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Impact of Power Generation Mix on Life Cycle Assessment and Carbon Footprint Greenhouse Gas Results Joe Marriott, H. Scott Matthews, and Chris T. Hendrickson

Keywords: carbon emissions electric utilities energy footprint energy use greenhouse gas (GHG) emissions industrial ecology

Supporting information is available on the JIE Web site

Summary The mix of electricity consumed in any stage in the life cycle of a product, process, or industrial sector has a significant effect on the associated inventory of emissions and environmental impacts because of large differences in the power generation method used. Fossil-fuel-fired or nuclear-centralized steam generators; large-scale and small-scale hydroelectric power; and renewable options, such as geothermal, wind, and solar power, each have a unique set of issues that can change the results of a life cycle assessment. This article shows greenhouse gas emissions estimates for electricity purchase for different scenarios using U.S. average electricity mix, state mixes, state mixes including imports, and a sector-specific mix to show how different these results can be. We find that greenhouse gases for certain sectors and scenarios can change by more than 100%. Knowing this, practitioners should exercise caution or at least account for the uncertainty associated with mix choice.

Address correspondence to: Joe Marriott Assistant Professor Department of Civil and Environmental Engineering University of Pittsburgh Pittsburgh, PA 15261 [email protected]  c 2010 by Yale University

DOI: 10.1111/j.1530-9290.2010.00290.x Volume 14, Number 6

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Introduction It is difficult to exaggerate the importance of electricity in the world today, no matter which aspect of the triple bottom line interests you. Indeed, electrification was cited by the U.S. National Academy of Engineering as the greatest engineering achievement of the 20th century (Wulf 2000). With regard to the economy, the generation and delivery of electric power is one of the largest industries in the United States; power generation creates about $300 billion in revenue and $40 billion in net profits per year, or about 2% of GDP in 2005 (BEA 2007; EIA 2007b). To support power production, utilities have supply chains that reach not only throughout the local economy but across the globe. In 1997, more than $40 billion was spent on the procurement and transportation of coal, petroleum, and natural gas in the United States (BEA 2002). But these macroeconomic numbers do not really demonstrate the importance of electricity, given that both industry and the general public rely on power generation. For example, the blackout that hit the eastern United States and Canada in August 2003 affected about 40 million people in only eight states and portions of Ontario for less than a day, yet it is estimated to have caused between $4 and $10 billion in damages and lost productivity, or nearly a quarter of the annual profit of the entire industry (EIA 2004). Electricity fuels productivity in nearly all other industries. From an environmental perspective, there is no parallel to the raw tonnage of a myriad of pollutants that the burning of fossil fuels expels into the atmosphere: Power generation produced nearly 40% of the total carbon dioxide (CO2 ) emissions in the United States in 2002 (EIA 2007c). But electricity is also a major aspect of the debate on equity in the world, given that the availability of power has a direct impact on nations’ ability to foster effective economic development. Despite this importance, electricity is treated casually in many analyses. Power is often seen as a homogenous commodity and treated as such, as if all kilowatt-hours (kWh) were equal. In effect, this approach uses the average mix of a country or region (the U.S. generation mix is 920

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Table 1 2000 and 2006 U.S. average generation mix (EIA 2007a) Generation type Coal Natural gas Petroleum Nuclear Hydro Geothermal Biomass Wind Solar Other

2000 (%)

2006 (%)

51.7 16.2 2.9 19.8 7.1 0.4 1.6 0.1 0.0 0.1

49.8 20.3 1.6 19.4 6.9 0.4 1.4 0.6 0.0 0.3

shown in table 1) or an average emission factor per kilowatt-hour to simplify what is, in reality, a very complex system. Tools available to practitioners, such as economic input-output life cycle assessment (EIO-LCA), imply an average electricity mix, without giving users an opportunity to modify it (Hendrickson et al. 2005). Life cycle assessment standards, such as the International Organization for Standardization’s ISO 14044 (ISO 1997), the Publicly Available Specification (PAS) 2050 (British Standards Institution 2008), or the greenhouse gas (GHG) protocol (WBCSD and WRI 2004), specify an average mix or nothing at all. It is important to understand that although all electrons may be equal when they are consumed, the means by which they were created are certainly not. Electrons have very different costs and impacts depending on how they were generated, and generation assets vary greatly in different regions. We argue here that the mix of electricity consumed in any stage in the life cycle of a product, process, or industrial sector can have a significant effect on both the economics and the associated inventory of emissions and environmental impacts because of large differences in the power generation methods used. The choice of mix can be an unanticipated source of uncertainty. This article shows where these “land mines” can occur by providing greenhouse gas results for electricity purchases for different scenarios using the U.S. average electricity mix, state mixes both including and ignoring imports, and a sector-specific mix. To illustrate the importance of power mix assumptions in life cycle assessment, we focus

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on industrial sectors and utilities in the United States, which has a relatively diverse mix of technologies at both the national and the state level. The take-away message is the same across the globe, however: In many scenarios, power mix matters.

Background Because of the regional variation of power generation assets, one can imagine cases in which the total output of a process or plant is unfairly burdened with the environmental impact of a generation type it does not use. Conversely, one can imagine multiple states or sectors taking credit for sustainable electricity purchases from wind or other renewable projects. For instance, the aluminum manufacturing sector uses considerable amounts of electricity in the smelting process. Although there are primary aluminum production facilities across the United States, more than a fifth (22.1%) of them are located in the Pacific Northwest, particularly in Washington and Oregon, which take advantage of low-cost (and low-emission) hydroelectric power. These states generated 75% and 74% of their power with hydroelectricity, respectively, in 2000 (U. S. Census Bureau 2002; EPA 2007a). Therefore, one would expect that if a generation mix could be assigned on a more specific basis, significant changes to the life cycle impacts would be made. In fact, the aluminum industry accounts for this in its own life cycle inventories, which state the percentage of hydropower used in primary aluminum production as more than 57% (IAI 2007). The role of electricity in producing carbon emissions has received new attention in recent years. With the proliferation of carbon footprint tools for both individuals and corporations and the movement of local, regional, and national governments toward implementation of renewable portfolio standards, better estimates are needed of the electricity being consumed by a particular region, sector, or process. The Federal Trade Commission of the United States has become increasingly interested in the results produced by carbon footprint tools, as some are being used as the basis for the growing market in carbon offsets (Hogue 2008).

Additionally, the cost to the environment and to human health from electricity generation is large, and although no method of power generation is completely benign, corporations and individuals should have as complete a picture of those impacts as is possible. In the United States, the national power generation mix is dominated by fossil fuel generation, with more than 70% coming from coal, natural gas, and petroleum (see table 1). These fuels all emit large amounts of carbon to the atmosphere during combustion, but there are significant variations in the amounts and make-up of their other emissions. Nuclear fuel has virtually no local emissions but creates large amounts of hazardous radioactive waste that must be managed. Hydroelectric power is renewable and has little waste but dramatically alters the ecosystem wherever dams are built and incurs a large impact during construction and from biomass decay in the reservoir (Pacca and Horvath 2002). Wind, solar, geothermal, and biomass generation are growing quickly, but they are still a relatively small portion of the generation portfolio. The impacts of these types are diverse, and certainly none is perfect (Bergerson and Lave 2004). There is also significant state and regional variation, because of either resource availability or policy decisions. For example, West Virginia generates 99% of its electricity with coal, whereas Idaho gets 99% from hydroelectricity (EPA 2007a). There is also a lot of interstate trading of electricity that happens as states with abundant resources and generation assets ship power to states lacking those things (Marriott and Matthews 2005). California imported 69 terawatt-hours (about 30% of its consumption) of electricity in 2000 and 46 TWh (almost 20%) in 2006. West Virginia annually ships to other states more than twice what it consumes (EIA 2007c; EPA 2007a).

Method We compare the greenhouse gas emissions of five sectors of the U.S. economy, as if those sectors had consumed electrical power from four different electricity generation mixes for the United States: the national generation mix, the state

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generation mix, the state mix with electricity imports, and a sector-specific mix. The national mix for the United States is readily available from a variety of sources. Table 1 shows the mix for 2000, which was used in the analysis, and the updated 2006 mix. There have been slight changes to the mix since 2000, with the 2006 data showing small decreases in the percentage of coal, petroleum, and nuclear generation and slight increases in natural gas, hydroelectric power, and other renewable methods (EIA 2007a). State generation profiles are aggregated from U.S. Energy Information Administration (EIA) plant-level data by the U.S. Environmental Protection Agency (EPA) and made available through the Emissions & Generation Resource Integrated Database (eGRID) for years up to 1998 (EIA 2007b; EPA 2007a). 1998 is the last year for which state-level net imports are available from eGRID. The state mixes including imports were created from updated data with a previously developed method. The state mixes with imports, which we call “consumption mixes” to distinguish between power generated in-state versus total power consumed, are created according to a linear optimization model that minimizes the distance electricity must “travel” to satisfy the demand of a net importer given a pool of net exporters (Marriott and Matthews 2005). This is a standard problem and solution in transportation logistics (Eppen et al. 1998), modified for

use in the power system. Figure 1 shows some example results. It includes the published generation mix for California along with an updated consumption mix. Although California has very little coal-fired generation in-state, as consumers demand additional electricity beyond the in-state capacity, California utilities are forced to import power from states such as Arizona and Wyoming, which have significant coal-fired generation assets. For the sector-specific mixes, we again use a previously developed method with updated data. For each sector in the economy, we establish a percentage of employees present in each state as a proxy for the productivity and, therefore, power consumption in each state. Knowing where each industry is “located” and the generation profile in those locations, we can create a profile for each sector (Marriott and Matthews 2005). The employment numbers and state mixes are updated with data made available since 2005 (U.S. Census Bureau 2002; Huang and Matthews 2007). The greenhouse gas output emission factors for CO2 , dinitrogen oxide (N2 O), and methane (CH4 ) in grams per kilowatt-hour (g/kWh) are taken from a variety of sources and are shown in table 2 (EPA 2007a). Values are shown for each generation type for which there are significant direct emissions of greenhouse gases. Nuclear, hydropower, biomass, wind, and solar power are assumed to have zero direct greenhouse gas emissions. Also included in table 2 are global

Figure 1 In-state electricity generation mix versus consumption mix (includes electricity imports) for California in 1998 (Marriott and Matthews 2005; EPA 2007a).

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Table 2 Greenhouse gas emission factors for different generation sources in the United States (EPA 2007b; IPCC 2007) GHG

GWP mult.

Coal

Natural gas

Oil

Biomass

Other

CO2 CH4 N2 0

1 23 296

969.8 0.006 0.015

514.8 0.001 0.001

758.4 0.007 0.006

0.050 0.020

0.050

Note: Values are in grams carbon dioxide equivalent per kilowatt-hour (g CO2 e/kWh). GHG = greenhouse gas; GWP mult. = global warming potential multiplier; CO2 = carbon dioxide; CH4 = methane; N2 O = dinitrogen oxide.

warming potential (GWP) multipliers to convert the global warming effects of methane and nitrogen oxides to their equivalent carbon dioxide emissions (CO2 e; IPCC 2007). We report on a selection of five sectors and states in this article. Sectors were chosen because they are representative of various stages of production life cycles and also different types of industries. Although production from these sectors could occur anywhere in the United States, representative states are chosen to show how various mix assumptions could change the results of inventories done for a particular sector. The six-digit North American Industry Classification System (NAICS) code is shown for each sector. The comparison results show grams per kilowatthour of GWP in CO2 e for one of the mixes described above. The coal mining sector (NAICS 212100) was chosen as being representative of labor-intensive raw material extraction industries. The states in which the majority of coal mines are located tend to have mixes dominated by coalfired power plants. The representative state chosen here is West Virginia, which is also the state with the highest percentage of coal mining jobs (22.3%). The steel manufacturing industry (NAICS 331111) is traditionally located near sources of coal, because coke is an important feedstock for the process, and this sector was chosen as representative of heavy manufacturing. Pennsylvania has 18.5% of the nation’s steel jobs and was chosen as the representative state. Automobile manufacturing (NAICS 336110) represents the durable goods sector. Although the life cycle greenhouse gases for automobiles are dominated by the burning of gasoline during the use phase, it is still important to consider the pro-

duction of the vehicle as well. Michigan still has more than 20% of the auto manufacturing jobs in the United States and was the state used in the analysis. Semiconductor manufacturing (NAICS 334413) is representative of the growing hightech industry in the United States, and California has nearly 40% (39.6%) of these jobs. Finally, hotels and motels (NAICS 721100) were chosen because they are representative of a service industry and also of industries that are well mixed in a geographic sense. The number of employees in the hotel and motel sector closely correlates with state populations, except in Hawaii. We hypothesized that the sector consumption mix for hotels and motels would be very close to the U.S. average generation mix, as it might be for retail or hospitals. Washington, DC, was chosen as the “state”1 in the analysis, not because it has the most employees (that is California, the most populous state, with 11.7% of country’s hotel employees) but because hotels are critical to the tourism and somewhat transient political industries, and the District of Columbia’s small size produces some unique results (U.S. Census Bureau 2002). The calculations with these data are very simple. Each of the four mixes is represented by a 12-element vector, and that vector is multiplied by the 12-element vector of carbon dioxide emission factors, then added to a similar multiplication for methane and nitrogen oxides emission factors. This produces a single-generation-mixspecific emission factor for greenhouse gases. Two cautionary notes are necessary before we present results: In applying our state and sector greenhouse gas emissions, we make several assumptions that have important implications. First, we use a 1998 generation mix for each state,

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and this may change over time, as shown in table 1 for the national mix, in response to new investments, operating changes, and policies. Also, individual plants or corporations may alter their own mix by purchasing from a particular type of generator. The average results reported here and in our Supporting Information on the Web can be adjusted for both projections of generation mixes and emission factors as well as for individual plant or firm electricity purchase strategies. Second, it is important to understand that the sector consumption mixes presented here are static indicators of past electricity consumption. The types of electricity used by a particular sector and the emissions associated with that use are based on a hypothetical snapshot that uses data from 1997–2006. The results do not have any inherent predictive ability beyond providing information on which assumptions can be based. Using them as a predictive model could produce misleading or unwanted results. To illustrate this point, consider the case in which a manufacturer moves a facility to a state with cheap hydro power to reduce expenses. As more individuals make similar choices, however, the static model results will no longer show what is going on in the market. Because very little new hydroelectricity generation is being installed in the United States due to the large ecosystem impacts, new power capacity that is required to power facilities will not come from hydroelectric dams. New capac-

ity will likely come from large-scale conventional fossil plants, which would drive the average electricity price up for all customers in the state in question. We elaborate further on this point in our conclusions.

Results The greenhouse gas emission factors for each mix are shown in figure 2 and also summarized in table 3. This summary table shows the GWP results from three of the mix scenarios. The U.S. average is not shown, because it is the same value no matter which sector one looks at. There are also high and low values, which show the range of possible values associated with state choice. We begin with analysis of the coal mining sector and sectors located in West Virginia. The state electricity emission factor is very high relative to the U.S. average of about 600 g/kWh because the West Virginia generation mix is 98% coal fired, the most carbon-intensive generation technology. A consumption mix, which includes electricity imports, does not change in this result because West Virginia is a net exporter of electricity, so both the consumption and the generation mixes are the same. West Virginia has the most greenhouse-gas-intensive power mix of all 50 states, at 961 g/kWh. By contrast, the state of Washington has one of the lowest greenhouse gas emission factors, only 123 g/kWh, due to the large

Figure 2 Greenhouse gas emissions per kilowatt-hour for West Virginia consumption generation and for the U.S. coal mining economic sector. GWP = global warming potential; g CO2 e/kWh = grams carbon dioxide equivalent per kilowatt-hour.

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Table 3 Full results for all five sectors in terms of global warming potential (grams carbon dioxide equivalent per kilowatt-hour [g CO2 e/kWh]) Sector

Sector mix

State assump. (% emp)

State (generation)

State (consumption)

State high

State low

Coal Semiconductors Automobiles Steel Hotels

664.8 429.0 618.3 597.8 567.8

WV (22.3) CA (39.6) MI (20.6) PA (18.5) DC (0.01)

961.1 276.9 709.3 579.7 958.4

961.1 405.1 663.8 579.7 961.1

961.1 949.7 961.1 961.1 961.1

122.9 15.2 15.2 15.2 15.2

Note: assump. = assumption; % emp = percentage of employees; WV = West Virginia; CA = California; MI = Michigan; PA = Pennsylvania; DC = District of Columbia.

percentage of hydropower in the Pacific Northwest. The last two columns of table 3 indicate the highest and lowest emission factors possible if the coal mine were located in another state. Vermont actually has the lowest emission factor, at 15 g/kWh, but the state has no coal mines. The sector average (which considers weighted average emissions across all states with coal mining employees) result is more carbon intense than the U.S. average because states with coal as a resource have more coal-fired power plants, which makes them more carbon intense than the U.S. average. Use of the U.S. average electricity mix would result in underestimates of greenhouse gas emissions for West Virginia industries and the coal mining sector.

The greenhouse gas emissions per unit of electricity results (figure 3) are relatively low for semiconductor manufacturing because of the influence of the California generation mix, which is shown in figure 1. California’s generation mix includes very little coal power—nearly all the fossil generation comes from natural gas. Note, however, the large increase in greenhouse gas as California’s total consumption is accounted for. California imports large amounts of electricity from states, such as Wyoming, that have power generated with coal. For other semiconductor manufacturers, the value could be an order of magnitude lower than California’s relatively carbon-friendly generation mix if the facility were located in Vermont. It could also be twice as

Figure 3 Greenhouse gas emissions per kilowatt-hour for the semiconductor manufacturing scenario. GWP = global warming potential; g CO2 e/kWh = grams carbon dioxide equivalent per kilowatt-hour. Marriott et al., Impact of Power Generation Mix on LCA and Carbon Footprints

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high if the facility bought electricity in Indiana. For California and the semiconductor industry, emissions from the U.S. average mix generally overestimate their greenhouse gas emissions by 30%, even though the consumption emissions in California are larger than the generation emissions per unit. Table 3 shows the full results for all five sectors. We had hypothesized that the hotel sector would have a sector emission factor very similar to that of the U.S. average, because the industry is geographically well mixed. The results do not entirely bear this out. Hotels had a sector emission factor of 568 g/kWh, which is lower than the U.S. average of 600 g/kWh. We expect that this is because hotels tend to be located in more populated areas that tend to have “cleaner” generation methods, such as hydro power, natural gas, or even nuclear power. In fact, most sectors have emission sectors that are much closer to the U.S. average, such as automobiles and steel. This result is similar to what we found in previous work (Marriott and Matthews 2005). Another interesting thing to note is that the state mix for the hotel sector, for which we chose Washington, DC, as an example state, changes dramatically as imports are included. This is because the District of Columbia generates a very small amount of power within its borders, all from petroleum-fired plants. When coal-fired power is imported, the associated emissions rise accordingly. Included in the Supporting Information for this article is a listing of all 50 state consumption mixes and all 500 sector mixes, so these results can be created for any sector or state of interest and with new emission factors.

Conclusions Our results show that whether one is developing an inventory or creating a carbon footprint, it is important to consider the source of one’s electricity. If better information, such as a specific purchase of wind power for a facility or a published mix from a utility, is unavailable, it is important to consider these types of data as at least upper and lower bounds on an average generation mix. Although many sectors may look like the U.S. average due to geographic mixing 926

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across regions, some major sectors that we care about, such as coal mines and semiconductors, are very different. And although the numerical results shown here are U.S.-centric, the conclusion can be generalized for any region of the world that has a diverse set of generation methods. There are still very important questions to answer regarding the appropriate spatial scale, given that state borders, from the perspective of the electrical grid, are as arbitrary as national borders. What we begin to show here is that the scale does matter. There are also valid concerns regarding the differences in the environmental impact of marginal versus average demands; the power plants that turn on to provide power at the margin potentially use different fuels and have different environmental impacts than the average mix of plants. The results that we show here are based on averages and, as such, should not be used a predictor of what future demand will look like for a particular sector or in a particular state. Rather, these results are an example of the variation that exists even on the average; there must be an understanding that at the margin, both the economics and the environmental impacts of power consumption change dramatically and with variability that is not predicted by our results. As with any analysis that uses primarily large government data sources, the present work is plagued by uncertainties in the data and the method. But, certainly, better data need to be publicly available to allow for this sort of calculation. Electricity is obviously not a homogeneous quantity at its source, and the data need to reflect this so that future inventories and carbon footprinting efforts do not need to rely on the interpretation of individual practitioners. In addition, the difference between consumption and generation matters at the state and sector levels from a greenhouse gas perspective and raises important allocation issues. If two states are implementing renewable portfolio standards and utilities in those states have negotiated the transfer of wind-generated electricity across the state border, which state gets credit for that wind power, the state that purchased and consumed the power, or the state that installed the turbines and generated the power? Failure to address this sort of double counting either in policy or in

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transparent data availability could lead to an overestimate of the effectiveness of renewable standards to increase the amount of renewable or carbon-free generation. In this article, we have shown that regional variations in the local generation mix can significantly affect greenhouse gas emission estimates relative to using a national generation mix assumption. Using consumption electricity mix rather than generation in a local area tends to reduce this local difference, because imported electricity is more representative of the generation mix for the nation as a whole. The greenhouse gas emissions of firms and economic sectors can similarly be affected by the local generation and consumption mix. The bottom line with electricity is that whether one is addressing its economic, environmental, or social impacts, the variation in the methods of generation and the complexity of the delivery system needs to be accounted for or, at the very least, discussed.

Note 1. Although technically not one of the 50 U.S. states, Washington, DC, is a distinct geographic entity that for many purposes acts as a state. Hence, statelevel data are available for Washington, DC.

References BEA (Bureau of Economic Analysis). 2002. 1997 benchmark input-output tables. Washington, DC: U.S. Department of Commerce. BEA. 2007. Real and nominal GDP. Washington, DC: U.S. Department of Commerce. Bergerson, J. and L. Lave. 2004. Life cycle analysis of power generation systems. Encyclopedia of Energy 3: 635–645. British Standards Institution. 2008. Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. London: British Standards Institution. EIA (Energy Information Administration). 2004. Electric power annual 2003. Washington, DC: U.S. Department of Energy. EIA. 2007a. Annual energy review 2006. Washington, DC: U.S. Department of Energy.

EIA. 2007b. Electric power annual 2006. Washington, DC: U.S. Department of Energy. EIA. 2007c. State electricity profiles 2006. Washington, DC: U.S. Department of Energy. EPA (U.S. Environmental Protection Agency). 2007a. Emissions & generation resource integrated database. Washington, DC: EPA. EPA. 2007b. Inventory of U.S. greenhouse gas emissions and sinks: 1990–2005. Washington, DC: EPA. Eppen, G. D., F. J. Gould, C. P. Schmidt, J. H. Moore, and L. R. Weatherford. 1998. Introductory management science, fifth edition. Upper Saddle River, NJ: Prentice Hall. Hendrickson, C. T., L. B. Lave, and H. S. Matthews. 2005. Environmental life cycle assessment of goods and services: An input-output approach, edited by C. T. Hendrickson et al., first edition. Washington, DC: Resources for the Future Press. Hogue, C. 2008. Green assertions. Chemical & Engineering News, 4 February, 24. Huang, Y. A. and H. S. Matthews. 2007. Regional visualization of national EIO-LCA model. Pittsburgh, PA: Green Design Institute, Carnegie Mellon University. IAI (International Aluminium Institute). 2007. Life cycle assessment of aluminium: Inventory data for the primary aluminium industry. London: IAI. IPCC (Intergovernmental Panel on Climate Change). 2007. Climate change 2007: The physical science basis, summary for policymakers. Cambridge, UK: Cambridge University Press. ISO (International Organization for Standardization). 1997. ISO 14040—Environmental management— Life cycle assessment—Principles and Framework. Geneva, Switzerland: ISO. Marriott, J. and H. S. Matthews. 2005. Environmental effects of interstate power trading on electricity consumption mixes. Environmental Science & Technology 39(22): 8584– 8590. Pacca, S. and A. Horvath. 2002. Greenhouse gas emissions from building and operating electric power plants in the Upper Colorado River Basin. Environmental Science & Technology 36(14): 3194– 3200. U.S. Census Bureau. 2002. 1997 economic census. Washington, DC: U.S. Census Bureau. WBCSD (World Business Council for Sustainable Development) and WRI (World Resources Institute). 2004. Greenhouse gas protocol: A corporate accounting and reporting standard. Washington, DC: WRI. Wulf, W. A. 2000. Great achievements and grand challenges. Bridge 30(3/4): 5–10.

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About the Authors Joe Marriott is a professor in the Department of Civil and Environmental Engineering at the University of Pittsburgh in Pittsburgh, Pennsylvania, USA. H. Scott Matthews is a professor of civil and environmental engi-

neering and of engineering and public policy at Carnegie Mellon University, which is also in Pittsburgh, Pennsylvania, USA. Chris T. Hendrickson is the Duquesne Light Professor of Civil and Environmental Engineering, also at Carnegie Mellon University.

Supporting Information Additional supporting information may be found in the online version of this article: Supporting Information S1. This supporting information contains a table with state generation and consumption mixes. Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

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