GIS-Based Regionalized Life Cycle Assessment: How Big Is SmallEnough? Methodology and Case Study of Electricity GenerationChristopher L. Mutel,*,† Stephan Pfister,†,‡ and Stefanie Hellweg†

†ETH Zurich, Institute of Environmental Engineering, 8093 Zurich, Switzerland‡Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, California93106-5131, United States

*S Supporting Information

ABSTRACT: We describe a new methodology for performing regionalized lifecycle assessment and systematically choosing the spatial scale of regionalized impactassessment methods. We extend standard matrix-based calculations to includematrices that describe the mapping from inventory to impact assessment spatialsupports. Uncertainty in inventory spatial data is modeled using a discrete spatialdistribution function, which in a case study is derived from empirical data. Theminimization of global spatial autocorrelation is used to choose the optimal spatialscale of impact assessment methods. We demonstrate these techniques onelectricity production in the United States, using regionalized impact assessmentmethods for air emissions and freshwater consumption. Case study results showimportant differences between site-generic and regionalized calculations, andprovide specific guidance for future improvements of inventory data sets and impactassessment methods.

■ INTRODUCTIONLife cycle assessment (LCA) is the process of calculating theenvironmental burdens of goods and services. In this paper weaddress two stages of LCA methodology: in the life cycleinventory stage, environmental flows (resource consumptionsand emissions) are quantified for the supply chain,manufacture, and eventual disposal of a product or service; inthe impact assessment stage, the damage caused by theenvironmental flows is calculated.Regionalization, in the context of LCA, is the recognition

that industrial production characteristics and the environmentalimpact of environmental flows vary throughout space. Pottingand Hauschild1 distinguished among site-generic, site-depend-ent, and site-specific assessments, where site-generic is globallyvalid, site-dependent operates on the regional scale, and site-specific is only locally applicable. As an alternative to the use ofspatial data, classification of processes into archetypalconditions such as high population density areas has beenproposed.2

Most inventory databases use a country or region code toindicate the location of supply chain processes. For example,the ecoinvent database uses ISO country codes and IIASAregion codes3 and the ILCD database adds latitude andlongitude coordinates.4 Plans for the next generation ofdatabases and database formats include detailed spatial data.5

Regionalized impact assessment methods are available for arange of impacts and spatial scales. Many of these methods usea gridded spatial scale or country-based spatial units.6−16

However, some authors have also used spatial units based onwatersheds, ecoregions, or population density.12,13,17 Recent

studies have concluded that the key to realizing the potential ofregionalized LCA is to choose appropriate spatial scales forimpact assessment methods.13,18

The computational structure of matrix-based LCA is wellestablished;19 however, there is no standard methodology forregionalized LCA calculations. Some regionalized LCA studiesassume that the spatial units of the inventory are the same asthe impact assessment, and use simple matching to calculate thesite-dependent environmental impacts.17,20 For example,Nansai et al.17 used population densities to create regionalizedimpact assessment characterization factors (CFs) assuming thatinventory and impact assessment share the same spatial scale.However, no studies have described an approach for uncertaininventory spatial data or matching different inventory andimpact assessment spatial units (IASUs).Several studies have imported geographic information

systems (GIS) based calculations into LCA or generic matrixsoftware,17,21 but there is currently no public software thatintegrates GIS databases into LCA calculations.In this paper, we review some key geographic concepts

needed to work with regionalized data. We propose a newtechnique to systematically determine an appropriate spatialsupport for regionalized impact assessment methods. We thendescribe a methodology to couple regionalized impact assess-ment methods with regionalized inventories and use a new

Received: September 6, 2011Revised: December 8, 2011Accepted: December 9, 2011Published: December 27, 2011

Article

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version of the open source Brightway software20 that directlyincludes GIS capabilities in the LCA calculation. Finally, weexamine a case study of electricity production in the UnitedStates.

■ MATERIALS AND METHODSSpatial Statistics and Regionalized LCA. The descrip-

tion of geographical objects and their spatial coordinates isknown as the spatial support of the data set.22 Basic locationinformation in GIS is composed of three geometricalprimitives: a point, a line, and a polygon. All three geometricaltypes can be present in the support of regionalized inventories.For example, a point could represent a factory, a line couldrepresent a transport route, and a polygon could represent anarea of activity, such as a farming region or a city. Polygons arealso used when more specific location data are not available,such as country-level data sets. Regionalized impact assessmentmethods have a spatial support of contiguous polygons, andideally completely cover the area of interest. Each polygonwould have a CF for each environmental flow, and the CFsrepresent the damage caused in that location. SupportingInformation (SI) Figure S1 shows an example of a regionalizedinventory data set and impact assessment method.Because location information in databases is always an

approximation of physical locations, spatial uncertainty isalways present.23 In inventory databases, uncertainty is causedby several factors, including measurement error, incompletebackground databases, and uneven sampling.24 Each type ofgeometrical representation adds its own uncertainty as well.Point locations can be inaccurate, and are often used torepresent the centroid of a larger area. Lines often representtransport routes, for which uncertainty derives primarily fromvariable route choices. Polygon boundaries, both natural andpolitical, can be vaguely defined and change over time.One well-known problem in spatial analysis that affects both

inventory data sets and impact assessment methods is called themodifiable areal unit problem (MAUP).25 The MAUP arisesfrom the need to model or describe continuous spatialphenomena with discrete geographic units. There are twocomponents of the MAUP. First, the scale problem: statisticalproperties such as data set averages, variance, and correlationcoefficients will vary as a function of the size of the spatial units.Second, the zoning problem: analytical results are sensitive tothe way that a set of data is partitioned into polygonal spatialunits, and different partitioning schemes will produce differentresults, even if the number of spatial units is the same. As theMAUP is an inherent problem of areal data, it cannot beavoided.26

The modifiable areal unit problem can be directly observedin the spatial support of regionalized impact assessmentmethods, but is also present in inventory data sets. The mostprominent example is the sensitivity of gridded impactassessment CFs to the size and origin of the grid. However,both the average and uncertainty of CFs and inventoryparameters are affected, as these data are usually derived froma number of spatial data sets at different scales, such asmeteorology, soil, land cover, economic activity, or populationdensity.There are important differences between the spatial support

of inventory data sets and impact assessment methods.Inventory data sets are drawn from raw or aggregated datathat are already located in space, and represent real objectsafactory, agricultural district, or state. These locations are not

chosen by the LCA practitioner, but are a property of the inputdata sets, and may have considerable spatial uncertainty.The spatial support of impact assessment methods, on the

other hand, is derived from choices made during the methoddevelopment. The level of spatial detail, computer representa-tion of the world, and size and origin of the grid, if applicable,are all explicitly chosen and provided by the method developer.Whereas the input data for the impact assessment method mayhave spatial uncertainty, and the CFs have uncertaintydistributions, the spatial support of the model output is precise.From the perspective of the LCA practitioner, therefore, thereis no spatial uncertainty in the locations of IASUs.

Regionalized Inventories. We propose handling spatialuncertainty in regionalized inventory data sets by using thegiven locations as the basis for a spatial uncertainty distribution.In this approach, buffers of increasing distance are drawnaround each inventory spatial unit, and a probability that theactivity occurs in each buffered region is assigned. Figure 1

shows a graphical example of a buffered inventory spatial unit.Buffer distances are based on the spatial data quality of eachinventory data set object. Probability values decrease withdistance from the original spatial unit.Within an inventory data set, spatial data can have varying

quality levels. In the case study, the inventory spatial data weredivided into two data quality classes, and wider buffers wereapplied to the inventories with low spatial data quality. Section2.5.1 in the SI provides details on these two buffer sets, whichwere derived using differences in reported plant locationsacross different databases to generate a spatial uncertaintydistribution.

Regionalized Impact Assessment. Spatial autocorrela-tion is the embodiment of the first law of geography:27,28

“everything is related to everything else, but near things aremore related than distant things”. Algorithms for calculatingglobal spatial autocorrelation quantify how well the values inneighboring spatial units can predict the value of the originalunit. In this paper, we use Moran’s calculation for global spatialautocorrelation,29 which ranges from −1 to 1.

Figure 1. Buffering an example inventory geographic unit to includespatial uncertainty. Impact assessment spatial units (IASUs) areoutlined, and the number shown in each IASU is the probability that atechnological process occurs in that IASU. Probabilities are calculatedaccording to eq 1.

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Calculating spatial autocorrelation requires specifying amatrix that defines the spatial relationships between differentgeographic units. This matrix is called the geographic weightsmatrix, W. A number of different methods have been proposedto calculate the geographic weights matrix, from simplemeasures of geographic contiguity to methods based onstatistics of the underlying data.30 We expect the spatialsupport of impact assessment methods to be heterogeneous,with many large and small spatial units, and therefore the spatialstructure cannot be adequately explained using simple geo-graphic measures such as contiguity.31 Instead, we used theAMOEBA algorithm, which uses the data values and spatialstructure of the spatial data to calculate the geographic weightsmatrix.32,33 AMOEBA iteratively finds a set of contiguousneighbors that maximize a simple information criterion basedon their data values, and then continues to expand outwarduntil no new neighbors are found. The SI provides moreinformation on AMOEBA and the geographic weights matrix.The minimization of global spatial autocorrelation allows for

an optimization of impact assessment spatial scale. The MAUPinforms us that modeled impact assessment CF values are atleast partially a function of the spatial scale of the model.Therefore, it is appropriate to choose a spatial scale thatmaximizes some objective measure of the spatial performanceof the model.34 The presence of global spatial autocorrelationin modeling results can be interpreted as spatial phenomenathat operate on a different spatial scale than the model.35,36 Theminimization of global spatial autocorrelation is thus a suitablecriterion to choose the spatial scale of impact assessment.Matching Regionalized Inventories and Impact

Assessment Methods. Each buffered inventory spatial unitneeds to be mapped onto the impact assessment spatialsupport. This matching is a change of support problem.Correctly calculating change of support can be difficult, anddifferent approaches produce widely varying results, because ofthe MAUP problem.22,37

The buffer probability distribution, aggregated over areaintersected with each IASU, shows the probability that theinventory falls within this IASU. Equation 1 shows how this wasmodeled:

∑=∑

⎡⎣⎢⎢

⎤⎦⎥⎥g p

A

Aijbz

bzbz j

j bz j

,

, (1)

where gij is the probability-weighted areal fraction of theinventory geometry i that falls within IASU j, Abz,j is the area ofbuffer zone bz intersecting IASU j, and pbz is the probabilitythat i occurs within buffer zone bz. The SI contains more detailson this procedure.As long as the inventory spatial unit intersected at least one

IASU, the inventory was matched to regionalized CFs.Inventory spatial units completely outside of the impactassessment spatial support (e.g., a plant in Europe, with impactassessment limited to North America) were assigned the site-generic CF.Computation Methodology. Matrix-based LCA19 de-

scribes the life cycle inventory in two matrices: Thetechnosphere matrix links technological process inputs tooutputs, and the biosphere matrix gives the environmentalflows associated with each technological process. In addition tothe two matrices, the impact assessment method character-ization vector indicates the damage done by each environ-

mental flow. For each LCA calculation, we construct a demandvector, which lists the amount of each technological outputdemanded. If we call the technosphere matrix A, the biospherematrix B, the impact category characterization vector c, and thedemand vector f, then the total environmental impact, h, isgiven by eq 2:

= − −B I Ah c fdiag( ) ( ) 1(2)

where diag(c) is a diagonal matrix constructed from thecharacterization vector c, and I is the identity matrix.A regionalized LCA does not change the technosphere or

biosphere matrices, but several additional matrices are neededto describe the spatial relationships between inventory andimpact assessment. A regionalized impact assessment methodprovides separate CFs for each geographical unit in themethod’s spatial support. The characterization vector c musttherefore be expanded to a regionalized characterization matrixR, which has rows of IASUs and columns of environmentalflows.We can encapsulate location-specific information by defining

two new matrices, M and G. The mapping matrix, M, has rowsof technological processes, and columns of inventory spatialunits. The purpose of this matrix is to indicate where atechnological process occurs. As each technological process islinked to one inventory spatial unit, each matrix element hasthe value of one if the process occurs in the spatial unit, andzero otherwise.The geographic transform matrix, G, describes the change of

spatial support between the impact assessment method and theinventory database and is composed of the matrix elements gij(eq 1). G has rows of inventory spatial units and columns ofIASUs. Each row in G should be normalized to sum to one, asrow values represent the proportional area of an inventoryspatial unit that is located in each IASU.The regionalized total environmental impact, hr, is given by

eq 3:

= ◦ − −MGR B I Ah f( ) [ ( ) diag( )]rT 1

(3)

where element-wise multiplication, also called the Hadamardproduct, is represented by the “◦” symbol. The demand vector fis transformed into a diagonal matrix so that the dimensions arecorrect, and the individual impacts of biosphere flows andtechnological processes can be calculated.Regionalized impact assessment methods may need multiple

spatial supports, as the characteristic scale for individualbiosphere flows may differ. To avoid overlapping impactassessment areas, separate geographic transform and mappingmatrices should be constructed for each impact assessmentspatial support. A separate regionalized characterization matrixis also needed, as each R is specific to one impact category.Equation 3 uses the same technosphere and biosphere

matrices as matrix-based LCA, and therefore Monte Carlouncertainty propagation can be directly applied to assessinventory uncertainty. Because the MGR term does not varyunder inventory uncertainty, it does not need to be recalculatedfor each iteration. Uncertainty distributions for CFs could alsobe applied by varying the R matrix.

Case Study Inventory and Impact Assessment Data.Several data sets were combined to form a life cycle inventorydatabase of all large electricity generators in the United Statesfor the year 2005. The eGRID database38 was combined withthe National Emissions Inventory data set (NEI)39 to get plant

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operation information, location, and air emissions. Evaporationfrom cooling systems was estimated using cooling system datafrom the Energy Information Administration40 and cooling-system specific evaporation rates.41 Evaporation from 1315dams was calculated using dam geometries42 and local climaticconditions.43 Each plant was also linked into the ecoinventdatabase,3 providing life cycle inventory data. Section 2 of theSI has detailed information on data set development.TRACI is an impact assessment method designed for

conditions in the United States, with regionalized factors foracidifying and eutrophying air emissions available on a statelevel for the continental United States.44

For the assessment of freshwater consumption we used themethod developed by Pfister et al.12 which provides CFs forimpacts on ecosystems, human health, and groundwaterresources for 11 828 watersheds across the globe, ranging insize from 4.5 to 340 million square kilometers. Thecharacterized water consumption is called “RED (Relevant forEnvironmental Deficiency) water.”45 In addition to thewatershed-scale CFs, we used an unpublished raster map ofecosystem damage CFs with a half-degree resolution to testdifferent spatial scales. Raster data with similar values wereaggregated into polygons. Similarity was determined bysplitting the raster data values into a set of bins, where eachbin had an equal number of values (see Section 4.1 of the SI fordetails). Contiguous data values in the same bin wereaggregated, and assigned that bin’s median data value.In addition to the regionalized CFs, both RED water and

TRACI use weighted averages to calculate site-generic CFs.Emissions and water withdrawals were used as loading factorsto calculate the weighted averages. RED water uses freshwaterwithdrawals from each watershed as the load factor whencalculating country averages, and TRACI used the backgroundemissions database from the ASTRAP model.46 For the REDwater method, we used the United States CF as the site-genericCF, as all impacting processes were located in the UnitedStates.

■ RESULTSSite-Generic and Regionalized LCA Scores for the

United States Electricity Grid Mix. Table 1 showsregionalized and site-generic scores for the United States gridmix for the selected impact assessment methods. There aresignificant differences between the regionalized and site-genericscores for the three freshwater consumption methods. Theregionalized ecosystem damage score is 30% higher than theenvironmental flow-weighted site-generic score, while thehuman health and resource consumption scores are 38 and33% lower. On the other hand, the different scores for bothTRACI methods are quite close. This similarity is due toelectrical power stations being major atmospheric sources ofboth acidifying and eutrophying emissions, and the site-genericCF being calculated using an emissions-loading weightedaverage. The TRACI method also has a relatively small ratiobetween the minimum and maximum regionalized CFsafactor of 5, as opposed to several orders of magnitude for thefreshwater regionalized CFs. Table 1 also shows that site-generic scores depend heavily on the methodology used tocalculate the site-generic CFs.Regional Impacts of U.S. Electricity Production. Figure

2 shows regional impact patterns for the five impact categoriesassessed in the case study. Freshwater consumption humanhealth impacts only occur in the Southwest, but there are

resource consumption impacts in Florida and the Northeast inaddition to the Southwest, and while ecosystem damageimpacts occur mainly in the Southwest, low-level impacts arecalculated throughout the continental United States. TheTRACI impact categoriesacidification and eutrophicationhave impacts concentrated in the Midwest and East. For bothcategories, this spatially concentrated impact derives from thelarge nitrogen and sulfur emissions of coal-fired power plants inthese regions, and not from region-specific CFs. The regionalpatterns observed for freshwater impact categories, on the otherhand, are driven by the varying regionalized CFs. Section 6 ofthe SI has a discussion on the relative impact of individualplants.The North American Electric Reliability Corporation

(NERC) defines 10 regions, mapped in section 5 of the SI,that have their own regulatory or technical independence. Weconstructed production mixes for each NERC region, andcalculated site-generic and regionalized impact assessmentscores for each. Depending on the region and impactassessment method, differences of up to 2 orders of magnitudebetween the two scores were observed (SI Figure S17).

Spatial Scale of Impact Assessment. To choose anoptimized spatial scale, we used the raster map of ecosystemdamage CFs to calculate autocorrelation scores at differentlevels of spatial aggregation. The results are shown in Figure 3,which shows that a minimum autocorrelation score is reachedwhen the CFs are geographically aggregated so that there areapproximately 10 000 separate geographic units.Figure 3 also includes the results of discretization method

sensitivity testing. We varied the bin cutoff values by 5−20%,moving hyphenated vertical lines in SI Figure S8 to the rightand left, to test the sensitivity of the autocorrelation score tothe chosen details of the aggregation method, and found thatautocorrelation scores can change by up to 0.1 with differentaggregation cutoffs.In addition to the above analysis, we created a new global

map of ecosystem damage CFs using the autocorrelation-

Table 1. Regionalized and Site-Generic LCA Results for 1Kilowatt-Hour of the United States Grid Mix with REDFreshwater Consumption and TRACI Impact AssessmentMethodsa

site-generic score

methodregionalized

score

environmentalflow-weighted

average

area-weightedaverage median

RED freshwater consumption, watershed spatial support12

ecosystem damage(PDF·m2/year)

7.01 5.39 7.69 1.55

human health(DALY)

2.42 × 10−8 3.7 × 10−8 4.50 ×10−7

0.0

resourceconsumption (MJ)

21.7 32.5 18.4 0.0

TRACI, state-level spatial support42

acidification (molesH+)

1.88 1.91 2.28 2.11

terrestrialeutrophication(potential in kg N)

4.94 × 10−4 5.05 × 10−4 4.77 ×10−4

4.54 ×10−4

aThe three site-generic columns use different methods to calculate theCFs. The biosphere flow-weighted average is provided by the methoddevelopers; the area-weighted averages are calculated using thewatershed and state areas, respectively, and the median is the medianof all regionalized CFs provided for the U.S.

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optimized spatial scale (available in the SI and SI Figures S12and S14). We chose the point from Figure 3 with the lowestautocorrelation score as the spatial scale. Instead of applying themedian value to the spatial units of each bin, we used themedian of the intersected raster values to increase fidelity andavoid CF quantization. This modified procedure dropped theglobal spatial autocorrelation value to 0.16. The RED water−watershed-aggregated level has 7761 spatial units with anonzero CF, and the autocorrelation-aggregated level has7739. Both methods have similar distributions of spatial unitsizes (SI Figure S10). Table 2 shows that the rank-ordercorrelation between the disaggregated and optimized spatialscales is high (>99%), and that the LCA score for the UnitedStates grid mix is slightly closer to the disaggregated score thanfor the watershed resolution.

■ DISCUSSION

Spatial Scale of Impact Assessment. Impact assessmentcategories and models are both complex and heterogeneous,and given the MAUP there will never be a perfect spatial scalefor impact assessment. Nevertheless, impact assessmentmethod developers must test and justify their spatial scalessomething none of the cited impact assessment methods did.The watershed-based ecosystem damage CFs had a low globalautocorrelation score, and when one considers how upstreamwithdrawals can affect downstream availability, the watershed-based scale could be appropriate for assessing ecosystemdamage. The watershed-scale resource consumption andhuman health CFs, however, had high global autocorrelationscores (0.94 and 0.71), and the given spatial support could beimproved upon. For ecosystem damage, the autocorrelation-optimized spatial scale was better able to group together areasof similar CFs and minimize variability within impactassessment spatial units. Minimization of variability is especiallyimportant for studies that compare two different processes, asthe results will be sensitive to the CFs at each process site.The minimization of global spatial autocorrelation is a

systematic approach that can find the spatial scale of theenvironmental processes that cause changes in CFs. In the casestudy, minimizing autocorrelation led to an aggregation of small

Figure 2. Regional patterns of impact for the U.S. electricity grid mix for five impact assessment categories. Power plant circles are logarithmicallyscaled by annual generation. Colored boxes show each grid cell’s contribution to the total regionalized LCA score of the USA grid mix.

Figure 3. Effect of spatial aggregation on spatial autocorrelation valuesfor the adjusted RED water freshwater consumption ecosystemdamage method.

Table 2. LCIA Scores for 1 kWh of U.S. Grid Mix andCorrelation Statistics for Ecosystem Damage Method at TwoSpatial Aggregation Levels

aggregation scale

LCIA score(PDF m2/

year)

number ofglobal spatial

units

CF rank-order correlationwith disaggregated data

(n = 52,907)

disaggregated 7.75 52,907 n/awatershed 7.01 11,828 0.783autocorrelationoptimized

7.39 10,353 0.996

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spatial units with similar CFs. Some aggregation in regionalizedimpact assessment methods may be desirable. Given theuncertainty in most impact assessment methods, large numbersof spatial units with similar CFs risks high precision but lowaccuracy. In addition, maps of impact assessment CFs areimportant results in themselves, and can be easier to interpretwith fewer spatial units. For example, the map ofautocorrelation-optimized CFs provides a better descriptionof the gradient in ecosystem vulnerability to freshwaterconsumption (as seen in SI Figures S13, S14, and S15). Thecalculation methodology proposed in this paper allows for allpolygon-based impact assessment method spatial scales. Inaddition, because spatial autocorrelation calculations arerelatively simple, global spatial autocorrelation can also beused as a screening tool to confirm the validity of the selectedspatial scale.Improvements in the procedure for choosing impact

assessment spatial scale are possible. Local indicators of spatialautocorrelation47 can allow for the fine-tuning of impactassessment spatial support by identifying individual spatial unitsthat are candidates for aggregation or disaggregation. Sensitivityanalysis on the MAUP scale and zoning effects could also beused to help choose appropriate spatial scales, as a good spatialscale should not be highly sensitive to small changes in spatialboundaries. Finally, a number of different discretizationtechniques are available.48 A discretization method that takesspatial relationships into account, in addition to similarity innumerical values, could allow for IASUs that better matchnatural features such as watersheds.Case Study. The case study is the first integrated LCA with

a regionalized inventory, including uncertainty about locationdata, and regionalized impact assessment with its own spatialscale. The case study results have practical implications, as theyidentify the most damaging generators and regions, allowing forefficient interventions to reduce total environmental burdenfrom electricity generation. The estimates developed forevaporation from dams are also a significant advance fromprevious published work,49 as site-specific climate data wereused.The case study also provides a quantitative assessment of the

impact of inventory spatial uncertainty. The spatial uncertaintydistributions allow us to compare a power plant’s given locationwith the range of possible locations, and in the case ofecosystem damage, the spatial uncertainty distribution of morethan 50% of all power plants intersected watersheds with CFs afactor of 2 different from those at the plant’s given location.However, the impact of spatial uncertainty is small relative tothe other uncertainties in impact assessment,50 yet can berelevant for specific impact categories.The case study results show the limits of the archetypal

alternative to regionalization. It would also be difficult tocategorize the generating plants into archetypes that wouldcapture the spatial pattern of the impacts considered in the casestudy without already knowing their locationsprecisely thedata gathering that the archetypal approach aims to avoid.Guidance for Data Set Developers. The use of geospatial

databases to separately model industrial facilities can also raiseconcerns about data quality that would be missed for averageddata sets. In the case study, large combustion plants hadcontinuous emission monitoring data for their actual emissions,while smaller plants used simple models to calculate their airemissions, as shown in SI Figure S6. The eGRID database doesnot distinguish between these two data quality levels. The

eGRID database also had many plants with impossible thermalefficiencies. Using disaggregated data for regionalized LCArequires clear and consistent documentation and checks fordata reasonableness.In some cases, inventory spatial data quality may not be

available or easily calculated. In these cases, generic measure ofspatial uncertainty, similar to the generic estimates of inventoryuncertainty,3 could be applied. However, data set developersshould prefer transparent data sets with spatial data qualityindicators.Many regionalized impact assessment methods do not

provide worldwide coverage, and it is not always clear whensite-generic CFs should be used. In the case study, site-genericCFs were applied for supply chain acidifying and eutrophyingemissions that occurred outside the continental United States.Regionalized impact assessment method developers shouldprovide guidance on when it is appropriate to use site-genericCFs.Furthermore, impact assessment method developers should

provide the background environmental flow amounts used forweighted averages for each spatial unit. This would allow forLCA practitioners to use loading-weighted averages, instead ofarea-weighted averages, in their calculations. Both RED waterand TRACI use loading-weighted averages to calculate site-generic CFs, and it would be consistent to use this approach inthe regionalized calculations as well.

Outlook. We have developed and implemented regionalizedlife cycle assessment, including methods to address uncertaintyin inventory spatial data and systematically choosing the mostappropriate spatial scale of impact assessment. There arepromising areas for future research, such as the inclusion ofrasterized data in life cycle inventories and application of theproposed methods to other regionalized inventories and impactassessment methods. Despite the increased complexity ofregionalized LCA, the benefits of reduced impact assessmentuncertainty, better supply chain modeling, and geographicinterpretation of results are widely recognized.1,18 With themethodology proposed here and the development ofregionalized inventories and impact assessment methods,regionalized LCA can transition from a research topic toroutine practice.

■ ASSOCIATED CONTENT

*S Supporting InformationDetailed description of the case study inventory development,modeling of inventory spatial uncertainty, matrix construction,regionalized LCA software, autocorrelation calculations, includ-ing maps, and more detailed regional and plant-level results.Additional Supporting Information includes the raster dataused in creating new spatial supports, a map of NERC regions,the data set used in the case study, individual plant-level resultsfor all regionalized impact assessment methods, and maps oftwo different spatial support for the freshwater consumptionecosystem damage impact assessment method. This material isavailable free of charge via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATION

Corresponding Author*E-mail: mutel@ifu.baug.ethz.ch; phone: +41-44-633-71-45;fax: +41-44-633-10-61.

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■ ACKNOWLEDGMENTS

The research was funded by the European Commission underthe seventh framework program on environment;ENV.2009.3.3.2.1: LC-IMPACT - Improved Life Cycle ImpactAssessment methods (LCIA) for better sustainability assess-ment of technologies, grant agreement 243827. We also thanktwo anonymous reviewers for their detailed and numerouscomments, Shanna Shaked and Sebastien Humbert for theirhelp with the IMPACT North America method, Jared Aldstadtfor help with the AMOEBA algorithm, Fritz Stauffer for krigingguidance, and Robert Mutel for text comments.

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