GIS-Based Regionalized Life Cycle Assessment - ACS Publications

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GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation Christopher 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, California 93106-5131, United States

‡

S Supporting Information *

ABSTRACT: We describe a new methodology for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. We extend standard matrix-based calculations to include matrices that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. We demonstrate these techniques on electricity production in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calculations, and provide specific guidance for future improvements of inventory data sets and impact assessment methods.

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INTRODUCTION Life cycle assessment (LCA) is the process of calculating the environmental burdens of goods and services. In this paper we address two stages of LCA methodology: in the life cycle inventory stage, environmental flows (resource consumptions and emissions) are quantified for the supply chain, manufacture, and eventual disposal of a product or service; in the impact assessment stage, the damage caused by the environmental flows is calculated. Regionalization, in the context of LCA, is the recognition that industrial production characteristics and the environmental impact of environmental flows vary throughout space. Potting and Hauschild1 distinguished among site-generic, site-dependent, and site-specific assessments, where site-generic is globally valid, site-dependent operates on the regional scale, and sitespecific is only locally applicable. As an alternative to the use of spatial data, classification of processes into archetypal conditions such as high population density areas has been proposed.2 Most inventory databases use a country or region code to indicate the location of supply chain processes. For example, the ecoinvent database uses ISO country codes and IIASA region codes3 and the ILCD database adds latitude and longitude coordinates.4 Plans for the next generation of databases and database formats include detailed spatial data.5 Regionalized impact assessment methods are available for a range of impacts and spatial scales. Many of these methods use a gridded spatial scale or country-based spatial units.6−16 However, some authors have also used spatial units based on watersheds, ecoregions, or population density.12,13,17 Recent © 2011 American Chemical Society

studies have concluded that the key to realizing the potential of regionalized LCA is to choose appropriate spatial scales for impact assessment methods.13,18 The computational structure of matrix-based LCA is well established;19 however, there is no standard methodology for regionalized LCA calculations. Some regionalized LCA studies assume that the spatial units of the inventory are the same as the impact assessment, and use simple matching to calculate the site-dependent environmental impacts.17,20 For example, Nansai et al.17 used population densities to create regionalized impact assessment characterization factors (CFs) assuming that inventory and impact assessment share the same spatial scale. However, no studies have described an approach for uncertain inventory spatial data or matching different inventory and impact assessment spatial units (IASUs). Several studies have imported geographic information systems (GIS) based calculations into LCA or generic matrix software,17,21 but there is currently no public software that integrates GIS databases into LCA calculations. In this paper, we review some key geographic concepts needed to work with regionalized data. We propose a new technique to systematically determine an appropriate spatial support for regionalized impact assessment methods. We then describe a methodology to couple regionalized impact assessment methods with regionalized inventories and use a new Received: Revised: Accepted: Published: 1096

September 6, 2011 December 8, 2011 December 9, 2011 December 27, 2011 dx.doi.org/10.1021/es203117z | Environ. Sci. Technol. 2012, 46, 1096−1103

Environmental Science & Technology

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version of the open source Brightway software20 that directly includes GIS capabilities in the LCA calculation. Finally, we examine a case study of electricity production in the United States.

chosen by the LCA practitioner, but are a property of the input data 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 method development. The level of spatial detail, computer representation 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 may have spatial uncertainty, and the CFs have uncertainty distributions, the spatial support of the model output is precise. From the perspective of the LCA practitioner, therefore, there is no spatial uncertainty in the locations of IASUs. Regionalized Inventories. We propose handling spatial uncertainty in regionalized inventory data sets by using the given locations as the basis for a spatial uncertainty distribution. In this approach, buffers of increasing distance are drawn around each inventory spatial unit, and a probability that the activity occurs in each buffered region is assigned. Figure 1

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MATERIALS AND METHODS Spatial Statistics and Regionalized LCA. The description of geographical objects and their spatial coordinates is known as the spatial support of the data set.22 Basic location information in GIS is composed of three geometrical primitives: a point, a line, and a polygon. All three geometrical types can be present in the support of regionalized inventories. For example, a point could represent a factory, a line could represent a transport route, and a polygon could represent an area of activity, such as a farming region or a city. Polygons are also used when more specific location data are not available, such as country-level data sets. Regionalized impact assessment methods have a spatial support of contiguous polygons, and ideally completely cover the area of interest. Each polygon would have a CF for each environmental flow, and the CFs represent the damage caused in that location. Supporting Information (SI) Figure S1 shows an example of a regionalized inventory data set and impact assessment method. Because location information in databases is always an approximation of physical locations, spatial uncertainty is always present.23 In inventory databases, uncertainty is caused by several factors, including measurement error, incomplete background databases, and uneven sampling.24 Each type of geometrical representation adds its own uncertainty as well. Point locations can be inaccurate, and are often used to represent the centroid of a larger area. Lines often represent transport routes, for which uncertainty derives primarily from variable route choices. Polygon boundaries, both natural and political, 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 the modifiable areal unit problem (MAUP).25 The MAUP arises from the need to model or describe continuous spatial phenomena with discrete geographic units. There are two components of the MAUP. First, the scale problem: statistical properties such as data set averages, variance, and correlation coefficients will vary as a function of the size of the spatial units. Second, the zoning problem: analytical results are sensitive to the way that a set of data is partitioned into polygonal spatial units, and different partitioning schemes will produce different results, even if the number of spatial units is the same. As the MAUP is an inherent problem of areal data, it cannot be avoided.26 The modifiable areal unit problem can be directly observed in the spatial support of regionalized impact assessment methods, but is also present in inventory data sets. The most prominent example is the sensitivity of gridded impact assessment CFs to the size and origin of the grid. However, both the average and uncertainty of CFs and inventory parameters are affected, as these data are usually derived from a number of spatial data sets at different scales, such as meteorology, soil, land cover, economic activity, or population density. 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 data that are already located in space, and represent real objectsa factory, agricultural district, or state. These locations are not

Figure 1. Buffering an example inventory geographic unit to include spatial uncertainty. Impact assessment spatial units (IASUs) are outlined, and the number shown in each IASU is the probability that a technological process occurs in that IASU. Probabilities are calculated according to eq 1.

shows a graphical example of a buffered inventory spatial unit. Buffer distances are based on the spatial data quality of each inventory data set object. Probability values decrease with distance 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 were divided into two data quality classes, and wider buffers were applied to the inventories with low spatial data quality. Section 2.5.1 in the SI provides details on these two buffer sets, which were derived using differences in reported plant locations across different databases to generate a spatial uncertainty distribution. Regionalized Impact Assessment. Spatial autocorrelation is the embodiment of the first law of geography:27,28 “everything is related to everything else, but near things are more related than distant things”. Algorithms for calculating global spatial autocorrelation quantify how well the values in neighboring spatial units can predict the value of the original unit. In this paper, we use Moran’s calculation for global spatial autocorrelation,29 which ranges from −1 to 1. 1097

dx.doi.org/10.1021/es203117z | Environ. Sci. Technol. 2012, 46, 1096−1103


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mental flow. For each LCA calculation, we construct a demand vector, which lists the amount of each technological output demanded. If we call the technosphere matrix A, the biosphere matrix B, the impact category characterization vector c, and the demand vector f, then the total environmental impact, h, is given by eq 2:

Calculating spatial autocorrelation requires specifying a matrix that defines the spatial relationships between different geographic units. This matrix is called the geographic weights matrix, W. A number of different methods have been proposed to calculate the geographic weights matrix, from simple measures of geographic contiguity to methods based on statistics of the underlying data.30 We expect the spatial support of impact assessment methods to be heterogeneous, with many large and small spatial units, and therefore the spatial structure cannot be adequately explained using simple geographic measures such as contiguity.31 Instead, we used the AMOEBA algorithm, which uses the data values and spatial structure of the spatial data to calculate the geographic weights matrix.32,33 AMOEBA iteratively finds a set of contiguous neighbors that maximize a simple information criterion based on their data values, and then continues to expand outward until no new neighbors are found. The SI provides more information on AMOEBA and the geographic weights matrix. The minimization of global spatial autocorrelation allows for an optimization of impact assessment spatial scale. The MAUP informs us that modeled impact assessment CF values are at least partially a function of the spatial scale of the model. Therefore, it is appropriate to choose a spatial scale that maximizes some objective measure of the spatial performance of the model.34 The presence of global spatial autocorrelation in modeling results can be interpreted as spatial phenomena that operate on a different spatial scale than the model.35,36 The minimization of global spatial autocorrelation is thus a suitable criterion to choose the spatial scale of impact assessment. Matching Regionalized Inventories and Impact Assessment Methods. Each buffered inventory spatial unit needs to be mapped onto the impact assessment spatial support. This matching is a change of support problem. Correctly calculating change of support can be difficult, and different approaches produce widely varying results, because of the MAUP problem.22,37 The buffer probability distribution, aggregated over area intersected with each IASU, shows the probability that the inventory falls within this IASU. Equation 1 shows how this was modeled:

⎡

gij =

∑ ⎢⎢pbz bz

⎣

Abz , j ⎤ ⎥ ∑j Abz , j ⎥⎦

h = diag(c)B(I − A)−1f

(2)

where diag(c) is a diagonal matrix constructed from the characterization vector c, and I is the identity matrix. A regionalized LCA does not change the technosphere or biosphere matrices, but several additional matrices are needed to describe the spatial relationships between inventory and impact assessment. A regionalized impact assessment method provides separate CFs for each geographical unit in the method’s spatial support. The characterization vector c must therefore be expanded to a regionalized characterization matrix R, which has rows of IASUs and columns of environmental flows. We can encapsulate location-specific information by defining two new matrices, M and G. The mapping matrix, M, has rows of technological processes, and columns of inventory spatial units. The purpose of this matrix is to indicate where a technological process occurs. As each technological process is linked to one inventory spatial unit, each matrix element has the value of one if the process occurs in the spatial unit, and zero otherwise. The geographic transform matrix, G, describes the change of spatial support between the impact assessment method and the inventory database and is composed of the matrix elements gij (eq 1). G has rows of inventory spatial units and columns of IASUs. Each row in G should be normalized to sum to one, as row values represent the proportional area of an inventory spatial unit that is located in each IASU. The regionalized total environmental impact, hr, is given by eq 3:

hr = (MGR )T ◦[B(I − A)−1diag(f )]

(3)

where element-wise multiplication, also called the Hadamard product, is represented by the “◦” symbol. The demand vector f is transformed into a diagonal matrix so that the dimensions are correct, and the individual impacts of biosphere flows and technological processes can be calculated. Regionalized impact assessment methods may need multiple spatial supports, as the characteristic scale for individual biosphere flows may differ. To avoid overlapping impact assessment areas, separate geographic transform and mapping matrices should be constructed for each impact assessment spatial support. A separate regionalized characterization matrix is 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 Carlo uncertainty propagation can be directly applied to assess inventory uncertainty. Because the MGR term does not vary under inventory uncertainty, it does not need to be recalculated for each iteration. Uncertainty distributions for CFs could also be applied by varying the R matrix. Case Study Inventory and Impact Assessment Data. Several data sets were combined to form a life cycle inventory database of all large electricity generators in the United States for the year 2005. The eGRID database38 was combined with the National Emissions Inventory data set (NEI)39 to get plant

(1)

where gij is the probability-weighted areal fraction of the inventory geometry i that falls within IASU j, Abz,j is the area of buffer zone bz intersecting IASU j, and pbz is the probability that i occurs within buffer zone bz. The SI contains more details on 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 impact assessment spatial support (e.g., a plant in Europe, with impact assessment limited to North America) were assigned the sitegeneric CF. Computation Methodology. Matrix-based LCA19 describes the life cycle inventory in two matrices: The technosphere matrix links technological process inputs to outputs, and the biosphere matrix gives the environmental flows associated with each technological process. In addition to the two matrices, the impact assessment method characterization vector indicates the damage done by each environ1098



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operation information, location, and air emissions. Evaporation from cooling systems was estimated using cooling system data from the Energy Information Administration40 and coolingsystem specific evaporation rates.41 Evaporation from 1315 dams was calculated using dam geometries42 and local climatic conditions.43 Each plant was also linked into the ecoinvent database,3 providing life cycle inventory data. Section 2 of the SI has detailed information on data set development. TRACI is an impact assessment method designed for conditions in the United States, with regionalized factors for acidifying and eutrophying air emissions available on a state level for the continental United States.44 For the assessment of freshwater consumption we used the method developed by Pfister et al.12 which provides CFs for impacts on ecosystems, human health, and groundwater resources for 11 828 watersheds across the globe, ranging in size from 4.5 to 340 million square kilometers. The characterized water consumption is called “RED (Relevant for Environmental Deficiency) water.”45 In addition to the watershed-scale CFs, we used an unpublished raster map of ecosystem damage CFs with a half-degree resolution to test different spatial scales. Raster data with similar values were aggregated into polygons. Similarity was determined by splitting the raster data values into a set of bins, where each bin had an equal number of values (see Section 4.1 of the SI for details). Contiguous data values in the same bin were aggregated, 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 factors to calculate the weighted averages. RED water uses freshwater withdrawals from each watershed as the load factor when calculating country averages, and TRACI used the background emissions database from the ASTRAP model.46 For the RED water method, we used the United States CF as the site-generic CF, as all impacting processes were located in the United States.

Table 1. Regionalized and Site-Generic LCA Results for 1 Kilowatt-Hour of the United States Grid Mix with RED Freshwater Consumption and TRACI Impact Assessment Methodsa site-generic score

method

regionalized score

environmental flow-weighted average

areaweighted average

RED freshwater consumption, watershed spatial support12 ecosystem damage 7.01 5.39 7.69 (PDF·m2/year) 3.7 × 10−8 4.50 × human health 2.42 × 10−8 10−7 (DALY) resource 21.7 32.5 18.4 consumption (MJ) TRACI, state-level spatial support42 acidification (moles 1.88 1.91 2.28 H+) 5.05 × 10−4 4.77 × terrestrial 4.94 × 10−4 10−4 eutrophication (potential in kg N)

median 1.55 0.0 0.0

2.11 4.54 × 10−4

a

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

resource consumption impacts in Florida and the Northeast in addition to the Southwest, and while ecosystem damage impacts occur mainly in the Southwest, low-level impacts are calculated throughout the continental United States. The TRACI impact categoriesacidification and eutrophication have impacts concentrated in the Midwest and East. For both categories, this spatially concentrated impact derives from the large nitrogen and sulfur emissions of coal-fired power plants in these regions, and not from region-specific CFs. The regional patterns observed for freshwater impact categories, on the other hand, are driven by the varying regionalized CFs. Section 6 of the SI has a discussion on the relative impact of individual plants. 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. We constructed production mixes for each NERC region, and calculated site-generic and regionalized impact assessment scores for each. Depending on the region and impact assessment method, differences of up to 2 orders of magnitude between the two scores were observed (SI Figure S17). Spatial Scale of Impact Assessment. To choose an optimized spatial scale, we used the raster map of ecosystem damage CFs to calculate autocorrelation scores at different levels of spatial aggregation. The results are shown in Figure 3, which shows that a minimum autocorrelation score is reached when the CFs are geographically aggregated so that there are approximately 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 right and left, to test the sensitivity of the autocorrelation score to the chosen details of the aggregation method, and found that autocorrelation scores can change by up to 0.1 with different aggregation cutoffs. In addition to the above analysis, we created a new global map of ecosystem damage CFs using the autocorrelation-

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RESULTS Site-Generic and Regionalized LCA Scores for the United States Electricity Grid Mix. Table 1 shows regionalized and site-generic scores for the United States grid mix for the selected impact assessment methods. There are significant differences between the regionalized and site-generic scores for the three freshwater consumption methods. The regionalized ecosystem damage score is 30% higher than the environmental flow-weighted site-generic score, while the human health and resource consumption scores are 38 and 33% lower. On the other hand, the different scores for both TRACI methods are quite close. This similarity is due to electrical power stations being major atmospheric sources of both acidifying and eutrophying emissions, and the site-generic CF being calculated using an emissions-loading weighted average. The TRACI method also has a relatively small ratio between the minimum and maximum regionalized CFsa factor of 5, as opposed to several orders of magnitude for the freshwater regionalized CFs. Table 1 also shows that sitegeneric scores depend heavily on the methodology used to calculate the site-generic CFs. Regional Impacts of U.S. Electricity Production. Figure 2 shows regional impact patterns for the five impact categories assessed in the case study. Freshwater consumption human health impacts only occur in the Southwest, but there are 1099



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

Table 2. LCIA Scores for 1 kWh of U.S. Grid Mix and Correlation Statistics for Ecosystem Damage Method at Two Spatial Aggregation Levels aggregation scale disaggregated watershed autocorrelation optimized

LCIA score (PDF m2/ year)

number of global spatial units

CF rank-order correlation with disaggregated data (n = 52,907)

7.75 7.01 7.39

52,907 11,828 10,353

n/a 0.783 0.996

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DISCUSSION Spatial Scale of Impact Assessment. Impact assessment categories and models are both complex and heterogeneous, and given the MAUP there will never be a perfect spatial scale for impact assessment. Nevertheless, impact assessment method developers must test and justify their spatial scales something none of the cited impact assessment methods did. The watershed-based ecosystem damage CFs had a low global autocorrelation score, and when one considers how upstream withdrawals can affect downstream availability, the watershedbased scale could be appropriate for assessing ecosystem damage. The watershed-scale resource consumption and human health CFs, however, had high global autocorrelation scores (0.94 and 0.71), and the given spatial support could be improved upon. For ecosystem damage, the autocorrelationoptimized spatial scale was better able to group together areas of similar CFs and minimize variability within impact assessment spatial units. Minimization of variability is especially important for studies that compare two different processes, as the 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 the environmental processes that cause changes in CFs. In the case study, minimizing autocorrelation led to an aggregation of small

Figure 3. Effect of spatial aggregation on spatial autocorrelation values for the adjusted RED water freshwater consumption ecosystem damage method.

optimized spatial scale (available in the SI and SI Figures S12 and S14). We chose the point from Figure 3 with the lowest autocorrelation score as the spatial scale. Instead of applying the median value to the spatial units of each bin, we used the median of the intersected raster values to increase fidelity and avoid CF quantization. This modified procedure dropped the global spatial autocorrelation value to 0.16. The RED water− watershed-aggregated level has 7761 spatial units with a nonzero CF, and the autocorrelation-aggregated level has 7739. Both methods have similar distributions of spatial unit sizes (SI Figure S10). Table 2 shows that the rank-order correlation between the disaggregated and optimized spatial scales is high (>99%), and that the LCA score for the United States grid mix is slightly closer to the disaggregated score than for the watershed resolution. 1100



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eGRID database also had many plants with impossible thermal efficiencies. Using disaggregated data for regionalized LCA requires clear and consistent documentation and checks for data reasonableness. In some cases, inventory spatial data quality may not be available or easily calculated. In these cases, generic measure of spatial uncertainty, similar to the generic estimates of inventory uncertainty,3 could be applied. However, data set developers should prefer transparent data sets with spatial data quality indicators. Many regionalized impact assessment methods do not provide worldwide coverage, and it is not always clear when site-generic CFs should be used. In the case study, site-generic CFs were applied for supply chain acidifying and eutrophying emissions that occurred outside the continental United States. Regionalized impact assessment method developers should provide guidance on when it is appropriate to use site-generic CFs. Furthermore, impact assessment method developers should provide the background environmental flow amounts used for weighted averages for each spatial unit. This would allow for LCA practitioners to use loading-weighted averages, instead of area-weighted averages, in their calculations. Both RED water and TRACI use loading-weighted averages to calculate sitegeneric CFs, and it would be consistent to use this approach in the regionalized calculations as well. Outlook. We have developed and implemented regionalized life cycle assessment, including methods to address uncertainty in inventory spatial data and systematically choosing the most appropriate spatial scale of impact assessment. There are promising areas for future research, such as the inclusion of rasterized data in life cycle inventories and application of the proposed methods to other regionalized inventories and impact assessment methods. Despite the increased complexity of regionalized LCA, the benefits of reduced impact assessment uncertainty, better supply chain modeling, and geographic interpretation of results are widely recognized.1,18 With the methodology proposed here and the development of regionalized inventories and impact assessment methods, regionalized LCA can transition from a research topic to routine practice.

spatial units with similar CFs. Some aggregation in regionalized impact assessment methods may be desirable. Given the uncertainty in most impact assessment methods, large numbers of spatial units with similar CFs risks high precision but low accuracy. In addition, maps of impact assessment CFs are important results in themselves, and can be easier to interpret with fewer spatial units. For example, the map of autocorrelation-optimized CFs provides a better description of the gradient in ecosystem vulnerability to freshwater consumption (as seen in SI Figures S13, S14, and S15). The calculation methodology proposed in this paper allows for all polygon-based impact assessment method spatial scales. In addition, because spatial autocorrelation calculations are relatively simple, global spatial autocorrelation can also be used as a screening tool to confirm the validity of the selected spatial scale. Improvements in the procedure for choosing impact assessment spatial scale are possible. Local indicators of spatial autocorrelation47 can allow for the fine-tuning of impact assessment spatial support by identifying individual spatial units that are candidates for aggregation or disaggregation. Sensitivity analysis on the MAUP scale and zoning effects could also be used to help choose appropriate spatial scales, as a good spatial scale should not be highly sensitive to small changes in spatial boundaries. Finally, a number of different discretization techniques are available.48 A discretization method that takes spatial relationships into account, in addition to similarity in numerical values, could allow for IASUs that better match natural features such as watersheds. Case Study. The case study is the first integrated LCA with a regionalized inventory, including uncertainty about location data, and regionalized impact assessment with its own spatial scale. The case study results have practical implications, as they identify the most damaging generators and regions, allowing for efficient interventions to reduce total environmental burden from electricity generation. The estimates developed for evaporation from dams are also a significant advance from previous published work,49 as site-specific climate data were used. The case study also provides a quantitative assessment of the impact of inventory spatial uncertainty. The spatial uncertainty distributions allow us to compare a power plant’s given location with the range of possible locations, and in the case of ecosystem damage, the spatial uncertainty distribution of more than 50% of all power plants intersected watersheds with CFs a factor of 2 different from those at the plant’s given location. However, the impact of spatial uncertainty is small relative to the other uncertainties in impact assessment,50 yet can be relevant for specific impact categories. The case study results show the limits of the archetypal alternative to regionalization. It would also be difficult to categorize the generating plants into archetypes that would capture the spatial pattern of the impacts considered in the case study without already knowing their locationsprecisely the data 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 raise concerns about data quality that would be missed for averaged data sets. In the case study, large combustion plants had continuous emission monitoring data for their actual emissions, while smaller plants used simple models to calculate their air emissions, as shown in SI Figure S6. The eGRID database does not distinguish between these two data quality levels. The

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ASSOCIATED CONTENT

S Supporting Information *

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

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; phone: +41-44-633-71-45; fax: +41-44-633-10-61. 1101



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ACKNOWLEDGMENTS

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REFERENCES

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The research was funded by the European Commission under the seventh framework program on environment; ENV.2009.3.3.2.1: LC-IMPACT - Improved Life Cycle Impact Assessment methods (LCIA) for better sustainability assessment of technologies, grant agreement 243827. We also thank two anonymous reviewers for their detailed and numerous comments, Shanna Shaked and Sebastien Humbert for their help with the IMPACT North America method, Jared Aldstadt for help with the AMOEBA algorithm, Fritz Stauffer for kriging guidance, and Robert Mutel for text comments.

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