of natural gas and gasoline. As in-dicated in table 2, the use phase for these twoconsumer products has much greater potentialhuman respiratory health impacts than the up-stream phase. By contrast, for more typical con-sumer products, the upstream phase impacts areusually larger than use phase impacts.

Potential human health respiratory impactscaused by the total life cycle of natural gas andgasoline purchases by Washington consumers in2005 totaled 1,035.6 and 6,839.0 metric tons ofPM2.5 equivalents, respectively. These impactsresulted from purchases during 2005 totaling, re-spectively, $523.5 million and $2,883 million in1997 dollars. The pollution intensities for humanrespiratory health impacts are 2.0 and 2.4 gramsePM2.5 per real dollar spent for natural gas andgasoline purchases, respectively, in 2005.

The Overall CEI and Its FiveSubindexes

After we compute aggregated environmen-tal impact scores for climate change, ecosys-tem toxicity, human health—respiratory andparticulates, human health—toxics, and humanhealth—carcinogens, calculating an index foreach of these five impact categories is straight-forward but, of course, subject to scrutiny. TheCEI model uses the score for 2000 as the baseand sets the impact index for 2000 equal to 100.For each subsequent year, the impact score forthat year is divided by the score for 2000 andmultiplied by 100.

There does not seem to be a credible, “ob-jective” method for combining indexes for theCEI model’s five environmental impact cate-gories into a single index number reflecting theoverall impact of Washington consumer spend-ing on the environment. The Building for Envi-ronmental and Economic Sustainability (BEES)model incorporates three sets of weights for ag-gregating impact category outcomes and instructsthe BEES model user to choose the one that bestsuits his or her decision criteria (Lippiatt 2007).Two of these impact category weighting sets arerelatively old—one from EPA’s Science Advi-

Table 4 Default weights for the overall consumerenvironmental index

Environmental Category WeightImpact Category in Overall CEI

Climate change 0.45Human health

Criteria air pollutants 0.14Toxics 0.07Carcinogens 0.13

Ecosystems toxicity 0.21

1.00

sory Board (SAB) that was developed for thepurpose of establishing priorities to protect theenvironment (EPA 1990), and the other fromHarvard University’s Kennedy School of Gov-ernment that was based on international com-parisons of environmental hazards (Clark andNorberg-Bohm 1992). The third weighting setwas developed in 2006 specifically for the currentupdate of BEES to version 4.0 and establishes aconsensus of experts and stakeholders on the ap-propriate weights for a dozen different health andenvironmental impact categories.

The three sets of weights are quite different incertain respects. For example, whereas the SABweighted climate change at 27% and ecosystemsand habitat at 45% in 1990, the 2006 BEES panelrated them at 45% and 21%, respectively. Thehuman health categories did not change that sub-stantially.

The CEI model allows the user to selectweights for its five impact categories. As a defaultfor use by WADOE, the CEI uses the 2006 BEESexpert consensus weights shown in table 4.29

Examples of CEI Results forWashington State Consumers

For the overall CEI, Washington consumershave increased their total environmental impactsby 25% since 2000. Even on a per capita ba-sis, consumer impacts have gone up almost 18%,mainly due to the 3.8% annual growth in percapita real income from 2000 to 2005. Incomegrowth explains more of the upsurge because pop-ulation growth in Washington has only averaged1.2% annually during 2000 through 2005.


F O RU M

Figure 2 Index of consumer life cycle impacts on greenhouse gas emissions for 2000 through 2005(2000 = 100).

It is no surprise that more people, eachspending more money, have a deleterious envi-ronmental impact unless spending patterns andproduction technologies change radically enoughto offset the impacts of both income and popu-lation growth. Note that this kind of result dif-fers from the CPI, which implicitly tries to holdspending constant, so as to measure just changesin price levels. Because the CEI attempts to iden-tify raw changes to environmental quality, realspending levels are not held constant. The modeldoes, however, hold money price levels constantby calculating all monetary flows in 1997 dollars.

On the brighter side, the CEI in 2005 wouldbe more than 1% below where it started in 2000if both population and per capita spending (inconstant dollars) had not increased. That is, if allelse remains equal, Washington consumers ap-parently are shifting the composition of their pur-chases in an environmentally friendly direction.For example, the quantity of both gasoline andmotor oil consumed in 2005 is below the 2002peak. It is also important to recall that the initialCEI model does not adjust for reductions in thepollution intensity of resource extraction, mate-

rial refining, and product manufacturing. Whenthe 2002 EIO-LCA model data that are now be-coming available are included in the CEI model,if there are reductions in upstream pollution in-tensities they also will be reflected in the CEI.

Among the five CEI components, the climatechange impacts of Washington consumers showthe most progress. As indicated by figure 2, ona constant real spending basis, the greenhouseimpacts of consumer expenditures and use anddisposal of goods and services declined by nearly7% between 2000 and 2005. Furthermore, GHGimpacts on a constant real spending basis did nottrend up in 2001 and 2002 as they did in theoverall CEI. Conversely, GHG emissions fromconsumption did grow in total by 18% and percapita by 11%.

Among the five CEI components, emissions ofhuman carcinogens and ecosystems toxics showsubstantial lack of progress over the period from2000 through 2005. Figure 3 shows the indexthat tracks emissions with the potential to causecancers in humans. Even on a per real dollarspent basis, human health impacts of the carcino-gens emitted as a result of Washington consumer


F O RU M

Figure 3 Index of consumer life cycle impacts on carcinogenic emissions for 2000 through 2005(2000 = 100).

behavior increased about 4% between 2000 and2005. This suggests that the shift away from pur-chases of GHG-generating products, such as gaso-line and motor oil, toward more climate-friendlygoods and services has not been accompanied bya shift away from products and services that havethe potential to harm public health, especially interms of emissions that are carcinogenic.

Finally, figure 4 shows the three trends forecosystems toxicity from Washington consump-tion. For emissions that are potentially harmfulto ecosystems, the constant spending index atfirst trended up to a 6% increase by 2003, be-fore falling back to 103.7 for 2004 and 2005.In total and per capita, the index of emissionsthat are toxic to ecosystems went up 31% and24%, respectively. These results and those forhuman health, in comparison with the results forclimate change, suggest that public media dis-cussion about global warming and its potentialimpacts may be having the unintended conse-quence of concentrating consumer attention onclimate change to the detriment of human andecosystems health.

There are at least three caveats to keep inmind with respect to consumption-driven im-pacts on human health and ecosystems:

1. The CEI model assumes that pesticide us-age remained at 2002 levels per dollarspent on lawn and garden products andmaintenance services for 2003 through2005 and that the phase-out of the insec-ticide diazinon produced the same steepdecline in its usage after 2002 that thephase-out of the insecticide chlorpyrifosproduced for that active ingredient’s us-age after 2000. No actual data on the ac-tive ingredients of pesticides purchased inWashington were available after 2002.

2. The pesticides profile used by commerciallawn and garden maintenance services maydiffer from the pesticides profile of con-sumer purchases. The CEI model uses theconsumer pesticide purchases profile to es-timate use phase emissions from consumerpurchases of lawn and garden maintenanceservices.


F O RU M

Figure 4 Index of consumer life cycle impacts on ecotoxicity emissions for 2000 through 2005(2000 = 100).

3. The CEI model’s use and disposal phasemodules at present contain no specificemissions data for household cleaningagents or for pharmaceuticals. The up-stream module contains no emissions datafor agricultural pesticides. Trends in theseemissions could alter some of the trendsjust discussed.

Furthermore, there is a more general caveatwith respect to the ecosystem component of theCEI. That is, the scope of this initial versionof the CEI lacks any measures for habitat dis-ruption, biodiversity decline, or ecosystems ser-vices degradation that may occur as a resultof consumer purchases and use of goods andservices.

Remarks on the PollutionIntensity of Consumer Spending

One can use the detailed purchasing patternsdata in the CEI model to illuminate why emis-

sions per real dollar spent for greenhouse gasesand human carcinogens moved in opposite di-rections between 2000 and 2005. Table 5 showsemissions intensity for greenhouse gases and hu-man carcinogens, the distribution of total emis-sions, and the distribution of total real spending(in 1997 dollars).

First, note that the composition of Washing-ton consumer spending changed between 2000and 2005. The portion of total real spending ac-counted for by food purchases dropped 2.0 per-centage points (pts), housing went up 1.7 pts,insurance and pensions rose by 1.1 pts, entertain-ment increased by 0.8 pts, transportation wentdown 0.6 pts, charitable contributions increased0.6 pts, clothing dropped 0.5 pts, and professionalservices decreased by 0.4 pts. Spending sharesfor other major product and services categorieschanged by 0.2 pts or less.

These changes in spending patterns providesome of the explanation for the opposite trends inoverall average GHG and carcinogenic emissionsper dollar. For example, meat and dairy caused


F O RU M

Tabl

e5

Clim

ate

chan

gean

dhu

man

carc

inog

enic

emiss

ions

inte

nsity

in20

00an

d20

05

Dist

ribut

ion

Em

issio

ns/d

olla

rsp

ent

Em

issio

ns/d

olla

rsp

enta

ndof

cons

umer

and

dist

ribut

ion

ofto

tal

dist

ribut

ion

ofto

tale

miss

ions

spen

ding

gree

nhou

sega

sem

issio

nsca

rcin

ogen

icto

hum

ans

2000

2005

2000

2000

2005

2005

2000

2000

2005

2005

Con

sum

ptio

nca

tego

ry(%

)(%

)(k

g/re

al$)

(%)

(kg/

real

$)(%

)(k

g/re

al$)

(%)

(kg/

real

$)(%

)

Food

13.6

11.6

1.11

15.4

1.07

13.5

4.84

E-0

412

.34.

75E

-04

10.1

Mea

t1.

61.

01.

873.

11.

862.

07.

70E

-04

2.3

7.64

E-0

41.

4D

airy

1.0

0.8

2.43

2.4

2.45

2.1

6.34

E-0

41.

16.

35E

-04

0.9

Frui

tsan

dve

geta

bles

1.5

1.1

1.03

1.6

1.03

1.2

4.36

E-0

41.

24.

36E

-04

0.9

Gra

ins

and

cere

als

1.2

0.8

1.01

1.2

0.98

0.9

3.50

E-0

40.

83.

51E

-04

0.5

Oth

er8.

37.

90.

847.

10.

847.

24.

40E

-04

6.8

4.40

E-0

46.

4

Alc

ohol

1.0

1.2

0.51

0.5

0.51

0.7

3.22

E-0

40.

63.

17E

-04

0.7

Hou

sing

33.9

35.6

1.24

43.1

1.13

44.1

5.79

E-0

436

.65.

96E

-04

39.0

Shel

ter

20.5

19.0

0.35

7.4

0.35

7.3

5.18

E-0

419

.84.

77E

-04

16.6

Util

ities

—el

ectr

icity

1.8

1.8

10.5

119

.310

.51

20.2

1.65

E-0

35.

51.

65E

-03

5.3

Util

ities

—oi

land

gas

0.6

0.5

10.7

07.

010

.48

5.5

5.63

E-0

40.

75.

77E

-04

0.5

Util

ities

—bi

omas

s0.

010.

012.

250.

03.

420.

03.

48E

-02

0.8

6.08

E-0

21.

4U

tiliti

es—

tele

phon

e2.

12.

50.

180.

40.

180.

51.

60E

-04

0.6

1.60

E-0

40.

7U

tiliti

es—

wat

er,s

ewer

,and

garb

age

0.8

0.7

5.52

4.5

5.35

4.2

2.05

E-0

40.

32.

03E

-04

0.3

Ope

ratio

nsan

dsu

pplie

s—m

ovin

gan

dst

orag

e0.

10.

12.

120.

22.

110.

25.

99E

-04

0.1

5.99

E-0

40.

1O

pera

tions

and

supp

lies—

carp

etan

dru

gs0.

10.

10.

870.

10.

840.

15.

29E

-04

0.1

5.29

E-0

40.

1O

pera

tions

and

supp

lies—

com

pute

rs1.

33.

50.

390.

50.

391.

59.

95E

-04

2.5

1.01

E-0

36.

4O

pera

tions

and

supp

lies—

othe

r6.

57.

50.

543.

60.

554.

55.

11E

-04

6.2

5.41

E-0

47.

4

App

arel

4.5

4.0

0.57

2.6

0.56

2.4

6.13

E-0

45.

16.

70E

-04

4.9

Tra

nspo

rtat

ion

18.3

17.7

1.69

31.5

1.63

31.6

9.47

E-0

432

.39.

71E

-04

31.5

Fuel

2.7

2.3

8.21

22.5

8.36

21.3

2.09

E-0

310

.52.

13E

-03

9.1

Mot

oroi

l0.

030.

022.

650.

12.

640.

11.

01E

-02

0.5

1.02

E-0

20.

3N

ewve

hicl

epu

rcha

ses

8.0

9.1

0.61

5.0

0.61

6.1

1.06

E-0

315

.81.

06E

-03

17.7

Publ

ictr

ansp

orta

tion

1.2

1.4

1.50

1.9

1.54

2.4

3.17

E-0

40.

73.

15E

-04

0.8

Oth

er6.

34.

80.

322.

10.

331.

74.

06E

-04

4.8

4.06

E-0

43.

6

Con

tinu

ed.


F O RU M

Tabl

e5

Con

tinue

d.

Dist

ribut

ion

Em

issio

ns/d

olla

rsp

ent

Em

issio

ns/d

olla

rsp

enta

ndof

cons

umer

and

dist

ribut

ion

ofto

tal

dist

ribut

ion

ofto

tale

miss

ions

spen

ding

gree

nhou

sega

sem

issio

nsca

rcin

ogen

icto

hum

ans

2000

2005

2000

2000

2005

2005

2000

2000

2005

2005

Con

sum

ptio

nca

tego

ry(%

)(%

)(k

g/re

al$)

(%)

(kg/

real

$)(%

)(k

g/re

al$)

(%)

(kg/

real

$)(%

)

Hea

lth

care

4.9

4.5

0.21

1.0

0.19

0.9

2.64

E-0

42.

42.

45E

-04

2.0

Ente

rtai

nmen

t5.

26.

40.

472.

50.

493.

44.

42E

-04

4.3

4.77

E-0

45.

6Pe

tfoo

dan

dsu

pplie

s0.

40.

41.

150.

41.

120.

54.

70E

-04

0.3

4.70

E-0

40.

4O

ther

4.9

5.9

0.46

2.1

0.48

2.9

4.39

E-0

44.

04.

76E

-04

5.2

Pers

onal

care

1.3

1.1

0.38

0.5

0.39

0.5

3.76

E-0

40.

93.

82E

-04

0.8

Rea

ding

0.4

0.3

0.37

0.2

0.36

0.1

2.54

E-0

40.

22.

53E

-04

0.2

Educ

atio

n2.

52.

30.

340.

90.

330.

83.

26E

-04

1.5

3.25

E-0

41.

4

Tob

acco

0.5

0.3

0.37

0.2

0.37

0.1

4.21

E-0

40.

44.

20E

-04

0.2

Prof

essi

onal

serv

ices

2.0

1.6

0.22

0.4

0.26

0.4

2.40

E-0

40.

92.

41E

-04

0.7

Con

trib

utio

ns2.

73.

30.

290.

80.

291.

02.

99E

-04

1.5

3.43

E-0

42.

1

Insu

ranc

ean

dpe

nsio

ns9.

210

.30.

030.

30.

030.

35.

73E

-05

1.0

5.03

E-0

50.

9

Ave

rage

/tot

al10

0.0

100.

00.

9810

0.0

0.91

100.

05.

36E

-04

100.

05.

44E

-04

100.

0


F O RU M

GHG emissions per dollar 1.9 and 2.5 timeshigher, respectively, than the overall averageemissions of 0.98 kilograms of CO2 equivalentsper dollar in 2000. Thus, the drop in share of totalspending of 0.6 pts for meat and 0.2 pts for dairycontributed importantly to the drop to 0.91 kg by2005 in overall average eCO2 emissions per realdollar spent.

Increased use by Washington consumers ofwood for home heating explains a substantial partof the increase in the level of human carcino-genic emissions between 2000 and 2005. Realpurchases of wood biomass for home heating wentup by nearly 32%, according to U.S. BLS expen-diture data and U.S. Energy Information Admin-istration (EIA) price data. Total wood biomassconsumed by Washington residents increased bymore than 77%, according to data from EIA. Thedifference in these two estimates is likely relatedto acquisition of wood for combustion from non-market sources, such as homeowner woodlots andstorm blow-down available to the public at nocharge.

Wood combustion at home causes carcino-genic emissions. The carcinogenic emissions in-tensity of wood biomass consumption is morethan two orders of magnitude higher than overallaverage human carcinogenic emissions per realdollar spent. As a result, increased home com-bustion of wood biomass helped push the overallaverage emissions level for human carcinogensper dollar of spending up by more than a percent-age point.

Another significant cause of the increase incarcinogenic emissions per dollar spent for 2005versus 2000 was increased real spending on newcomputers—up from 1.3% of spending in 2000to 3.5% in 2005. Production of computers causescarcinogenic emissions per constant dollar ofretail purchase price approximately double theemissions level for the average dollar of consumerspending.

The gallons of vehicle fuels purchased byWashington consumers also rose 12% in 2005versus 2000, and a greater percentage of thosegallons were diesel, which raised the averagelevel for carcinogenic emissions. The carcino-genic emissions intensity per dollar for vehiclefuels is at least four times larger than the overallaverage.

Conversely, because price increases for vehi-cle fuels and home heating oil and gas were muchlarger than for most other consumer goods andservices, the proportion of total real spending onvehicle fuels and home heating fossil fuels actu-ally fell in 2005 compared with 2000. This helpedreduce the overall average for GHG emissions perreal dollar spent in 2005.

The increased spending on new computersand on entertainment also pushed down averageGHG intensity per real dollar. For each real dollarof consumer spending, the computer and enter-tainment services life cycles have GHG emissionsthat are about 60% below the average for all goodsand services.

One can also use the detailed spending andpollution intensity data on the 700 products andservices tracked by the CEI to indicate the per-ils of relying only on greenhouse gas emissionsintensities when making environmental policychoices. Table 6 ranks a number of consumerspending items according to their GHG emis-sions per real dollar of purchases. The items listedat the top of the table are all the purchases thatrank above the 2005 average level for GHG emis-sions intensity—0.9 kilograms per dollar spent.These include home heating and vehicular fuels,electricity, and dairy and meat purchases. Butthere also are others that are not so frequentlydiscussed, such as water and sewer utilities, air-line trips, pet food, and sugar.

The most interesting implication of the datain table 6, however, may be not what itemsshow up at the top of the list for GHG emis-sions intensities per dollar of spending but thefact that life cycle emissions intensities for theother four environmental impacts differ impor-tantly from GHG intensities. For example, com-puters are well below average on the GHG inten-sity list but very much above average for humanand ecosystem toxics emissions intensities. As an-other example, purchases of consumer durables,such as cars and refrigerators, rank low on life cy-cle GHG emissions intensity but are more thanthree times more emissions intense than the av-erage for ecosystem toxics. Of course, if one addsthe fuels for vehicles and the electricity for re-frigerators into the life cycle impacts of thesepurchases, their GHG emissions intensities risesubstantially.


F O RU M

Table 6 2005 pollution intensities for various consumer items in Washington State

GHG Particulates Toxics Carcinogens EcotoxicsConsumer item (kg/$) (g/$) (kg/$) (g/$) (g/$)

Natural gas heating fuel 10.9 2.0 1.3 0.5 7.3Heating oil 10.7 7.1 2.8 2.6 11.0Electricity 10.5 14.9 1.6 1.6 41.8Diesel vehicle fuel 9.0 7.6 2.5 2.6 9.9Gasoline vehicle fuel 8.3 2.4 1.4 2.1 8.9Water/sewer utilities 7.8 0.4 0.5 0.2 4.6Bottled gas heating fuel 6.8 1.5 1.3 0.6 8.0Wood heating fuel 3.4 260.8 1.0 60.8 45.0Dairy 2.4 1.7 1.5 0.0 8.7Meat 1.9 2.3 1.8 0.0 9.5Airline trips 1.8 0.5 0.7 0.3 4.8Ship cruises 1.4 5.2 0.7 0.3 5.6Sugar 1.4 1.4 1.3 0.8 44.0Pet food 1.2 1.1 1.1 0.5 7.7Train trips 1.1 2.1 0.7 0.3 6.8Fruits and vegetables 1.0 1.7 1.0 0.0 6.5Grains and cereals 1.0 2.7 0.8 0.0 5.4Fish 0.9 0.9 0.9 0.0 5.4Refrigerators 0.7 0.7 1.6 0.7 32.3Laundry 0.7 0.6 3.0 1.2 11.3New cars and light trucks 0.6 0.7 1.7 1.1 32.5Bus trips 0.6 2.8 0.8 0.4 5.8Clothing 0.6 0.8 1.2 0.7 15.0Televisions 0.5 0.5 1.1 0.5 10.7Computers 0.4 0.4 2.4 1.0 13.0Hospital room 0.4 0.4 1.2 0.5 5.3Newspaper 0.4 0.4 0.6 0.2 3.1Cigarettes 0.4 2.4 1.1 0.4 5.2College tuition 0.3 0.3 1.0 0.4 4.8Postage and delivery services 0.3 0.2 1.7 0.7 6.5Movie, theater, and ballet 0.2 0.2 0.5 0.2 2.8Health care 0.2 0.2 0.6 0.2 2.7Dating service 0.1 0.1 0.5 0.2 2.6Insurance 0.1 0.1 0.4 0.1 1.4Average 0.9 1.1 1.1 0.5 10.0

Note: GHG = greenhouse gas.

“What If” CalculationCapabilities of the CEI Model

The CEI model provides numerous opportu-nities to calculate “what if” scenarios. For exam-ple, the model reveals that the average householdin Washington that recycled 100% of the curb-side recyclable waste materials, food scraps, andyard debris it generated at home and at work in2005 reduced GHG emissions by as much as if

the household cut back on its use of gasoline anddiesel fuels by more than 40% for the year. Thisresult is due to the GHG reductions made possibleby using recycled rather than virgin raw materi-als to make new products and by using composton lawns and gardens to reduce the applicationsof synthetic fertilizers and pesticides. The magni-tude of this GHG reduction potential for the av-erage Washington household in 2005 amountedto nearly 4 metric tons eCO2. This is also more


F O RU M

than 40% of the GHG emissions caused by the av-erage Washington household’s purchases of elec-tricity in 2005.

CEI Model Limitations, DataGaps, and Uncertainties

Despite legions of bookkeepers, accountants,auditors, and census takers, economists still needmany simplifying assumptions to construct indi-cators of price change, output, and economic vi-tality. An environmental index is perhaps evenmore problematic, given the scarcity of pollutantemissions and impacts data relative to the amountof information on monetary transactions.

In part, methodological limitations are whythe CEI model does not have more robust mea-sures for the impacts of pollutants and toxics onecosystems. As yet, there are no particularly com-pelling time series statistics on habitat vitality,biodiversity, or ecosystem services productivitythat reflect how those impacts are related to con-sumption of specific products or services.

But even for the human health impact cate-gories, there are data gaps that are cause for con-cern. TRI emissions data are not collected formany producers of goods and services. Agricul-ture, dry cleaning, auto repair, and smaller busi-nesses in general are among the important exclu-sions from TRI reporting requirements. Findingrobust emissions profiles for these economic sec-tors is an important initiative for future improve-ments in the CEI model.

In addition, new home construction is cur-rently not included in the CEI model. The BLSexpenditures survey classifies mortgage princi-pal payments as asset transactions rather thanconsumption expenditures. This makes economicsense, and it makes some sense for the CEI as well.The mortgage principal payment is a pure finan-cial transfer and has no environmental impactsassociated with it. Additionally, many mortgageprincipal payments are for purchases of existinghomes, not newly constructed dwellings.

Nonetheless, new housing construction is re-sponsible for a significant portion of consumers’environmental impacts. Thus, the CEI modelwould benefit from development and use of amethodology for amortizing those new home con-

struction impacts and including them in the an-nual impacts tracked in the CEI.

Finally, as already indicated, future updates ofthe CEI will be able to rely on the in-processupdate of CMU’s EIO-LCA model for the 2002BEA EIO data and corresponding pollution re-lease profiles. This will allow the CEI to revealtrends in environmental impacts related to tech-nological change in industries in addition to thetrends from changes in consumer purchasing pat-terns exhibited in the initial CEI.

Imports

One other CEI model limitation is worthyof discussion by itself. EIO-LCA models in gen-eral (including the 1997 US EIO-LCA modelused here) estimate emissions for the resourceextraction, refining, and product manufacturingsteps in a product’s life cycle under the assump-tion that products are produced entirely in theUnited States (the so-called “domestic produc-tion assumption”).

Weber and Matthews (2007) compared thecarbon pollution intensity of domestic produc-tion with the carbon pollution intensity of prod-ucts and services imported into the United Statesfrom its seven largest trading partners. One of thefindings of their study is that the United Statesproduced 22% of the world’s fossil fuel carbondioxide emissions in 2004 but consumed prod-ucts and services responsible for between 25%and 26% of world greenhouse gas emissions inthat year. On this basis, the CEI model underesti-mates greenhouse gas emissions from resource ex-traction, refining, and production by about 15%.

Upstream, use, and end-of-life managementof the goods and services purchased by Washing-ton consumers generated 98 million metric tonsof carbon dioxide equivalent (MTCO2e) green-house gases in 2000. Total generation increasedsteadily to 116 million MTCO2e by 2005.

The resource extraction and refining step andthe product manufacturing step of the upstreamphase accounted for 68% of total carbon gener-ation in 2000. This share increased to 70% by2005. A 15% undercounting error in the green-house gas emissions for these steps would total10.1 million MTCO2e in 2000, increasing to 12.1million by 2005.


F O RU M

Nonetheless, we recall that the climatechange index component of the CEI reached118.1 in 2005. Adjusting the greenhouse gasemissions estimates in 2000 and 2005 to includea 15% imports impact would raise the 2005 in-dex to 118.4, a change of only three tenths of1%. This does not imply that one should ignorethe potential bias in the CEI due to its exclusionof the pollution intensity of imports. However,it does show that substantial errors in estimatingabsolute pollution levels in each year may not beas serious when one is mainly interested in trendsand changes over time.

Conclusions

This study’s success at integrating diverse datafrom a wide variety of sources into the CEI modelindicates that it is possible to create a relativelyrobust index to quantify the environmental im-pacts of consumer purchases and track changesin those impacts over time. Much remains to bedone in better understanding the environmen-tal impacts when consumers use certain products,such as household cleaners, yard and garden pesti-cides and fertilizers, and pharmaceuticals. Muchalso needs to be done to better track the im-pacts of agricultural practices and services pro-vided by smaller businesses, such as dry cleanersand auto repair shops. When data such as theseeventually become available, they can be read-ily incorporated in the CEI model with some ofthe same chaining and benchmarking techniquesused to incorporate new products and changingconsumer choices into the calculations of priceindexes.

Due to the CEI’s coverage of nearly 700 goodsand services, this study also adds additional de-tail to some of the accepted generalizations on theenvironmental impacts of consumers. For exam-ple, the relatively higher environmental impactof meat and dairy consumption versus grains andvegetables is well understood. But the high en-vironmental impact of sugar consumption shownin table 6 is perhaps not so well known. Further-more, the trade-off between the climate changebenefits of wood use for home heating and thehuman health and ecosystem toxicity damages il-lustrated in table 6 has not been much discussed.

As this latter point illustrates, an importantaspect of the CEI is that it encompasses multipleimpacts. This allows the CEI to be used for what-if analyses that examine environmental trade-offs that may be induced by policies that changeconsumption patterns to better evaluate whetherenvironmental benefits outweigh environmentalcosts.

Furthermore, cross-sectional research could bedone to assess differences in the environmentalimpacts of various states or metropolitan areasusing CEI-like methods. This might facilitate as-sessments of the effectiveness of various programstargeted at reducing the impacts of consumers.

Notes

1. The Department of Ecology of the State of Wash-ington is typically referred to as Ecology in informaldiscourse. We have created the WADOE acronymto avoid confusion for international readers.

2. WADOE indicated an interest in tracking theharmful substances used in manufacture of prod-ucts and incorporated in products. Discussions onthis possibility concluded that time series data onharmful substances used in resource extraction andmanufacturing are quite limited, that harmful sub-stances sometimes are combined to yield less harm-ful components in a product, and that harmfulsubstances may be neutralized in the productionprocess or effectively encapsulated in the product.Hence, the decision was made to use pollutantemissions as indicators of public health and en-vironmental impacts from consumption. The sup-position is that there likely is a high correlationbetween lower pollutant emissions over a product’slife cycle and lower use of harmful substances formaking the product itself in the first place.

3. See Bare and colleagues (2003) and Lippiatt (2007)for a description and discussion of other environ-mental impacts as well as of the climate change, hu-man health, and ecosystems toxicity impacts thatare included in the initial CEI model.

4. Measurement of manufacturing efficiency changesis not implemented in the results presented in thisarticle.

5. See BLS (2005) for a detailed description of thesesurveys. The periodic surveys are available onlineat www.bls.gov/cex/#publications.

6. For example, recycling avoids bauxite mining, re-fining of bauxite into alumina, and manufacture ofalumina into can sheet, because recycled aluminumcans replace the can sheet made from the virgin raw


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material bauxite in manufacture of new aluminumcans. Similarly, recycling avoids tree harvest andthe pulping of trees by substituting repulped paperand cardboard for virgin tree pulp as feedstocks forpaper and paperboard manufacturing.

7. Hendrickson and colleagues (2006) outline theeconomic theory behind Carnegie Mellon’s EIO-LCA model.

8. The Clean Air Act requires EPA to set airquality standards for six commonly occurringair pollutants—particulate matter, ground-levelozone, carbon monoxide, sulfur oxide (SOx), ni-trogen oxide (NOx), and lead. EPA calls these pol-lutants “criteria” air pollutants because it regulatesthem by developing human-health-based or envi-ronmentally based criteria for setting permissiblelevels.

9. Volume 10, number 3. See the work of Tukker andJansen (2006) for a review of the studies in thatissue.

10. See also the work by Brower and Leon (1999).11. The data and calculations for use phase emissions

from motor oil are detailed in the SupplementaryMaterial on the Web.

12. Weber and Matthews (2008a) estimate that pro-duction accounts for 83% of greenhouse gas emis-sions associated with food consumption, whereastransportation accounts for just 11%. As indicatedin Table 7 by the pollution intensity of meat anddairy compared with the pollution intensity ofgrains and vegetables, eating lower on the foodchain is another way to significantly reduce green-house gases from food consumption.

13. The emissions profile for combustion of wood inhome fireplaces and woodstoves estimated by theWashington Department of Ecology Air QualitySection is detailed in the Supplementary Materialon the Web.

14. The Supplementary Material on the Web containsa table that shows pollutant emissions profiles forlandfills and waste-to-energy (WTE) combustionfacilities from the DST database.

15. See EPA (2006) for a detailed description of thedata and methods that support WARM.

16. One kilogram (kg, SI) ≈ 2.204 pounds (lb). 1British Thermal Unit (BTU) ≈ 1,055.1 joules (j)≈ .2522 kilocalories.

17. See EPA (1998) for U.S. recycling rates for pa-per, glass, plastics, and metals for 1997, the yearof the EIO-LCA table used to calculate the up-stream environmental impacts in the CEI model.Residential recycling rates by material for Wash-ington were provided in an Excel spreadsheet byWADOE specifically for the CEI project.

18. Strictly speaking, the adjustment for increased ordecreased recycled content should be based on uti-lization rate differentials rather than recycling ratedifferentials. Adjustments based on recycling ratestend to overstate the increase or decrease in lev-els of actual recycled content from increased ordecreased recycling. Recycling rates typically mea-sure material collection and do not adjust for ma-terial losses to disposal at facilities that process col-lected materials for shipment to recycled-contentproduct manufacturers. Nor do recycling rates ac-count for additional recycled material losses duringrecycled-content product manufacturing. Unfortu-nately, data on utilization rates are generally notavailable.

19. Some materials recycled in Washington State, es-pecially paper and cardboard, are sent out of thecountry to recycled-content manufacturers. Be-cause the CEI is based on the EIO model forthe United States, the implicit assumption here isthat virgin-content versus recycled-content man-ufacturing environmental impact differentials out-side the United States are the same as inside thecountry.

20. See the work by Wackernagel and colleagues(2005) and Wiedmann and coleagues (2006).

21. Separate from Toffel and Marshall’s (2004) con-cerns are additional relevant concerns about theability to broadly associate HTP values with spe-cific versus average releases in the United States.For example, an HTP value for arsenic may berepresentative of a midpoint of a range of ef-fects but may seriously overestimate or underes-timate the site-specific effect when a particular re-lease of arsenic occurs at, for example, a mine orsmelter.

22. Sulfur and nitrogen oxide gases tend to form par-ticulate matter when emitted to the atmosphere.

23. See a description of the CalTOX model, refer-ences, and downloadable manual and software athttp://eetd.lbl.gov/IED/ERA/caltox/index.html.

24. Solomon and colleagues (2007), Table 2.14, pp.212–213.

25. Further discussion on the aggregation methodologyis available in the work of Morris and colleagues(2007).

26. The notation ePM2.5 is used here because theindicator pollutants for human toxicity and hu-man carcinogenicity are toluene and benzene, re-spectively. Denoting toluene equivalents and ben-zene equivalents with an e prefix rather than suf-fix seems less awkward—for example, eTolueneseems preferable to Toluenee. Hence the nota-tions for indicator pollutants used in the CEI are


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eCO2 for carbon dioxide equivalents for climatechange, ePM2.5 for PM2.5 equivalents for humanhealth—respiratory, eToluene for toluene equiva-lents for human health—toxics, eBenzene for ben-zene equivalents for human health—carcinogens,and e2,4-D for 2,4-D equivalents for ecosystemstoxicity.

27. Mobile source characterization factors for PM10and SO2 are the same, but NOx is 0.0502.

28. According to the BEA breakdown for the retailprice of gasoline, 45.1% of the retail price is due toproducer prices, 2.7% is due to transportation costs,32.9% is due to wholesale costs, and 19.3% is dueto retail costs. Because of the differing pollutionintensities of these life cycle sectors for gasoline,production accounts for 70.3% of ePM2.5 emis-sions, transportation acounts for 6.4%, wholesaleaccounts for 9.9%, and retail accounts for 13.4%of the potential for human health respiratoryimpacts.

29. The BEES panel gave ecosystems toxicity and habi-tat a combined weight of 21% among the weight-ings for climate change, the three human healthimpacts, and the two ecosystem impacts. Eventhough the CEI model does not as yet contain ahabitat impact category, ecosystems toxicity usesthe combined weight to better balance the rela-tive importance of ecosystems in comparison withhuman health and climate change.

Acknowledgements

The authors thank the anonymous refereesand the journal editor for their many sugges-tions that helped improve this article. We alsothank our co-contributors to the CEI develop-ment project—Frank Ackerman, Rick Hlavka,and Michelle Morris, as well as staff at the Wash-ington Department of Ecology. Any errors in thearticle remain our responsibility.

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About the Authors

Dr. Jeffrey Morris is an economist and prin-cipal at Sound Resource Management Group, aconsulting firm based in Olympia, Washington,USA. Dr. Scott Matthews is an associate pro-fessor in the Department of Civil and Environ-mental Engineering and research director for theGreen Design Institute at Carnegie Mellon Uni-versity in Pittsburgh, Pennsylvania, USA.


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Supplementary Material

Additional Supplementary Material may be found in the online version of this article:

Supplement S1: This supplement provides additional information on (1) disposal facility emis-sions used in the consumer environmental index (CEI) to calculate human health and ecosystemstoxicity impacts from product and packaging disposal, (2) derivation of use phase emissions formotor oil and (3) home fireplace & woodstove wood combustion emissions.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supple-mentary materials supplied by the authors. Any queries (other than missing material) should bedirected to the corresponding author for the article.


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